This document proposes a method for annotating faces in images without supervision by mining the web. The method has two steps:
1. It ranks faces retrieved from a text-based search engine based on a local density score, which measures how similar a face is to its neighbors. Faces with higher scores are considered more relevant.
2. It then improves this ranking by modeling it as a classification problem, where faces are classified as the queried person or not. Multiple weak classifiers are trained on different subsets and combined via bagging to reduce noise from the unlabeled data. The faces are then re-ranked based on the classifier probabilities. Repeating this process iteratively improves the ranking.
Prediction of Student's Performance with Deep Neural NetworksCSCJournals
The performance of education has a big part in people's life. The prediction of student's performance in advance is very important issue for education. School administrators and students' parents impact on students' performance. Hence, academic researchers have developed different types of models to improve student performance. The main goal to reveal of this study is to search the best model of neural network models for the prediction of the performance of the high school students. For this purpose, five different types of neural network models have been developed and compared to their results. The data set obtained from Taldykorgan Kazakh Turkish High School (in Kazakhstan) students was used. Test results show that proposed two types of neural network model are predicted students' real performance efficiently and provided better accuracy when the test of today’s and future’s samples have similar characteristics.
Proposing a new method of image classification based on the AdaBoost deep bel...TELKOMNIKA JOURNAL
Image classification has different applications. Up to now, various algorithms have been presented
for image classification. Each of these methods has its own weaknesses and strengths. Reducing error rate
is an issue which many researches have been carried out about it. This research intends to optimize
the problem with hybrid methods and deep learning. The hybrid methods were developed to improve
the results of the single-component methods. On the other hand, a deep belief network (DBN) is a generative
probabilistic modelwith multiple layers of latent variables and is used to solve the unlabeled problems. In
fact, this method is anunsupervised method, in which all layers are one-way directed layers except for
the last layer. So far, various methods have been proposed for image classification, and the goal of this
research project was to use a combination of the AdaBoost method and the deep belief network method to
classify images. The other objective was to obtain better results than the previous results. In this project, a
combination of the deep belief network and AdaBoost method was used to boost learning and the network
potential was enhanced by making the entire network recursive. This method was tested on the MINIST
dataset and the results were indicative of a decrease in the error rate with the proposed method as compared
to the AdaBoost and deep belief network methods.
MULTI-LEVEL FEATURE FUSION BASED TRANSFER LEARNING FOR PERSON RE-IDENTIFICATIONgerogepatton
Most of the currently known methods treat person re-identification task as classification problem and used commonly neural networks. However, these methods used only high-level convolutional feature or to express the feature representation of pedestrians. Moreover, the current data sets for person reidentification is relatively small. Under the limitation of the number of training set, deep convolutional networks are difficult to train adequately. Therefore, it is very worthwhile to introduce auxiliary data sets help training. In order to solve this problem, this paper propose a novel method of deep transfer learning, and combines the comparison model with the classification model and multi-level fusion of the
convolution features on the basis of transfer learning. In a multi-layers convolutional network, the characteristics of each layer of network are the dimensionality reduction of the previous layer of results, but the information of multi-level features is not only inclusive, but also has certain complementarity. We can using the information gap of different layers of convolutional neural networks to extract a better feature expression. Finally, the algorithm proposed in this paper is fully tested on four data sets (VIPeR, CUHK01, GRID and PRID450S). The obtained re-identification results prove the effectiveness of the algorithm.
Prediction of Student's Performance with Deep Neural NetworksCSCJournals
The performance of education has a big part in people's life. The prediction of student's performance in advance is very important issue for education. School administrators and students' parents impact on students' performance. Hence, academic researchers have developed different types of models to improve student performance. The main goal to reveal of this study is to search the best model of neural network models for the prediction of the performance of the high school students. For this purpose, five different types of neural network models have been developed and compared to their results. The data set obtained from Taldykorgan Kazakh Turkish High School (in Kazakhstan) students was used. Test results show that proposed two types of neural network model are predicted students' real performance efficiently and provided better accuracy when the test of today’s and future’s samples have similar characteristics.
Proposing a new method of image classification based on the AdaBoost deep bel...TELKOMNIKA JOURNAL
Image classification has different applications. Up to now, various algorithms have been presented
for image classification. Each of these methods has its own weaknesses and strengths. Reducing error rate
is an issue which many researches have been carried out about it. This research intends to optimize
the problem with hybrid methods and deep learning. The hybrid methods were developed to improve
the results of the single-component methods. On the other hand, a deep belief network (DBN) is a generative
probabilistic modelwith multiple layers of latent variables and is used to solve the unlabeled problems. In
fact, this method is anunsupervised method, in which all layers are one-way directed layers except for
the last layer. So far, various methods have been proposed for image classification, and the goal of this
research project was to use a combination of the AdaBoost method and the deep belief network method to
classify images. The other objective was to obtain better results than the previous results. In this project, a
combination of the deep belief network and AdaBoost method was used to boost learning and the network
potential was enhanced by making the entire network recursive. This method was tested on the MINIST
dataset and the results were indicative of a decrease in the error rate with the proposed method as compared
to the AdaBoost and deep belief network methods.
MULTI-LEVEL FEATURE FUSION BASED TRANSFER LEARNING FOR PERSON RE-IDENTIFICATIONgerogepatton
Most of the currently known methods treat person re-identification task as classification problem and used commonly neural networks. However, these methods used only high-level convolutional feature or to express the feature representation of pedestrians. Moreover, the current data sets for person reidentification is relatively small. Under the limitation of the number of training set, deep convolutional networks are difficult to train adequately. Therefore, it is very worthwhile to introduce auxiliary data sets help training. In order to solve this problem, this paper propose a novel method of deep transfer learning, and combines the comparison model with the classification model and multi-level fusion of the
convolution features on the basis of transfer learning. In a multi-layers convolutional network, the characteristics of each layer of network are the dimensionality reduction of the previous layer of results, but the information of multi-level features is not only inclusive, but also has certain complementarity. We can using the information gap of different layers of convolutional neural networks to extract a better feature expression. Finally, the algorithm proposed in this paper is fully tested on four data sets (VIPeR, CUHK01, GRID and PRID450S). The obtained re-identification results prove the effectiveness of the algorithm.
