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computing, Power and Communication Technologies (IC2PCT), IEEE International Conference on

作者： Kuldeep Singh Kaswan Jagjit Singh Dhatterwal Rishika Yadav Kiran Malik Aanjey Mani Tripathi School of Computing Science and Engineering Galgotias University Greater Noida UP India Artificial Intelligence and Data Science Koneru Lakshmaiah Education Foundation Guntur AP India Department of Computer Science and Engineering Graphic Era Hills University Dehradun Uttarakhand India Department of Computer Science and Engineering MRIEM Rohtak India School of Computing Science and Engineering Galgotias University Greater Noida India

Federated Learning, a disruptive and novel aspect of machine learning, is at the forefront of decentralized, privacy-conscious data processing. This in-depth review study navigates the complex environment of Federated Learning Ecosystems, attempting to extract the core of the topic while shining light on its research horizons, pressing difficulties, and promising approaches. Our trip begins with a look at the fundamental concepts of Federated Learning, emphasizing its importance in the landscape of distributed machine learning paradigms. We track its development via historical milestones and key contributions, offering context for the field’s increasing research. This study focuses on the many features of Federated Learning research, including privacy-preserving techniques, communication protocols, aggregation approaches, and real-world applications. We aggregate major results and advances within these domains, highlighting notable authors and their contributions. However, the route to Federated Learning’s full fulfillment is plagued with difficulties, including questions about privacy, communication efficiency, and scalability. In the future, we will enlighten the growing paths of Federated Learning research, forecasting trends, providing insights into overcoming present problems, and visualizing its integration into diverse fields. To summarize, this review article serves as a guidepost for scholars, practitioners, and enthusiasts wanting to understand the vast domain of Federated Learning. It captures the core of this dynamic discipline, offers strategic counsel for resolving its obstacles, and encourages optimism about its bright future within the domain of distributed machine learning paradigms.

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STRUCTURE-ENHANCED PROTEIN INSTRUCTION TUNING: TOWARDS GENERAL-PURPOSE PROTEIN UNDERSTANDING

arXiv

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arXiv 2024年

作者： Wu, Wei Wang, Chao Chen, Liyi Yin, Mingze Zhu, Yiheng Fu, Kun Ye, Jieping Xiong, Hui Wang, Zheng School of Artificial Intelligence and Data Science University of Science and Technology of China China College of Computer Science and Technology Zhejiang University China Alibaba Cloud Computing China China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong

Proteins, as essential biomolecules, play a central role in biological processes, including metabolic reactions and DNA replication. Accurate prediction of their properties and functions is crucial in biological applications. Recent development of protein language models (pLMs) with supervised fine tuning provides a promising solution to this problem. However, the fine-tuned model is tailored for particular downstream prediction task, and achieving general-purpose protein understanding remains a challenge. In this paper, we introduce Structure-Enhanced Protein Instruction Tuning (SEPIT) framework to bridge this gap. Our approach integrates a noval structure-aware module into pLMs to inform them with structural knowledge, and then connects these enhanced pLMs to large language models (LLMs) to generate understanding of proteins. In this framework, we propose a novel twostage instruction tuning pipeline that first establishes a basic understanding of proteins through caption-based instructions and then refines this understanding using a mixture of experts (MoEs) to learn more complex properties and functional information with the same amount of activated parameters. Moreover, we construct the largest and most comprehensive protein instruction dataset to date, which allows us to train and evaluate the general-purpose protein understanding model. Extensive experimental results on open-ended generation and closed-set answer tasks demonstrate the superior performance of SEPIT over both closed-source general LLMs and open-source LLMs trained with protein knowledge. Copyright © 2024, The Authors. All rights reserved.

