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Artificial Intelligence and Image Processing (IWAIIP), International Workshop on

作者： Sanjay Kukreja Tarun Kumar Vishal Bharate Amit Purohit Abhijit Dasgupta Debashis Guha Department of Machine Learning SP Jain School of Global Management Mumbai India COE AI-ML eClerx Services Ltd. Chandigarh India COE AI-ML eClerx Services Ltd. Pune India COE AI-ML eClerx Services Ltd. Mumbai India Department of Data Science SP Jain School of Global Management Mumbai India

This research paper aims to present a comprehensive survey of vector databases and vector embedding techniques. A concise overview of the evolution, architecture, advantages and challenges of vector databases are presented in this paper. To convert unstructured data into vectors, various embedding techniques with their in-depth technical description are also surveyed in this research paper. The existing vector databases’ characteristics and features are described to select an appropriate vector database. The embedding and indexing techniques are thoroughly discussed to help the research community experiment with combining them to develop their application. The challenges of vector databases, like selecting distance metrics, dimensionality, data integrity, cost, etc. are presented. This information on vector database systems will help researchers to exhibit further advancements in their data science applications.

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Cross-Store Next-Basket Recommendation

Cross-Store Next-Basket Recommendation

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IEEE International Conference on data Mining (ICDM)

作者： Liang-Chen Ma Ya Li Zi-Feng Mai Fei-Yao Liang Chang-Dong Wang Min Chen Mohsen Guizani School of Computer Science and Engineering Sun Yat-sen University Guangzhou China School of Electronics and Information Guangdong Polytechnic Normal University Guangzhou China Guangdong Provincial Key Laboratory of Intellectual Property and Big Data Guangzhou China School of Computer Science and Engineering South China University of Technology Guangzhou China Pazhou Laboratory Guangzhou China Machine Learning Department Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI) Abu Dhabi UAE

ISBN: (数字)9798331506681

ISBN: (纸本)9798331506698

Next-basket recommendation (NBR) infers a set of items that a user will interact with in the next basket. Existing methods often struggle with the data sparsity problem, particularly when the number of baskets is significantly large due to diverse user behaviors. Cross-domain recommendation (CDR) can effectively alleviate this problem in NBR by transferring knowledge across different domains. Nevertheless, these methods often rely on the similarities of overlapping users, which leads to the negative transfer problem and ignores the overlapping items that are general in real-world scenarios like chain stores. In this paper, we provide a clear symbolic definition of cross-store recommendation (CSR) and distinguish it from CDR. We also propose a novel CSNBR model for cross-store next-basket recommendation task. To fully model the transferable collaborative information between two stores, we learn the embeddings of users, baskets, and items by two intra-store bipartite graphs, and use an inter-store unified bipartite graph to transfer the previously learned knowledge. Furthermore, to alleviate the negative transfer problem, we propose to reconstruct the inter-store unified bipartite graph by utilizing user embeddings obtained from the transfer layer and the disentanglement layer. We also employ two sequence encoders to model the historical sequential information at basket-level and item-level. Extensive experiments conducted on real-world datasets demonstrate the effectiveness of the CSNBR model.

关键词： Neural networks Collaboration data models Bipartite graph data mining

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The leave-one-covariate-out conditional randomization test

arXiv

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arXiv 2020年

作者： Katsevich, Eugene Ramdas, Aaditya Department of Statistics and Data Science Machine Learning Department Carnegie Mellon University

Conditional independence testing is an important problem, yet provably hard without assumptions. One of the assumptions that has become popular of late is called "model-X", where we assume we know the joint distribution of the covariates, but assume nothing about the conditional distribution of the outcome given the covariates. Knockoffs is a popular methodology associated with this framework, but it suffers from two main drawbacks: only one-bit p-values are available for inference on each variable, and the method is randomized with significant variability across runs in practice. The conditional randomization test (CRT) is thought to be the "right" solution under model-X, but usually viewed as computationally inefficient. This paper proposes a computationally efficient leave-one-covariate-out (LOCO) CRT that addresses both drawbacks of knockoffs. LOCO CRT produces valid p-values that can be used to control the familywise error rate, and has nearly zero algorithmic variability. For L1 regularized M-estimators, we develop an even faster variant called L1ME CRT, which reuses computation by leveraging a novel observation about the stability of the cross-validated lasso to removing inactive variables. Last, for multivariate Gaussian covariates, we present a closed form expression for the LOCO CRT p-value, thus completely eliminating resampling in this important special case. Copyright © 2020, The Authors. All rights reserved.

