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Control Conference (ICC), 2019 Sixth Indian

作者： Vasumathi R Subhadeep Kumar Ramkrishna Pasumarthy Nirav Bhatt Department of Electrical Engineering Robert Bosch Center of Data Sciences and Artificial Intelligence Indian Institute of Technology Madras Chennai India Department of Electrical Engineering Robert Bosch Center of Data Sciences and Artificial Intelligence and Prospective Center of Excellence for Network Systems Learning Control and Evolution Group Indian Institute of Technology Madras Chennai India Department of Biotechnology Robert Bosch Center of Data Sciences and Artificial Intelligence Prospective Center of Excellence for Network Systems Learning Control and Evolution Group Indian Institute of Technology Madras Chennai India

Accurate position estimation, especially in indoor environments, presents a formidable challenge despite significant advancements in estimation methodologies. The integration of additional sensors is often imperative to refine estimates, particularly within indoor settings. The choice of additional sensors depends on the specific applications and operational range. This study focuses on devising a position estimation framework utilizing the internal sensors such as the Inertial Measurement Unit (IMU) and wheel encoders within a scaled-down Electric Vehicle. The proposed approach entails a combination of crafting a digital filter and implementing a Kalman filter to effectively attenuate noise and errors in the IMU data, thereby enhancing the position estimate, and the methodology is validated through experiments. This work contributes to the broader discourse on advancing indoor position estimation while bypassing the need for external positioning systems.

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Can a bifurcation diagram contain loops?

Can a bifurcation diagram contain loops?

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2021 International Conference "Nonlinearity, Information and Robotics", NIR 2021

作者： Palshin, Gleb P. Ryabov, Pavel E. Sokolov, Sergei V. Financial University Department of Data Analysis and Machine Learning Moscow Russia Moscow Institute of Physics and Technology National Research University Department of Theoretical Mechanics Moscow Region Dolgoprudny Russia

ISBN: (纸本)9781665424066

The bifurcation diagram plays a major role in the study of the phase topology of completely Liouville-integrable Hamiltonian systems. In the works of A.T. Fomenko and A.V. Bolsinov, the problem of the permissible form of bifurcation diagrams is formulated. In particular, can a bifurcation diagram contain loops? The answer is positive. In this paper, the critical set of the integral mapping and the bifurcation diagram, which contains or almost contains a loop, is given by the example of the vortex dynamics problem. © 2021 IEEE.

关键词： Bifurcation (mathematics)

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Approaches Adopted by Researchers to Measure the Quality of the Experience of People Working from Home: a Scoping Review

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Journal of technology in Behavioral Science 2022年第4期7卷 451-467页

作者： Federici, Stefano De Filippis, Maria Laura Mele, Maria Laura Borsci, Simone Bracalenti, Marco Bifolchi, Giovanni Gaudino, Giancarlo Amendola, Massimo Cocco, Antonello Simonetti, Emilio Department of Philosophy Social and Human Sciences and Education University of Perugia Perugia Italy Department Learning Data Analysis and Technology Cognition Data and Education CODE Group Faculty of Behavioural Management and Social Sciences University of Twente Enschede Netherlands Department of Surgery and Cancer Faculty of Medicine NIHR London IVD Imperial College London United Kingdom DGTCSI-ISCTI Superior Institute of Communication and Information Technologies Ministry of Economic Development Rome Italy Department of Public Service Prime Minister’s Office Rome Italy

Working from home (WFH) remotely is a modality of working that requires the careful design of systems of rules and tools to enable people to exchange information and perform actions. WFH is expected to expand after the COVID-19 pandemic. How to assess and compare in a reliable way the experience of workers with different (sociotechnical) systems of WFH is a central point to supporting the diffusion of acceptable modalities of working. However, the concept of experience and how it can be measured in the domain in WFH is yet to be clearly characterized. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology for scoping reviews, we systematically map the approaches used by researchers to assess WFH, identify which aspects are usually investigated, and examine how such aspects are usually measured in terms of questions and tools. Literature is collected using Scopus and Web of Science. Thirty-four records out of 323 focusing either on validating a scale, presenting theoretically the experience of workers or testing this empirically are included in the qualitative synthesis. The results highlight a lack of unified terminology and tools, with assessments of workers’ experience mainly characterized by survey approaches and qualitative questions. Clustering together the most investigated aspects in the literature and reviewing how these aspects are assessed, we propose a list of 10 relevant overarching dimensions and attempt to define workers’ experience in the domain of WFH remotely. This definition can be used as a tool by researchers aiming to assess the experience of workers in order to inform the design or redesign of the sociotechnical systems that enable WFH. © 2022, The Author(s).