Eat it, Review it: A New Approach for Review Predictionvivatechijri
Deep Learning has achieved significant improvement in various machine learning tasks. Nowadays,
Recurrent Neural Network (RNN) and Long Short Term Memory (LSTM) have been increasing its popularity on
Text Sequence i.e. word prediction. The ability to abstract information from image or text is being widely
adopted by organizations around the world. A basic task in deep learning is classification be it image or text.
Current trending techniques such as RNN, CNN has proven that such techniques open the door for data analysis.
Emerging technologies such has Region CNN, Recurrent CNN have been under consideration for the analysis.
Recurrent CNN is being under development with the current world. The proposed system uses Recurrent Neural
Network for review prediction. Also LSTM is used along with RNN so as to predict long sentences. This system
focuses on context based review prediction and will provide full length sentence. This will help to write a proper
reviews by understanding the context of user.
An Extensive Review on Generative Adversarial Networks GAN’sijtsrd
This paper is to provide a high level understanding of Generative Adversarial Networks. This paper will be covering the working of GAN’s by explaining the background idea of the framework, types of GAN’s in the industry, it’s advantages and disadvantages, history of how GAN’s are developed and enhanced along the timeline and some applications where GAN’s outperforms themselves. Atharva Chitnavis | Yogeshchandra Puranik "An Extensive Review on Generative Adversarial Networks (GAN’s)" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd42357.pdf Paper URL: https://www.ijtsrd.comcomputer-science/artificial-intelligence/42357/an-extensive-review-on-generative-adversarial-networks-gan’s/atharva-chitnavis
The field of Artificial Intelligence (AI) has been revitalized in this decade, primarily due to the large-scale application of Deep Learning (DL) and other Machine Learning (ML) algorithms. This has been most evident in applications like computer vision, natural language processing, and game bots. However, extraordinary successes within a short period of time have also had the unintended consequence of causing a sharp difference of opinion in research and industrial communities regarding the capabilities and limitations of deep learning. A few questions you might have heard being asked (or asked yourself) include:
a. We don’t know how Deep Neural Networks make decisions, so can we trust them?
b. Can Deep Learning deal with highly non-linear continuous systems with millions of variables?
c. Can Deep Learning solve the Artificial General Intelligence problem?
The goal of this seminar is to provide a 1000-feet view of Deep Learning and hopefully answer the questions above. The seminar will touch upon the evolution, current state of the art, and peculiarities of Deep Learning, and share thoughts on using Deep Learning as a tool for developing power system solutions.
Natural Language Processing is a programmed approach to analyze text that is based on both a set of theories and a set of technologies. This forum aims to bring together researchers who have designed and build software that will analyze, understand, and generate languages that humans use naturally to address computers.
TL;DR: This tutorial was delivered at KDD 2021. Here we review recent developments to extend the capacity of neural networks to “learning to reason” from data, where the task is to determine if the data entails a conclusion.
The rise of big data and big compute has brought modern neural networks to many walks of digital life, thanks to the relative ease of construction of large models that scale to the real world. Current successes of Transformers and self-supervised pretraining on massive data have led some to believe that deep neural networks will be able to do almost everything whenever we have data and computational resources. However, this might not be the case. While neural networks are fast to exploit surface statistics, they fail miserably to generalize to novel combinations. Current neural networks do not perform deliberate reasoning – the capacity to deliberately deduce new knowledge out of the contextualized data. This tutorial reviews recent developments to extend the capacity of neural networks to “learning to reason” from data, where the task is to determine if the data entails a conclusion. This capacity opens up new ways to generate insights from data through arbitrary querying using natural languages without the need of predefining a narrow set of tasks.
Part of the ongoing effort with Skater for enabling better Model Interpretation for Deep Neural Network models presented at the AI Conference.
https://conferences.oreilly.com/artificial-intelligence/ai-ny/public/schedule/detail/65118
Model of Differential Equation for Genetic Algorithm with Neural Network (GAN...Sarvesh Kumar
The work is carried on the application of differential equation (DE) and its computational technique of genetic algorithm and neural (GANN) in C#, which is frequently used in globalised world by human wings. Diagrammatical and flow chart presentation is the major concerned for easy undertaking of these two concepts with indication of its present and future application is the new initiative taken in this paper along with computational approaches in C#. Little observation has been also pointed during working, functioning and development process of above algorithm in C# under given boundary value condition of DE for genetic and neural. Operations of fitness function and Genetic operations were completed for behavioural transmission of chromosome.
Data mining is the knowledge discovery in databases and the gaol is to extract patterns and knowledge from
large amounts of data. The important term in data mining is text mining. Text mining extracts the quality
information highly from text. Statistical pattern learning is used to high quality information. High –quality in
text mining defines the combinations of relevance, novelty and interestingness. Tasks in text mining are text
categorization, text clustering, entity extraction and sentiment analysis. Applications of natural language
processing and analytical methods are highly preferred to turn
Handwriting identification using deep convolutional neural network methodTELKOMNIKA JOURNAL
Handwriting is a unique thing that produced differently for each person. Handwriting has a characteristic that remain the same with single writer, so a handwriting can be used as a variable in biometric systems. Each person have a different form of handwriting style but with a small possibility that same characters have something commons. We propose a handwriting identification method using sentence segmented handwriting forms. Sentence form is used to get more complete handwriting characteristics than using a single characters or words. Dataset used is divided into three categories of images, binary, grayscale, and inverted binary. All datasets have same image with different in color and consist of 100 class. Transfer learning used in this paper are pre-trained model VGG19. Training was conducted in 100 epochs. Highest result is grayscale images with genuince acceptance rate of 92.3% and equal error rate of 7.7%.
