Ranking using pairwise preferences
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Comparison of three ranking algorithms : Rank Centrality, Rank Aggregation via NNM, and SVM-Rank Aggregation
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The document discusses several collaborative filtering techniques for making recommendations, including k-nearest neighbors (kNN), naive Bayes classification, singular value decomposition (SVD), and probabilistic models. It provides examples of how these methods work, such as using ratings from similar users to predict a user's rating for an item (kNN), and decomposing a ratings matrix to capture relationships between users and items (SVD). The techniques vary in their assumptions, complexity, and ability to incorporate additional user/item metadata. Evaluation on new data is important to ensure the methods generalize well beyond the training data.
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Principal Component Analysis (PCA) is a technique used to simplify complex data sets by identifying patterns in the data and expressing it in such a way to highlight similarities and differences. It works by subtracting the mean from the data, calculating the covariance matrix, and determining the eigenvectors and eigenvalues to form a feature vector representing the data in a lower dimensional space. PCA can be used to represent image data as a one dimensional vector by stacking the pixel rows of an image and applying this analysis to multiple images.
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Similar to Ranking using pairwise preferences
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The document discusses several collaborative filtering techniques for making recommendations:
1) Nearest neighbor techniques like k-NN make predictions based on the ratings of similar users. They require storing all user data but can be fast with appropriate data structures.
2) Naive Bayes classifiers treat each item's ratings independently; they make strong assumptions but require less data.
3) Dimensionality reduction techniques like SVD decompose the user-item rating matrix to find latent factors. Weighted SVD handles missing data.
4) Probabilistic models like mixtures of multinomials and aspect models represent additional user metadata but have more parameters.
Download
The document discusses several collaborative filtering techniques for making recommendations, including k-nearest neighbors (kNN), naive Bayes classification, singular value decomposition (SVD), and probabilistic models. It provides examples of how these methods work, such as using ratings from similar users to predict a user's rating for an item (kNN), and decomposing a ratings matrix to capture relationships between users and items (SVD). The techniques vary in their assumptions, complexity, and ability to incorporate additional user/item metadata. Evaluation on new data is important to ensure the methods generalize well beyond the training data.
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This is my invited talk at the Workshop on Machine Learning and Data Mining for Sports Analytics (co-located with ECML/PKDD 2018)
Winning in Sports with Networks
Network analysis methods can be used for sports analytics applications like team and lineup ranking. SportsNetRank ranks teams based on their win-loss network using PageRank centrality. LinNet evaluates lineups based on their matchup network using network embeddings. It learns latent representations of lineups using node2vec and predicts outcomes of new lineup matchups. LinNet outperforms adjusted plus-minus and PageRank in predicting unseen lineup matchups, with probabilities well calibrated and Brier scores around 0.19. Substitution networks also show potential for explaining team performance. Further work could optimize network embeddings and model lineup ability curves.
Recent advances on low-rank and sparse decomposition for moving object detection
(RFIA 2016) Recent advances on low-rank and sparse decomposition for moving object detection: matrix and tensor-based approaches. RFIA 2016, workshop/atelier: Enjeux dans la détection d’objets mobiles par soustraction de fond.
Introduction to Machine Learning
This document provides an introduction to machine learning, covering key concepts such as definition, stages of the machine learning process, and types of machine learning algorithms. It discusses supervised machine learning techniques including regression to predict continuous values and classification to predict categorical values. Unsupervised machine learning techniques covered include clustering to discover inherent groupings in unlabeled data. Specific algorithms like linear regression, logistic regression, and k-means clustering are explained along with examples and evaluation methods.
Aaa ped-20-Recommender Systems: Model-based collaborative filtering
This is the part where we will talk about model-based filtering. In here, we will see two types of models: SVM and SVD (for Singular Value Decomposition). We will introduce you to a useful library : Surprise library.
