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International Conference for Convergence of technology (I2CT)

作者： Akash K Rao Vishnu K Menon Arnav Bhavsar Shubhajit Roy Chowdhury Ramsingh Negi Varun Dutt Applied Cognitive Science Laboratory Indian Institute of Technology Mandi Mandi India School of Computing and Electrical Engineering Indian Institute of Technology Mandi Mandi India Cognitive Control and Machine Learning Group Institute of Nuclear Medicine and Allied Sciences Delhi India

ISBN: (数字)9798350394474

ISBN: (纸本)9798350394481

Attention is the brain's mechanism for selectively processing specific stimuli while filtering out irrelevant information. Characterizing changes in attention following long-term interventions (such as transcranial direct current stimulation (tDCS)) has seldom been emphasized in the literature. To classify attention performance post-tDCS, this study uses functional connectivity and machine learning algorithms. Fifty individuals were split into experimental and control conditions. On Day 1, EEG data was obtained as subjects executed an attention task. From Day 2 through Day 8, the experimental group was administered 1mA tDCS, while the control group received sham tDCS. On Day 10, subjects repeated the task mentioned on Day 1. Functional connectivity metrics were used to classify attention performance using various machine learning methods. Results revealed that combining the Adaboost model and recursive feature elimination yielded a classification accuracy of 91.84%. We discuss the implications of our results in developing neurofeedback frameworks to assess attention.

关键词： Measurement Machine learning algorithms Filtering Machine learning Brain modeling Electroencephalography Task analysis

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Classification of attention performance post-longitudinal tDCS via functional connectivity and machine learning methods

arXiv

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arXiv 2024年

作者： Rao, Akash K. Menon, Vishnu K. Bhavsar, Arnav Chowdhury, Shubhajit Roy Negi, Ramsingh Dutt, Varun Applied Cognitive Science Laboratory Indian Institute of Technology Mandi Mandi India School of Computing and Electrical Engineering Indian Institute of Technology Mandi Mandi India Cognitive Control and Machine learning group Institute of Nuclear Medicine and Allied Sciences Delhi India

关键词： Electroencephalography

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Multi-Channel Hypergraph Convolution Group Recommendation with Member Information Enhancement 25

Multi-Channel Hypergraph Convolution Group Recommendation wi...

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25th IEEE International Conferences on High Performance computing and Communications, 9th International Conference on Data Science and Systems, 21st IEEE International Conference on Smart City and 9th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Chen, Tianhao Gao, Qian Fan, Jun Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Shandong Jinan250014 China Qilu University of Technology Shandong Academy of Sciences Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Shandong Jinan250353 China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Shandong Jinan250014 China China Telecom Digital Intelligence Techonology Co Ltd Shandong Jinan250101 China

ISBN: (纸本)9798350330014

Group recommendation involves comprehensively considering various aspects, including members and items, to predict the overall interests of a group and recommend suitable items through a recommendation system. With the rapid evolution of the Internet, online group activities have become increasingly prevalent, making group recommendation a highly discussed topic within the realm of recommendation systems. However, current research in group recommendation still confronts the following challenges. Firstly, a predominant portion of group recommendation research focuses solely on aggregating group-level information, neglecting the valuable contribution of higher-order member-level insights to group recommendation. Moreover, some aggregation strategies overly prioritize fairness, thereby disregarding common real-world patterns. Secondly, previous studies have primarily relied on aggregating individual member interests to establish group preferences, lacking a holistic consideration of group dynamics. To tackle these challenges, this study introduces an innovative approach by implementing multi-channel hypergraph convolution within the member perspective. This approach aims to effectively extract higher-order insights from members by utilizing a member information enhancement module. By establishing group interconnections based on similarity and employing an adaptive fusion network to amalgamate multiple viewpoints, a final representation is derived. Experimental results demonstrate that our proposed model surpasses baseline models by 3% to 4% in terms of hit rate and accuracy, validating the efficacy of our approach. © 2023 IEEE.

关键词： Recommender systems

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Social-enhanced recommendation using graph-based contrastive learning 25

Social-enhanced recommendation using graph-based contrastive...