Eat it, Review it: A New Approach for Review Predictionvivatechijri
Deep Learning has achieved significant improvement in various machine learning tasks. Nowadays,
Recurrent Neural Network (RNN) and Long Short Term Memory (LSTM) have been increasing its popularity on
Text Sequence i.e. word prediction. The ability to abstract information from image or text is being widely
adopted by organizations around the world. A basic task in deep learning is classification be it image or text.
Current trending techniques such as RNN, CNN has proven that such techniques open the door for data analysis.
Emerging technologies such has Region CNN, Recurrent CNN have been under consideration for the analysis.
Recurrent CNN is being under development with the current world. The proposed system uses Recurrent Neural
Network for review prediction. Also LSTM is used along with RNN so as to predict long sentences. This system
focuses on context based review prediction and will provide full length sentence. This will help to write a proper
reviews by understanding the context of user.
An Extensive Review on Generative Adversarial Networks GAN’sijtsrd
This paper is to provide a high level understanding of Generative Adversarial Networks. This paper will be covering the working of GAN’s by explaining the background idea of the framework, types of GAN’s in the industry, it’s advantages and disadvantages, history of how GAN’s are developed and enhanced along the timeline and some applications where GAN’s outperforms themselves. Atharva Chitnavis | Yogeshchandra Puranik "An Extensive Review on Generative Adversarial Networks (GAN’s)" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd42357.pdf Paper URL: https://www.ijtsrd.comcomputer-science/artificial-intelligence/42357/an-extensive-review-on-generative-adversarial-networks-gan’s/atharva-chitnavis
The field of Artificial Intelligence (AI) has been revitalized in this decade, primarily due to the large-scale application of Deep Learning (DL) and other Machine Learning (ML) algorithms. This has been most evident in applications like computer vision, natural language processing, and game bots. However, extraordinary successes within a short period of time have also had the unintended consequence of causing a sharp difference of opinion in research and industrial communities regarding the capabilities and limitations of deep learning. A few questions you might have heard being asked (or asked yourself) include:
a. We don’t know how Deep Neural Networks make decisions, so can we trust them?
b. Can Deep Learning deal with highly non-linear continuous systems with millions of variables?
c. Can Deep Learning solve the Artificial General Intelligence problem?
The goal of this seminar is to provide a 1000-feet view of Deep Learning and hopefully answer the questions above. The seminar will touch upon the evolution, current state of the art, and peculiarities of Deep Learning, and share thoughts on using Deep Learning as a tool for developing power system solutions.
Natural Language Processing is a programmed approach to analyze text that is based on both a set of theories and a set of technologies. This forum aims to bring together researchers who have designed and build software that will analyze, understand, and generate languages that humans use naturally to address computers.
TL;DR: This tutorial was delivered at KDD 2021. Here we review recent developments to extend the capacity of neural networks to “learning to reason” from data, where the task is to determine if the data entails a conclusion.
The rise of big data and big compute has brought modern neural networks to many walks of digital life, thanks to the relative ease of construction of large models that scale to the real world. Current successes of Transformers and self-supervised pretraining on massive data have led some to believe that deep neural networks will be able to do almost everything whenever we have data and computational resources. However, this might not be the case. While neural networks are fast to exploit surface statistics, they fail miserably to generalize to novel combinations. Current neural networks do not perform deliberate reasoning – the capacity to deliberately deduce new knowledge out of the contextualized data. This tutorial reviews recent developments to extend the capacity of neural networks to “learning to reason” from data, where the task is to determine if the data entails a conclusion. This capacity opens up new ways to generate insights from data through arbitrary querying using natural languages without the need of predefining a narrow set of tasks.
Part of the ongoing effort with Skater for enabling better Model Interpretation for Deep Neural Network models presented at the AI Conference.
https://conferences.oreilly.com/artificial-intelligence/ai-ny/public/schedule/detail/65118
Model of Differential Equation for Genetic Algorithm with Neural Network (GAN...Sarvesh Kumar
The work is carried on the application of differential equation (DE) and its computational technique of genetic algorithm and neural (GANN) in C#, which is frequently used in globalised world by human wings. Diagrammatical and flow chart presentation is the major concerned for easy undertaking of these two concepts with indication of its present and future application is the new initiative taken in this paper along with computational approaches in C#. Little observation has been also pointed during working, functioning and development process of above algorithm in C# under given boundary value condition of DE for genetic and neural. Operations of fitness function and Genetic operations were completed for behavioural transmission of chromosome.
Data mining is the knowledge discovery in databases and the gaol is to extract patterns and knowledge from
large amounts of data. The important term in data mining is text mining. Text mining extracts the quality
information highly from text. Statistical pattern learning is used to high quality information. High –quality in
text mining defines the combinations of relevance, novelty and interestingness. Tasks in text mining are text
categorization, text clustering, entity extraction and sentiment analysis. Applications of natural language
processing and analytical methods are highly preferred to turn
Handwriting identification using deep convolutional neural network methodTELKOMNIKA JOURNAL
Handwriting is a unique thing that produced differently for each person. Handwriting has a characteristic that remain the same with single writer, so a handwriting can be used as a variable in biometric systems. Each person have a different form of handwriting style but with a small possibility that same characters have something commons. We propose a handwriting identification method using sentence segmented handwriting forms. Sentence form is used to get more complete handwriting characteristics than using a single characters or words. Dataset used is divided into three categories of images, binary, grayscale, and inverted binary. All datasets have same image with different in color and consist of 100 class. Transfer learning used in this paper are pre-trained model VGG19. Training was conducted in 100 epochs. Highest result is grayscale images with genuince acceptance rate of 92.3% and equal error rate of 7.7%.
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MW2010: Rich Cherry, A Walk in the Park: The Balboa Park Online Collaborative...museums and the web
A presentation from Museums and the Web 2010
Balboa Park Online Collaborative is a collaborative technology project involving 17 museums, performing arts venues, gardens, and the San Diego Zoo in Balboa Park, San Diego. This paper will present the challenges we faced in managing the creation and execution of a common strategy and framework during our first year. We will share the successes and lessons learned as well as the compromises required to move the institutions of Balboa Park forward and manage the project effectively.