关键词： Random processes

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Contrastive learning for Adapting Language Model to Sequential Recommendation

Contrastive Learning for Adapting Language Model to Sequenti...

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IEEE International Conference on data Mining (ICDM)

作者： Fei-Yao Liang Wu-Dong Xi Xing-Xing Xing Wei Wan Chang-Dong Wang Min Chen Mohsen Guizani School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Contribution during internship at NetEase Games UX Center NetEase Games Guangzhou China Guangdong Provincial Key Laboratory of Intellectual Property and Big Data Guangzhou China School of Computer Science and Engineering South China University of Technology Guangzhou China Pazhou Laboratory Guangzhou China Machine Learning Department Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI) Abu Dhabi UAE

ISBN: (数字)9798331506681

ISBN: (纸本)9798331506698

With the explosive growth of information, recommendation systems have emerged to alleviate the problem of information overload. In order to improve the performance of recommendation systems, many existing methods introduce Large Language Models to extract textual information from description text. However, Large Language Models are trained on large-scale generic textual data and may face a semantic gap for downstream recommendation tasks. To address the above issues, we propose Contrastive learning for Adapting Language Model to Sequential Recommendation (CLA-Rec). In CLA-Rec, we first extract text embeddings from description text using Large Language Models and align the text embeddings learned by Large Language Models with the collaborative information through contrastive learning to obtain high-quality item representations. Through semantic alignment, we bridge the semantic gap between Large Language Models and the recommendation task. To map textual information and collaborative information into user representations, we utilize a Transformer model to learn user representations and capture user preferences by combining the semantically aligned item representations. Extensive experiments on three public datasets demonstrate that our method outperforms state-of-the-art approaches on multiple evaluation metrics, illustrating the effectiveness of the CLA-Rec model in adapting Large Language Models to recommendation tasks.

关键词： Measurement Adaptation models Large language models Semantics Collaboration Contrastive learning Transformers data models data mining Recommender systems

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Sampling Strategies for Compressive Imaging Under Statistical Noise

Sampling Strategies for Compressive Imaging Under Statistica...

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International Conference on Sampling Theory and Applications (SampTA)

作者： Frederik Hoppe Felix Krahmer Claudio Mayrink Verdun Marion I. Menzel Holger Rauhut Chair of Mathematics of Information Processing RWTH Aachen University Aachen Germany Department of Mathematics Technical University of Munich Munich Germany Munich Center for Machine Learning Munich Germany Munich Data Science Institute Technical University of Munich AImotion Bavaria Technische Hochschule Ingolstadt Ingolstadt Germany Department of Physics Technical University of Munich Garching Germany GE Healthcare Munich Germany

Most of the compressive sensing literature in signal processing assumes that the noise present in the measurement has an adversarial nature, i.e., it is bounded in a certain norm. At the same time, the randomization introduced in the sampling scheme usually assumes an i.i.d. model where rows are sampled with replacement. In this case, if a sample is measured a second time, it does not add additional information. For many applications, where the statistical noise model is a more accurate one, this is not true anymore since a second noisy sample comes with an independent realization of the noise, so there is a fundamental difference between sampling with and without replacement. Therefore, a more careful analysis must be performed. In this short note, we illustrate how one can mathematically transition between these two noise models. This transition gives rise to a weighted LASSO reconstruction method for sampling without replacement, which numerically improves the solution of high-dimensional compressive imaging problems.

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RecCoder: Reformulating Sequential Recommendation as Large Language Model-Based Code Completion

RecCoder: Reformulating Sequential Recommendation as Large L...

