Many latent (factorized) models have been
proposed for recommendation tasks like collaborative filtering and for ranking tasks like
document or image retrieval and annotation.
Common to all those methods is that during inference the items are scored independently by their similarity to the query in the
latent embedding space. The structure of the
ranked list (i.e. considering the set of items
returned as a whole) is not taken into account. This can be a problem because the
set of top predictions can be either too diverse (contain results that contradict each
other) or are not diverse enough
Exploiting Entity Linking in Queries For Entity RetrievalFaegheh Hasibi
Slides for the ICTIR 2016 paper: "Exploiting Entity Linking in Queries For Entity Retrieval"
The premise of entity retrieval is to better answer search queries by returning specific entities instead of documents. Many queries mention particular entities; recognizing and linking them to the corresponding entry in a knowledge base is known as the task of entity
linking in queries. In this paper we make a first attempt at bringing together these two, i.e., leveraging entity annotations of queries in the entity retrieval model. We introduce a new probabilistic component and show how it can be applied on top of any term based entity retrieval model that can be emulated in the Markov Random Field framework, including language models, sequential dependence models, as well as their fielded variations. Using a standard entity retrieval test collection, we show that our extension brings consistent improvements over all baseline methods, including the current state-of-the-art. We further show that our extension is robust against parameter settings.
The document discusses fuzzy logical databases and an efficient algorithm for evaluating fuzzy equi-joins. It introduces fuzzy concepts in databases, defines a new measure for fuzzy equality, and proposes a fuzzy equi-join based on this measure. It then presents a sort-merge join algorithm called SMFEJ that uses interval ordering to efficiently evaluate the fuzzy equi-join in two phases: sorting relations on join attributes, and joining tuples with overlapping ranges.
This paper proposes using additional context features from neighboring semantic arguments to improve the accuracy of automatic semantic argument classification. The researchers augment baseline features, which consider each argument independently, with semantic context features that capture the interdependence between arguments of the same predicate. Their experimental results show significant improvement in classification accuracy on the PropBank test set when exploiting argument interdependence through additional context features. Classification accuracy increases to 90.50%, an 18% relative error reduction over the baseline.
Learning Collaborative Agents with Rule Guidance for Knowledge Graph ReasoningDeren Lei
Walk-based models have shown their advantages in knowledge graph (KG) reasoning by achieving decent performance while providing interpretable decisions. However, the sparse reward signals offered by the KG during traversal are often insufficient to guide a sophisticated walk-based reinforcement learning (RL) model. An alternate approach is to use traditional symbolic methods (e.g., rule induction), which achieve good performance but can be hard to generalize due to the limitation of symbolic representation. In this paper, we propose RuleGuider, which leverages high-quality rules generated by symbolic-based methods to provide reward supervision for walk-based agents. Experiments on benchmark datasets show that RuleGuider improves the performance of walk-based models without losing interpretability.
This document proposes a generalized definition language for implementing an object-based fuzzy class model. It begins by reviewing related work on defining fuzzy classes and identifying limitations in existing approaches. It then summarizes the authors' previous work developing a generalized fuzzy class structure and model. The document introduces several new data types for representing different types of fuzzy attributes. Finally, it proposes a formal definition language for the fuzzy class model that utilizes the new data types to define fuzzy class structure and accurately represent fuzzy data types and attribute values. The language is intended to serve as a data definition language for object-based fuzzy database systems.
Visualizing and Managing Folksonomies, SASWeb 2011 workshop, at UMAP 2011Antonella Dattolo
1) The document proposes a formal model called Folkview to dynamically visualize and manage folksonomies through the use of multiple agents.
2) In Folkview, a folksonomy is represented as a labeled multigraph composed of different structural dimensions, each modeled as an autonomous agent.
3) The agents can manage semantic and structural properties, cooperate to provide personalized and dynamic views of the folksonomy to users.
The document provides information about cluster analysis and hierarchical agglomerative clustering. It discusses how hierarchical agglomerative clustering works by successively merging the most similar clusters together based on a distance measure between clusters. It begins with each object as its own cluster and merges them into larger clusters, forming a dendrogram, until all objects are in a single cluster. Different linkage criteria like single, complete, and average linkage can be used to define the distance between clusters.
Gleaning Types for Literals in RDF with Application to Entity SummarizationKalpa Gunaratna
ESWC 2016 talk about how to compute types (ontology classes) for literals and add semantics to them, making them richer. Then utilize them in an entity summarization usecase.
Exploiting Entity Linking in Queries For Entity RetrievalFaegheh Hasibi
Slides for the ICTIR 2016 paper: "Exploiting Entity Linking in Queries For Entity Retrieval"
The premise of entity retrieval is to better answer search queries by returning specific entities instead of documents. Many queries mention particular entities; recognizing and linking them to the corresponding entry in a knowledge base is known as the task of entity
linking in queries. In this paper we make a first attempt at bringing together these two, i.e., leveraging entity annotations of queries in the entity retrieval model. We introduce a new probabilistic component and show how it can be applied on top of any term based entity retrieval model that can be emulated in the Markov Random Field framework, including language models, sequential dependence models, as well as their fielded variations. Using a standard entity retrieval test collection, we show that our extension brings consistent improvements over all baseline methods, including the current state-of-the-art. We further show that our extension is robust against parameter settings.
The document discusses fuzzy logical databases and an efficient algorithm for evaluating fuzzy equi-joins. It introduces fuzzy concepts in databases, defines a new measure for fuzzy equality, and proposes a fuzzy equi-join based on this measure. It then presents a sort-merge join algorithm called SMFEJ that uses interval ordering to efficiently evaluate the fuzzy equi-join in two phases: sorting relations on join attributes, and joining tuples with overlapping ranges.
This paper proposes using additional context features from neighboring semantic arguments to improve the accuracy of automatic semantic argument classification. The researchers augment baseline features, which consider each argument independently, with semantic context features that capture the interdependence between arguments of the same predicate. Their experimental results show significant improvement in classification accuracy on the PropBank test set when exploiting argument interdependence through additional context features. Classification accuracy increases to 90.50%, an 18% relative error reduction over the baseline.
Learning Collaborative Agents with Rule Guidance for Knowledge Graph ReasoningDeren Lei
Walk-based models have shown their advantages in knowledge graph (KG) reasoning by achieving decent performance while providing interpretable decisions. However, the sparse reward signals offered by the KG during traversal are often insufficient to guide a sophisticated walk-based reinforcement learning (RL) model. An alternate approach is to use traditional symbolic methods (e.g., rule induction), which achieve good performance but can be hard to generalize due to the limitation of symbolic representation. In this paper, we propose RuleGuider, which leverages high-quality rules generated by symbolic-based methods to provide reward supervision for walk-based agents. Experiments on benchmark datasets show that RuleGuider improves the performance of walk-based models without losing interpretability.
This document proposes a generalized definition language for implementing an object-based fuzzy class model. It begins by reviewing related work on defining fuzzy classes and identifying limitations in existing approaches. It then summarizes the authors' previous work developing a generalized fuzzy class structure and model. The document introduces several new data types for representing different types of fuzzy attributes. Finally, it proposes a formal definition language for the fuzzy class model that utilizes the new data types to define fuzzy class structure and accurately represent fuzzy data types and attribute values. The language is intended to serve as a data definition language for object-based fuzzy database systems.
Visualizing and Managing Folksonomies, SASWeb 2011 workshop, at UMAP 2011Antonella Dattolo
1) The document proposes a formal model called Folkview to dynamically visualize and manage folksonomies through the use of multiple agents.
2) In Folkview, a folksonomy is represented as a labeled multigraph composed of different structural dimensions, each modeled as an autonomous agent.
3) The agents can manage semantic and structural properties, cooperate to provide personalized and dynamic views of the folksonomy to users.
The document provides information about cluster analysis and hierarchical agglomerative clustering. It discusses how hierarchical agglomerative clustering works by successively merging the most similar clusters together based on a distance measure between clusters. It begins with each object as its own cluster and merges them into larger clusters, forming a dendrogram, until all objects are in a single cluster. Different linkage criteria like single, complete, and average linkage can be used to define the distance between clusters.
Gleaning Types for Literals in RDF with Application to Entity SummarizationKalpa Gunaratna
ESWC 2016 talk about how to compute types (ontology classes) for literals and add semantics to them, making them richer. Then utilize them in an entity summarization usecase.
This document discusses tensor decompositions for learning latent variable models. It presents five applications of latent variable modeling: topic modeling, understanding human communities, recommender systems, feature learning, and computational biology. It describes the statistical framework of discovering hidden structure through latent variable models and graphical modeling. A key challenge is efficiently learning latent variable models, as maximum likelihood is NP-hard. The document proposes using tensor decompositions to efficiently and consistently learn latent variable models from data in a guaranteed manner. It outlines experimental results on social networks that demonstrate the tensor approach outperforms variational methods in accuracy and speed.
Patterns that only occur in objects belonging to a
single class are called Jumping Emerging Patterns (JEP). JEP
based Classifiers are considered one of the successful classification
systems. Due to its comprehensibility, simplicity and strong
differentiating abilities JEPs have captured significant recognition.
However, discovery of JEPs in a large pattern space is normally a
time consuming and challenging task because of their exponential
behaviour. In this work a novel method based on genetic
algorithm (GA) is proposed to discover JEPs in large pattern
space. Since the complexity of GA is lower than other algorithms,
so we have combined the power of JEPs and GA to find high
quality JEPs from datasets to improve performance of
classification system. Our proposed method explores a set of high
quality JEPs from pattern search space unlike other methods in
literature that compute complete set of JEPs, Large numbers of
duplicate and redundant JEPs are filtered out during their
discovery process. Experimental results show that our proposed
Genetic-JEPs are effective and accurate for classification of a
variety of data sets and in general achieve higher accuracy than
other standard classifiers.
The document discusses higher order learning and the role of higher order relations in machine learning. It describes how higher order relations between instances can provide additional context compared to considering instances independently. Specifically, it discusses how higher order relations have been shown to improve performance in statistical relational learning, text classification, and supervised learning algorithms like LSI. The document also proposes approaches for leveraging higher order relations in supervised learning tasks and for unsupervised learning through an extension of association rule mining.
Linear discriminant analysis (LDA) is a method used to classify observations into categories. LDA finds a linear combination of features that best separates two or more classes of objects. It assumes normal distributions of data and equal class prior probabilities. LDA seeks projections of high-dimensional data onto a line or plane that best separates the classes.
This document discusses fuzzy logical databases and an efficient algorithm for evaluating fuzzy equi-joins. It begins with an introduction to fuzzy concepts in databases, including representing imprecise data using fuzzy sets and membership functions. It then defines a new measure for fuzzy equality that is used to define a fuzzy equi-join. The document proposes a sort-merge join algorithm that sorts relations based on a partial order of intervals to efficiently evaluate the fuzzy equi-join in two phases: sorting and joining. Experimental results are said to show a significant improvement in efficiency when using this algorithm.
