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IEEE India Conference (INDICON)

作者： Biswajit Bhowmik Sunil Kumar Sanjay Ramapogu Raju Anupam Prakash Omkar Mense Department of Computer Science and Engineering Maharshi Sushrut CAS Lab BRICS Lab National Institute of Technology Surathkal Mangalore Karnataka India

ISBN: (数字)9798350391282

ISBN: (纸本)9798350391299

Optimizing tensor product matrix computations is critical for enhancing computational efficiency in high-performance applications. Traditional algorithms, like the Split algorithm, often struggle due to the unique properties of each matrix involved. This paper presents a novel heuristic method that optimizes the selection of cutting points and matrix ar-rangement, significantly reducing redundant calculations and minimizing memory usage. The proposed approach adapts to the varying characteristics of tensor products, improving performance across different computational scenarios. Enhancing floating-point operation efficiency and CPU utilization delivers substantial speed and efficiency gains, particularly in large-scale tensor product matrix operations, offering a robust solution for complex computational tasks.

关键词： Wireless sensor networks Tensors High dimensional data Scalability Heuristic algorithms High performance computing Robustness Computational efficiency Distributed computing Optimization

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Enhancing Next-Item Recommendation Through Adaptive User Group Modeling

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Journal of Social Computing 2023年第2期4卷 112-124页

作者： Nengjun Zhu Lingdan Sun Jian Cao Xinjiang Lu Runtong Li the School of Computer Engineering and Science Shanghai UniversityShanghai 200444China the Department of Computer Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China the Business Intelligence Lab Baidu ResearchBeijing 100085China the Department of Electronic Engineering Tsinghua UniversityBeijing 100084China

Session-based recommender systems are increasingly applied to next-item ***,existing approaches encode the session information of each user independently and do not consider the interrelationship between *** work is based on the intuition that dynamic groups of like-minded users exist over *** considering the impact of latent user groups,we can learn a user’s preference in a better *** this end,we propose a recommendation model based on learning user embeddings by modeling long and short-term dynamic latent user ***,we utilize two network units to learn users’long and short-term sessions,***,we employ two additional units to determine the affiliation of users with specific latent groups,followed by an aggregation of these latent group ***,user preference representations are shaped comprehensively by considering all these four aspects,based on an attention ***,to avoid setting the number of groups manually,we further incorporate an adaptive learning unit to assess the necessity for creating a new group and learn the representation of emerging groups *** experiments prove our model outperforms multiple state-of-the-art methods in terms of Recall,mean average precision(mAP),and area under curve(AUC)metrics.

关键词： session-based recommender user group modeling attention mechanism adaptive learning

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A Design Space of Behavior Change Interventions for Responsible Data science 25

A Design Space of Behavior Change Interventions for Responsi...

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30th International Conference on Intelligent User Interfaces, IUI 2025

作者： Dong, Ziwei Barrett, Teanna Patil, Ameya B Shoda, Yuichi Battle, Leilani Wall, Emily Computer Science Department Emory University AtlantaGA United States Computer Science and Engineering/Social Futures Lab University of Washington SeattleWA United States

ISBN: (纸本)9798400713064

Behavior change theories, rooted in psychology and sociology, offer valuable insights into why and how individuals and groups modify their actions and decisions. By leveraging these theories in the context of responsible data science, we can better understand and influence the behaviors of data scientists, who play a central role in ensuring ethical outcomes by collecting data, developing, and deploying models. In this paper, we present a comprehensive design space for behavior change interventions aimed at promoting responsible behaviors in data science, structured around the 5W1H interrogative framework (Why, Who, What, When, Where, and How). This framework provides a practical guide for developing effective interventions designed to promote responsible behaviors in data science. We showcase the usability of this design space by using it to characterize existing responsible data science intervention tools. We further demonstrate its utility through two usage scenarios to show how the design space can be applied during the ideation phase for building effective tools to foster responsible data science practices. Our work equips the data science community with resources to create effective interventions that not only ensure technical excellence but also foster ethical responsibility, ultimately benefiting society through the responsible use of data. © 2025 Copyright held by the owner/author(s).