[notebook](https://colab.research.google.com/drive/1Xt3DImn43eMrEMMZadByrD1_bSS0fmGh)
Uncertainty aware multidimensional ensemble data visualization and exploration
This document summarizes an approach for uncertainty-aware multidimensional projection of ensemble data. The key contributions are a new dissimilarity measure between ensemble data objects based on both mean distance and distribution distance, and an enhanced Laplacian-based projection scheme. The approach first estimates probability distributions for each ensemble data object using kernel density estimation. It then projects a subset of control points using MDS and interpolates the remaining points based on nearest neighbors. Visualization widgets allow exploration of projection results and uncertainty quantification. The approach is demonstrated on synthetic, NBA player statistic, and weather simulation datasets.
Building the Professional of 2020: An Approach to Business Change Process Int...
Building the Professional of 2020: An Approach to Business Change Process Int...Dr Harris Apostolopoulos EMBA, PfMP, PgMP, PMP, IPMO-E
CRAM (Change Risk Assessment Model) is a novel model approach which can significantly contribute to the missing formality of business models especially in the change(s) risk assessment area.
Project Management has long established the need for risk management techniques to be utilised in the succinct definition of associated risks in projects and agreement on countervailing actions as an aim to reduce scope creep, increase the probability of on-time and in-budget delivery.
Uncontrolled changes, regardless of size and complexity, can certainly pose as risks, of any magnitude, to projects and affect project success or even an organisation’s coherence.
introduction to machine learning 3c-feature-extraction.pptx
This document discusses feature extraction and dimensionality reduction techniques. It begins by defining feature extraction as mapping a set of features to a reduced feature set that maximizes classification ability. It then explains principal component analysis (PCA) and how it works by finding orthogonal directions that maximize data variance. However, PCA does not consider class information. Linear discriminant analysis (LDA) is then introduced as a technique that finds projections by maximizing between-class distance and minimizing within-class distance to better separate classes. LDA thus provides a "good projection" for classification tasks.
Predicting SPARQL query execution time and suggesting SPARQL queries based on...
This document discusses predicting SPARQL query execution time using machine learning techniques. It first provides context on assisting users and agents in querying and consuming semantic web data. It then outlines predicting query times and suggesting similar queries from history. The document discusses applying the scientific method used for analyzing algorithms to understand query performance. It describes using machine learning to represent SPARQL queries as feature vectors to predict execution times, with the key challenge being effective feature engineering. An experiment uses support vector machine regression to predict times for test queries from DBpedia with mixed results.
04 Classification in Data Mining
Overview of common classification techniques in Data Mining: regression and Bayesian models, KNN, decision trees, neural networks.
RankSRGAN
1) RankSRGAN is a GAN method for single image super-resolution that introduces a Ranker to mimic perceptual metrics and provide differentiable loss functions.
2) The Ranker is trained on image pairs ranked by perceptual metrics to learn the ranking behavior, and provides a rank-content loss to constrain the SRGAN generator.
3) Experiments show RankSRGAN achieves better perceptual quality than SRGAN and ESRGAN according to metrics and user studies, and the Ranker learns metric behaviors better than regression.
Information retrieval 10 vector and probabilistic models
Vector space model or term vector model is an algebraic model for representing text documents (and any objects, in general) as vectors of identifiers, such as, for example, index terms. It is used in information filtering, information retrieval, indexing and relevancy rankings.
Principal component analysis and lda
PCA and LDA are dimensionality reduction techniques. PCA transforms variables into uncorrelated principal components while maximizing variance. It is unsupervised. LDA finds axes that maximize separation between classes while minimizing within-class variance. It is supervised and finds axes that separate classes well. The document provides mathematical explanations of how PCA and LDA work including calculating covariance matrices, eigenvalues, eigenvectors, and transformations.
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Data Mining: Implementation of Data Mining Techniques using RapidMiner software
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THEMATIC APPERCEPTION TEST(TAT) cognitive abilities, creativity, and critic...