IJERD (www.ijerd.com) International Journal of Engineering Research and Devel...IJERD Editor
This document summarizes and compares various multi-relational decision tree learning algorithms. It first introduces the MRDTL algorithm, which constructs decision trees from relational databases by using selection graphs to represent patterns at nodes. The document then discusses limitations of MRDTL related to running time and handling missing values. It proposes an improved MRDTL-2 algorithm that uses a naive Bayesian classifier for preprocessing to address missing values, and tuple ID propagation instead of complex data structures to improve running time. Finally, it describes the RDC algorithm, which uses tuple ID propagation and information gain to build decision trees from relational databases.
The document provides an overview of database concepts, the relational data model, relational algebra, structured query language (SQL), and SQL commands. It defines key concepts like relations, attributes, tuples, domains, and keys. It describes relational algebra operations like selection, projection, join, and set operations. It also outlines the different components of SQL like DDL, DML, DCL and provides examples of SQL commands for data definition, manipulation, and control.
This document summarizes several key dimensionality reduction algorithms:
1) FOCUS and RELIEF are two of the earliest and most widely cited dimensionality reduction algorithms. FOCUS implements feature selection by preferring consistent hypotheses with few features, while RELIEF ranks features based on distance between instances of the same and different classes.
2) RELIEF was later expanded to handle multi-class problems (RELIEF-F) and incomplete data (RELIEF-D). Liu and Dash also reviewed dimensionality reduction methods and categorized them based on their generation and evaluation procedures.
3) Liu et al. proposed the LVF (Las Vegas Filter) algorithm for feature selection, which uses random search guided by in
A Real Time Application of Soft Set in Parameterization Reduction for Decisio...IJECEIAES
In each and every field of science and technology Information science plays an important role. Sometimes information science is facing different types of problems to handle the data and information. Data Uncertainty is one of the challenging difficulties to handle. In past, there are several theories like fuzzy set, Rough set, Probability etc.to dealing with uncertainty. Soft set theory is the youngest theory to deal with uncertainty. In this paper we discussed how to find reducts. This paper focuses on how we can transform a sample data set to binary valued information system. We are also going to reduce the dimension of data set by using the binary valued information that results a better decision.
The document proposes a category vector space approach for multi-class classification problems. It discusses limitations of combining binary classifiers and motivates mapping categories to a geometric vector space to maximize margins. It outlines solving classification and regression in this category space using support vector machines with kernels to project vectors between spaces. Preliminary results apply this to a 3-class face recognition problem using a Gaussian radial basis function classifier.
Introduction Of Sinostrat Solution (Sn)Julien Charre
Sinostrat Solutions is an internet marketing consulting company that offers various services including consulting, marketing solutions, branding and design, and web development. They provide services such as website audits, web design, content management system integration, search engine optimization, social media marketing, and online public relations. Sinostrat aims to help clients increase their online visibility, traffic, and brand recognition through strategic internet marketing solutions.
This document discusses a study on student aspirations for higher education in Central Queensland, Australia. The study surveyed 258 high school students about their educational and career aspirations. It found that 58% aspired to careers requiring a university degree, but many lacked knowledge about university pathways. Students from disadvantaged backgrounds had fewer "navigational nodes" between current situations and aspirations. The study concludes students need reliable information on post-secondary options to help resource their university aspirations.
The document contains quotes from various authors about choices and their consequences. It discusses how the choices we make define who we are and shape our lives and futures. Several quotes emphasize that our choices have consequences, whether intended or not, and that happiness comes from the choices we make rather than external factors. While we have freedom to choose, we cannot choose the results of our choices.
European Utility Week - US Business Utility ModelsPaul De Martini
This document summarizes key points about changing US utility business models:
1) Policy drivers like renewable portfolio standards are spurring more distributed energy resource (DER) adoption by customers. Over 80% of the US population is under policies equivalent to the EU's 20/20/20 plan.
2) DER capacity like solar, demand response, and backup generation is projected to reach 30% of total US capacity by 2020, coming entirely from customers.
3) Increasing variable renewable generation and customer DER are changing utility operational needs and requiring more flexible resources to balance the grid.
4) Utilities will need to develop new strategies like offering differentiated energy solutions and facilitating DER markets to maintain competitive advantages
Leonard Sweet responds to accusations and criticisms of his theology and writings. He acknowledges some past works could be improved but denies endorsing New Age or emergent theology. While quoting outside sources, he aimed to evangelize, not compromise orthodoxy. He critiques how "emerging church" lacks passion for salvation and separates Jesus from his teachings. Reviews of Sweet's book "Nudge" criticize his view that evangelism should "nudge" people to the God within them, not introduce them to Jesus, but Sweet argues traditional evangelism is flawed and his goal is to revolutionize how Christians reach others.
EFFICIENTLY PROCESSING OF TOP-K TYPICALITY QUERY FOR STRUCTURED DATAcsandit
This work presents a novel ranking scheme for structured data. We show how to apply the
notion of typicality analysis from cognitive science and how to use this notion to formulate the
problem of ranking data with categorical attributes. First, we formalize the typicality query
model for relational databases. We adopt Pearson correlation coefficient to quantify the extent
of the typicality of an object. The correlation coefficient estimates the extent of statistical
relationships between two variables based on the patterns of occurrences and absences of their
values. Second, we develop a top-k query processing method for efficient computation. TPFilter
prunes unpromising objects based on tight upper bounds and selectively joins tuples of highest
typicality score. Our methods efficiently prune unpromising objects based on upper bounds.
Experimental results show our approach is promising for real data.
DIRECTIONS READ THE FOLLOWING STUDENT POST AND RESPOND EVALUATE I.docxlynettearnold46882
DIRECTIONS: READ THE FOLLOWING STUDENT POST AND RESPOND EVALUATE ITS CONTENT. PLEASE CITE ALL REFERENCES
Katie Kessler
Unit 2 Discussion 1
Top of Form
The word “noir” is used to remember the scaling of measurement in psychology (Embretson, 2004). In short, the letters stand for nominal, ordinal, interval and ratio (Embretson, 2004). To give a brief introduction of what each scale measures, “nominal is the simplest way to measure” because it focuses on categorizing measurements on a scale of category, according to Embretson (2004). An example of nominal is eye color. “Ordinal measures in terms of ranking, interval measures scores of tests that focus on unobservable mental functioning and ratio focuses on measuring activities in the physical world, such as someone’s running time” (Embretson, 2004). With different scales of measurement, there are two methods to compare sets of data. These include norm-referenced and criterion-referenced testing. According to Embretson (2004) norm-referenced testing “yields information on a testtaker’s standing or ranking relative to some comparison group of testtakers.” In other words, it focuses on the performance of peers. Criterion-referenced testing is a little different because it focuses on examining individual’s scores to a set standard (Embretson, 2004).
The ability for ordinal measurement scale to be utilized on a standardized test as a norm-referenced test is high since an ordinal scale is based upon ranking and norm-referenced testing gathers information on the examinees ranking compared to a group of testtakers. For example, a study conducted on decision making with the use of ordinal variables states that ordinal measurement scales has the ability to be utilized by norm-referenced testing (Barua, Kademane, Das, Gubbiyappa, Verma, & Al-Dubai, 2014).On the other hand, ordinal scaling would not be a strong measurement for criterion-referenced testing because it focuses on the ranking rather than the measurement of the scores to be close to a set standard.
Ratio scaling directs its focus on measuring objects and activities in the physical world which would be beneficial for criterion-referenced testing instead of norm-referenced testing. Imagine a marathon runner who was trying to beat the world’s fastest time running a marathon. Criterion-referenced testing allows the runner to be aware of the set standard the marathon runner needs to beat to be the best and set a new standard. Norm-referenced testing would not be as useful because the marathon runner would not have the standard measurement he or she needs to beat. However, the marathon runner would be aware of the relative time he or she needs to beat to be the best. That is not as helpful as the criterion-referenced testing because runners need an exact number instead of a relative number in comparison to other runners.
Norm-referenced data would be collected by “the standards relative to a group, such as means and standard deviation.
Evaluation of subjective answers using glsa enhanced with contextual synonymyijnlc
Evaluation of subjective answers submitted in an exam is an essential but one of the most resource consuming educational activity. This paper details experiments conducted under our project to build a software that evaluates the subjective answers of informative nature in a given knowledge domain. The paper first summarizes the techniques such as Generalized Latent Semantic Analysis (GLSA) and Cosine Similarity that provide basis for the proposed model. The further sections point out the areas of improvement in the previous work and describe our approach towards the solutions of the same. We then discuss the implementation details of the project followed by the findings that show the improvements achieved. Our approach focuses on comprehending the various forms of expressing same entity and thereby capturing the subjectivity of text into objective parameters. The model is tested by evaluating answers submitted by 61 students of Third Year B. Tech. CSE class of Walchand College of Engineering Sangli in a test on Database Engineering.
Found this paper really interesting. It delves into the learning behaviors of Deep Learning Ensembles and compares them Bayesian Neural Networks, which theoretically does the same thing. This answers why Deep Ensembles Outperform
This tutorial covers machine learning approaches for learning to rank documents in information retrieval systems. It discusses how early IR methods did not incorporate machine learning. It then covers: (1) ordinal regression approaches that learn multiple thresholds to account for the ordered nature of relevance labels; (2) optimizing pairwise preferences between documents, which decomposes the problem and allows for efficient algorithms; (3) directly optimizing rank-based evaluation measures like MAP and NDCG using structural SVMs, boosting, or smooth approximations to allow for gradient descent optimization of discontinuous objectives. The goal is to outperform traditional IR methods by applying machine learning techniques to learn good ranking functions.
This document discusses tensor decompositions for learning latent variable models. It presents five applications of latent variable modeling: topic modeling, understanding human communities, recommender systems, feature learning, and computational biology. It describes the statistical framework of discovering hidden structure through latent variable models and graphical modeling. A key challenge is efficiently learning latent variable models, as maximum likelihood is NP-hard. The document proposes using tensor decompositions to efficiently and consistently learn latent variable models from data in a guaranteed manner. It outlines experimental results on social networks that demonstrate the tensor approach outperforms variational methods in accuracy and speed.
Patterns that only occur in objects belonging to a
single class are called Jumping Emerging Patterns (JEP). JEP
based Classifiers are considered one of the successful classification
systems. Due to its comprehensibility, simplicity and strong
differentiating abilities JEPs have captured significant recognition.
However, discovery of JEPs in a large pattern space is normally a
time consuming and challenging task because of their exponential
behaviour. In this work a novel method based on genetic
algorithm (GA) is proposed to discover JEPs in large pattern
space. Since the complexity of GA is lower than other algorithms,
so we have combined the power of JEPs and GA to find high
quality JEPs from datasets to improve performance of
classification system. Our proposed method explores a set of high
quality JEPs from pattern search space unlike other methods in
literature that compute complete set of JEPs, Large numbers of
duplicate and redundant JEPs are filtered out during their
discovery process. Experimental results show that our proposed
Genetic-JEPs are effective and accurate for classification of a
variety of data sets and in general achieve higher accuracy than
other standard classifiers.