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IEEE International Conference on data Mining (ICDM)

作者： Kai-Huang Lai Wu-Dong Xi Xing-Xing Xing Wei Wan Chang-Dong Wang Min Chen Mohsen Guizani School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Contribution during internship at NetEase Games UX Center NetEase Games Guangzhou China Guangdong Provincial Key Laboratory of Intellectual Property and Big Data Guangzhou China School of Computer Science and Engineering South China University of Technology Guangzhou China Pazhou Laboratory Guangzhou China Machine Learning Department Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI) Abu Dhabi UAE

ISBN: (数字)9798331506681

ISBN: (纸本)9798331506698

In the evolving landscape of sequential recommendation systems, the application of Large Language Models (LLMs) is increasingly prominent. However, current attempts typically utilize general-purpose LLMs, which present a mismatch in capability and a large semantic gap relative to the specialized needs of recommendation tasks. To tackle these issues, we introduce RecCoder, an innovative model that reformulates sequential recommendation as a code completion task. This approach leverages the superior reasoning capability of code LLMs as a backbone, aligning well with the requirements of recommendation systems. To bridge the semantic gap, RecCoder creates extra tokens for each item and employs item content to initialize token embeddings. Furthermore, we have developed a suite of Semantic Adaptation Fine-tuning tasks, tailored to enhance the model's acquisition of both content and collaborative semantic information, thus aligning the model's intrinsic capabilities with the unique demands of recommendation tasks. Through extensive testing on three public datasets, RecCoder has shown remarkable improvements over existing models in terms of recommendation accuracy and efficiency. This success highlights the substantial yet previously underexplored potential of code LLMs in improving recommendation accuracy and efficiency, suggesting a promising new direction for future research in this area. The implementation code is accessible at https://***/AllminerLab/Code-for-RecCoder-master.

关键词： Adaptation models Codes Accuracy Large language models Semantics Cognition data models data mining Recommender systems Testing

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Is L2 Physics-Informed Loss Always Suitable for Training Physics-Informed Neural Network?

arXiv

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arXiv 2022年

作者： Wang, Chuwei Li, Shanda He, Di Wang, Liwei School of Mathematical Sciences Peking University China Machine Learning Department School of Computer Science Carnegie Mellon University United States National Key Laboratory of General Artificial Intelligence School of Intelligence Science and Technology Peking University China Center for Data Science Peking University China Zhejiang Lab China

The Physics-Informed Neural Network (PINN) approach is a new and promising way to solve partial differential equations using deep learning. The L2 Physics-Informed Loss is the de-facto standard in training Physics-Informed Neural Networks. In this paper, we challenge this common practice by investigating the relationship between the loss function and the approximation quality of the learned solution. In particular, we leverage the concept of stability in the literature of partial differential equation to study the asymptotic behavior of the learned solution as the loss approaches zero. With this concept, we study an important class of high-dimensional non-linear PDEs in optimal control, the Hamilton-Jacobi-Bellman (HJB) Equation, and prove that for general Lp Physics-Informed Loss, a wide class of HJB equation is stable only if p is sufficiently large. Therefore, the commonly used L2 loss is not suitable for training PINN on those equations, while L∞ loss is a better choice. Based on the theoretical insight, we develop a novel PINN training algorithm to minimize the L∞ loss for HJB equations which is in a similar spirit to adversarial training. The effectiveness of the proposed algorithm is empirically demonstrated through experiments. Our code is released at https://***/LithiumDA/L_inf-PINN. Copyright © 2022, The Authors. All rights reserved.

关键词： Partial differential equations

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Artificial Intelligence-Driven Protein Folding System Employing Alpha Fold

Artificial Intelligence-Driven Protein Folding System Employ...

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Multi-Agent Systems for Collaborative Intelligence (ICMSCI), International Conference on

作者： Sowmiya R M. Pravin Kumar T. M. Sathish Kumar Rasika P M. Poornima Devi S. Priya Dharshini Department of Artificial Intelligence and Data Science SNS College of Engineering Coimbatore Tamil Nadu India Department of Medical Electronics Velalar College of Engineering and Technology (Autonomous) Erode Tamil Nadu India Department of Electronics and Communication Engineering K.S.R. College of Engineering Namakkal Tamil Nadu India Department of Pharmaceutics SNS College of Pharmacy and Health Sciences Coimbatore Tamil Nadu India Department of Artificial Intelligence and Machine Learning SNS College of Technology Coimbatore Tamil Nadu India Department of Pharmaceutics Periyar College of Pharmaceutical Sciences Tiruchirappalli Tamil Nadu India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