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Diagnosis of Malignant Lymphoma Cancer Using Hybrid Optimized Techniques Based on Dense Neural Networks

arXiv

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arXiv 2024年

作者： Aly, Salah A. Bakhiet, Ali Balat, Mazen Computing & Data Science College Badya University October 6th City Giza Egypt Computer Science Department Culture & Science October 6th City October 6 City Giza Egypt Computer Science Department E-JUST Alexandria Egypt

Lymphoma diagnosis, particularly distinguishing between subtypes, is critical for effective treatment but remains challenging due to the subtle morphological differences in histopathological images. This study presents a novel hybrid deep learning framework that combines DenseNet201 for feature extraction with a Dense Neural Network (DNN) for classification, optimized using the Harris Hawks Optimization (HHO) algorithm. The model was trained on a dataset of 15,000 biopsy images, spanning three lymphoma subtypes: Chronic Lymphocytic Leukemia (CLL), Follicular Lymphoma (FL), and Mantle Cell Lymphoma (MCL). Our approach achieved a testing accuracy of 99.33%, demonstrating significant improvements in both accuracy and model interpretability. Comprehensive evaluation using precision, recall, F1-score, and ROC-AUC underscores the model's robustness and potential for clinical adoption. This framework offers a scalable solution for improving diagnostic accuracy and efficiency in oncology. © 2024, CC0.

关键词： Lung cancer

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Multimodal Fusion with Deep Neural Network Architecture for Alzheimer's Disease Prediction 15th

Multimodal Fusion with Deep Neural Network Architecture for ...

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15th International Conference on Soft computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired computing, NaBIC 2023

作者： Venkatesh, Swarna Saritha, Kuppala Gogula, Sreenivasulu Chitteti, Chengamma Yalavarthi, Sikhi Swaraja, K. ECE CMR Technical Campus Hyderabad India CSE Presidency University Bangalore India Vardhaman College of Engineering Hyderabad India Department of Data Science School of Computing Mohan Babu University Andhra Pradesh Tirupati India Lexisnexis Risk Solutions Inc. Oklahomacity United States ECE GRIET Hyderabad India

ISBN: (纸本)9783031810824

Alzheimer's disease occurs due to neurodegenerative disorder, innovative approach is needed for early detection of disease. A method combining multimodal data fusion with adaptive capabilities of Adam optimization algorithm to enhance Alzheimer's disease prediction have been proposed. The proposed multimodal fusion approach combines neuroimaging, clinical, and genetic data into a deep neural network architecture, in order to achieve better accuracy,. Utilization of multimodal data helps to capture the view of disease, from structural brain changes to clinical assessments. This approach not only enhances predictive accuracy but also provides valuable insights into the mechanisms of Alzheimer's disease prediction. Utilization of Adam's optimization algorithm helps the model to converge faster making the proposed method highly effective. Combining the strengths of multimodal fusion and Adam optimization, the proposed method can be utilized for early Alzheimer's disease diagnosis, leading to improved patient outcomes and improved accuracy in Alzheimer's disease prediction. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep neural networks

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Reactivity,Regioselectivity,and Synthetic Application of 2-Pyrenyl Units in Scholl Reactions

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CCS Chemistry 2023年第9期5卷 2069-2077页

作者： Sai Ho Pun Ethan Chi Ho Wen Zeming Xia Han Chen Felix RFischer Qian Miao Department of Chemistry The Chinese University of Hong KongHong Kong Department of Chemistry University of CologneCologne Department of Chemistry University of CaliforniaBerkeleyCalifornia 94720 Materials Sciences Division Lawrence Berkeley National LaboratoryBerkeleyCalifornia 94720 Kavli Energy NanoScience Institute at the University of California Berkeley and the Lawrence Berkeley National Laboratory BerkeleyCalifornia 94720 Bakar Institute of Digital Materials for the Planet Division of ComputingData Scienceand SocietyUniversity of CaliforniaBerkeleyCalifornia 94720

We herein report the reactivity and regioselectivity of 2-pyrenyl as a coupling unit in Scholl *** the basis of the Scholl reactions of hexaarylbenzene substrates,we have found that pyrenyl units are preferably oxidized over naphthyl and phenyl units under appropriate Scholl reaction conditions,allowing divergent synthesis through a highly controllable intramolecular coupling *** find that the C1 and C3 positions of the 2-pyrenyl unit are the favorable sites for intramolecular coupling while C4 is not reactive to allow further *** reactivity and regioselectivity pattern can be explained by the spin density distribution,which shows that carbon-carbon bonds preferably form at sites with higher positive spin *** by these findings,we successfully synthesized a double helicene and a sextuple helicene through the controlled Scholl reactions of 2-pyrenyl units.