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hematic appreciation test is a psychological assessment tool used to measure an individual's appreciation and understanding of specific themes or topics. This test helps to evaluate an individual's ability to connect different ideas and concepts within a given theme, as well as their overall comprehension and interpretation skills. The results of the test can provide valuable insights into an individual's cognitive abilities, creativity, and critical thinking skillsThe use of Nauplii and metanauplii artemia in aquaculture (brine shrimp).pptx
Although Artemia has been known to man for centuries, its use as a food for the culture of larval organisms apparently began only in the 1930s, when several investigators found that it made an excellent food for newly hatched fish larvae (Litvinenko et al., 2023). As aquaculture developed in the 1960s and ‘70s, the use of Artemia also became more widespread, due both to its convenience and to its nutritional value for larval organisms (Arenas-Pardo et al., 2024). The fact that Artemia dormant cysts can be stored for long periods in cans, and then used as an off-the-shelf food requiring only 24 h of incubation makes them the most convenient, least labor-intensive, live food available for aquaculture (Sorgeloos & Roubach, 2021). The nutritional value of Artemia, especially for marine organisms, is not constant, but varies both geographically and temporally. During the last decade, however, both the causes of Artemia nutritional variability and methods to improve poorquality Artemia have been identified (Loufi et al., 2024).
Brine shrimp (Artemia spp.) are used in marine aquaculture worldwide. Annually, more than 2,000 metric tons of dry cysts are used for cultivation of fish, crustacean, and shellfish larva. Brine shrimp are important to aquaculture because newly hatched brine shrimp nauplii (larvae) provide a food source for many fish fry (Mozanzadeh et al., 2021). Culture and harvesting of brine shrimp eggs represents another aspect of the aquaculture industry. Nauplii and metanauplii of Artemia, commonly known as brine shrimp, play a crucial role in aquaculture due to their nutritional value and suitability as live feed for many aquatic species, particularly in larval stages (Sorgeloos & Roubach, 2021).
在线办理(salfor毕业证书)索尔福德大学毕业证毕业完成信一模一样
学校原件一模一样【微信：741003700 】《(salfor毕业证书)索尔福德大学毕业证》【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
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3:国家专业人才认证中心颁发入库证书
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5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
原版制作(carleton毕业证书)卡尔顿大学毕业证硕士文凭原版一模一样
原版纸张【微信：741003700 】【(carleton毕业证书)卡尔顿大学毕业证】【微信：741003700 】学位证，留信认证（真实可查，永久存档）offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原海外各大学 Bachelor Diploma degree, Master Degree Diploma
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留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
Thornton ESPP slides UK WW Network 4_6_24.pdf
ESPP presentation to EU Waste Water Network, 4th June 2024 “EU policies driving nutrient removal and recycling
and the revised UWWTD (Urban Waste Water Treatment Directive)”
Phenomics assisted breeding in crop improvement
As the population is increasing and will reach about 9 billion upto 2050. Also due to climate change, it is difficult to meet the food requirement of such a large population. Facing the challenges presented by resource shortages, climate
change, and increasing global population, crop yield and quality need to be improved in a sustainable way over the coming decades. Genetic improvement by breeding is the best way to increase crop productivity. With the rapid progression of functional
genomics, an increasing number of crop genomes have been sequenced and dozens of genes influencing key agronomic traits have been identified. However, current genome sequence information has not been adequately exploited for understanding
the complex characteristics of multiple gene, owing to a lack of crop phenotypic data. Efficient, automatic, and accurate technologies and platforms that can capture phenotypic data that can
be linked to genomics information for crop improvement at all growth stages have become as important as genotyping. Thus,
high-throughput phenotyping has become the major bottleneck restricting crop breeding. Plant phenomics has been defined as the high-throughput, accurate acquisition and analysis of multi-dimensional phenotypes
during crop growing stages at the organism level, including the cell, tissue, organ, individual plant, plot, and field levels. With the rapid development of novel sensors, imaging technology,
and analysis methods, numerous infrastructure platforms have been developed for phenotyping.
Bob Reedy - Nitrate in Texas Groundwater.pdf
Presented at June 6-7 Texas Alliance of Groundwater Districts Business Meeting
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Describing and Interpreting an Immersive Learning Case with the Immersion Cub...