Session: Collaboration Outcomes - Part 1 [organizations]
See: http://www.archimuse.com/mw2010/abstracts/prg_335002335.html
A NOVEL WEB IMAGE RE-RANKING APPROACH BASED ON QUERY SPECIFIC SEMANTIC SIGNAT...Journal For Research
Image re-ranking, is an effective way to improve the results of web-based image search. Given a query keyword, a pool of images are initailly retrieved primarily based on textual data, the remaining images are re-ranked based on their visual similarities with the query image corresponding to the user input. A major challenge is that the similarities of visual features don't well correlate with images’ semantic meanings that interpret users’ search intention. Recently people proposed to match pictures in a semantic space that used attributes or reference categories closely associated with the semantic meanings of images as basis. Even though, learning a universal visual semantic space to characterize extremely diverse images from the internet is troublesome and inefficient. In this thesis, we propose a completely distinctive image re-ranking framework that learns completely different semantic spaces for numerous query keywords automatically at the on-line stage. The visual features of images are projected into their corresponding semantic spaces to induce semantic signatures. At the online stage, images are re-ranked by scrutiny their semantic signatures obtained from the semantic spaces such that by the query keyword. The proposed query-specific semantic signatures considerably improve both the accuracy and efficiency of image re-ranking.
FACE EXPRESSION IDENTIFICATION USING IMAGE FEATURE CLUSTRING AND QUERY SCHEME...Editor IJMTER
Web mining techniques are used to analyze the web page contents and usage details. Human facial
images are shared in the internet and tagged with additional information. Auto face annotation techniques are used
to annotate facial images automatically. Annotations are used in online photo search and management.
Classification techniques are used to assign the facial annotation. Supervised or semi-supervised machine learning
techniques are used to train the classification models. Facial images with labels are used in the training process.
Noisy and incomplete labels are referred as weak labels. Search-based face annotation (SBFA) is assigned by
mining weakly labeled facial images available on the World Wide Web (WWW). Unsupervised label refinement
(ULR) approach is used for refining the labels of web facial images with machine learning techniques. ULR
scheme is used to enhance the label quality using graph-based and low-rank learning approach. The training phase
is designed with facial image collection, facial feature extraction, feature indexing and label refinement learning
steps. Similar face retrieval and voting based face annotation tasks are carried out under the testing phase.
Clustering-Based Approximation (CBA) algorithm is applied to improve the scalability. Bisecting K-means
clustering based algorithm (BCBA) and divisive clustering based algorithm (DCBA) are used to group up the
facial images. Multi step Gradient Algorithm is used for label refinement process. The web face annotation scheme
is enhanced to improve the label quality with low refinement overhead. Noise reduction is method is integrated
with the label refinement process. Duplicate name removal process is integrated with the system. The indexing
scheme is enhanced with weight values for the labels. Social contextual information is used to manage the query
facial image relevancy issues.
Recognizing Celebrity Faces in Lot of Web ImagesIJERA Editor
Now a dayscelebrityrelatedqueriesrankingconstantlyamong all the image queries.On the other hand celebrity images on web provide a greatopportunity for constructing large scale training datasets to advance face recognition. Collecting and labelingcelebrity faces fromgeneral web images is a challengingtask. In thisproblemwe are using the surroundingtext in web images such as name, location, time etc., then the image isannotedusing image annotation system and nameassignment system thenfinding the near duplicate image and at lastgetting the correct result.In thiswayusercanidentify the person in the web images.
MULTI-LEVEL FEATURE FUSION BASED TRANSFER LEARNING FOR PERSON RE-IDENTIFICATIONijaia
Most of the currently known methods treat person re-identification task as classification problem and used commonly neural networks. However, these methods used only high-level convolutional feature or to express the feature representation of pedestrians. Moreover, the current data sets for person reidentification is relatively small. Under the limitation of the number of training set, deep convolutional networks are difficult to train adequately. Therefore, it is very worthwhile to introduce auxiliary data sets to help training. In order to solve this problem, this paper propose a novel method of deep transfer learning, and combines the comparison model with the classification model and multi-level fusion of the convolution features on the basis of transfer learning. In a multi-layers convolutional network, the characteristics of each layer of network are the dimensionality reduction of the previous layer of results, but the information of multi-level features is not only inclusive, but also has certain complementarity. We can using the information gap of different layers of convolutional neural networks to extract a better feature expression. Finally, the algorithm proposed in this paper is fully tested on four data sets (VIPeR, CUHK01, GRID and PRID450S). The obtained re-identification results prove the effectiveness of the algorithm.
Comparison of Various Web Image Re - Ranking TechniquesIJSRD
Image re-ranking, is an quite an efficient way to improve the results that are fetched from the web-based image search query. Given a query keyword for the image, a pool of images are first retrieved based on textual information, then the images are re-ranked based on their visual similarities with the query image according to the user input. But when, the images’ visual features do not match with the semantic meanings of the users’ entered query or keyword, it becomes a major challenge to make available the actual searched image. Hence, in this paper, the various Web image Re- ranking techniques are studied, on how it approaches towards the Web Image search that the user has input in query.
Face Recognition for Human Identification using BRISK Feature and Normal Dist...ijtsrd
Face recognition is a kind of automatic human identification from face images has been performed widely research in image processing and machine learning. Face image, facial information of the person is presented and unique information for each person even two person possessed the same face. We propose a methodology for automatic human classification based on Binary Robust Invariant Scalable Keypoints BRISK feature of face images and the normal distribution model. In our proposed methodology, the normal distribution model is used to represent the statistical information of face image as a global feature. The human name is the output of the system according to the input face image. Our proposed feature is applied with Artificial Neural Networks to recognize face for human identification. The proposed feature is extracted from the face image of the Extended Yale Face Database B to perform human identification and highlight the properties of the proposed feature. Khin Mar Thi "Face Recognition for Human Identification using BRISK Feature and Normal Distribution Model" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26589.pdfPaper URL: https://www.ijtsrd.com/computer-science/multimedia/26589/face-recognition-for-human-identification-using-brisk-feature-and-normal-distribution-model/khin-mar-thi
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
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Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
2. Figure 2. Large variations in facial expres-
sions, poses, illumination conditions and oc-
clusions making face recognition difficult.
