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International Symposium on Parallel and Distributed Processing (IPDPS)

作者： Daniel Nichols Alexander Movsesyan Jae-Seung Yeom Abhik Sarkar Daniel Milroy Tapasya Patki Abhinav Bhatele Department of Computer Science University of Maryland Center for Applied Scientific Computing Lawrence Livermore National Laboratory

ISBN: (数字)9798350387117

ISBN: (纸本)9798350387124

A variety of hardware architectures, both CPUs and GPUs, are used today to build supercomputers and parallel clusters. Often times, users can choose which hardware platform they want to run on. Modern scientific workflows have multiple computational tasks, and each task may be better suited for a different architecture in terms of performance. Deciding where to run an application or workflow task is not straightforward because of the complexity of applications, and hardware architectures, which makes performance predictions challenging. Hence, modeling the performance of scientific applications across a variety of architectures is important for achieving the best performance. In this paper, we present a machine learning based methodology to model the relative performance of applications across multiple architectures using hardware performance counters. Our machine learning model can predict the relative performance of an application with a mean absolute error of 0.11, and can be used effectively to make performance-aware and multi-architecture scheduling decisions, reducing makespan by up to 20%.

关键词： Schedules Distributed processing Computational modeling computer architecture Machine learning Predictive models Hardware

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FedEdge: Accelerating Edge-Assisted Federated Learning 23

FedEdge: Accelerating Edge-Assisted Federated Learning

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32nd ACM World Wide Web Conference, WWW 2023

作者： Wang, Kaibin He, Qiang Chen, Feifei Jin, Hai Yang, Yun School of Computer Science and Technology Huazhong University of Science and Technology China Department of Computing Technologies Swinburne University of Technology Australia School of Information Technology Deakin University Australia National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781450394161

Federated learning (FL) has been widely acknowledged as a promising solution to training machine learning (ML) model training with privacy preservation. To reduce the traffic overheads incurred by FL systems, edge servers have been included between clients and the parameter server to aggregate clients' local models. Recent studies on this edge-assisted hierarchical FL scheme have focused on ensuring or accelerating model convergence by coping with various factors, e.g., uncertain network conditions, unreliable clients, heterogeneous compute resources, etc. This paper presents our three new discoveries of the edge-assisted hierarchical FL scheme: 1) it wastes significant time during its two-phase training rounds;2) it does not recognize or utilize model diversity when producing a global model;and 3) it is vulnerable to model poisoning attacks. To overcome these drawbacks, we propose FedEdge, a novel edge-assisted hierarchical FL scheme that accelerates model training with asynchronous local federated training and adaptive model aggregation. Extensive experiments are conducted on two widely-used public datasets. The results demonstrate that, compared with state-of-the-art FL schemes, FedEdge accelerates model convergence by 1.14 × -3.20 ×, and improves model accuracy by 2.14% - 6.63%. © 2023 ACM.

关键词： Edge computing

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Federated Learning for Enhanced Intrusion Detection in Smart City Environments

Federated Learning for Enhanced Intrusion Detection in Smart...

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International Conference on Open Source Systems and Technologies (ICOSST)

作者： Soomaiya Hamid Narmeen Zakaria Bawany Center for Computing Research Department of Computer Science and Software Engineering Jinnah University for Women Karachi Pakistan

ISBN: (数字)9798331508692

ISBN: (纸本)9798331508708

The integration of IoT devices in smart cities enhances urban infrastructure, services, and governance but also introduces significant cybersecurity challenges. Traditional centralized Intrusion Detection Systems (IDS) face several issues, including data privacy concerns and high-power consumption due to centralized data processing. These challenges increase the risks of unauthorized access, data breaches, and privacy violations, undermining user trust and compliance with privacy regulations. Additionally, the centralization of data and processing leads to higher power consumption, making these systems less sustainable for widespread deployment in smart cities. This research addresses these issues by proposing a Federated Learning (FL)based intrusion detection framework for smart cities. FL enables collaborative and privacy-preserving model training across distributed IoT devices, mitigating the need to share sensitive data centrally. By aggregating local model updates, FL ensures data privacy and distributes the computational workload, significantly reducing power consumption compared to traditional centralized systems. The proposed model leverages advanced AI techniques and is trained using the IoTID20 dataset. The Flower framework, utilizing the FedAvg algorithm, facilitates the federated learning process. Our experimental results demonstrate that the global model achieves 98% accuracy, with individual clients achieving accuracies of around 85% to 98%. This approach provides continuous learning mechanisms, anomaly detection, and ensemble learning capabilities, enhancing the resilience of federated intrusion detection systems against emerging threats and adversarial attacks. This research systematically investigates the application of federated learning for intrusion detection in smart city networks, addressing key challenges and advancing the state-of-the-art in decentralized cybersecurity solutions. The proposed framework offers a robust, scalable, and privacyco

关键词： Data privacy Power demand Accuracy Smart cities Federated learning Computational modeling Intrusion detection Data models Internet of Things computer security

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SeerahGPT: Retrieval Augmented Generation based Large Language Model

SeerahGPT: Retrieval Augmented Generation based Large Langua...