Current descriptions of immersive learning cases are often difficult or impossible to compare. This is due to a myriad of different options on what details to include, which aspects are relevant, and on the descriptive approaches employed. Also, these aspects often combine very specific details with more general guidelines or indicate intents and rationales without clarifying their implementation. In this paper we provide a method to describe immersive learning cases that is structured to enable comparisons, yet flexible enough to allow researchers and practitioners to decide which aspects to include. This method leverages a taxonomy that classifies educational aspects at three levels (uses, practices, and strategies) and then utilizes two frameworks, the Immersive Learning Brain and the Immersion Cube, to enable a structured description and interpretation of immersive learning cases. The method is then demonstrated on a published immersive learning case on training for wind turbine maintenance using virtual reality. Applying the method results in a structured artifact, the Immersive Learning Case Sheet, that tags the case with its proximal uses, practices, and strategies, and refines the free text case description to ensure that matching details are included. This contribution is thus a case description method in support of future comparative research of immersive learning cases. We then discuss how the resulting description and interpretation can be leveraged to change immersion learning cases, by enriching them (considering low-effort changes or additions) or innovating (exploring more challenging avenues of transformation). The method holds significant promise to support better-grounded research in immersive learning.
ESR spectroscopy in liquid food and beverages.pptx
With increasing population, people need to rely on packaged food stuffs. Packaging of food materials requires the preservation of food. There are various methods for the treatment of food to preserve them and irradiation treatment of food is one of them. It is the most common and the most harmless method for the food preservation as it does not alter the necessary micronutrients of food materials. Although irradiated food doesn’t cause any harm to the human health but still the quality assessment of food is required to provide consumers with necessary information about the food. ESR spectroscopy is the most sophisticated way to investigate the quality of the food and the free radicals induced during the processing of the food. ESR spin trapping technique is useful for the detection of highly unstable radicals in the food. The antioxidant capability of liquid food and beverages in mainly performed by spin trapping technique.
Authoring a personal GPT for your research and practice: How we created the Q...
Thematic analysis in qualitative research is a time-consuming and systematic task, typically done using teams. Team members must ground their activities on common understandings of the major concepts underlying the thematic analysis, and define criteria for its development. However, conceptual misunderstandings, equivocations, and lack of adherence to criteria are challenges to the quality and speed of this process. Given the distributed and uncertain nature of this process, we wondered if the tasks in thematic analysis could be supported by readily available artificial intelligence chatbots. Our early efforts point to potential benefits: not just saving time in the coding process but better adherence to criteria and grounding, by increasing triangulation between humans and artificial intelligence. This tutorial will provide a description and demonstration of the process we followed, as two academic researchers, to develop a custom ChatGPT to assist with qualitative coding in the thematic data analysis process of immersive learning accounts in a survey of the academic literature: QUAL-E Immersive Learning Thematic Analysis Helper. In the hands-on time, participants will try out QUAL-E and develop their ideas for their own qualitative coding ChatGPT. Participants that have the paid ChatGPT Plus subscription can create a draft of their assistants. The organizers will provide course materials and slide deck that participants will be able to utilize to continue development of their custom GPT. The paid subscription to ChatGPT Plus is not required to participate in this workshop, just for trying out personal GPTs during it.
Equivariant neural networks and representation theory
Or: Beyond linear.
Abstract: Equivariant neural networks are neural networks that incorporate symmetries. The nonlinear activation functions in these networks result in interesting nonlinear equivariant maps between simple representations, and motivate the key player of this talk: piecewise linear representation theory.
Disclaimer: No one is perfect, so please mind that there might be mistakes and typos.
dtubbenhauer@gmail.com
Corrected slides: dtubbenhauer.com/talks.html
20240520 Planning a Circuit Simulator in JavaScript.pptx
Evaporation step counter work. I have done a physical experiment.
(Work in progress.)
Deep Software Variability and Frictionless Reproducibility
Deep Software Variability and Frictionless ReproducibilityUniversity of Rennes, INSA Rennes, Inria/IRISA, CNRS
The ability to recreate computational results with minimal effort and actionable metrics provides a solid foundation for scientific research and software development. When people can replicate an analysis at the touch of a button using open-source software, open data, and methods to assess and compare proposals, it significantly eases verification of results, engagement with a diverse range of contributors, and progress. However, we have yet to fully achieve this; there are still many sociotechnical frictions.