Best viewed in color.
• The bagging framework helps to leverage noises in the
unsupervised labeling process.
Our contribution is two-fold:
Figure 1. A news photo and its caption. Ex- • We propose a general framework to boost the face re-
tracted faces are shown on the top. These trieval performance of text-based search engines by vi-
faces might be returned for the query of sual consistency learning. The framework seamlessly
person-Bush. integrates data mining techniques such as supervised
learning and unsupervised learning based on bagging.
Our framework requires only a few parameters and
works stably.
or non-person-X (the un-queried person). The faces are
ranked according to a relevancy score that is inferred from • We demonstrate its feasibility with a practical web
the classifier’s probability output. Since annotation data is mining application. A comprehensive evaluation on a
not available, the rank list from the previous step is used to large face dataset of many people was carried out and
assign labels for a subset of faces. This subset is then used confirmed that our approach is promising.
to train a classifier using supervised methods such as sup-
port vector machines (SVM). The trained classifier is used
to re-rank faces in the original input set. This step is re- 2. Related Work
peated a number of times to get the final ranked list. Since
automatically assigning labels from the ranked list is not re- There are several approaches for re-ranking and learn-
liable, the trained classifiers are weak. To obtain the final ing models from web images. Their underlying assump-
strong classifier, we use the idea of ensemble learning [6] in tion is that text-based search engines return a large frac-
which weak classifiers trained on different subsets are com- tion of relevant images. The challenge is how to model
bined to improve the stability and classification accuracy of what is common in the relevant images. One approach
single classifiers. The learned classifier can be further used is to model this problem in a probabilistic framework in
for recognizing new facial images of the queried person. which the returned images are used to learn the parame-
The second stage improves the ranked list and recogni- ters of the model. For examples, as described by Fergus et
tion performance for the following reasons: al. [12], objects retrieved using an image search engine are
re-ranked by extending the constellation model. Another
• Supervised learning methods, such as SVM, provide proposal, described in [15], uses a non-parametric graphi-
a strong theoretical background for finding the opti- cal model and an interactive framework to simultaneously
mal decision boundary even with noisy data. Further- learn object class models and collect object class datasets.
more, recent studies [20, 17] suggest that SVM clas- The main contribution of these approaches is probabilistic
sifiers provide probability outputs that are suitable for models that can be learned with a small number of training
ranking. images. However, these models are complicated since they
384
3. require several hundred parameters for learning and are sus- 3 Proposed Framework
ceptible to over-fitting. Furthermore, to obtain robust mod-
els, a small amount of supervision is required to select seed Given a set of images returned by any text-based search
images. engine for a queried person (e.g. ’George Bush’), we per-
Another study [4, 3] proposed a clustering-based method form a ranking process and learning of person X’s model
for associating names and faces in news photos. To solve as follows:
the problem of ambiguity between several names and one
• Step 1: Detect faces and eye positions, and then per-
face, a modified k-means clustering process was used in
form face normalizations.
which faces are assigned to the closest cluster (each clus-
ter corresponding to one name) after a number of iterations. • Step 2: Compute an eigenface space and project the
Although the result was impressive, it is not easy to apply it input faces into this subspace.
to our problem since it is based on a strong assumption that
requires a perfect alignment when a news photo only has • Step 3: Estimate the ranked list of these faces using
one face and its caption only has one name. Furthermore, Rank-By-Local-Density-Score.
a large number of irrelevant faces (more than 12%) have to
be manually eliminated before clustering. • Step 4: Improve this ranked list using Rank-By-
Bagging-ProbSVM.
A graph-based approach was proposed by Ozkan and
Duygulu [16], in which a graph is formed from faces as Steps 1 and 2 are typical for any face processing system,
nodes, and the weights of edges linked between nodes are and they are described in section 4.2. The algorithms used
the similarity of faces, is closely related to our problem. in Steps 3 and 4 are described in section 3.1 and section 3.2,
Assuming that the number of faces of the queried person is respectively. Figure 3 illustrates the proposed framework.
larger than that of others and that these faces tend to form
the most similar subset among the set of retrieved faces, 3.1 Ranking by Local Density Score
this problem is considered equal to the problem of finding
the densest subgraph of a full graph; and can therefore be
solved by taking an available solution [9]. Although, exper-
imental results showed the effectiveness of this method, it is
still questionable whether the densest subgraph intuitively
describes most of the relevant faces of the queried person
and it is easy to extend for the ranking problem. Further-
more, choosing an optimal threshold to convert the initial
graph into a binary one is difficult and rather ad hoc due to
the curse of dimensionality.
An advantage of the methods [4, 3, 16] is they are fully
unsupervised. However, a disadvantage is that no model
is learned for predicting new images of the same category.
Furthermore, they are used for performing hard categoriza-
Figure 4. An example of faces retrieved for
tion on input images that are in applicable for re-ranking.
person-Donald Rumsfeld. Irrelevant faces
The balance of recall and precision was not addressed. Typ-
are marked with a star. Irrelevant faces might
ically, these approaches tend to ignore the recall to obtain
form several clusters, but the relevant faces
high precision. This leads to the reduction in the number of
form the largest cluster.
collected images.