The document discusses higher order learning and the role of higher order relations in machine learning. It describes how higher order relations between instances can provide additional context compared to considering instances independently. Specifically, it discusses how higher order relations have been shown to improve performance in statistical relational learning, text classification, and supervised learning algorithms like LSI. The document also proposes approaches for leveraging higher order relations in supervised learning tasks and for unsupervised learning through an extension of association rule mining.
Linear discriminant analysis (LDA) is a method used to classify observations into categories. LDA finds a linear combination of features that best separates two or more classes of objects. It assumes normal distributions of data and equal class prior probabilities. LDA seeks projections of high-dimensional data onto a line or plane that best separates the classes.
This document discusses fuzzy logical databases and an efficient algorithm for evaluating fuzzy equi-joins. It begins with an introduction to fuzzy concepts in databases, including representing imprecise data using fuzzy sets and membership functions. It then defines a new measure for fuzzy equality that is used to define a fuzzy equi-join. The document proposes a sort-merge join algorithm that sorts relations based on a partial order of intervals to efficiently evaluate the fuzzy equi-join in two phases: sorting and joining. Experimental results are said to show a significant improvement in efficiency when using this algorithm.
IJERD (www.ijerd.com) International Journal of Engineering Research and Devel...IJERD Editor
This document summarizes and compares various multi-relational decision tree learning algorithms. It first introduces the MRDTL algorithm, which constructs decision trees from relational databases by using selection graphs to represent patterns at nodes. The document then discusses limitations of MRDTL related to running time and handling missing values. It proposes an improved MRDTL-2 algorithm that uses a naive Bayesian classifier for preprocessing to address missing values, and tuple ID propagation instead of complex data structures to improve running time. Finally, it describes the RDC algorithm, which uses tuple ID propagation and information gain to build decision trees from relational databases.
The document provides an overview of database concepts, the relational data model, relational algebra, structured query language (SQL), and SQL commands. It defines key concepts like relations, attributes, tuples, domains, and keys. It describes relational algebra operations like selection, projection, join, and set operations. It also outlines the different components of SQL like DDL, DML, DCL and provides examples of SQL commands for data definition, manipulation, and control.
This document summarizes several key dimensionality reduction algorithms:
1) FOCUS and RELIEF are two of the earliest and most widely cited dimensionality reduction algorithms. FOCUS implements feature selection by preferring consistent hypotheses with few features, while RELIEF ranks features based on distance between instances of the same and different classes.
2) RELIEF was later expanded to handle multi-class problems (RELIEF-F) and incomplete data (RELIEF-D). Liu and Dash also reviewed dimensionality reduction methods and categorized them based on their generation and evaluation procedures.
3) Liu et al. proposed the LVF (Las Vegas Filter) algorithm for feature selection, which uses random search guided by in
A Real Time Application of Soft Set in Parameterization Reduction for Decisio...IJECEIAES
In each and every field of science and technology Information science plays an important role. Sometimes information science is facing different types of problems to handle the data and information. Data Uncertainty is one of the challenging difficulties to handle. In past, there are several theories like fuzzy set, Rough set, Probability etc.to dealing with uncertainty. Soft set theory is the youngest theory to deal with uncertainty. In this paper we discussed how to find reducts. This paper focuses on how we can transform a sample data set to binary valued information system. We are also going to reduce the dimension of data set by using the binary valued information that results a better decision.
The document proposes a category vector space approach for multi-class classification problems. It discusses limitations of combining binary classifiers and motivates mapping categories to a geometric vector space to maximize margins. It outlines solving classification and regression in this category space using support vector machines with kernels to project vectors between spaces. Preliminary results apply this to a 3-class face recognition problem using a Gaussian radial basis function classifier.
Introduction Of Sinostrat Solution (Sn)Julien Charre
Sinostrat Solutions is an internet marketing consulting company that offers various services including consulting, marketing solutions, branding and design, and web development. They provide services such as website audits, web design, content management system integration, search engine optimization, social media marketing, and online public relations. Sinostrat aims to help clients increase their online visibility, traffic, and brand recognition through strategic internet marketing solutions.
This document discusses a study on student aspirations for higher education in Central Queensland, Australia. The study surveyed 258 high school students about their educational and career aspirations. It found that 58% aspired to careers requiring a university degree, but many lacked knowledge about university pathways. Students from disadvantaged backgrounds had fewer "navigational nodes" between current situations and aspirations. The study concludes students need reliable information on post-secondary options to help resource their university aspirations.
The document contains quotes from various authors about choices and their consequences. It discusses how the choices we make define who we are and shape our lives and futures. Several quotes emphasize that our choices have consequences, whether intended or not, and that happiness comes from the choices we make rather than external factors. While we have freedom to choose, we cannot choose the results of our choices.
European Utility Week - US Business Utility ModelsPaul De Martini
This document summarizes key points about changing US utility business models:
1) Policy drivers like renewable portfolio standards are spurring more distributed energy resource (DER) adoption by customers. Over 80% of the US population is under policies equivalent to the EU's 20/20/20 plan.
2) DER capacity like solar, demand response, and backup generation is projected to reach 30% of total US capacity by 2020, coming entirely from customers.
3) Increasing variable renewable generation and customer DER are changing utility operational needs and requiring more flexible resources to balance the grid.
4) Utilities will need to develop new strategies like offering differentiated energy solutions and facilitating DER markets to maintain competitive advantages
Leonard Sweet responds to accusations and criticisms of his theology and writings. He acknowledges some past works could be improved but denies endorsing New Age or emergent theology. While quoting outside sources, he aimed to evangelize, not compromise orthodoxy. He critiques how "emerging church" lacks passion for salvation and separates Jesus from his teachings. Reviews of Sweet's book "Nudge" criticize his view that evangelism should "nudge" people to the God within them, not introduce them to Jesus, but Sweet argues traditional evangelism is flawed and his goal is to revolutionize how Christians reach others.
EFFICIENTLY PROCESSING OF TOP-K TYPICALITY QUERY FOR STRUCTURED DATAcsandit
This work presents a novel ranking scheme for structured data. We show how to apply the
notion of typicality analysis from cognitive science and how to use this notion to formulate the
problem of ranking data with categorical attributes. First, we formalize the typicality query
model for relational databases. We adopt Pearson correlation coefficient to quantify the extent
of the typicality of an object. The correlation coefficient estimates the extent of statistical
relationships between two variables based on the patterns of occurrences and absences of their
values. Second, we develop a top-k query processing method for efficient computation. TPFilter
prunes unpromising objects based on tight upper bounds and selectively joins tuples of highest
typicality score. Our methods efficiently prune unpromising objects based on upper bounds.
Experimental results show our approach is promising for real data.
DIRECTIONS READ THE FOLLOWING STUDENT POST AND RESPOND EVALUATE I.docxlynettearnold46882
DIRECTIONS: READ THE FOLLOWING STUDENT POST AND RESPOND EVALUATE ITS CONTENT. PLEASE CITE ALL REFERENCES
Katie Kessler
Unit 2 Discussion 1
Top of Form
The word “noir” is used to remember the scaling of measurement in psychology (Embretson, 2004). In short, the letters stand for nominal, ordinal, interval and ratio (Embretson, 2004). To give a brief introduction of what each scale measures, “nominal is the simplest way to measure” because it focuses on categorizing measurements on a scale of category, according to Embretson (2004). An example of nominal is eye color. “Ordinal measures in terms of ranking, interval measures scores of tests that focus on unobservable mental functioning and ratio focuses on measuring activities in the physical world, such as someone’s running time” (Embretson, 2004). With different scales of measurement, there are two methods to compare sets of data. These include norm-referenced and criterion-referenced testing. According to Embretson (2004) norm-referenced testing “yields information on a testtaker’s standing or ranking relative to some comparison group of testtakers.” In other words, it focuses on the performance of peers. Criterion-referenced testing is a little different because it focuses on examining individual’s scores to a set standard (Embretson, 2004).
The ability for ordinal measurement scale to be utilized on a standardized test as a norm-referenced test is high since an ordinal scale is based upon ranking and norm-referenced testing gathers information on the examinees ranking compared to a group of testtakers. For example, a study conducted on decision making with the use of ordinal variables states that ordinal measurement scales has the ability to be utilized by norm-referenced testing (Barua, Kademane, Das, Gubbiyappa, Verma, & Al-Dubai, 2014).On the other hand, ordinal scaling would not be a strong measurement for criterion-referenced testing because it focuses on the ranking rather than the measurement of the scores to be close to a set standard.
Ratio scaling directs its focus on measuring objects and activities in the physical world which would be beneficial for criterion-referenced testing instead of norm-referenced testing. Imagine a marathon runner who was trying to beat the world’s fastest time running a marathon. Criterion-referenced testing allows the runner to be aware of the set standard the marathon runner needs to beat to be the best and set a new standard. Norm-referenced testing would not be as useful because the marathon runner would not have the standard measurement he or she needs to beat. However, the marathon runner would be aware of the relative time he or she needs to beat to be the best. That is not as helpful as the criterion-referenced testing because runners need an exact number instead of a relative number in comparison to other runners.
Norm-referenced data would be collected by “the standards relative to a group, such as means and standard deviation.
Evaluation of subjective answers using glsa enhanced with contextual synonymyijnlc
Evaluation of subjective answers submitted in an exam is an essential but one of the most resource consuming educational activity. This paper details experiments conducted under our project to build a software that evaluates the subjective answers of informative nature in a given knowledge domain. The paper first summarizes the techniques such as Generalized Latent Semantic Analysis (GLSA) and Cosine Similarity that provide basis for the proposed model. The further sections point out the areas of improvement in the previous work and describe our approach towards the solutions of the same. We then discuss the implementation details of the project followed by the findings that show the improvements achieved. Our approach focuses on comprehending the various forms of expressing same entity and thereby capturing the subjectivity of text into objective parameters. The model is tested by evaluating answers submitted by 61 students of Third Year B. Tech. CSE class of Walchand College of Engineering Sangli in a test on Database Engineering.
Found this paper really interesting. It delves into the learning behaviors of Deep Learning Ensembles and compares them Bayesian Neural Networks, which theoretically does the same thing. This answers why Deep Ensembles Outperform
This tutorial covers machine learning approaches for learning to rank documents in information retrieval systems. It discusses how early IR methods did not incorporate machine learning. It then covers: (1) ordinal regression approaches that learn multiple thresholds to account for the ordered nature of relevance labels; (2) optimizing pairwise preferences between documents, which decomposes the problem and allows for efficient algorithms; (3) directly optimizing rank-based evaluation measures like MAP and NDCG using structural SVMs, boosting, or smooth approximations to allow for gradient descent optimization of discontinuous objectives. The goal is to outperform traditional IR methods by applying machine learning techniques to learn good ranking functions.
Automated Essay Scoring Using Generalized Latent Semantic AnalysisGina Rizzo
The document describes an automated essay scoring system called Generalized Latent Semantic Analysis (GLSA) that was developed to improve upon existing Latent Semantic Analysis (LSA)-based systems. GLSA forms an n-gram by document matrix instead of a word by document matrix to better capture word order. Experimental results showed GLSA achieved higher accuracy levels compared to both existing LSA techniques and human graders when evaluating essays. The proposed GLSA system demonstrates the potential for automated essay scoring to surpass human grading performance.