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作者： Xue, Peng Gao, Qian Fan, Jun Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Shandong Jinan250014 China Qilu University of Technology Shandong Academy of Sciences Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Shandong Jinan250353 China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Shandong Jinan250014 China China Telecom Digital Intelligence Techonology Co Ltd Shandong Jinan250101 China

ISBN: (纸本)9798350330014

The social network-based recommendation model use social network information to mitigate data sparsity issues and improve the accuracy of recommendation models. However, In most social network-based recommendation algorithms, the social neighbors' contributions are difficult to distinguish from the central user's, neglecting hidden correlations in social infor-mation. In order to solve this problem, this paper presents a graph-based contrastive learning framework for social-enhanced recommendation (SoGCLR) that utilizes implicit social information captured by a new social relation attention mechanism, en-rich user representations, and improve model robustness through graph-based contrastive learning. Specifically, the paper captures the degree of contribution of each neighbor in the social graph to the central user through a social relation attention layer, thus obtaining hidden correlations in social information, and further integrates this with user information in the user-item interaction graph to enrich user representations. In addition, the paper incorporates graph-based contrastive learning into the recommendation task using cross-layer contrastive learning, calculating contrastive loss and mapping nodes with similar but different exposure rates to nearby regions to mitigate exposure bias issues. Results from Ciao and Epinions demonstrate that SoGCLR reduces RMSE and MAE by 1.33% to 1.84% compared with baseline models. © 2023 IEEE.

关键词： Recommender systems

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A Triangular Stable Node Network based on Self-supervised Learning for personalized prediction

A Triangular Stable Node Network based on Self-supervised Le...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Liu, Qing Gao, Qian Fan, Jun Zhang, Zhiqiang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Qilu University of Technology Shandong Academy of Sciences Jinan250014 China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology Shandong Academy of Sciences Jinan250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan250014 China China Telecom Digital Intelligence Techonology Co Ltd. No.1999 Shunhua road Shandong Jinan250101 China

ISBN: (纸本)9798350368741

In recent years, research has illuminated the potency of implicit data processing in enhancing user preferences. Nevertheless, barriers remain in breaking through the constraints of implicit information. This study aims to bridge this gap by firstly constructing a triangular stable node network model, tailored to manage implicit information with precision. Recognizing the challenge of pinpointing novel structures within large-scale graphs, we propose SLTSNN-a triangular stable node network based on self-supervised learning for personalized prediction. SLTSNN innovates by maximizing mutual information between graph-level and patch-level representations, while augmenting graph representations through extended enhanced representations. Additionally, it incorporates triangular data associations and introduces a triangular allocation attention network, which emphasizes strongly correlated preference features among similar users. Furthermore, SLTSNN employs contrastive learning to maximize mutual information between graph vectors and hidden representations, distinguishing between high-order global and local representations. The model's effectiveness in enhancing user preferences and capturing new graph structures is evidenced by its performance on hit rate and normalized discounted cumulative gain metrics across three datasets. © 2025 IEEE.

关键词： graph contrastive learning graph neural networks recommender systems self-supervised learning

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A Novel Federated Learning Based Intrusion Detection System for IoT Networks

A Novel Federated Learning Based Intrusion Detection System ...

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IEEE International Conference on Communications (ICC)

作者： Rabaie Benameur Amine Dahane Sami Souihi Abdelhamid Mellouk Laboratory of Industrial Computing and Networks (RIIR) University of Oran 1 Oran Algeria Institute of Applied Science and Technology ISTA Oran Algeria Image Signal and Intelligent Systems (LiSSi) Laboratory TincNET Research Team Univ. Paris-Est Cr eteil France

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

In the realm of IoT platforms, susceptibility to cyber-attacks is a pressing concern, necessitating the deployment of Intrusion Detection Systems (IDS). Constructing a scalable, accurate, and lightweight model without compromising data privacy poses a formidable challenge. This study assesses classical and novel approaches employing federated learning (FL) to train IDS models. Optimization through Knowledge Distillation (KD) techniques aims to enhance computational efficiency. Experimental results reveal the efficacy of federated learning, achieving an 84.5% accuracy for 15 attack types, and an impressive performance for binary network attack classification. Notably, these models exhibit shorter inference times compared to cutting-edge machine learning models trained on the Edge-IIoTset dataset, offering promising advancements in IoT security.

关键词： Data privacy Accuracy Federated learning Computational modeling Image edge detection Intrusion detection Pressing

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A Comparison of Low Complexity CNN Models and Data Enhancement for Efficient Android Implementation of Leaves Recognition

A Comparison of Low Complexity CNN Models and Data Enhanceme...

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International Symposium on Advanced Topics in Electrical Engineering (ATEE)

作者： Alin-Gabriel Cococi Radu Dogaru Doctoral School in Electronics Telecommunications and Information Technology University "Politehnica" of Bucharest Romania Dept. of Applied Electronics and Information Eng Natural Computing Laboratory University "Politehnica" of Bucharest Romania

Classification of flora data is a high complexity problem due to the similarity of plants. Identification and determination of plant species is a task that most of the general public is unable to do and it can be a challenge even for the expertise of qualified individuals. This paper studies the possibility of using lightweight convolutional neural networks on resources constrained devices or platforms to accurately classify leaves of plants. Several low complexity convolutional neural networks are considered for the problem of leaves recognition in terms of accuracy, precision, latency and complexity. A novel scheme for enhancement and augmentation of the leaf’s images is proposed and demonstrated as capable to improve performance. MobileNet, L-CNN and NL-CNN models with Android implementations in Tensorflow Lite1 are considered, demonstrating the capability to build a portable intelligent instrument capable to identify plants by their leaves with an accuracy of 95.3%.