关键词： Assessment Experience Remote working Satisfaction Workers’ experience Working from home

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Cross-Store Next-Basket Recommendation 24

Cross-Store Next-Basket Recommendation

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24th IEEE International Conference on data Mining, ICDM 2024

作者： Ma, Liang-Chen Li, Ya Mai, Zi-Feng Liang, Fei-Yao Wang, Chang-Dong Chen, Min Guizani, Mohsen School of Electronics and Information Guangdong Polytechnic Normal University Guangzhou China School of Computer Science and Engineering Sun Yat-sen 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 Abu Dhabi United Arab Emirates

ISBN: (纸本)9798331506681

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. © 2024 IEEE.

关键词： Graph embeddings

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Contrastive learning for Adapting Language Model to Sequential Recommendation 24

Contrastive Learning for Adapting Language Model to Sequenti...

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24th IEEE International Conference on data Mining, ICDM 2024

作者： Liang, Fei-Yao Xi, Wu-Dong Xing, Xing-Xing Wan, Wei Wang, Chang-Dong Chen, Min Guizani, Mohsen School of Computer Science and Engineering Sun Yat-sen University Guangzhou China NetEase Games China 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 Abu Dhabi United Arab Emirates

ISBN: (纸本)9798331506681

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. © 2024 IEEE.

关键词： Contrastive learning

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Enhancing the Prediction Accuracy of Brain Tumor Detection by using Fuzzy C-Means Segmentation and Support Vector Machine

Enhancing the Prediction Accuracy of Brain Tumor Detection b...

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IoT Based Control Networks and Intelligent Systems (ICICNIS), International Conference on

作者： M Revathi P Kalaiarasi S Jeevitha G Akilandasowmya Artificial Intelligence & Data Science St. Joseph's Institute of Technology Chennai India Department of Machine Learning Kalasalingam University Krishnankoil India Information Technology Jerusalem College of Engineering Chennai India Computer and Communication Engineering Sri Sairam Institute of Technology Chennai India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

Brain is an important organ of the human body that helps in the proper functioning of the all the organs. Without brain, the functions of other organs will not happen since they cannot run without the commands of brain. Brain tumor refers to a situation where an abnormal group of cells are found in some part of the brain. There are many types of brain tumor that are identified. The tumors in brain is independent of age and it can grow at anytime. The symptoms that is exhibited by the brain tumor depends on the size of the tumor cell as well as its location. Machine learning algorithms helps in the earlier identification of the disease which can be used to give proper treatment. In this paper, a fuzzy c means based segmentation of image is done. Then classification of the segmented image is done by using Support Vector Machine. The results shows 98% percentage of accuracy.

关键词： Support vector machines Image segmentation Accuracy Machine learning algorithms Brain tumors Biological systems Intelligent systems Diseases

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End-to-end Autonomous Driving in Heterogeneous Traffic Scenario Using Deep Reinforcement learning

End-to-end Autonomous Driving in Heterogeneous Traffic Scena...

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European Control Conference (ECC)

作者： Soumyajit Chakraborty Subhadeep Kumar Nirav Bhatt Ramkrishna Pasumarthy Department of Electrical Engineering Indian Instiute of Technology Madras Chennai India Department of Electrical Engineering Robert Bosch Center of Data Sciences and Artificial Intelligence Indian Institute of Technology Madras Chennai India Department of Biotechnology Center of Data Sciences and Artificial Intelligence Prospective Center of Excellence for Network Systems Learning Control and Evolution Group Indian Institute of Technology Madras Chennai India Department of Electrical Engineering Robert Bosch Center of Data Sciences and Artificial Intelligence and Prospective Center of Excellence for Network Systems Learning Control and Evolution Group Indian Institute of Technology Madras Chennai India

In this paper, we propose an end-to-end autonomous driving architecture for safe maneuvering in heterogeneous traffic using a reinforcement learning (RL) algorithm. Using the proposed architecture we develop an RL agent that can make driving decisions directly from the sensor data. We formulate the autonomous driving problem as a Markov Decision Process and propose different architectures using Deep $Q -$Networks for two types of sensor data - top view images of the autonomous vehicle (AV) and its surrounding vehicles and information on relative position and velocities of the surrounding vehicles w.r.t the AV. We consider a highway scenario and analyze the performance of the RL agent using the proposed architectures using the highway-env simulator. We compare the driving performance of the AV for both sensor types and discuss their efficacy under varying traffic densities.

