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32nd ACM International Conference on Multimedia, MM 2024

作者： Li, Ao Liu, Huijun Sheng, Jinrong Chen, Zhongming Ge, Yongxin School of Big Data and Software Engineering Chongqing University Chongqing China College of Computer Science Chongqing University Chongqing China

ISBN: (纸本)9798400706868

Weakly-supervised Temporal Action Localization (WTAL) following a localization-by-classification paradigm has achieved significant results, yet still grapples with confounding arising from ambiguous snippets. Previous works have attempted to distinguish these ambiguous snippets from action snippets without investigating the underlying causes of their formation, thus failing to effectively eliminate the bias on both action-context and action-content. In this paper, we revisit WTAL from the perspective of structural causal model to identify the true origins of confounding, and propose an efficient dual-confounding eliminating framework to alleviate these biases. Specifically, we construct a Substituted Confounder Set (SCS) to eliminate the confounding bias on action-context by leveraging the modal disparity between RGB and FLOW. Then, a Multi-level Consistency Mining (MCM) method is designed to mitigate the confounding bias on action-content by utilizing the consistency between discriminative snippets and corresponding proposals at both the feature and label levels. Notably, SCS and MCM could be seamlessly integrated into any two-stream models without additional parameters by Expectation-Maximization (EM) algorithm. Extensive experiments on two challenging benchmarks including THUMOS14 and ActivityNet-1.2 demonstrate the superior performance of our method. © 2024 ACM.

关键词： Expectation maximization algorithm

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Enhanced Heart Disease Classification: An Ensemble-Based Deep Learning Approach

Enhanced Heart Disease Classification: An Ensemble-Based Dee...

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2024 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems, SPICES 2024

作者： Nivaashini, M. Sathish Kumar, G. Uma Maheshwari, G. Soundariya, R.S. Kumar B, Arun Vijaya, G. Sri Eshwar College of Engineering Department of Artificial Intelligence and Data Science Tamil Nadu Coimbatore India Dr. Mahalingam College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore India Bannari Amman Institute of Technology Department of Computer Technology Tamil Nadu Sathyamangalam India Karpagam Academy of Higher Education Faculty of Engineering Department of Artificial Intelligence and Data Science Tamil Nadu Coimbatore India Sri Krishna College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798350376135

The WHO's assessment indicates a rising prevalence of heart disease, exacerbated by challenges in detection and diagnosis in India due to population growth. Machine Learning (ML) is increasingly used for precise judgments in healthcare, aiming to accurately diagnose heart-related illnesses. This study proposes a Deep Learning (DL) ensemble method, combining ML classifiers to enhance accuracy. The ensemble model outperforms AdaBoost, XGBoost, Random Forest, Decision Tree, SVM, and Logistic Regression with 98.3% accuracy on a UCI dataset. Correlation-based Feature Selection (CFS) for relevant characteristic selection improves the model's accuracy, recall, precision, and f1-score. This research contributes to improved disease prediction and categorization in cardiovascular health, vital for effective healthcare interventions and outcomes. © 2024 IEEE.

关键词： Contrastive Learning

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Minimal Counters, Maximum Insight: Simplifying System Performance With HPC Clusters for Optimized Monitoring

IEEE Computer Architecture Letters

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IEEE computer Architecture Letters 2025年第1期24卷 177-180页

作者： Shukla, Shubhi Singh, Abhijeet Chakraborty, Rajdeep Chakraborty, Anirban Rathod, Tejas Mumbaikar, Harshal Munigala, Manoj Kumar Madhusudhan, K.N. Mitra, Pabitra Mukhopadhyay, Debdeep Indian Institute of Technology Kharagpur Centre for Computational Data Sciences India Indian Institute of Technology Kharagpur Computer Science and Engineering Department India Intel Corporation Bangalore India

As computer systems become more complex, evaluating performance requires tracking various hardware performance counters that capture the system's internal activities. While these counters provide valuable insights, their growing number makes it challenging to identify the most relevant ones for performance analysis. In this paper, we investigate the correlation between performance counter values and overall system performance, while also exploring the inter-correlation between different counters. Our findings demonstrate that specific counters are strongly correlated with key performance metrics and that significant redundancy exists among counters. By leveraging these relationships, we propose a method for selecting a small, representative set of performance counters. This streamlined set can further be used to accurately predict performance score across various workloads and system configurations. © 2002-2011 IEEE.