关键词： helicene polycyclic aromatics pyrene Scholl reactions spin density

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Correction to: Advanced model based machine learning technique for early stage prediction of ankylosing spondylitis under timely analysis with featured textures

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Multimedia Tools and Applications 2024年第26期83卷 68415-68420页

作者： Ahammad, Shaik Hasane Jayaraj, R. Shibu, S. Sujatha, V. Prathima, Ch Leo, L. Megalan Prabu, R. Thandaiah Hossain, Md.Amzad Rashed, Ahmed Nabih Zaki Department of ECE Koneru Lakshmaiah Education Foundation Vaddeswaram India Department of Data Science and Buisness Systems School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India Department of Electronics and Communication Engineering Panimalar Engineering College Chennai India Department of Master of Computer Applications S.A. Engineering College Chennai India Department of Data Science School of Computing Mohan Babu University Erstwhile Sree Vidyanikethan Engineering College) Tirupati India Deptartment of Electronics and Communication Engineering Sathyabama Institute of Science and Technology Chennai India Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences SIMATS Saveetha University Chennai India Department of Electrical and Electronic Engineering Jashore University of Science and Technology Jashore Bangladesh Electronics and Electrical Communications Engineering Department Faculty of Electronic Engineering Menoufia University Menouf Egypt

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AUVs' Distributed Mission Planning System for Effective Underwater Chlorophyll Sampling

AUVs' Distributed Mission Planning System for Effective Unde...

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International Conference on Unmanned Vehicle Systems-Oman (UVS)

作者： Somaiyeh MahmoudZadeh Amirmehdi Yazdani Yashar Kalantari Bekir Ciftler College of Computing and IT UDST University Doha Qatar School of IT Deakin University Melbourne VIC Australia School of Engineering and Energy Murdoch University Perth WA Australia Department of Computer Science University-South Tehran Branch Tehran Iran Department of Data Science & AI College of Computing and IT UDST University Doha Qatar

This paper presents a distributed mission planning system that enables a team of autonomous underwater vehicles (AUVs) for effective underwater chlorophyll sampling. The proposed system is developed based on a distributed algorithm that optimizes the real-time task allocation and mission planning of AUVs in a cooperative fashion while considering environmental and vehicular constraints to ensure efficient and coordinated sampling efforts. A realistic case study of a chlorophyll sampling mission in Australia's Great Barrier is utilized to investigate the performance of the proposed system in a restricted mission time and in the exposure to the uncertainty of the mission. Simulation studies confirm the effectiveness and resilience of the proposed system and showcase its potential as a valuable tool for advancing underwater chlorophyll sampling missions.

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A Comprehensive Empirical Study of Query Performance Across GPU DBMSes

A Comprehensive Empirical Study of Query Performance Across ...

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作者： Suh, Young-Kyoon An, Junyoung Tak, Byungchul Na, Gap-Joo School of Computer Science and Engineering Department of Data Convergence Computing Kyungpook National University Korea Republic of School of Computer Science and Engineering Kyungpook National University Korea Republic of Artifcial Intelligence Research Laboratory Electronics and Telecommunications Research Institute Korea Republic of

In recent years, GPU database management systems (DBMSes) have rapidly become popular largely due to their remarkable acceleration capability obtained through extreme parallelism in query evaluations. However, there has been relatively little study on the characteristics of these GPU DBMSes for a better understanding of their query performance in various contexts. Also, little has been known about what the potential factors could be that affect the query processing jobs within the GPU DBMSes. To fill this gap, we have conducted a study to identify such factors and to propose a structural causal model, including key factors and their relationships, to explicate the variances of the query execution times on the GPU DBMSes. We have also established a set of hypotheses drawn from the model that explained the performance characteristics. To test the model, we have designed and run comprehensive experiments and conducted in-depth statistical analyses on the obtained empirical data. As a result, our model achieves about 77% amount of variance explained on the query time and indicates that reducing kernel time and data transfer time are the key factors to improve the query time. Also, our results show that the studied systems should resolve several concerns such as bounded processing within GPU memory, lack of rich query evaluation operators, limited scalability, and GPU under-utilization. © 2022 ACM.