Artificial Intelligence (AI) is a main role to solve the lot of real-live issues. One of the significant challenges in the bioinformatics that researchers have been identified recently is understand the protein folding. First of all, people want to give a big hand off to the advancements because, day to day activities is performed in 3-dimensional basis that is formed based on a sequence of amino acids using protein folds. The folding of proteins is mostly related to three major issues. The first issue is the lack of a specific folding code. There is a folding mechanism in the second one. The ability to ascertain the 3-dimensional structure of proteins presents the last challenge. The structural components and microscopic machinery that make up human cells are called proteins. They provide vital tasks include storing oxygen, breaking down food, and creating scaffolding to support cells in maintaining their structure. Here, Amino acids is formed based on a single chain mechanism because amino acids folds safely and it performs a unique structure that is incorporated here. All living things, including viruses, are composed of proteins, including every element of human body and every other organism. The computational approach can be used to predict the structure of minuscule proteins. In this research work explained about how artificial intelligence is incorporated in predictions of protein structure and artificial intelligence give the solution for the problem of problem folding.

关键词： Proteins Hands Computer viruses Microscopy Amino acids Organisms Artificial intelligence machinery Collaborative intelligence Periodic structures

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Personalised Speech-Based PTSD Prediction Using Weighted-Instance learning

Personalised Speech-Based PTSD Prediction Using Weighted-Ins...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Alexander Kathan Shahin Amiriparian Andreas Triantafyllopoulos Alexander Gebhard Sabrina Milkus Jonas Hohmann Pauline Muderlak Jürgen Schottdorf Richard Musil Björn W. Schuller EIHW – Chair of Embedded Intelligence for Healthcare and Wellbeing University of Augsburg Germany CHI – Chair of Health Informatics MRI Technical Univsersity of Munich Germany MCML – Munich Center for Machine Learning Germany Department of Psychiatry and Psychotherapy University Hospital LMU Munich Germany Zentrumspraxis Friedberg Germany GLAM – Group on Language Audio & Music Imperial College London UK MDSI – Munich Data Science Institute Germany

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Post-traumatic stress disorder (PTSD) is a prevalent disorder that can develop in people who have experienced very stressful, shocking, or distressing events. It has great influence on peoples’ daily life and can affect their mental, physical, or social wellbeing, which is why a timely and professional treatment is required. In this paper, we propose a personalised speech-based PTSD prediction approach using a newly collected dataset which consists of 15 participants, including speech recordings from people with PTSD and healthy controls. In addition, the dataset includes data before and after a clinical intervention so that the prediction can be analysed at different points in time. In our experiments, we demonstrate the superiority of the personalised approach, achieving a best area under the ROC curve (AUC) of 82 % and a best relative improvement of 7 % points compared to the non-personalised model.

关键词： Biological system modeling Human factors Recording Engineering in medicine and biology

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Customer Segmentation Using Clustering Analysis

Customer Segmentation Using Clustering Analysis

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Information Technology, Electronics and Intelligent Communication Systems (ICITEICS), IEEE International Conference on

作者： Moses Makuei Jiet Aahash Kamble Chetan Puri Prajyot Yesankar Prateek Verma Rajendra Rewatkar Department of Computer Science & Design Faculty of Engineering and Technology Datta Meghe Institute of Higher Education & Research (DU) Wardha Maharashtra India Department of Artificial Intelligence & Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education & Research (DU) Wardha Maharashtra India Department of Artificial Intelligence & Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education & Research (DU) Wardha Maharashtra India Department of Biomedical Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education & Research (DU) Wardha Maharashtra India

ISBN: (数字)9798350382693

ISBN: (纸本)9798350382709

This research focuses on the crucial role of the clustering technique in data mining, specifically in market forecasting and planning. The study presents a comprehensive report on utilizing the k-means clustering technique and data visualization tools to develop a real- time system for a supermarket, predicting sales across various annual cycles. An open-source dataset of a mall with n = 200 customers divided into k = 3 groups, demonstrated high accuracy in predicting day-to-day sales statistics when compared with actual results. The research highlights the effectiveness of the clustering technique in real-time sales prediction for supermarkets.

关键词： Sociology Refining Decision making data visualization Prediction algorithms Real-time systems Planning

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