Our approach combines a number of advances over the
existing approaches. Specifically, we learn a model for each Among the faces retrieved by text-based search engines
query from the returned images for purposes such as re- for a query of person-X, as shown in Figure 4, relevant
ranking and predicting new images. However, we used an faces usually look similar and form the largest cluster. One
unsupervised method to select training samples automati- approach of re-ranking these faces is to cluster based on vi-
cally, which is different from the methods proposed by Fer- sual similarity. However, to obtain ideal clustering results is
gus et al. and Li et al. [12, 15]. This unsupervised method impossible since these faces are high dimensional data and
is different from the one by Ozkan and Duygulu [16] in the the clusters are in different shapes, sizes, and densities. In-
modeling of the distribution of relevant images. We use stead, a graph-based approach was proposed by Ozkan and
density-based estimation rather than the densest graph. Duygulu [16] in which the nodes are faces and edge weights
385
4. Figure 3. The proposed framework for re-ranking faces returned by text-based search engines.
are the similarities between two faces. With the observation Algorithm 1: Rank-By-Local-Density-Score
that the nodes (faces) of the queried person are similar to Step 1: For each face p, compute LDS(p, k),
each other and different from other nodes in the graph, the where k is the number of neighbors of p
densest component of the full graph the set of highly con- and is the input of the ranking process.
nected nodes in the graph will correspond to the face of the Step 2: Rank these faces using LDS(p, k)
queried person. The main drawback of this approach is it (The higher the score the more relevant).
needs a threshold to convert the initial weighted graph to a
binary graph. Choosing this threshold in high dimensional
spaces is difficult since different persons might have differ- 3.2 Ranking by Bagging of SVM Classi-
ent optimal thresholds. fiers
We use the idea of density-based clustering described by
Ester et al. and Breunig et al. [11, 7] to solve this problem. One limitation of the local density score based ranking
Specifically, we define the local density score (LDS) of a is it cannot handle faces of another person strongly associ-
point p (i.e. a face) as the average distance to its k-nearest ated in the k-neighbor set (for example, many duplicates).
neighbors. Therefore, another step is proposed for handling this case.
distance(p, q) As a result, we have a model that can be used for both re-
q∈R(p,k)
LDS(p, k) = ranking current faces and predicting new incoming faces.
k The main idea is to use a probabilistic model to measure
where R(p, k) is the set of k - neighbors of p, and the relevancy of a face to person-X, P (person − X|f ace).
distance(p, q) is the similarity between p and q. Since the labels are not available for training, we use the
Since faces are represented in high dimensional feature input rank list found from the previous step to extract a sub-
space, and face clusters might have different sizes, shapes, set of faces lying at the top and bottom of the ranked list to
and densities, we do not directly use the Euclidean distance form the training set. After that, we use SVM with prob-
between two points in this feature space for distance(p, q). abilistic output [17] implemented in LibSVM [8] to learn
Instead, we use another similarity measure defined by the the person-X model. This model is applied to faces of the
number of shared neighbors between two points. The effi- original set, and the output probabilistic scores are used to
ciency of this similarity measure for density-based cluster- re-rank these faces. Since it is not guaranteed that faces ly-
ing methods was described in [10]. ing at two ends of the input rank list correctly correspond to
|R(q, k) ∩ R(p, k)| the faces of person-X and faces of non person-X, we adopt
distance(p, q) = the idea of a bagging framework [6] in which randomly se-
k
lecting subsets to train weak classifiers, and then combining
Therefore
these classifiers help reduce the risk of using noisy training
q∈R(p,k) |R(q, k) ∩ R(p, k)| sets.
LDS(p, k) =
k2 The details of the Rank-By-Bagging-ProbSVM-
A high value of LDS(p, k) indicates a strong association InnerLoop method, improving an input rank list by
between p and its neighbors. Therefore, we can use this combining weak classifiers trained from subsets annotated
local density score to rank faces. Faces with higher scores by that rank list are described in Algorithm 2.
are considered to be potential candidates that are relevant to Given an input ranked list, Rank-By-Bagging-ProbSVM-
person-X, while faces with lower scores are considered as InnerLoop is used to improve this list. We repeat the process
outliers and thus are potential candidates for non-person-X. a number of times whereby the ranked list output from the
Algorithm 1 describes these steps. previous step is used as the input ranked list of the next
386
5. Algorithm 2: Rank-By-Bagging-ProbSVM-InnerLoop 4 Experiments
Step 1: Train a weak classifier, hi .
Step 1.1: Select a set Spos including p% of top ranked faces 4.1 Dataset
∗
and then randomly select a subset Spos from Spos .
∗
Label faces in Spos as positive samples. We used the dataset described by Berg et al. [4] for our
Step 1.2: Select a set Sneg including p% of bottom ranked
∗
experiments. This dataset consists of approximately half a
faces and then randomly select a subset Sneg from Sneg . million news photos and captions from Yahoo News col-
∗
Label faces in Sneg as negative samples. lected over a period of roughly two years. This dataset is
∗ ∗
Step 1.3: Use Spos and Sneg to train a weak better than datasets collected from image search engines
classifier, hj , using LibSVM [8] with probability outputs. such as Google that usually limit the total number of re-
i
Step 2: Compute ensemble classifier Hi = j=1 hj . turned images to 1,000. Furthermore, it has annotations that
Step 3: Apply Hi to the original face set and form the are valuable for evaluation of methods. Note that these an-
rank list, Ranki , using the output probabilistic scores. notations are used for evaluation purpose only. Our method
Step 4: Repeat steps 1 to 3 is fully unsupervised, so it assumes the annotations are not
until Dist2RankList(Ranki−1, Ranki ) <= . available at running time.
Step 5: Return Hi = i hj .j=1 Only frontal faces were considered since current frontal
face detection systems [19] work in real time and have ac-
Algorithm 3: Rank-By-Bagging-ProbSVM-OuterLoop curacies exceeding 95%. 44,773 faces were detected and
Step 1: Rankcur = normalized to the size of 86×86 pixels.
Rank-By-Bagging-ProbSVM-InnerLoop(Rankprev). We selected fifteen government leaders, including
Step 2: dist = Dist2RankList(Rankprev , Rankcur ). George W. Bush (US), Vladimir Putin (Russia), Ziang
Step 3: Rankf inal = Rankcur . Jemin (China), Tony Blair (UK), Junichiro Koizumi
Step 4: Rankprev = Rankcur . (Japan), Roh Moo-hyun (Korea), Abdullah Gul (Turkey),
Step 5: Repeat steps 1 to 4 and other key individuals, such as John Paul II (the Former
until dist <= . Pope) and Hans Blix (UN), because their images frequently
Step 6: Return Rankf inal . appear in the dataset [16]. Variations in each person’s name
were collected. For example, George W. Bush, President
step. In this way, the iterations significantly improve the Bush, U.S. President, etc., all refer to the current U.S. pres-
final ranked list. The details are described in Algorithm 3. ident.