关键词： Complex networks

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Facial Expressions and EEG Signals for Emotion Recognition Using Deep2 Face Algorithm 10

Facial Expressions and EEG Signals for Emotion Recognition U...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Swetha, S. Babu, Nirmal Varghese Karunya Institute of Technology and Science Division of Computer Science and Engineering Karunya Nagar Tamil Nadu Coimbatore India Karunya Institute of Technology and Science Division of Data Science and Cyber Security Karunya Nagar Tamil Nadu Coimbatore India

ISBN: (纸本)9798350384369

Emotions, the essence of human connection and understanding, take center stage in this pioneering research on emotion recognition. We look at identifying common emotions including joy, sorrow, neutral, anger, fear, and surprise. Introducing an innovative approach, we seamlessly blend advanced computer vision algorithms and intricate signal processing techniques to concurrently analyze facial expressions and EEG signals. The heart of our methodology lies in the utilization of the state-of- the-art Deep Face Analyzer, guided by the deep2face algorithm for meticulous interpretation of facial features, coupled with a spectrographic analyzer translating EEG signals into spectrograms. The overarching goal is to enhance the accuracy and resilience of emotion recognition, with the added dimension of a real-time Waveform Extractor for dynamic evaluation of audio signals. Within the landscape of diverse algorithms and modalities, the Hybrid Model (CNN + LSTM) emerges as a standout performer, achieving an impressive 88.2% accuracy by synergizing facial expressions and EEG signals. The strategic integration of long short-term memory (LSTM) networks with convolutional neural networks (CNNs) underscores the model's exceptional capacity to capture the nuanced intricacies of human emotions. Beyond mere technological advancement, this study underscores the importance of embracing multiple modalities for a holistic understanding of emotions, positioning the Hybrid Model as a powerful and nuanced tool for precise and multiclass emotion identification across diverse real-world scenarios. In essence, our research charts a path where the realm of emotions converges with technological innovation, fostering a deeper understanding of the human experience © 2024 IEEE.

关键词： Emotion Recognition

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An Empirical Analysis of Load Balancing Issues in Fog Computing Environment 2

An Empirical Analysis of Load Balancing Issues in Fog Comput...

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2nd International Conference on data science and Information System, ICDSIS 2024

作者： Kumar, K. R. Prasanna Siddesh, G.M. Ananda Babu, J. M S Ramaiah Institute Of Technology Department Of Computer Science & Engineering Bangalore India M S Ramaiah Institute Of Technology Department Of Artificial Intelligence & Data Science Bangalore India Malnad College Of Engineering Department Of Information Science And Engineering Hassan India

ISBN: (纸本)9798350381665

Fog Computing (FC) is a distributed paradigm that complements cloud computing in terms of service delivery. By extending storage and computation of the network edge, fog systems enable location awareness and mobility support, while also resolving the issue of service computing for delay-sensitive applications. A crucial component of fog networks is load balancing, which prevents fog nodes from becoming under or overloaded. In recent years, only a limited number of studies have been conducted on load-balancing methods in fog networks. This paper provides a thorough assessment of the load-balancing techniques used in fog computing, covering approximate, accurate, fundamental, and hybrid methods. This paper also examines load balancing metrics in fog networks, including all benefits and drawbacks associated with the selected load balancing algorithms. The evaluation methods considered for each study are investigated. Additionally, the primary unresolved issues and potential developments for these systems are discussed. © 2024 IEEE.

关键词： Virtualization

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Dementia Disorder Analysis Using Optimized Deep ResiNet Based on Cognitive Approach 1st

Dementia Disorder Analysis Using Optimized Deep ResiNet Base...