This document discusses using unsupervised support vector analysis to increase the efficiency of simulation-based functional verification. It describes applying an unsupervised machine learning technique called support vector analysis to filter redundant tests from a set of verification tests. By clustering similar tests into regions of a similarity metric space, it aims to select the most important tests to verify a design while removing redundant tests, improving verification efficiency. The approach trains an unsupervised support vector model on an initial set of simulated tests and uses it to filter future tests by comparing them to support vectors that define regions in the similarity space.
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EXPERT OPINION AND COHERENCE BASED TOPIC MODELINGijnlc
In this paper, we propose a novel algorithm that rearrange the topic assignment results obtained from topic
modeling algorithms, including NMF and LDA. The effectiveness of the algorithm is measured by how much
the results conform to expert opinion, which is a data structure called TDAG that we defined to represent the
probability that a pair of highly correlated words appear together. In order to make sure that the internal
structure does not get changed too much from the rearrangement, coherence, which is a well known metric
for measuring the effectiveness of topic modeling, is used to control the balance of the internal structure.
We developed two ways to systematically obtain the expert opinion from data, depending on whether the
data has relevant expert writing or not. The final algorithm which takes into account both coherence and
expert opinion is presented. Finally we compare amount of adjustments needed to be done for each topic
modeling method, NMF and LDA.
This document describes a new genetic algorithm (GA)-based system for predicting the future performance of individual stocks. The system uses GAs for inductive machine learning rather than optimization. It is compared to a neural network system using data from over 1,600 stocks. The study finds that the GA system can predict stock returns 12 weeks in the future and that combining GA and neural network forecasts provides synergistic benefits.
Object segmentation by alignment of poselet activations to image contoursirisshicat
This document proposes techniques to segment objects in images using a combination of bottom-up (image edges) and top-down (object detector) cues. The key techniques are:
1. Extending an existing poselet detector to 19 additional categories beyond people. Poselets can predict masks for parts of objects.
2. Non-rigidly aligning the predicted poselet masks to image contours to increase segmentation precision and remove false positives.
3. Spatially smoothing the aligned masks while ensuring non-overlapping object regions using a variational technique.
4. Refining the segmentation further based on self-similarity of small image patches. The approach achieves competitive results on the PASCAL VOC benchmark
HOLISTIC EVALUATION OF XML QUERIES WITH STRUCTURAL PREFERENCES ON AN ANNOTATE...ijseajournal
With the emergence of XML as de facto format for storing and exchanging information over the Internet, the search for ever more innovative and effective techniques for their querying is a major and current concern of the XML database community. Several studies carried out to help solve this problem are mostly oriented towards the evaluation of so-called exact queries which, unfortunately, are likely (especially in the case of semi-structured documents) to yield abundant results (in the case of vague queries) or empty results (in the case of very precise queries). From the observation that users who make requests are not necessarily interested in all possible solutions, but rather in those that are closest to their needs, an important field of research has been opened on the evaluation of preferences queries. In this paper, we propose an approach for the evaluation of such queries, in case the preferences concern the structure of the document. The solution investigated revolves around the proposal of an evaluation plan in three phases: rewriting-evaluation-merge. The rewriting phase makes it possible to obtain, from a partitioningtransformation operation of the initial query, a hierarchical set of preferences path queries which are holistically evaluated in the second phase by an instrumented version of the algorithm TwigStack. The merge phase is the synthesis of the best results.
This paper addresses the problem of determinizing probabilistic data to enable its use in legacy systems that only accept deterministic data. It proposes a query-aware approach that selects deterministic representations of probabilistic data to optimize the quality of answers to queries over that data. A key contribution is a branch-and-bound algorithm that finds an approximate near-optimal solution to the NP-hard problem of minimizing the expected cost of query answers. Empirical results demonstrate the algorithm is efficient and achieves high quality results close to the optimal.
Query Aware Determinization of Uncertain Objectsnexgentechnology
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www.nexgenproject.com
Mobile: 9751442511,9791938249
Telephone: 0413-2211159.
NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
Query aware determinization of uncertainnexgentech15
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Puducherry.
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www.nexgenproject.com
Mobile: 9751442511,9791938249
Telephone: 0413-2211159.
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This technical report explores using set-valued attributes for decision tree induction algorithms. Conventional algorithms use single-valued attributes, but the authors argue set-valued attributes can improve accuracy and speed. They describe modifying decision tree algorithms for splitting, pruning, and classification when attributes can have set values. Experiments show the proposed approach works well with only simple pre-pruning needed to limit excessive instance replication across tree branches. The set-valued approach is intended to better handle noise and variability in data values.
The document presents an approach for end-to-end argument mining in student essays that involves identifying argument components and their relationships. It describes a pipeline approach that first performs argument component identification and then relation identification. It also proposes performing joint inference over the outputs using an integer linear programming framework to address errors from the pipeline approach and directly optimize F-score. The approach is evaluated on a corpus of 90 annotated student essays and yields an 18.5% relative reduction in error over the pipeline system.
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Advanced Tactics and Engagement Strategies for Google+Sunny Kr
Succeeding Liam Walsh is Dan Petrovic, the CEO of Dejan SEO, and a well-known search engine specialist. A well-traveled and seasoned presenter, Dan brought a myriad of underutilised Google + capabilities to our attention.
A scalable gibbs sampler for probabilistic entity linkingSunny Kr
This document summarizes a research paper that proposes a scalable Gibbs sampling approach for probabilistic entity linking. The approach formulates entity linking as probabilistic inference in a topic model where each topic corresponds to a Wikipedia article. It introduces an efficient Gibbs sampling scheme that exploits the sparsity in the Wikipedia-LDA model to allow inference over millions of topics. Experimental results show it achieves state-of-the-art performance on the Aida-CoNLL dataset.
Through a detailed analysis of logs of activity for all Google employees, this paper shows how the Google Docs suite (documents, spreadsheets and slides) enables and increases collaboration within Google. In particular, visualization and analysis of the evolution of Google’s collaboration network show that new employees, have started collaborating more quickly and with more people as usage of Docs has grown.
Think Hotels - Book Cheap Hotels WorldwideSunny Kr
This document provides information on booking hotels through an online service. It allows users to choose from over 25,000 hotel locations worldwide and offers the best prices with a single click. Additional details are provided about available accommodations.
Times after the concord stagecoach was on displaySunny Kr
The document summarizes recent events and developments at the San Diego Historical Society:
1) The Society opened a new exhibition called "Nikkei Youth Culture: Past, Present, Future" in their newly dedicated Youth Gallery, focusing on the experiences of Japanese American youth over time.
2) They also advanced the second phase of their core exhibition "Place of Promise" highlighting the diverse histories that formed the foundation of San Diego. Artifacts like a stagecoach and quilts will be featured.
3) The Balboa Art Conservation Center is partnering with the Society to evaluate collection needs and make recommendations for funding conservation, storage, and management efforts.
Wireframes are basic sketches of an app or website that communicate design ideas without visual elements like fonts or colors. They allow teams to align expectations early on and are used by business decision makers to visualize requirements, developers to understand technical needs, and QA testers to write test scripts. The process involves starting with requirements, creating visual mockups that are iterated based on feedback, building the product while updating designs, and keeping wireframes current throughout development and testing.
comScore Inc. - 2013 Mobile Future in FocusSunny Kr
This document provides an overview and analysis of the mobile and connected device landscape in the United States and internationally. Some of the key points covered include:
- Smartphone and tablet adoption has surged, with over 120 million smartphone owners and nearly 50 million tablet owners in the US. This widespread adoption is ushering in a new "Brave New Digital World" of multi-platform media consumption.
- Mobile channels now account for over 1 in 3 minutes of digital media time spent, demonstrating the rise of multi-platform consumption as a new reality. Leading digital properties are extending their reach by 29% on average through mobile.
- The top mobile platforms are Android and iOS, which combined control nearly 90% of the
Search results clustering (SRC) is a challenging algorithmic
problem that requires grouping together the results returned
by one or more search engines in topically coherent clusters,
and labeling the clusters with meaningful phrases describing
the topics of the results included in them.
This document discusses the importance of reproducibility in human computation tasks. It argues that for the results of human computation to be meaningful and informative, they must be reproducible by different human contributors working independently. Reproducibility ensures the results are not due to chance and can reflect the underlying properties of the task. The document outlines sources of variability in human judgments and draws similarities between human computation and content analysis in behavioral sciences, where reproducibility is crucial. It suggests ensuring reproducibility through clear task design and measurement.
Algorithmic entropy can be seen as a special case of entropy as studied in
statistical mechanics. This viewpoint allows us to apply many techniques
developed for use in thermodynamics to the subject of algorithmic information theory. In particular, suppose we fix a universal prefix-free Turing
Optimizing Budget Constrained Spend in Search AdvertisingSunny Kr
Search engine ad auctions typically have a signicant frac-
tion of advertisers who are budget constrained, i.e., if al-
lowed to participate in every auction that they bid on, they
would spend more than their budget. This yields an im-
portant problem: selecting the ad auctions in which these
advertisers participate, in order to optimize dierent system
It is well-known that SRPT is optimal for minimizing
ow time on machines that run one job at a time.
However, running one job at a time is a big under-
utilization for modern systems where sharing, simultane-
ous execution, and virtualization-enabled consolidation
are a common trend to boost utilization. Such machines,
used in modern large data centers and clouds, are
powerful enough to run multiple jobs/VMs at a time
subject to overall CPU, memory, network, and disk
capacity constraints.
Motivated by this pr
HyperLogLog in Practice: Algorithmic Engineering of a State of The Art Cardin...Sunny Kr
Cardinality estimation has a wide range of applications and
is of particular importance in database systems. Various
algorithms have been proposed in the past, and the HyperLogLog algorithm is one of them
We present a linear regression method for predictions on a small data
set making use of a second possibly biased data set that may be much
larger. Our method ts linear regressions to the two data sets while
penalizing the dierence between predictions made by those two models.
Auctions for perishable goods such as internet ad inventory need to make real-time allocation
and pricing decisions as the supply of the good arrives in an online manner, without knowing the
entire supply in advance. These allocation and pricing decisions get complicated when buyers
Approximation Algorithms for the Directed k-Tour and k-Stroll ProblemsSunny Kr
In the Asymmetric Traveling Salesman Problem (ATSP), the input is a directed n-vertex graph G = (V; E) with nonnegative edge lengths, and the goal is to nd a minimum-length tour, visiting
each vertex at least once. ATSP, along with its undirected counterpart, the Traveling Salesman
problem, is a classical combinatorial optimization problem
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?Speck&Tech
ABSTRACT: A prima vista, un mattoncino Lego e la backdoor XZ potrebbero avere in comune il fatto di essere entrambi blocchi di costruzione, o dipendenze di progetti creativi e software. La realtà è che un mattoncino Lego e il caso della backdoor XZ hanno molto di più di tutto ciò in comune.
Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
BIO: Sostenitrice del software libero e dei formati standard e aperti. È stata un membro attivo dei progetti Fedora e openSUSE e ha co-fondato l'Associazione LibreItalia dove è stata coinvolta in diversi eventi, migrazioni e formazione relativi a LibreOffice. In precedenza ha lavorato a migrazioni e corsi di formazione su LibreOffice per diverse amministrazioni pubbliche e privati. Da gennaio 2020 lavora in SUSE come Software Release Engineer per Uyuni e SUSE Manager e quando non segue la sua passione per i computer e per Geeko coltiva la sua curiosità per l'astronomia (da cui deriva il suo nickname deneb_alpha).
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
AI 101: An Introduction to the Basics and Impact of Artificial IntelligenceIndexBug
Imagine a world where machines not only perform tasks but also learn, adapt, and make decisions. This is the promise of Artificial Intelligence (AI), a technology that's not just enhancing our lives but revolutionizing entire industries.
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.
Taking AI to the Next Level in Manufacturing.pdfssuserfac0301
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
“An Outlook of the Ongoing and Future Relationship between Blockchain Technologies and Process-aware Information Systems.” Invited talk at the joint workshop on Blockchain for Information Systems (BC4IS) and Blockchain for Trusted Data Sharing (B4TDS), co-located with with the 36th International Conference on Advanced Information Systems Engineering (CAiSE), 3 June 2024, Limassol, Cyprus.
Full-RAG: A modern architecture for hyper-personalizationZilliz
Mike Del Balso, CEO & Co-Founder at Tecton, presents "Full RAG," a novel approach to AI recommendation systems, aiming to push beyond the limitations of traditional models through a deep integration of contextual insights and real-time data, leveraging the Retrieval-Augmented Generation architecture. This talk will outline Full RAG's potential to significantly enhance personalization, address engineering challenges such as data management and model training, and introduce data enrichment with reranking as a key solution. Attendees will gain crucial insights into the importance of hyperpersonalization in AI, the capabilities of Full RAG for advanced personalization, and strategies for managing complex data integrations for deploying cutting-edge AI solutions.
Fueling AI with Great Data with Airbyte WebinarZilliz
This talk will focus on how to collect data from a variety of sources, leveraging this data for RAG and other GenAI use cases, and finally charting your course to productionalization.
How to Get CNIC Information System with Paksim Ga.pptxdanishmna97
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
CAKE: Sharing Slices of Confidential Data on BlockchainClaudio Di Ciccio
Presented at the CAiSE 2024 Forum, Intelligent Information Systems, June 6th, Limassol, Cyprus.
Synopsis: Cooperative information systems typically involve various entities in a collaborative process within a distributed environment. Blockchain technology offers a mechanism for automating such processes, even when only partial trust exists among participants. The data stored on the blockchain is replicated across all nodes in the network, ensuring accessibility to all participants. While this aspect facilitates traceability, integrity, and persistence, it poses challenges for adopting public blockchains in enterprise settings due to confidentiality issues. In this paper, we present a software tool named Control Access via Key Encryption (CAKE), designed to ensure data confidentiality in scenarios involving public blockchains. After outlining its core components and functionalities, we showcase the application of CAKE in the context of a real-world cyber-security project within the logistics domain.
Paper: https://doi.org/10.1007/978-3-031-61000-4_16
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
Infrastructure Challenges in Scaling RAG with Custom AI modelsZilliz
Building Retrieval-Augmented Generation (RAG) systems with open-source and custom AI models is a complex task. This talk explores the challenges in productionizing RAG systems, including retrieval performance, response synthesis, and evaluation. We’ll discuss how to leverage open-source models like text embeddings, language models, and custom fine-tuned models to enhance RAG performance. Additionally, we’ll cover how BentoML can help orchestrate and scale these AI components efficiently, ensuring seamless deployment and management of RAG systems in the cloud.
Generating privacy-protected synthetic data using Secludy and MilvusZilliz
During this demo, the founders of Secludy will demonstrate how their system utilizes Milvus to store and manipulate embeddings for generating privacy-protected synthetic data. Their approach not only maintains the confidentiality of the original data but also enhances the utility and scalability of LLMs under privacy constraints. Attendees, including machine learning engineers, data scientists, and data managers, will witness first-hand how Secludy's integration with Milvus empowers organizations to harness the power of LLMs securely and efficiently.
Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
Monitoring and Managing Anomaly Detection on OpenShift.pdf
Latent Structured Ranking
1. Latent Structured Ranking
Jason Weston John Blitzer
Google, New York, USA. Google, Mountain View, USA.
jweston@google.com blitzer@google.com
Abstract For example, in the task of collaborative filtering, one
is required to rank items according to their similarity
Many latent (factorized) models have been to the user, and methods which learn latent represen-
proposed for recommendation tasks like col- tations of both users and items have proven very ef-
laborative filtering and for ranking tasks like fective. In particular, Singular Value Decomposition
document or image retrieval and annotation. (SVD) (Billsus and Pazzani, 1998; Bell et al., 2009)
Common to all those methods is that dur- and Non-negative Matrix Factorization (NMF) (Lee
ing inference the items are scored indepen- and Seung, 2001) are two standard methods that at
dently by their similarity to the query in the inference time use equation (1), although the meth-
latent embedding space. The structure of the ods to learn the actual parameters U and V them-
ranked list (i.e. considering the set of items selves are different. In the task of document retrieval,
returned as a whole) is not taken into ac- on the other hand, one is required to rank text docu-
count. This can be a problem because the ments given a text query. The classical method Latent
set of top predictions can be either too di- Semantic Indexing (LSI) (Deerwester et al., 1990) is
verse (contain results that contradict each an unsupervised approach that learns from documents
other) or are not diverse enough. In this pa- only, but still has the form of equation (1) at test time.
per we introduce a method for learning latent More recently, supervised methods have been proposed
structured rankings that improves over ex- that learn the latent representation from (query, doc-
isting methods by providing the right blend ument) relevance pairs, e.g. the method Polynomial
of predictions at the top of the ranked list. Semantic Indexing (SSI) (Bai et al., 2009). Finally,
Particular emphasis is put on making this for multiclass classification tasks, particularly when
method scalable. Empirical results on large involving thousands of possible labels, latent models
scale image annotation and music recommen- have also proven to be very useful, e.g. the Wsabie
dation tasks show improvements over existing model achieves state-of-the-art results on large-scale
approaches. image (Weston et al., 2011) and music (Weston et al.,
2012) annotation tasks. Moreover, all these models
not only perform well but are also efficient in terms of
1 INTRODUCTION computation time and memory usage.
Scoring a single item as in equation (1) is not the end
Traditional latent ranking models score the ith item goal of the tasks described above. Typically for re-
di ∈ RD given a query q ∈ RD using the following comendation and retrieval tasks we are interested in
scoring function: ranking the items. This is achieved by, after scoring
f (q, di ) = q W di = q U V di , (1) each individual item using f (q, di ), sorting the scores,
largest first, to produce a ranked list. Further, typi-
where W = U V has a low rank parameterization, cally only the top few results are then presented to the
and hence q U can be thought of as the latent rep- user, it is thus critical that the method used performs
resentation of the query and V di is equivalently the well for those items. However, one potential flaw in
latent representation for the item. The latent space the models described above is that scoring items indi-
is n-dimensional, where n D, hence U and V are vidually as in eq. (1) does not fully take into account
n × D dimensional matrices. This formulation covers the joint set of items at the top of the list (even when
a battery of different algorithms and applications. optimizing top-of-the-ranked-list type loss functions).
2. The central hypothesis of this paper is that latent method without structure as well as other standard
ranking methods could be improved if one were to take baselines. We also provide some analysis of why we
into account the structure of the ranked list during in- think this is happening.
ference. In particular this would allow the model to
The rest of the paper is as follows. Section 2 describes
make sure there is the right amount of consistency and
our method, Latent Structured Ranking (LaSR). Sec-
diversity in the predictions.
tion 3 discusses previous work and connects them to
Let us suppose for a given query that some of the pre- our method. Section 4 describes our empirical results
dictions at the top of the ranked list are accurate and and finally Section 5 concludes.
some are inaccurate. A model that improves the con-
sistency of the predictions might improve overall accu-
2 METHOD
racy. A structured ranking model that predicts items
dependent on both the query and other items at the
Given a query q ∈ Q our task is to rank a set of doc-
top of the ranked list can achieve such a goal. To give
uments or items D. That is, we are interested in out-
a concrete example, in a music recommendation task ¯
putting (and scoring) a permutation d of the set D,
you might not want to recommend both “heavy metal” ¯j is the jth item in the predicted ranked list.
where d
and “60s folk” in the same top k list. In that case, a
Our ultimate goal will be to design models which take
structured model which encodes item-item similarities
into account not just individual document scores but
as well as query-item similarities could learn this by
the (learned) relative similarities of documents in dif-
representing those two items with very different latent
ferent positions as well.
embedding vectors such that their pairwise item-item
contribution is a large negative value, penalizing both
items appearing in the top k. Note that a structured 2.1 SCORING PERMUTATIONS BY
ranking model can do this despite the possibility that SCORING INDIVIDUAL ITEMS
both items are a good match to the query, so an un-
structured model would find this difficult to achieve. Let us begin by proposing methods for using the stan-
dard latent model of eq. (1) to score permutations.
Conversely, if improved results are gained from encour- We need a method for transforming the scores for
aging the top ranked items to be a rather diverse set for single documents into scores for permutations. Such
a particular query, then a structured model can learn transformations have been studied in several previous
to predict that instead. For example in the task of doc- works, notably (Le and Smola, 2007). They show that
ument retrieval, for ambiguous queries like “jaguar”, finding maximally scoring permutations from single
which may refer either to a Panthera or to the car man- documents can be cast as a linear assignment prob-
ufacturer, diversity should be encouraged. The goal of lem, solvable in polynomial time with the Hungarian
a structured ranker is to learn the optimal tradeoff be- algorithm.
tween consistency and diversity on a case-by-case (per
query) basis. As latent parameters are being learnt for For the vanilla model we propose here, however, we can
each query type this is indeed possible. use a simple parameterization which allows for infer-
ence by sorting. For any given permutation we assign
In this work we propose a latent modeling algorithm a score as follows:
that attempts to do exactly what we describe above.
¯
|d|
Our model learns to predict a ranked list that takes
¯
fvanilla (q, d) = ¯
wi (q U V di ), (2)
into account the structure of the top ranked items by
learning query-item and item-item components. Infer- i=1
ence then tries to find the maximally scoring set of doc-
where for each position i in the permutation, we asso-
uments. It should be noted that while there has been
ciate a weight wi , where wi can be any weights such
strong interest in building structured ranking models
that w1 > w2 > · · · > w|d| ≥ 0. For example, one can
¯
recently (Bakir et al., 2007), to our knowledge this is 1
just set wi = i . Inference using this model is then
the first approach of this type to do so for latent mod-
performed by calculating:
els. Further, the design of our algorithm is also partic-
ularly tuned to work on large scale datasets which are ¯
Fvanilla (q) = argmaxd [fvanilla (q, d )].