To determine the number of iterations of Rank- We performed simple string search in captions to check
By-Bagging-ProbSVM-InnerLoop and Rank-By-Bagging- whether a caption contained one of these names. The faces
ProbSVM-OuterLoop, we use the Kendall − tau dis- extracted from the corresponding image associated with this
tance [13], which is a metric that counts the number of pair- caption were returned. The faces retrieved from the differ-
wise disagreements between two lists. The larger the dis- ent name queries were merged into one set and used as input
tance, the more dissimilar the two lists are. The Kendall − for ranking.
tau distance between two lists, τ1 and τ2 , is defined as fol- Figure 5 shows the distribution of retrieved faces from
lows: this method and the corresponding number of relevant faces
for these fifteen individuals. In total, 5,603 faces were re-
K(τ1 , τ2 ) = K i,j (τ1 , τ2 ) trieved in which 3,374 faces were relevant. On average, the
(i,j)∈P accuracy was 60.22%.
where P is the set of unordered pairs of distinct elements
in τ1 and τ2 . K i,j (τ1 , τ2 ) = 0 if i and j are in the same 4.2 Face Processing
order in τ1 and τ2 , and K i,j (τ1 , τ2 ) = 1 if i and j are in the
opposite order in τ1 and τ2 . We used an eye detector to detect the positions of the
Since the maximum value of K(τ1 , τ2 ) is N (N − 1)/2, eyes of the detected faces. The eye detector, built with the
where N is the number of members of the list, the normal- same approach as that of Viola and Jones [19], had an ac-
ized Kendall tau distance can be written as follows: curacy of more than 95%. If the eye positions were not
detected, predefined eye locations were assigned. The eye
K(τ1 , τ2 )
Knorm (τ1 , τ2 ) = . positions were used to align faces to a predefined canonical
N (N − 1)/2
pose.
Using this measure for checking when the loops stop To compensate for illumination effects, the subtraction
means that if the ranked list does not change significantly of the bestfit brightness plane followed by histogram equal-
after a number of iterations, it is reasonable to stop. ization was applied. This normalization process is shown in
387
6. lated as follows:
Nrel
Recall =
Nhit
Nrel
P recision =
Nret
Precision and recall are only used to evaluate the quality
of an unordered set of retrieved faces. To evaluate ranked
lists in which both recall and precision are taken into ac-
count, average precision is usually used. The average pre-
cision is computed by taking the average of the interpolated
precision measured at the 11 recall levels of 0.0, 0.1, 0.2, ...,
Figure 5. Distribution of retrieved faces and
1.0.
relevant faces of 16 individuals used in ex-
The interpolated precision pinterp at a certain recall level
periments. Due to space limitation, bars cor-
r is defined as the highest precision found for any recall
responding to George Bush (2,282 vs. 1,284)
level q ≥ r:
and Tony Blair (682 vs. 323) were cut-off at
the upper limit of the graph.
pinterp = maxr ≥r p(r )
In addition, to evaluate the performance of multiple
Figure 6. queries, we used mean average precision, which is the mean
We then used principle component analysis [18] to re- of average precisions computed from queries3 .
duce the number of dimensions of the feature vector for face
representation. Eigenfaces were computed from the origi- 4.4 Parameters
nal face set returned using the text-based query method. The
number of eigenfaces used to form the eigen space was se- The parameters of our method include:
lected so that 97% of the total energy was retained [5]. The
number of dimensions of these feature spaces ranged from • p: the fraction of faces at the top and bottom of the
80 to 500. ranked list that are used to form a positive set Spos and
negative set Sneg for training weak classifiers in Rank-
By-Bagging-ProbSVM-InnerLoop. We empirically se-
lected p = 20% (i.e 40% samples of the rank list were
used) since a larger p will increase the number of incor-
rect labels, and a smaller p will cause over-fitting. In
∗
addition, Spos consists of 0.7 × |Spos | samples that are
selected randomly with replacement from Spos . This
sampling strategy is adopted from the bagging frame-
∗
Figure 6. Face normalization. (top) faces with work [6]. The same setting was used for Sneg .
detected eyes, (bottom) faces after normal-
ization process. • : the maximum Kendall tau distance Knorm (τ1 , τ 2)
between two rank lists τ 1 and τ2 . This value is used to
determine when the inner loop and the outer loop stop.
We set = 0.05 for balancing between accuracy and
processing time. Note that a smaller requires more
4.3 Evaluation Criteria iterations, making the system’s speed slower.
• kernel: the kernel type is used for the SVM. The de-
We evaluated the retrieval performance with measures fault is a linear kernel that is defined as: k(x, y) =
that are commonly used in information retrieval, such as x ∗y. We have tested other kernel types such as RBF or
precision, recall, and average precision. Given a queried polynomial, but the performance did not change much.
person and letting Nret be the total number of faces re- Therefore, we used the linear kernel for simplicity.
turned, Nrel the number of relevant faces, and Nhit the total
number of relevant faces, recall and precision can be calcu- 3 http://trec.nist.gov/pubs/trec10/appendices/measures.pdf
388
7. 4.5 Results • Supervised Learning (SVM-SUP): We randomly se-
lected a portion p of the data with annotations to train
4.5.1 Performance Comparison with Existing Ap- the classifier; and then used this classifier to re-rank
proaches the remaining faces. This process was repeated five
times and the average performance was reported. We
We performed a comparison between our proposed method used a range of portion p values for experiments: p =
with other existing approaches. 1%, 2%, 3%, ..., 5%.
• Text Based Baseline (TBL): Once faces corresponding
with images whose captions contain the query name
are returned, they are ranked in time order. This is a
rather naive method in which no prior knowledge be-
tween names and faces is used.
• Distance-Based Outlier (DBO): We adopted the idea
of distance-based outliers detection for ranking [14].