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1st International Conference on data engineering and Machine Intelligence, ICDEMI 2023

作者： Duraipandian, Kavitha Ambigapathy, Murugan Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Tamil Nadu Chennai600089 India Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chennai603203 India

ISBN: (纸本)9789819776153

Early warning signs of dementia may include memory loss, difficulty with problem-solving, confusion, changes in mood and behavior, and impaired communication skills. People impacted by dementia often make a loss on things, money, etc. In the social environment, the disease is considered as one of the serious issues. In order to correctly identify dementia, automated methods of diagnosis have grown essential. Conventional non-imaging components are capable of accurately identifying dementia as well as readily integrating the practice of medicine, despite the fact that neuro-images are currently fully utilized in the most recent technologies. To develop a powerful dementia screening tool, researchers combined machine learning with an enormous amount of non-imaging variables. In order to dramatically lessen the obstacles to employing the diagnostic tool, this project makes use of the predictive capability of non-imaging data. The suggested system was completely equipped to detect and evaluate cognitive signs associated with dementia using speech data, keystroke evaluation, and so on. The optimized Deep Residual Network (ODRN) is used to identify brain fluctuations, the sound information is subsequently analyzed with frequency extractors, and the voice pitch is further examined. A deep neural network is used to evaluate keystrokes while contrasting them to Kaggle data on odd stroke rates. On the ensemble method developed here, the system at hand has a precision of 96.66% performance. The overall processing delay of 52 s is achieved. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep neural networks

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Understanding and improving fairness in cognitive diagnosis

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science China(Information sciences) 2024年第5期67卷 99-114页

作者： Zheng ZHANG Le WU Qi LIU Jiayu LIU Zhenya HUANG Yu YIN Yan ZHUANG Weibo GAO Enhong CHEN Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology& School of Data Science University of Science and Technology of China State Key Laboratory of Cognitive Intelligence School of Computer Science and Information Engineering Hefei University of Technology

Intelligent education is a significant application of artificial intelligence. One of the key research topics in intelligence education is cognitive diagnosis, which aims to gauge the level of proficiency among students on specific knowledge concepts(e.g., Geometry). To the best of our knowledge, most of the existing cognitive models primarily focus on improving diagnostic accuracy while rarely considering fairness issues; for instance, the diagnosis of students may be affected by various sensitive attributes(e.g., region). In this paper,we aim to explore fairness in cognitive diagnosis and answer two questions:(1) Are the results of existing cognitive diagnosis models affected by sensitive attributes?(2) If yes, how can we mitigate the impact of sensitive attributes to ensure fair diagnosis results? To this end, we first empirically reveal that several wellknown cognitive diagnosis methods usually lead to unfair performances, and the trend of unfairness varies among different cognitive diagnosis models. Then, we make a theoretical analysis to explain the reasons behind this phenomenon. To resolve the unfairness problem in existing cognitive diagnosis models, we propose a general fairness-aware cognitive diagnosis framework, FairCD. Our fundamental principle involves eliminating the effect of sensitive attributes on student proficiency. To achieve this, we divide student proficiency in existing cognitive diagnosis models into two components: bias proficiency and fair *** design two orthogonal tasks for each of them to ensure that fairness in proficiency remains independent of sensitive attributes and take it as the final diagnosed result. Extensive experiments on the Program for International Student Assessment(PISA) dataset clearly show the effectiveness of our framework.

关键词： fairness intelligent education cognitive diagnosis psychometrics adversarial learning

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Hybrid GCN-LSTM Framework for Enhanced Schizophrenia Diagnosis: Integrating Spatial and Temporal Insights from Brain Connectivity data 1

Hybrid GCN-LSTM Framework for Enhanced Schizophrenia Diagnos...

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1st International Conference on Innovations in Communications, Electrical and computer engineering, ICICEC 2024

作者： Anitha, R. Hemalatha Prathyusha Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu Tiruvallur India R.M.D. Engineering College Department of Computer Science and Engineering Tamil Nadu Kavaraipettai601206 India

ISBN: (数字)9798350376517

ISBN: (纸本)9798350376517

A new hybrid approach for diagnosing schizophrenia combines graph convolutional networks (GCNs) with long short-term memory (LSTM) networks, leveraging GCNs for spatial connectivity and LSTMs for temporal modeling. This integration allows a simultaneous examination of spatial and sequential brain connectivity patterns, offering a comprehensive view of schizophrenia's complex dynamics. By extracting critical features from brain connectivity data, the hybrid GCN-LSTM model enhances research insights and improves clinical interpretability. Testing and validation with real-world data demonstrate the model's superior performance over existing methods, advancing clinical decision support in psychiatry. Additionally, this approach not only improves our understanding of schizophrenia's mechanisms but also shows potential for broader applications, such as early and accurate dementia diagnosis. The study marks significant progress in applying advanced machine learning to clinical research. © 2024 IEEE.