关键词： Data science

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LR-Based Performance Evaluation of MoCs

LR-Based Performance Evaluation of MoCs

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International Conference on VLSI Systems, Architecture, Technology and Applications (VLSI-SATA)

作者： Pallabi Hazarika Biswajit Bhowmik Department of Computer Science and Engineering Swami Vivekananda NoC Lab BRICS Lab National Institute of Technology Karnataka Surathkal Mangalore Bharat

ISBN: (数字)9798350362268

ISBN: (纸本)9798350362275

In the recent decade, on-chip communication net-works have developed into a potent platform for tackling chal-lenging and significant computation issues. However, many applications cannot achieve high-performance communication needs due to the seamless integration of computing cores in systems-on-chip (SoCs). Subsequently, a network-on-chip (NoC) has emerged as a prominent on-chip communication infrastructure in SoCs. Performance analysis of NoC's is essential for its architectural design and is traditionally evaluated employing a simulator. How-ever, simulation-based performance evaluation is relatively slow and may take a long time with varying architectural NoC sizes. This paper presents an AI-based approach for investigating mesh-based NoC (MoC) performance over the traditional simulation-based performance evaluation. The proposed framework targets to reach two objectives- quickly and accurately evaluation of various NoC performance metrics. Simulations are performed at varying architectural setups on a set of mesh NoCs to generate the training dataset for the proposed framework. Consequently, the framework satisfactorily predicts different performance metrics. For example, network and packet latency; hop count; switch, channel, and total power consumption; and total area are in the range of 58.14-88.49 and 58.69-106.97 cycles; 6.231-6.257; 1.44-13.02, 13.73-129.06, and 25.26- 177.44 $\mu W;$ and 1.35874 $\mu m^{2}$ , respectively while the proposed framework is applied on the 9 x 9 mesh NoC. The metrics are with 94% accuracy and predicted at very significantly less time. The LR model saves 99.45 % evaluation time resulting in the speedup of 260 x than a simulation - based method.

关键词： Performance evaluation Training Power demand Accuracy Systems architecture Switches Network-on-chip

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Enhancing MPI Communication Efficiency for Grid-Based Stencil Computations

Enhancing MPI Communication Efficiency for Grid-Based Stenci...

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Computing, Semiconductor, Mechatronics, Intelligent Systems and Communications (COSMIC), International Conference on

作者： Santosh Iranagouda Goudar Premkumar SJ Nayaka K K Girish Biswajit Bhowmik Department of Computer Science and Engineering Maharshi Sushrut CAS Lab BRICS Lab National Institute of Technology Karnataka Surathkal Mangalore Bharat

ISBN: (数字)9798331517892

ISBN: (纸本)9798331517908

In parallel computing, where efficiency and speed are crucial, the Message Passing Interface (MPI) is a fundamental paradigm for managing large-scale distributed memory systems. MPI is critical to complex computational tasks, particularly in grid-based computations that solve intricate numerical problems by discretizing spatial domains into structured grids. However, MPI Cartesian communicators exhibit limitations in handling these computations effectively, especially when managing large-scale data exchanges and complex stencil patterns. This paper addresses these challenges by presenting an integrated approach that combines MPI collective and Cartesian communication methods. The proposed solution simplifies data distribution, eliminates redundant interfaces, and enhances communication efficiency. Experimental results show a 43% reduction in execution time and a 40% decrease in communication overhead, with scalability improvements achieving 12.5x speedup using 64 processes. These quantitative outcomes demonstrate the advan-tages of the proposed method over conventional MPI Cartesian approaches, establishing it as a reliable framework for advancing High-Performance Computing (HPC) capabilities in grid-based applications.