Inspired by David Donoho's vision, this talk aims to revisit the three crucial pillars of frictionless reproducibility (data sharing, code sharing, and competitive challenges) with the perspective of deep software variability.
Our observation is that multiple layers — hardware, operating systems, third-party libraries, software versions, input data, compile-time options, and parameters — are subject to variability that exacerbates frictions but is also essential for achieving robust, generalizable results and fostering innovation. I will first review the literature, providing evidence of how the complex variability interactions across these layers affect qualitative and quantitative software properties, thereby complicating the reproduction and replication of scientific studies in various fields.
I will then present some software engineering and AI techniques that can support the strategic exploration of variability spaces. These include the use of abstractions and models (e.g., feature models), sampling strategies (e.g., uniform, random), cost-effective measurements (e.g., incremental build of software configurations), and dimensionality reduction methods (e.g., transfer learning, feature selection, software debloating).
I will finally argue that deep variability is both the problem and solution of frictionless reproducibility, calling the software science community to develop new methods and tools to manage variability and foster reproducibility in software systems.
Exposé invité Journées Nationales du GDR GPL 2024
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Ranking using pairwise preferences
- 1. Ranking Using Pairwise Preferences Himanshi Sinha(12202) Lakshmi Bansal(13014) Sweta Sharma(12392)
- 2. Problem Definition • There is a set of n items and one is given pairwise comparisons among these items. The problem is to compare the algorithms on the basis of the number of comparisons required to converge to an optimal ranking.
- 3. Algorithms Implemented • Rank Centrality: – Each pair(i,j) of items is compared ‘K’ number of times – The transition matrix P is defined using the outcome of these comparisons. – The Limiting distribution π of the matrix P defines the numerical scores of the n items. Sahand Negahban, Sewoong Oh, and Devavrat Shah. Rank centrality: Ranking from pair-wise comparisons arXiv preprint arXiv:1209.1688, 2012
- 4. Rank Aggregation via NNM • Each pair (i,j) item is compared ‘K’ times and a skew- symmetric matrix Y is defined as follows: • Then singular-value-projection is used to find Rank-2 approximation of the matrix Y. • This approximation is being used to calculate the final scores of the items. David F Gleich and Lek-heng Lim. Rank aggregation via nuclear norm minimization. In Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
- 5. SVM-Rank Aggregation • Inducing the Dataset: SP = {vij,zij}i<j as consisting of vectors vij = (Pi-Pj), where Pi denotes i-th column of P, together with binary labels zij = sign(Pji-Pij). • We find the hyperplane separating the above dataset. • Calculate scores for each item using w vector Arun Rajkumar and Shivani Agarwal. A statistical convergence perspective of algorithms for rank aggregation from pairwise data. In Proceedings of the 31st International Conference on Machine Learning, 2014.
- 6. DATASETS • Dataset generated synthetically using Bradley-Terry- Luce model with which we also get the true ranking for evaluating correctness of our implementations. • From REAL data set of SOC(strict ordering complete list) we generated pair wise data which also gives us the ground truth.
- 7. Pair-wise Disagreement error(BTL) Rank Centrality:
- 8. • Rank-Aggregation via NNM • SVM Rank Aggregation
- 10. • Rank-Aggregation via NNM • SVM Rank Aggregation
- 11. DL1 error(SOC) Rank Centrality: • N = 24, k=64
- 12. • Rank-Aggregation via NNM • SVM Rank Aggregation
- 14. Conclusion and Future Work • It can be seen from the results that Rank Centrality performs better than NNM and SVM-Aggregation on PDE metric for BTL Dataset. NNM performs slightly better than SVM. • We can see from DL1 error on BTL and SOC Dataset that Rank Centrality is more robust to missing values as compared to NNM and SVM Rank Aggregation. • The Empirical time taken by each of the algorithms increases as we increase the number of items n. Rank Centrality and NNM are more time efficient than SVM Aggregation.
- 15. THANK YOU