¯
the common case for latent models, e.g. in collabora-
tive filtering and large scale annotation and ranking In this case, computing the best-scoring assignment is
tasks. We provide empirical results on two such large simply a matter of sorting documents by their scores
scale datasets, on a music recommendation task, and from eq. (1). To see this note that the score of any
an image annotation task, that show our structured unsorted pair can be increased by sorting, since the
method brings accuracy improvements over the same positional weights wi are fixed and decreasing.
3. 2.2 LATENT STRUCTURED RANKING 2.4 MODEL INFERENCE
The fundamental hypothesis of this paper is that in- At test time for a given query we need to compute:
cluding knowledge about the structure of the rankings
¯ ¯
Flsr (q) = argmaxd [flsr (q, d )]. (5)
at inference time will improve the overall set of ranked
items. That is, we want to define a model where the
¯
score of a document di does not only depend on the Just as inference in the vanilla model can be cast as
query q but also on the other items and their respec- a linear assignment problem, inference in the LaSR
tive positions as well. What is more, we would prefer model can be cast as a quadratic assignment prob-
a model that places more weight on the top items in lem (Lacoste-Julien et al., 2006). This is known to be
a permutation (indeed, this is reflected by common NP hard, so we must approximate it. In this section,
ranking losses like MAP and precision@k). we briefly discuss several alternatives.
This leads us to propose the following class of Latent • Linear programming relaxation: Since we know
Structured Ranking (LaSR) models: we can cast our problem as quadratic assign-
ment, we could consider directly using the linear
¯
|d| ¯
|d|
programming relaxation suggested by (Lacoste-
¯
flsr (q, d) = ¯
wi (q U V di )+ ¯ ¯
wi wj (di S S dj ) . Julien et al., 2006). In our experiments, however,
i=1 i,j=1
we have tens of thousands of labels. Solving even
(3) this relaxed LP per query is computationally in-
In addition to the parameters of eq. (2), we now intro- feasible for problems of this size. We note that
duce the additional parameter S. S takes into account Wsabie’s (Weston et al., 2011) sampling-based
the structure of the predicted ranked list. S S is a low technique is an attempt to overcome even linear
rank matrix of item-item similarities where S is a n×D time inference for this problem.
matrix, just like U and V , and must also be learnt by
the model using training data. • Greedy structured search: we could also consider
a greedy approach to approximately optimizing
¯
eq. (5) as follows: (i) pick the document d1 ∈ D
2.3 CHOICE OF THE wi PARAMETERS
that maximizes:
The weights w are crucial to the usefulness of the ma- ¯ ¯ ¯ ¯
trix in the second term of eq. (3). If wi = 1 for all i fgreedy (q, d1 ) = w1 (qU V d1 ) + (w1 )2 (d1 S S d1 )
then the entire second term would always be the same (6)
¯
no matter what choice of ranking d one chooses. If the and then fix that document as the top ranked pre-
position weights wi are decreasing, however, then the diction. (ii) Find the second best document de-
structural term S is particularly meaningful at the top pendent on the first, by maximizing (for N = 2):
of the list. ¯ ¯
fgreedy (q, dN ) = wN (qU V dN )q
As suggested before in Section 2.1 we could choose
N
wi = 1 . In that case the items that are at the top
i
+ ¯ ¯
wi wN (di S S dN ).
of the predicted ranked list dominate the overall score
i=1
from the second term. In particular, the pairwise item-
item similarities between items in the top-ranked po- Finally, (iii) repeat the above, greedily adding one
sitions play a role in the overall choice of the entire more document each iteration by considering the
¯
ranked list d. Our model can hence learn the consis- above equation for N = 3, . . . , k up to the number
tency vs. diversity tradeoff within the top k we are of desired items to be presented to the user.
interested in. This method has complexity O(k 2 |D|). Its biggest
However, if one knows in advance the number of items drawback is that the highest-scoring document is
one wishes to show to the user (i.e. the top k) then one chosen using the vanilla model. Even if we could
could choose directly to only take into account those improve our score by choosing a different docu-
predictions: ment, taking into account the pairwise scores with
other permutation elements, this algorithm will
wi = 1/i, if i ≤ k, and 0 otherwise. (4) not take advantage of it. Another way to look at
this is, precision@1 would be no better than using
As we will see this also has some computational ad- the vanilla model of eq. (1).
vantages due to its sparsity, and will in fact be our The greedy procedure also permits beam search
method of choice in the algorithm we propose. variants. Using a beam of M candidates this gives
4. a complexity of O(M k 2 |D|). This is tractable at 2.5 LEARNING
test time, but the problem is that during (online)
learning one would have to run this algorithm per We are interested in learning a ranking function where
query, which we believe is still too slow for the the top k retrieved items are of particular interest as
cases we consider here. they will be presented to the user. We wish to optimize
all the parameters of our model jointly for that goal.
• Iterative search: Motivated by the defects in As the datasets we intend to target are large scale,
greedy search and LP relaxation, we propose one stochastic gradient descent (SGD) training seems a vi-
last, iterative method. This method is analo- able option. However, during training we cannot af-
gous to inference by iterated conditional modes ford to perform full inference during each update step
in graphical models (Besag, 1986). (i) On iter- as otherwise training will be too slow. A standard
ation t = 0 predict with an unstructured model loss function that already addresses that issue for the
(i.e. do not use the second term involving S): unstructured case which is often used for retrieval is
¯
|d| the margin ranking criterion (Herbrich et al., 2000;
¯
fiter:t=0 (q, d) = ¯
wi (qU V di ). (7) Joachims, 2002). In particular, it was also used for
i=1 learning factorized document retrieval models in Bai
As mentioned before, computing the best ranking et al. (2009). The loss can be written as:
¯
d just involves sorting the scores qU V di and or- m
dering the documents, largest first. Utilizing the errAU C = max(0, 1 − f (qi , di ) + f (qi , d− )).
sparse choice of wi = 1/i, if i ≤ k, and 0 otherwise i=1 d− =di
described in Section 2.3 we do not have to sort the (10)
entire set, but are only required to find the top k For each training example i = 1, . . . , m, the posi-
which can be done in O(|D| log k) time using a tive item di is compared to all possible negative items
heap. Let us denote the predicted ranked list as d− = di , and one assigns to each pair a cost if the neg-
¯
d0 and in general on each iteration t we are going ative item is larger or within a “margin” of 1 from the
¯
to make predictions dt . (ii) On subsequent itera- positive item. These costs are called pairwise viola-
tions, we maximize the following scoring function: tions. Note that all pairwise violations are considered
¯
|d| equally if they have the same margin violation, inde-
¯
fiter:t>0 (q, d) = ¯
wi (qU V di ) pendent of their position in the list. For this reason
i=1 the margin ranking loss might not optimize the top k
¯
|d| very accurately as it cares about the average rank.
+ ¯ ¯
wi wj (di S S dt−1 ). (8) For the standard (unstructured) latent model case,
j
i,j=1 the problem of optimizing the top of the rank list
¯
As dt−1 is now fixed on iteration t, the per- has also recently been addressed using sampling tech-
document di scores niques (Weston et al., 2011) in the so-called WARP
¯
|d| (Weighted Approximately Ranked Pairwise) loss. Let
¯
(qU V di ) + ¯ ¯t−1
wj (di S S dj ) (9) us first write the predictions of our model for all items
¯
in the database as a vector f (q) where the ith element
j=1
¯ (q) = f (q, di ). One then considers a class of rank-
is fi
are now independent of each other. Hence, they ing error functions:
can be calculated individually and, as before, can
be sorted or the top k can be found, dependent m
on the choice of w. If we use the sparse w of eq. errW ARP = ¯
L(rankdi (f (qi ))) (11)
(4) (which we recommend) then the per-document i=1
scores are also faster to compute as we only re-
¯
where rankdi (f (qi )) is the margin-based rank of the
quire:
labeled item given in the ith training example:
k
¯
(qU V di ) + ¯ ¯t−1
wj (di S S dj ). ¯
rankdi (f (q)) = ¯ ¯
θ(1 + fj (q) ≥ fi (q)) (12)
j=1
j=i
Overall this procedure then has complexity
O(T k|D|) when running for T steps. While this where θ is the indicator function, and L(·) transforms
does not look at first glance to be any faster than this rank into a loss:
the greedy or beam search methods at testing r
time, it has important advantages at training time L(r) = αi , with α1 ≥ α2 ≥ · · · ≥ 0. (13)
as we will see in the next section. i=1
5. The main idea here is to weight the pairwise viola- Algorithm 1 LaSR training algorithm
tions depending on their position in the ranked list. Input: Training pairs {(qi , di )}i=1,...,l .
Different choices of α define different weights (impor- Initialize model parameters Ut , Vt and St (we use
tance) of the relative position of the positive examples 1
mean 0, standard deviation √d ) for each t.
in the ranked list. In particular it was shown that by for t = 0, . . . , T do
choosing αi = 1/i a smooth weighting over positions is repeat
given, where most weight is given to the top position, if t = 0 then
with rapidly decaying weight for lower positions. This f (q, d) = qU0 V0 d.
is useful when one wants to optimize precision at k for else
a variety of different values of k at once Usunier et al. k
f (q, d) = qUt Vt d + j=1 wj d St St dt−1 ¯
j
(2009). (Note that choosing αi = 1 for all i we have end if
the same AUC optimization as equation (10)). Pick a random training pair (q, d+ ).
We can optimize this function by SGD following the Compute f (q, d+ ).
authors of Weston et al. (2011), that is samples are Set N = 0.
drawn at random, and a gradient step is made for each repeat
draw. Due to the cost of computing the exact rank Pick a random document d− ∈ D, d = di .
in (11) it is approximated by sampling. That is, for Compute f (q, d− ).
a given positive label, one draws negative labels until N = N + 1.
a violating pair is found, and then approximates the until f (q + , d+ ) < f (q + , d− ) + 1 or N ≥ |D| − 1
rank with if f (q + , d+ ) < f (q + , d− ) + 1 then
Make a gradient step to minimize:
¯ |D| − 1 L( |D|−1 ) max(1−f (q + , d+ )+f (q + , d− ), 0).
N
rankd (f (q)) ≈
N Project weights to enforce constraints,
i.e. if ||Uti || > C then Uti ← (CUti )/||Uti ||,
where . is the floor function, |D| is the number of i = 1, . . . , D (and likewise for Vt and St ).
items in the database and N is the number of trials end if
in the sampling step. Intuitively, if we need to sample until validation error does not improve.
more negative items before we find a violator then the For each training example, compute the top k
¯
ranking documents dt , i = 1, . . . , k for iteration
rank of the true item is likely to be small (it is likely i
to be at the top of the list, as few negatives are above t using f (q, d) defined above.
it). end for
This procedure for optimizing the top of the ranked
list is very efficient, but it has a disadvantage with of using eq. (8) we instead use:
respect to structured learning: we cannot simply sam- ¯
|d|
ple and score items any longer as we need to somehow ¯ ¯
fiter:t>0 (q, d) = wi (qUt Vt di )
score entire permutations. In particular, it is not di-
i=1
rectly applicable to several of the structured prediction ¯
|d|
approaches like LP, greedy or beam search. That is be- ¯ ¯t−1
¯ + wi wj (di St St dj ). (14)
cause we cannot compute the score of fi independently
i,j=1
because they depend on the ranking of all documents,
which then makes the sampling scheme invalid. How- on iteration t, where Ut , Vt and St are separate matri-
ever, for (a variant of) the iterative algorithm which ces for each iteration. This decouples the learning at
we described in the previous section the WARP (or each iteration. Essentially, we are using a cascade-like
AUC) technique can still be used. architecture of t models trained one after the other.