关键词： Graphics processing unit

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Vision-Based Multimodal Interfaces: A Survey and Taxonomy for Enhanced Context-Aware System Design 25

Vision-Based Multimodal Interfaces: A Survey and Taxonomy fo...

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2025 CHI Conference on Human Factors in computing Systems, CHI 2025

作者： Hu, Yongquan Gowen' Tang, Jingyu Gong, Xinya Zhou, Zhongyi Zhang, Shuning Elvitigala, Don Samitha Mueller, Florian Gfloyd' Hu, Wen Quigley, Aaron J. School of Computer Science and Engineering University of New South Wales Sydney Australia School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China The University of Tokyo Tokyo Japan Tsinghua University Beijing China Exertion Games Lab Department of Human-Centred Computing Monash University Melbourne Australia CSIRO's Data61 Sydney Australia University of New South Wales Sydney Australia

ISBN: (纸本)9798400713941

The recent surge in artificial intelligence, particularly in multimodal processing technology, has advanced human-computer interaction, by altering how intelligent systems perceive, understand, and respond to contextual information (i.e., context awareness). Despite such advancements, there is a significant gap in comprehensive reviews examining these advances, especially from a multimodal data perspective, which is crucial for refining system design. This paper addresses a key aspect of this gap by conducting a systematic survey of data modality-driven Vision-based Multimodal Interfaces (VMIs). VMIs are essential for integrating multimodal data, enabling more precise interpretation of user intentions and complex interactions across physical and digital environments. Unlike previous task- or scenario-driven surveys, this study highlights the critical role of the visual modality in processing contextual information and facilitating multimodal interaction. Adopting a design framework moving from the whole to the details and back, it classifies VMIs across dimensions, providing insights for developing effective, context-aware systems. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Human computer interaction

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Negation Blindness in Large Language Models: Unveiling the ‘NO Syndrome’ in Image Generation

arXiv

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arXiv 2024年

作者： Nadeem, Mohammad Sohail, Shahab Saquib Cambria, Erik Schuller, Björn W. Hussain, Amir Department of Computer Science Aligarh Muslim University Aligarh India School of Computing Science and Engineering VIT Bhopal University MP Sehore466114 India College of Computing and Data Science Nanyang Technological University Singapore Technical University of Munich Germany Imperial College London United Kingdom School of Computing Engineering and the Built Environment Edinburgh Napier University Scotland United Kingdom

Foundational Large Language Models (LLMs) have changed the way we perceive technology. They have been shown to excel in tasks ranging from poem writing and coding to essay generation and puzzle solving. With the incorporation of image generation capability, they have become more comprehensive and versatile AI tools. At the same time, researchers are striving to identify the limitations of these tools to improve them further. Currently identified flaws include hallucination, biases, and bypassing restricted commands to generate harmful content. In the present work, we have identified a fundamental limitation related to the image generation ability of LLMs, and termed it "The NO Syndrome". This negation blindness refers to LLMs inability to correctly comprehend ‘NO’ related natural language prompts to generate the desired images. Interestingly, all tested LLMs including GPT-4, Gemini, and Copilot were found to be suffering from this syndrome. To demonstrate the generalization of this limitation, we carried out simulation experiments and conducted entropy-based and benchmark statistical analysis tests on various LLMs in multiple languages, including English, Hindi, and French. We conclude that the NO syndrome is a significant flaw in current LLMs that needs to be addressed. A related finding of this study showed a consistent discrepancy between image and textual responses as a result of this NO syndrome. We posit that the introduction of a ‘negation context-aware’ reinforcement learning based feedback loop between the LLM’s textual response and generated image could help ensure the generated text is based on both the LLM’s correct contextual understanding of the negation query and the generated visual output. © 2024, CC BY-NC-SA.

关键词： Reinforcement learning

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