关键词： Performance evaluation Electrical engineering Electric potential Databases Instruments Data models Complexity theory

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Forte: An Interactive Visual Analytic Tool for Trust-Augmented Net Load Forecasting

arXiv

引用

arXiv 2023年

作者： Bhattacharjee, Kaustav Kundu, Soumya Chakraborty, Indrasis Dasgupta, Aritra Department of Data Science New Jersey Institute of Technology United States Optimization and Control Group Pacific Northwest National Laboratory United States Center for Applied Scientific Computing Lawrence Livermore National Laboratory United States

Accurate net load forecasting is vital for energy planning, aiding decisions on trade and load distribution. However, assessing the performance of forecasting models across diverse input variables, like temperature and humidity, remains challenging, particularly for eliciting a high degree of trust in the model outcomes. In this context, there is a growing need for data-driven technological interventions to aid scientists in comprehending how models react to both noisy and clean input variables, thus shedding light on complex behaviors and fostering confidence in the outcomes. In this paper, we present Forte, a visual analytics-based application to explore deep probabilistic net load forecasting models across various input variables and understand the error rates for different scenarios. With carefully designed visual interventions, this web-based interface empowers scientists to derive insights about model performance by simulating diverse scenarios, facilitating an informed decision-making process. We discuss observations made using Forte and demonstrate the effectiveness of visualization techniques to provide valuable insights into the correlation between weather inputs and net load forecasts, ultimately advancing grid capabilities by improving trust in forecasting models. © 2023, CC BY-NC-SA.

关键词： Decision making

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Detecting and Exploiting Unit Commitment Structures in MIPs 6

Detecting and Exploiting Unit Commitment Structures in MIPs

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6th International Conference on Power and Energy technology, ICPET 2024

作者： Su, Zhao-Gang Chen, Sheng-Jie Li, Yu-Fei Chen, Liang Kou, Caixia Cai, Zhi Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China Institute of Applied Mathematics Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China College of Sciences Beijing University of Posts and Telecommunications Beijing China Beijing Key Laboratory of Research and System Evaluation of Power Dispatching Automation Technology China Electric Power Research Institute Beijing China

ISBN: (纸本)9798350375855

The unit commitment (UC) problem has been extensively researched in the literature, which is typically formulated as a mixed integer programming (MIP) problem. However, current studies lack effective methods to identify potential UC problem structures within a general MIP problem, making it challenging to leverage well-established valid inequalities for UC problems to accelerate the solution process. In this paper, we present a novel detection method that automatically identifies UC structures within an MIP problem by analyzing the relationships between variables and constraints. Moreover, we enhance the MIP formulation by incorporating three well-studied classes of valid inequalities as cutting planes, based on the identified UC structures. Our experiments demonstrate that our proposed UC detection method greatly improves computational efficacy of a standard MIP solver compared to the default setting. © 2024 IEEE.

关键词： Mixed-integer linear programming

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PP-Grading: Hierarchical Authorization using Polarization Pruning

PP-Grading: Hierarchical Authorization using Polarization Pr...

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IEEE International Symposium on Information (IT) in Medicine and Education, ITME

作者： Yujia Zhu Ruoxi Wang Daoxun Xia School of Big Data and Computer Science Guizhou Normal University Guiyang China Key Laboratory of Information and Computing Science Guizhou Province Guizhou Normal University Guiyang China Engineering Laboratory for Applied Technology of Big Data in Education Guizhou Normal University Guiyang China

ISBN: (数字)9798350356670

ISBN: (纸本)9798350356687

With the popularity of neural networks, more and more service providers are providing us with convenient neural network services. In this situation, it is necessary for service providers to grant hierarchical authorization of the networks to meet the service requirements of different users. Pruning technology, as a mainstream method for modifying neural network structures, can also be applied in the hierarchical authorization of neural networks. This article uses the method of polarization pruning to decentralize and grade neural networks. Firstly, we extract the scale factors of the batch normalization(BN) layer as a measure of the importance of each channel. Then, sparse methods are used to screen for scale factors. In order to make the selected scale factors have more obvious and reasonable truncation intervals, we also use polarization methods to induce clustering of scale factors. According to different grading requirements, we prune the channels corresponding to different quantities of scale factors. The experiment proves that our method has good grading performance on different datasets and different neural networks. Moreover, good grading results can be achieved by pruning only a small portion of the channels.

关键词： Authorization Accuracy Neural networks Education Information technology

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