Note that if a global optimum is reached for each t
The method is as follows. In the first iteration the
then the solution should always be the same or im-
model scores in eq. (7) are independent and so we
prove over step t − 1, as one could pick the weights
can train using the WARP (or AUC) loss. We then
¯ that give exactly the same solution as for step t − 1.
have to compute d0 (the ranking of items) for each
training example for use in the next iteration. Note So far, the one thing we have failed to mention is reg-
that using the sparse w of eq. (4) this is O(D log k) to ularization during learning. One can regularize the
compute, and storage is also only a |D| × k matrix of parameters by preferring smaller weights. We con-
¯
top items. After computing d0 , in the second iteration strain them using ||Sti || ≤ C, ||Uti || ≤ C, ||Vti || ≤ C,
we are again left with independent scoring functions i = 1, . . . , |D|. During SGD one projects the parame-
¯
fi as long as we make one final modification, instead ters back on to the constraints at each step, following
6. the same procedure used in several other works, e.g. ranking structure to make predictions dependent on
Weston et al. (2011); Bai et al. (2009). We can opti- the query and the other predicted items during infer-
mize hyperparameters of the model such as C and the ence by encoding this in the model itself. That is, in
learning rate for SGD using a validation set. our work we explicitly seek to use (and learn) inter-
document similarity measures.
Overall, our preferred version of Latent Structured
Ranking that combines all these design decisions is There has been work on taking into account inter-
given in Algorithm 1. document similarities during ranking. The most fa-
mous and prominent idea is pseudo-relevance feed-
3 PRIOR WORK back via query expansion (Rocchio, 1971). Pseudo-
relevance works by doing normal retrieval (e.g. us-
In the introduction we already mentioned several la- ing cosine similarity in a vector space model), to find
tent ranking methods: SVD (Billsus and Pazzani, an initial set of most relevant documents, and then
1998; Bell et al., 2009), NMF (Lee and Seung, 2001), assuming that the top k ranked documents are rele-
LSI (Deerwester et al., 1990), PSI (Bai et al., 2009) vant, and performing retrieval again by adjusing the
and Wsabie (Weston et al., 2011). We should men- cosine similarity based on previously retrieved docu-
tion that many other methods exist as well, in partic- ments. In a sense, LaSR is also a pseudo-relevance
ular probabilistic methods like pLSA (Hofmann, 1999) feedback technique, but where inter-document simi-
and LDA (Blei et al., 2003). None of those methods, larities are learned to minimize ranking loss.
whether they are supervised or unsupervised, take into More recently, some authors have investigated incorpo-
the structure of the ranked list as we do in this work, rating inter-document similarity during ranking. Qin
and we will use several of them as baselines in our et al. (2008) have investigated incorporating a fixed
experiments. document-document similarity feature in ranking. In
There has been a great deal of recent work on struc- their work, however, they did not score permutations.
tured output learning (Bakir et al., 2007), particularly Instead, each document was associated with a rele-
for linear or kernel SVMs (which are not latent embed- vance score and the authors treated learning as a struc-
ding methods). In methods like Conditional Random tured regression problem. For a situation with implicit
Fields (Lafferty et al., 2001), SVM-struct (Tsochan- feedback, Raman et al. (2012) investigate an inference
taridis et al., 2004) LaSO (Daum´ III and Marcu,
e technique similar to our greedy algorithm. Volkovs
2005) and SEARN (Daum´ et al., 2009) one learns
e and Zemel (2009) also explored listwise ranking us-
to predict an output which has structure, e.g. for se- ing pairwise document interactions in a probabilistic
quence labeling, parse tree prediction and so on. Pre- setup. To the best of our knowledge, however, none
dicting ranked lists can also be seen in this framework. of these methods investigate a learned inter-document
In particular LaSO (Daum´ III and Marcu, 2005) is
e similarity (i.e. latent parameters for that goal), which
a general approach that considers approximate infer- is the most powerful feature of LaSR.
ence using methods like greedy approximation or beam
search that we mentioned in Section 2.4. As we said 4 EXPERIMENTS
before, due to the large number of items we are rank-
ing many of those approaches are infeasible. In our We considered two large scale tasks to test our pro-
method, scalabality is achieved using a cascade-like posed method. The first is a music recommenda-
training setup, and in this regard is related to (Weiss tion task with over 170,000 artists (possible queries
et al., 2010). However, unlike that work, we do not or items) and 5 million training pairs. The second is a
use it to prune the set of items considered for ranking, task of large scale image annotation with over 15,000
we use it to consider pairwise item similarities. labels and 7 million training examples.
The problem of scoring entire permutations for rank-
ing is well-known and has been investigated by many 4.1 MUSIC RECOMMENDATION TASK
authors (Yue et al., 2007a,b; Le and Smola, 2007; Xia
et al., 2008). These works have primarily focused on The first task we conducted experiments on is a large
on using knowledge of the structure (in this case the scale music recommendation task. Given a query
predicted positions in the ranked list) in order to op- (seed) artist, one has to recommend to the user other
timize the right metric, e.g. MAP or precision@k. artists that go well together with this artist if one were
In that sense, methods like Wsabie which uses the listening to both in succession, which is the main step
WARP loss already use structure in the same way. In in playlisting and artist page recommendation on sites
our work we also optimize top-of-the-ranked-list met- like last.fm, music.google.com and programs such
rics by using WARP, but in addition we also use the as iTunes and http://the.echonest.com/.
7. ods the latent dimension n = 50. For LaSR, we give
Table 1: Recommendation Results on the music rec-
results for iterations t = 0, . . . , 2, where t = 0 does
ommendation task. We report for recall at 5, 10, 30
not use the structure. LaSR with t = 0 already out-
and 50 for our method and several baselines.
performs SVD and NMF. LaSR optimizes the top of
Method R@5 R@10 R@30 R@50 the ranked list at training time (via the WARP loss),
NMF 3.76% 6.38% 13.3% 17.8% whereas SVD and NMF do not, which explains why it
SVD 4.01% 6.93% 13.9% 18.5% can perform better here on top-of-the-list metrics. We
LaSR (t = 0) 5.60% 9.49% 18.9% 24.8% tested LaSR t = 0 using the AUC loss (10) instead of
LaSR (t = 1) 6.65% 10.73% 20.1% 26.7%
LaSR (t = 2) 6.93% 10.95% 20.3% 26.5%
WARP (11) to check this hypothesis and we obtained
a recall at 5, 10, 30 and 50 of 3.56%, 6.32%, 14.8%
and 20.3% respectively which are slightly worse, than,
Table 2: Changing the embedding size on the music but similar to SVD, thus confirming our hypothesis.
recommendation task. We report R@5 for various di- For LaSR with t = 1 and t = 2 our method takes into
mensions n. account the structure of the ranked list at inference
time, t = 1 outperforms iteration t = 0 that does not
Method n= 25 50 100 200 use the structure. Further slight gains are obtained
NMF 2.82% 3.76% 3.57% 4.82%
with another iteration (t = 2).
SVD 3.61% 4.01% 4.53% 5.28%
LaSR (t = 0) 5.23% 5.60% 6.24% 6.42%
We used the “Last.fm Dataset - 1K users” dataset Changing the Embedding Dimension The re-
available from http://www.dtic.upf.edu/∼ocelma/ sults so far were all with latent dimension n = 50. It
MusicRecommendationDataset/lastfm-1K.html. could be argued that LaSR with t > 0 has more capac-
This dataset contains (user, timestamp, artist, song) ity (more parameters) than competing methods, and
tuples collected from the Last.fm (www.lastfm.com) those methods could have more capacity by increasing
API, representing the listening history (until May their dimension n. We therefore report results for var-
5th, 2009) for 992 users and 176,948 artists. Two ious embedding sizes (n = 10, 25, 50, 100) in Table 2.
consecutively played artists by the same user are The results show that LaSR (t = 0) consistently out-
considered as a (query, item) pair. Hence, both qi and performs SVD and NMF for all the dimensions tried,
di are D = 176, 948 sparse vectors with one non-zero but even with 200 dimensions, the methods that do not
value (a one) indicating which artist they are. One use structure (SVD, NMF and LaSR t = 0) are still
in every five days (so that the data is disjoint) were outperformed by LaSR that does use structure (t > 0)
left aside for testing, and the remaining data was even with n = 50 dimensions.
used for training and validation. Overall this gave
5,408,975 training pairs, 500,000 validation pairs (for
hyperparameter tuning) and 1,434,568 test pairs.
Analysis of Predictions We give two example
We compare our Latent Structured Ranking approach queries and the top ranked results for LaSR with and
to the same approach without structure by only per- without use of structure ((t = 0) and (t = 1)) in Table
forming one iteration of Algorithm 1. We used k = 20 3. The left-hand query is a popular artist “Bob Dy-
for eq. (4). We also compare to two standard methods lan”. LaSR (t = 0) performs worse than (t = 1) with
of providing latent recommendations, Singular Value “Wilco” in positon 1 - the pair (“Bob Dylan”,“Wilco”)
Decomposition (SVD) and Non-negative Matrix Fac- only appears 10 times in the test set, whereas (“Bob
torization (NMF). For SVD the Matlab implemen- Dylan”, “The Beatles”) appears 40 times, and LaSR
tation is used, and for NMF the implementation at (t = 1) puts the latter in the top position. In general
http://www.csie.ntu.edu.tw/∼cjlin/nmf/ is used. t = 1 improves the top ranked items over t = 0, re-
moving or demoting weak choices, and promoting some
Main Results We report results comparing NMF, better choices. For example, “Sonic Youth” which is
SVD and our method, Latent Structured Ranking a poor match is demoted out of the top 20. The sec-
(LaSR) in Table 1. For every test set (query, item) ond query is a less popular artist “Plaid” who make
pair we rank the document set D according to the electronic music. Adding structure to LaSR again im-
query and record the position of the item in the ranked proves the results in this case by boosting relatively
list. We then measure the recall at 5, 10, 30 and 50. more popular bands like “Orbital” and “µ − ziq”,
(Note that because there is only one item in the pair, and relatively more related bands like “Four-Tet” and
precision at k is equal to recall@k divided by k.) We “Squarepusher”, whilst demoting some lesser known
then average the results over all pairs. For all meth- bands.