Given a threshold dmin , for each point p, we counted
the number of points q so that dist(p, q) ≤ dmin ,
where dist(p, q) is the Euclidean distance between p
and q in the feature space mentioned in section 4.2.
This number was then used as the score to rank faces.
We selected a range of dmin values for experiments:
dmin = 10, 15, 20, ..., 90.
• Densest Sub-Graph based Method (DSG): We re- Figure 7. Performance comparison of meth-
implemented the densest sub-graph based method [16] ods. Due to different settings, performances
for ranking. Once the densest subgraph was found af- are superimposed for better evaluation.
ter an edge elimination process, we counted the num-
ber of surviving edges of each node (i.e face) and used
this number as the ranking score. To form the graph, Figure 7 shows a performance comparison of these meth-
the Euclidean distance dist(p, q) was used to assign ods. Our proposed methods (LDS and UEL-LDS) out-
the weight for the edge linked between node p and perform other unsupervised methods such as TBL, DBO
node q. DSG require a threshold θ to convert the and DSG. Furthermore, the performance of the DBO and
weighted graph to the binary graph before searching DSG methods are sensitive to the distance threshold, while
for the densest subgraph. We selected a range of θ the performance of our proposed method is less sensitive.
values that are the same as the values used in DBO: It confirms that the similarity measure using shared near-
θ = 10, 15, 20, ..., 90. est neighbors is reliable for estimation of the local den-
sity score. The performance of UEL-LDS is slightly bet-
• Local Density Score (LDS): This is the first stage of ter than LDS since the training sets labeled automatically
our proposed method. It requires the input value k to from the ranked list are noisy. However, UEL-LDS im-
compute the local density score. Since we do not know proves significantly even when the performance of LDS is
the number of returned faces from text-based search poor. These performances are worse than that of SVM-SUP
engines, we used another input value f raction defined using a small number of labeled samples.
as the fraction of neighbors and estimated k by the for- Figure 8 shows an example of the top 50 faces ranked
mula: k = f raction ∗ N , where N is the number of using the TBL, DBO, DSG and LDS methods. The perfor-
returned faces. We used a range of f raction values mance of DBO is poor since a low threshold is used. This
for experiments: f raction = 5%, 10%, 15%, ..., 50%. ranks irrelevant faces that are near duplicates (rows 2 and 3
For a large number of returned faces, we set k to the in Figure 8(b)) higher than relevant faces. This explains the
maximum value of 200: k = 200. same situation with DSG.
• Unsupervised Ensemble Learning Using Local Den- 4.5.2 Performance of Ensemble Classifiers
sity Score (UEL-LDS): This is a combination of rank-
ing by local density scores and then the ranked list is In Figure 9, we show the performance of five single clas-
used for training a classifier to boost the rank list. sifiers and that of five ensemble classifiers. The ensemble
389
8. Precision return a large fraction of relevant images is satisfied. Fig-
Method at top 20 Recall Precision ure 12 shows an example where this assumption is broken.
GoogleSE 79.33 100.00 57.08 Consequently, as shown in Figure 13, the model learned by
UEL-LDS 89.00 72.50 76.41 this set performed poorly in recognizing new faces returned
SVM-SUP-05 85.00 73.14 76.46 by GoogleSE. Our approach solely relies on the above as-
SVM-SUP-10 90.67 74.94 78.30 sumption; therefore, it is not affected by the ranking of text-
based search engines.
Table 1. Comparison of different methods on
The iteration of bagging SVM classifiers does not guar-
the new test set returned by Google Image
antee a significant improvement in performance. The aim
Search Engine.
of our future work is to study how to improve the quality of
the training sets used in this iteration.
classifier k is formed by combining single classifiers from 1 6 Conclusion
to k. It clearly indicates that the ensemble classifier is more
stable than single weak classifiers.
We presented a method for ranking faces retrieved us-
ing text-based correlation methods in searches for a specific
4.5.3 New Face Annotation person. This method learns the visual consistency among
faces in a two-stage process. In the first stage, a relative den-
We conducted another experiment to show the effectiveness
sity score is used to form a ranked list in which faces ranked
of our approach in which learned models are used to anno-
at the top or bottom of the list are likely to be relevant or ir-
tate new faces of other databases. We used each name in the
relevant faces, respectively. In the second stage, a bagging
list as a query to obtain the top 500 images from the Google
framework is used to combine weak classifiers trained on
Image Search Engine (GoogleSE). Next, these images were
subsets labeled from the ranked list into a strong classifier.
processed using the steps described in section 4.2: extract-
This strong classifier is then applied to the original set to
ing faces, detecting eyes and doing normalization. We pro-
re-rank faces on the basis of the output probabilistic scores.
jected these faces to the PCA subspace trained for that name
Experiments on various face sets showed the effectiveness
and used the learned model to re-rank faces.
of this method. Our approach is beneficial when there are
There were 4,103 faces (including false positives - non- several faces in a returned image, as shown in Figure 11.
faces detected as faces) detected from 7,500 returned im-
ages. We manually labeled these faces and there were 2,342
relevant faces. On average, the accuracy of the GoogleSE is References
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Figure 8. Top 50 faces ranked by the methods
TBL, DBO, DSG and LDS. Irrelevant faces are
marked with a star.
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10. Figure 9. Performance of the ensemble clas-
sifiers and single classifiers.
(a) - 5 irrelevant faces
Figure 12. Example in which portion of rel-
evant faces is dominant, but it is difficult to
group all these faces into one cluster due
(b) - no any irrelevant face to large facial variations. In feature space,
the largest cluster formed from relevant faces
is not largest cluster among those formed
Figure 10. Top 20 faces ranked by Google from all returned faces. Irrelevant faces are
Image Search Engine (a) and ranked using marked with a star.
our learned model (b). Irrelevant faces are
marked with a star.
Figure 13. Many irrelevant faces annotated
using the model learned from the data set
Figure 11. Image returned by GoogleSE for shown in Figure 12. Irrelevant faces are
query ’Gerhard Schroeder’. GoogleSE was marked with a star.
unable to accurately identify who the queried
person was, while the learned model of our
approach accurately identified him.
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