关键词： Clinical research

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Improving Heart Disease Prediction with Stacked Ensemble Learning: A Comparison of Binary and Multiclass 1

Improving Heart Disease Prediction with Stacked Ensemble Lea...

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1st International Conference for Women in Computing, InCoWoCo 2024

作者： Amirthaa, M. Mary, Gerardine Immaculate Julian, Anitha Selvarathinam, Anto Lourdu Xavier Raj School of Electronics Engineering Vellore Institute of Technology Vellore India Saveetha Engineering College Computer Science and Engineering Tamilnadu Chennai India Data Science and Analytics Grand Valley State University MI United States

ISBN: (纸本)9798331518943

Heart disease prediction remains a critical area of research due to its significant impact on public health. This paper, titled 'Improving Heart Disease Prediction with Stacked Ensemble Learning: A Comparison of Binary and Multiclass,' presents a comprehensive study comparing the performance of binary and multiclass classification models for heart disease prediction. Utilizing two distinct datasets, one for binary classification and one for multiclass classification. The study evaluates various machine learning and deep learning models, with a focus on stacked ensemble methods. The Binary Classification Results leveraging the Preprocessing-and-PCA-on-Heart-Disease-dataset, the study evaluated several models including Random Forest, Support Vector Machine (SVM), XGBoost, Gradient Boosting, Deep Learning model, and Logistic Regression. The results indicate that the Stacking Ensemble model achieved the highest accuracy of 90.2%, outperforming individual models where Random Forest (88.5%), SVM (89.1%), XGBoost (89.7%), Gradient Boosting (88.0%) and Deep Learning (89.1%). The Multiclass Classification study using the UCI Heart Disease dataset, when applied the same models, the Stacking Ensemble model achieved an accuracy of 64.1%, which, although superior to other individual models such as Random Forest (58.1%), SVM (54.3%), Gradient Boosting (59.2%) and Deep Learning model (57.6%) fell short compared to XGBoost, which attained an accuracy of 65.8%. This outcome highlights the challenges associated with multiclass classification in heart disease prediction. © 2024 IEEE.

关键词： Contrastive Learning

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IoT Authentication Protocols: Classification, Trend and Opportunities

IEEE Transactions on Sustainable Computing

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IEEE Transactions on Sustainable Computing 2024年第3期10卷 515-533页

作者： N.alsheavi, Amar Hawbani, Ammar Wang, Xingfu Othman, Wajdy Zhao, Liang Liu, Zhi Alsamhi, Saeed Hamood Al-Qaness, Mohammed A. A. University of Science and Technology of China School of Computer Science and Technology Anhui Hefei230027 China Shenyang Aerospace University School of Computer Science Shenyang110136 China Nankai University Haihe Laboratory of ITAI School of Cyber Science Tianjin300459 China The University of Electro-Communications Department of Computer and Network Engineering Tokyo Japan University of Galway Insight Centre for Data Analytics Galway Ireland IBB University Faculty of Engineering Ibb Yemen Zhejiang Normal University College of Physics and Electronic Information Engineering Jinhua321004 China Zhejiang Institute of Optoelectronics Jinhua321004 China

This paper reviews three main aspects of authentication protocols of Internet of Things (IoT): classifications and limitations, current trends, and opportunities. First, we explore the significance of IoT authentication protocols in ensuring secure communication and the protection of transmitted and received data, focusing on the classifications and associated limitations. Second, we discuss the latest developments and trends, such as using blockchain technology and machine learning to enhance authentication protocols. Third, we highlight the future opportunities, including the development of human-centric authentication designs and improved platform interoperability. At the end of this paper, we provided some insights gained for the new researcher, offering analyses of the trends and challenges in this field, giving recommendations for improving IoT authentication protocols, and emphasizing the need for further research and cooperation to develop advanced security solutions. © 2016 IEEE.

关键词： Authentication Protocol

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