8. Table 3: Music Recommendation results for our method LaSR with (t = 1) and without (t = 0) using the
structure. We show top ranked results for a popular query, “Bob Dylan” (folk rock music) and a less popular
query “Plaid” (electronic music). Total numbers of train and test pairs for given artist pairs are in square
brackets, and totals for all artists shown are given in the last row. Artists where the two methods differ are
labeled with an asterisk. Adding structure improves the results, e.g. unrelated bands like “Sonic Youth” are
demoted in the “Bob Dylan” query, and relatively more popular bands like Orbital and µ−ziq and more related
bands like Four-Tet and Squarepusher are boosted for the “Plaid” query.
LaSR t = 0 (no structure) LaSR t = 1 (structured ranking) LaSR t = 0 (no structure) LaSR t = 1 (with structured ranking)
Query: Bob Dylan Query: Bob Dylan Query: Plaid Query: Plaid
Wilco [53,10] The Beatles [179,40] Boards Of Canada [27,5] Boards Of Canada [27,5]
The Rolling Stones [40,9] Radiohead [61,16] Autechre [13,1] Aphex Twin [9,3]
The Beatles [179,40] The Rolling Stones [40,9] Aphex Twin [9,3] Autechre [13,1]
R.E.M. [35,12] Johnny Cash [49,11] Biosphere [6,1] Biosphere [6,1]
Johnny Cash [49,11] The Cure [38,10] Wagon Christ [3,1] Squarepusher [11,2]
Beck [42,18] David Bowie [48,12] Amon Tobin [6,3] Future Sound Of London [5,2]
David Bowie [48,12] Wilco [53,10] Arovane [5,1] Four Tet* [6,2]
Pixies [31,4] Pink Floyd* [30,8] Future Sound Of London [5,2] Massive Attack* [4,2]
Belle And Sebastian [28,4] U2* [40,16] The Orb [3,2] Arovane [5,1]
The Beach Boys* [22,6] The Smiths [25,7] Squarepusher [11,2] Air [9]
The Cure [38,10] Sufjan Stevens [23,4] Bola [5,2] The Orb [3,2]
Arcade Fire* [34,6] R.E.M. [35,12] Chris Clark* [4,2] Isan [3,1]
Radiohead [61,16] Belle And Sebastian [28,4] Kettel* [3,0] Amon Tobin [6,3]
Sonic Youth* [35,9] Beck [42,18] Ulrich Schnauss [7,1] Bola [5,2]
Bruce Springsteen [41,11] The Shins* [22,13] Apparat* [3,0] Orbital* [7,1]
The Smiths [25,7] Pixies [31,4] Isan [3,1] Murcof [4]
The Velvet Underground* [29,11] Ramones* [36,8] Air [9] Ulrich Schnauss [7,1]
Sufjan Stevens [23,4] Bruce Springsteen [44,11] Clark* [0,0] Wagon Christ [3,1]
Tom Waits* [19,13] Death Cab For Cutie* [32,7] Murcof [4,0] µ-Ziq* [5,3]
(Train,Test) totals = [835,213] (Train,Test) totals = [856,220] (Train,Test) totals = [126,27] (Train,Test) totals = [138,33]
Table 4: Image Annotation Results comparing Wsabie with LaSR. The top 10 labels of each method is shown,
the correct label (if predicted) is shown in bold. In many cases LaSR can be seen to improve on Wsabie by
demoting bad predictions e.g. war paint, soccer ball, segway, denture, rottweiler, reindeer, tv-antenna, leopard
frog (one example from each of the first 8 images). In the last 3 images neither method predicts the right label
(armrest, night snake and heifer) but LaSR seems slightly better (e.g. more cat, snake and cow predictions).
Input Image Wsabie LaSR Input Image Wsabie LaSR
workroom, life workroom, salon, tv-antenna,
office, war paint, day nursery, transmission line,
scissors, shears,
day nursery, schoolroom, student, shears, refracting
tinsnips, windmill,
homeroom, salon, homeroom, garden rake,
telescope, scissors,
foundling hospital, clothespress, day tv-antenna, forceps,
electrical cable,
teacher, sewing school, sewing room, safety harness,
chain wrench,
room, canteen study medical
astronomical
soccer ball, tent, instrument,
shelter tent, tent, telescope, wire, boat
inflated ball, fly plyers
camping, pack tent, hook,
tent, shelter tent, leopard frog,
fly tent, mountain tureen, glazed
camping, pack tent sauceboat, sugar
tent, tent flap, earthenware, slop
magpie, field tent, bowl, cream pitcher,
two-man tent, field bowl, sauceboat,
white admiral tureen, spittoon,
tent, pop tent spittoon, cullender,
butterfly pitcher,
roller skating, roller skate wheel, earthenware, punch
earthenware,
skateboard, roller skating, bowl, sugar bowl,
rainbow fish, slop
cross-country skiing, skateboard, in-line cream pitcher
bowl
skating, segway, skate, roller skate, frogmouth,
hockey stick, cross-country skiing, redpoll, frogmouth, soft-coated wheaten
skateboarding, skateboarding, screech owl, possum, terrier, gray
skating rink, crutch, unicycle, skate, grey parrot, finch, squirrel, cairn
roller skate wheel skating gypsy moth, gray terrier, gypsy moth,
round-bottom flask, squirrel, lycaenid tabby cat, chinchilla
partial denture,
flask, rummer, butterfly, cairn laniger, egyptian
round-bottom flask,
tornado lantern, terrier cat, burmese cat,
flask, panty girdle,
spotlight, foetus, oil abyssinian cat
night-light,
lamp, sconce, garter snake,
patchouly, organza, grass snake, garter
infrared lamp, european viper, grass
organdy, baby, wig snake, ribbon snake,
decanter snake, california
common kingsnake,
whipsnake, common
rubber boot, combat boot, black rattler,
kingsnake, eastern
rottweiler, pillar, riding boot, rubber western ribbon
ground snake,
combat boot, calf, boot, leg covering, snake, european
northern ribbon
riding boot, trouser, rubber, trouser, viper, pickerel frog
snake, puff adder,
watchdog, shepherd tabis boot, trouser spiny anteater,
whipsnake, black
dog, leg leg, boot, lawn tool eastern ground snake
rattler
black bear, black
black bear, yak,
reaper, seeder, gun vulture, labiated
reaper, reindeer, bear, american black
carriage, farm bear, greater gibbon,
cannon, steamroller, bear, stocky horse,
machine, combine, vulture, american
seeder, plow, calf, wild boar, bull,
plow, caribou, black bear, cuckoo,
tractor, combine, draft horse, black
haymaker, tractor, black sheep, buffalo,
cannon, caribou angus
trench mortar, yak,
9. models trained with a ranking loss instead, which we
Table 5: Summary of Test Set Results on Im-
refer to as Rank SVM, as it is an online version of
agenet. Recall at 1, 5, 10, Mean Average Precision
the pairwise multiclass (ranking) loss of (Weston and
and Mean Rank are given.
Watkins, 1999; Crammer and Singer, 2002). Finally,
Algorithm r@1 r@5 r@10 MAP MR
One-vs-Rest 2.83% 8.48% 13.2% 0.065 667 we compare to Wsabie a (unstructured) latent rank-
Rank SVM 5.35% 14.1% 19.3% 0.102 804 ing method which has yielded state-of-the-art perfor-
Wsabie 8.39% 19.6% 26.3% 0.144 626 mance on this task. For all methods, hyperparameters
LaSR (t = 1) 9.45% 22.1% 29.1% 0.161 523 are chosen via the validation set.
Results The overall results are given in Table 5.
4.2 IMAGE ANNOTATION TASK One-vs-Rest performs relatively poorly (2.83% re-
call@1), perhaps because there are so many classes
ImageNet (Deng et al., 2009) (http://www. (over 15,000) that the classifiers are not well calibrated
image-net.org/) is a large scale image database to each other (as they are trained independently). The
organized according to WordNet (Fellbaum, 1998). multiclass ranking loss of Rank SVM performs much
WordNet is a graph of linguistic terms, where each better (5.35% recall@1) but is still outperformed by
concept node consists of a word or word phrase, Wsabie (8.39% recall@1). Wsabie uses the WARP
and the concepts are organized within a hierarchical loss to optimize the top of the ranked list and its good
structure. ImageNet is a growing image dataset that performance can be explained by the suitability of this
attaches quality-controlled human-verified images loss function for measure like recall@k. LaSR with
to these concepts by collecting images from web t = 0 is essentially identical to Wsabie in this case
search engines and then employing annotators to and so we use that model as our “base learner” for
verify whether the images are good matches for those iteration 0. LaSR (t = 1), that does use structure,
concepts, and discarding them if they are not. For outperforms Wsabie with a recall@1 of 9.45%.
many nouns, hundreds or even thousands of images
are labeled. We can use this dataset to test image Some example annotations are given in Table 4. LaSR
annotation algorithms. We split the data into train seems to provide more consistent results than Wsabie
and test and try to learn to predict the label (an- on several queries (with less bad predictions in the top
notation) given the image. For our experiments, we k) which improves the overall results, whilst maintain-
downloaded the “Spring 2010” release which consists ing the right level of diversity on others.
of 9 million images and 15,589 possible concepts (this
is a different set to (Weston et al., 2011) but our 5 CONCLUSION
baseline results largely agree). We split the data into
80% for training, 10% for validation and 10% for
In this paper we introduced a method for learning
testing.
a latent variable model that takes into account the
Following (Weston et al., 2011) we employ a feature structure of the predicted ranked list of items given
representation of the images which is an ensemble of the query. The approach is quite general and can po-
several representations which is known to perform bet- tentially be applied to recommendation, annotation,
ter than any single representation within the set (see classification and information retrieval tasks. These
e.g. (Makadia et al., 2008)). We thus combined mul- problems often involve millions of examples or more,
tiple feature representations which are the concatena- both in terms of the number of training pairs and the
tion of various spatial (Grauman and Darrell, 2007) number of items to be ranked. Hence, many other-
and multiscale color and texton histograms (Leung wise straight-forward approaches to structured predic-
and Malik, 1999) for a total of about 5 × 105 di- tion approaches might not be applicable in these cases.
mensions. The descriptors are somewhat sparse, with The method we proposed is scalable to these tasks.
about 50,000 non-zero weights per image. Some of the
Future work could apply latent structured ranking to
constituent histograms are normalized and some are
more applications, for example in text document re-
not. We then perform Kernel PCA (Schoelkopf et al.,
trieval. Moreover, it would be interesting to explore
1999) on the combined feature representation using
using other algorithms as the “base algorithm” which
the intersection kernel (Barla et al., 2003) to produce
we add the structured predictions to. In this work, we
a 1024 dimensional input vector for training.
used the approach of (Weston et al., 2011) as our base
We compare our proposed approach to several base- algorithm, but it might also be possible to make struc-
lines: one-versus-rest large margin classifiers (One-vs- tured ranking versions of algorithms like Non-negative
Rest) of the form fi (x) = wi x trained online to per- matrix factorization, Latent Semantic Indexing or Sin-
form classification over the 15,589 classes, or the same gular Value Decomposition as well.
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