关键词： Mechatronics Scalability Message passing Memory management Reinforcement learning Parallel processing Real-time systems Computational efficiency Reliability Intelligent systems

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Leveraging Augmented Reality,Semantic-Segmentation,and VANETs for Enhanced Driver’s Safety Assistance

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computers, Materials & Continua 2024年第1期78卷 1443-1460页

作者： Sitara Afzal Imran Ullah Khan Irfan Mehmood Jong Weon Lee Mixed Reality and Interaction Lab Department of SoftwareSejong UniversitySeoul05006Korea Faculty of Engineering and Digital Technologies School of Computer ScienceAI and ElectronicsUniversity of BradfordBradfordUK

Overtaking is a crucial maneuver in road transportation that requires a clear view of the road ***,limited visibility of ahead vehicles can often make it challenging for drivers to assess the safety of overtaking maneuvers,leading to accidents and *** this paper,we consider atrous convolution,a powerful tool for explicitly adjusting the field-of-view of a filter as well as controlling the resolution of feature responses generated by Deep Convolutional Neural Networks in the context of semantic image *** article explores the potential of seeing-through vehicles as a solution to enhance overtaking ***-through vehicles leverage advanced technologies such as cameras,sensors,and displays to provide drivers with a real-time view of the vehicle ahead,including the areas hidden from their direct line of *** address the problems of safe passing and occlusion by huge vehicles,we designed a see-through vehicle system in this study,we employed a windshield display in the back car together with cameras in both *** server within the back car was used to segment the car,and the segmented portion of the car displayed the video from the front *** see-through system improves the driver’s field of vision and helps him change lanes,cross a large car that is blocking their view,and safely overtake other *** network was trained and tested on the Cityscape dataset using semantic *** transparent technique will instruct the driver on the concealed traffic situation that the front vehicle has *** our findings,we have achieved 97.1% *** article also discusses the challenges and opportunities of implementing see-through vehicles in real-world scenarios,including technical,regulatory,and user acceptance factors.

关键词： Overtaking safety augmented reality VANET V2V deep learning

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An Efficient Long Short-Term Memory and Gated Recurrent Unit Based Smart Vessel Trajectory Prediction Using Automatic Identification System Data

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computers, Materials & Continua 2024年第10期81卷 1789-1808页

作者： Umar Zaman Junaid Khan Eunkyu Lee Sajjad Hussain Awatef Salim Balobaid Rua Yahya Aburasain Kyungsup Kim Department of Computer Engineering Chungnam National UniversityDaejeon34134Republic of Korea Department of Environmental IT Engineering Chungnam National UniversityDaejeon34134Republic of Korea Autonomous Ship Research Center Samsung Heavy IndustriesDaejeon34051Republic of Korea ICAROS Center(PRISMA Lab) University of Naples Federico IINaples80131Italy Department of Computer Science College of Computer Science and Information TechnologyJazan UniversityJazan45142Saudi Arabia

Maritime transportation,a cornerstone of global trade,faces increasing safety challenges due to growing sea traffic *** study proposes a novel approach to vessel trajectory prediction utilizing Automatic Identification System(AIS)data and advanced deep learning models,including Long Short-Term Memory(LSTM),Gated Recurrent Unit(GRU),Bidirectional LSTM(DBLSTM),Simple Recurrent Neural Network(SimpleRNN),and Kalman *** research implemented rigorous AIS data preprocessing,encompassing record deduplication,noise elimination,stationary simplification,and removal of insignificant *** were trained using key navigational parameters:latitude,longitude,speed,and *** aware processing through trajectory segmentation and topological data analysis(TDA)was employed to capture dynamic *** using a three-month AIS dataset demonstrated significant improvements in prediction *** GRU model exhibited superior performance,achieving training losses of 0.0020(Mean Squared Error,MSE)and 0.0334(Mean Absolute Error,MAE),with validation losses of 0.0708(MSE)and 0.1720(MAE).The LSTM model showed comparable efficacy,with training losses of 0.0011(MSE)and 0.0258(MAE),and validation losses of 0.2290(MSE)and 0.2652(MAE).Both models demonstrated reductions in training and validation losses,measured by MAE,MSE,Average Displacement Error(ADE),and Final Displacement Error(FDE).This research underscores the potential of advanced deep learning models in enhancing maritime safety through more accurate trajectory predictions,contributing significantly to the development of robust,intelligent navigation systems for the maritime industry.

关键词： Trajectory prediction AIS data smart vessel deep learning LSTM GRU

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Optimizing Lender Portfolios: A P2P Lending Recommendation Approach

Optimizing Lender Portfolios: A P2P Lending Recommendation A...