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RecCoder: Reformulating Sequential Recommendation as Large Language Model-Based Code Completion 24

RecCoder: Reformulating Sequential Recommendation as Large L...

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24th IEEE International Conference on data Mining, ICDM 2024

作者： Lai, Kai-Huang Xi, Wu-Dong Xing, Xing-Xing Wan, Wei Wang, Chang-Dong Chen, Min Guizani, Mohsen School of Computer Science and Engineering Sun Yat-sen University Guangzhou China NetEase Games China 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 Abu Dhabi United Arab Emirates

ISBN: (纸本)9798331506681

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. © 2024 IEEE.

关键词： Semantics

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Disease and Medications Text Visualization Using Scattertext

Disease and Medications Text Visualization Using Scattertext

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2025 International Conference on Intelligent Control, Computing and Communications, IC3 2025

作者： Rama Krishna, K. Sandhya, Kaipa Praveen Gujjar, J. Devadas, Raghavendra M. Hiremani, Vani Sapna, R. Impact college of Engineering and Applied Sciences Department of Artificial Intelligence and Machine Learning Bengaluru India Impact college of Engineering and applied sciences Department of Data Science Bengaluru India Faculty of Management Studies Bengaluru India Department of Information Technology Manipal India University Pune India

ISBN: (纸本)9798331527495

Before text data can be analysed and visualised, it must be thoroughly cleaned due to its messy nature. data visualizations use the data to tell an engaging and simple-to-read story. That is what the Scattertext tool does. Scatter text allows which terms stand out in your corpora and how their frequency varies across categories. The dataset used in this paper includes reviews of drugs. This dataset offers a useful window into how people discuss their thoughts and experiences using various drugs. The drug names, user ratings, and the actual written reviews are among the details included in the dataset. This dataset consists of drug reviews collected from various sources, hence expressing a wide range of opinions and feelings. Each row in this dataset represents the experience a user had with a given medication and hence allows us to explore those emotions that are associated with any particular set of drugs. Understanding opinion expressed in the review about drugs might go a long way in helping decision-making processes in both healthcare and pharmaceuticals. The scatter plot distinguishes between labelled points and unlabelled ones by using a custom algorithm for Scattertext. By hovering their cursor over the labelled areas done client-side, users can interactively see which terms correspond to which points. © 2025 IEEE.

关键词： data visualization

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Existence of Direct Density Ratio Estimators

arXiv

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arXiv 2025年

作者： Banzato, Erika Drton, Mathias Saraf-Poor, Kian Shi, Hongjian Department of Statistical Sciences University of Padova Italy TUM School of Computation Information and Technology Munich Data Science Institute Technical University of Munich Munich Center for Machine Learning Germany Department of Statistics Columbia University United States TUM School of Computation Information and Technology Technical University of Munich Germany

Many two-sample problems call for a comparison of two distributions from an exponential family. Density ratio estimation methods provide ways to solve such problems through direct estimation of the differences in natural parameters. The term direct indicates that one avoids estimating both marginal distributions. In this context, we consider the Kullback–Leibler Importance Estimation Procedure (KLIEP), which has been the subject of recent work on differential networks. Our main result shows that the existence of the KLIEP estimator is characterized by whether the average sufficient statistic for one sample belongs to the convex hull of the set of all sufficient statistics for data points in the second sample. For high-dimensional problems it is customary to regularize the KLIEP loss by adding the product of a tuning parameter and a norm of the vector of parameter differences. We show that the existence of the regularized KLIEP estimator requires the tuning parameter to be no less than the dual norm-based distance between the average sufficient statistic and the convex hull. The implications of these existence issues are explored in applications to differential network analysis. Copyright © 2025, The Authors. All rights reserved.

关键词： Parameter estimation

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