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Innovation in Technology (ASIANCON), Asian Conference on

作者： Varshini Sannapareddy Umais Rifah Anusha Hegde H Biswajit Bhowmik Department of Computer Science and Engineering Ishwarchandra Vidyasagar AIT Lab BRICS Laboratory National Institute of Technology Karnataka Mangalore Bharat

ISBN: (数字)9798350354218

ISBN: (纸本)9798350354225

The proliferation of peer-to-peer (P2P) lending platforms has ushered in a new era of financial accessibility, but it has also brought to the forefront the growing concern of loan defaults. This paper explores the increasing significance of P2P lending platforms and addresses the critical issue of loan default prediction. The study focuses on the application of machine learning techniques, specifically employing the Random Forest algorithm and logistic regression, to train a predictive model for assessing the likelihood of default within a loan portfolio. The primary objective is to enhance the decision-making process for lenders by recommending optimal loan portfolios based on the predictive insights generated by the model. By leveraging the capabilities of this robust algorithm, the research aims to contribute to the advancement of risk assessment methodologies in P2P lending, ultimately fostering more informed and secure lending practices on these platforms. We trained and compared logistic Reression and random forest models and derived resultant optimal portfolio by considering both the models which is intended to give better results than a single model.

关键词： Logistic regression Technological innovation Correlation Predictive models Prediction algorithms Peer-to-peer computing Risk management Portfolios Recommender systems Random forests

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Drone-Assisted Load Distribution Framework for Traffic Optimization in IoT Networks 17

Drone-Assisted Load Distribution Framework for Traffic Optim...

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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Soni, Priyanka Mense, Omkar Manohar Kanti Addya, Sourav National Institute of Technology Cloud and Smart System Services Lab Department of Computer Science and Engineering Karnataka India

ISBN: (纸本)9798331531195

Device connectivity has been redefined by the rapid development of the Internet of Things (IoT) technology, enabling diverse applications in areas such as smart cities, smart homes, and healthcare. These applications produce massive amounts of data, making it a significant challenge to offload tasks to cloud servers. However, the physical distance between devices often leads to latency issues for IoT systems, impacting time-sensitive applications. Fog computing tackles this issue by processing data closer to IoT devices. However, a high user density can strain the capacity of a macro base station, potentially leading to congestion. Drone Base Stations (DBSs) provide a flexible solution by acting as mobile relays to reduce latency and traffic burden. To address this issue, this work proposes a drone-assisted load distribution strategy in IoT networks. In our proposed approach, we deploy DBS efficiently using a greedy optimization technique. However, after the deployment of DBS, an optimal base station assignment framework dynamically connects user equipment to the most suitable base station, helping to minimize latency and enhance network performance. This dual-phase approach provides a scalable and practical solution for realtime IoT applications. Through extensive simulations, ensures that our proposed approach achieves a more balanced assignment compared to baseline algorithms. © 2025 IEEE.

关键词： Drones

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Resource Allocation in 6G Network for High-Speed Train Using D2D Outband Communication 3rd

Resource Allocation in 6G Network for High-Speed Train Using...

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3rd International Conference on Machine Vision and Augmented Intelligence, MAI 2023

作者： Garg, Aakansha Arya, Rajeev Singh, Maheshwari Prasad Wireless Sensor Network Lab Department of Electronics and Communication Engineering Patna India Department of Computer Science and Engineering National Institute of Technology Patna India

ISBN: (纸本)9789819743582

The growth of 6G results in rising data transmission rate in static scenario. To achieve fast communication and computation with quality service in high-speed train is a challenge. This is due to fast handover and Doppler shift. To effectively communicate in high-speed train, the placement of a mobile relay (MR) with small server at the rooftop of train coach is proposed. The base station sends channel state information to mobile relay which stores it in the stack. The resource assignment has been conducted by an improved grey wolf optimization algorithm. The focus is at improving throughput with reduction in latency and energy consumption. The simulation outcomes manifests that the presented algorithm carry-out finer in comparison of the Hungarian algorithm. The accomplished results are refined almost three times in respect of energy consumption, latency, and throughput. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Resource allocation

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