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International Conference on Open Source Systems and Technologies (ICOSST)

作者： Surayya Obaid Narmeen Zakaria Bawany Department of Computer Science and Software Engineering Jinnah Univesity for Women Center for Computing Research Karachi Pakistan

ISBN: (数字)9798331508692

ISBN: (纸本)9798331508708

Large Language Models(LLMs) have become widely recognized in recent years for their exceptional performance in language generation capabilities. As a result, an unprecedented rise is seen in its use cases in various domains specifically involving Natural Language Processing(NLP). These models however perform suboptimally when exploited in the field of studies where authenticity of the generated content is a critical aspect. One such domain is usage of LLM for exploration of the life of Prophet Muhammad S.A.W(commonly referred to as Seerah). It is of utmost significance to ensure the authenticity and reliability in the sources used and reported by the LLM due to the sensitive nature of the domain. The contemporary LLMs, however, lack the explainability in their response due to their inherent black-box nature. In our study, we have presented a novel LLM named SeerahGPT that addresses this challenge with the help of retrieval-augmented generation (RAG). This technique enables the model to utilize both parametric and nonparametric memories for generating response of queries. Our model, built on the Llama-2-7b architecture, employs Sentence Transformer embedding to effectively retrieve relevant information. The model's capabilities are augmented by integrating it with a corpus having Islamic texts such as the Quranic translation and Hadith collections, and historical accounts. The model's performance is benchmarked against its base model using both quantitative and qualitative metrics. The comparative analysis with Llama-2-7b revealed that SeerahGPT incorporation with external knowledge sources, provided more authentic and verifiable responses, despite the others exhibiting greater fluency. Performance metrics such as BLEU, ROUGE, and METEOR indicated SeerahGPT's better accuracy and contextual handling. This study paves way for analysis of such sensitive domains in more efficient way that can be utilized in other complex domains such as Islamic theology and Fiqh or legal

关键词： Measurement Accuracy Translation Law Large language models Retrieval augmented generation Transformers Reliability Meteors Context modeling

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Wearable AI Reveals the Impact of Intermittent Fasting on Stress Levels in School Children During Ramadan

Wearable AI Reveals the Impact of Intermittent Fasting on St...

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21st International Conference on Informatics, Management, and Technology in Healthcare, ICIMTH 2023

作者： Ahmed, Arfan Aziz, Sarah Abd-Alrazaq, Alaa Qidwai, Uvais Farooq, Faisal Sheikh, Javaid AI Center for Precision Health Weill Cornell Medicine-Qatar Doha Qatar Department of Computer Science and Engineering Qatar University Qatar Center for Digital Health and Precision Medicine Qatar Computing Research Institute Doha Qatar

ISBN: (纸本)9781643684000

Intermittent fasting has been practiced for centuries across many cultures globally. Recently many studies have reported intermittent fasting for its lifestyle benefits, the major shift in eating habits and patterns is associated with several changes in hormones and circadian rhythms. Whether there are accompanying changes in stress levels is not widely reported especially in school children. The objective of this study is to examine the impact of intermittent fasting during Ramadan on stress levels in school children as measured using wearable artificial intelligence (AI). Twenty-nine school children (aged 13-17 years and 12M / 17F ratio) were given Fitbit devices and their stress, activity and sleep patterns analyzed 2 weeks before, 4 weeks during Ramadan fasting and 2 weeks after. This study revealed no statistically significant difference on stress scores during fasting, despite changes in stress levels being observed for 12 of the participants. Our study may imply intermittent fasting during Ramadan poses no direct risks in terms of stress, suggesting rather it may be linked to dietary habits, furthermore as stress score calculations are based on heart rate variability, this study implies fasting does not interfere the cardiac autonomic nervous system. © 2023 The authors and IOS Press.

关键词： Wearable technology

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Identification of Key Genes as Potential Drug Targets for Gastric Cancer

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Tsinghua science and Technology 2023年第4期28卷 649-664页

作者： Md.Tofazzal Hossain Md.Selim Reza Yin Peng Shengzhong Feng Yanjie Wei Center for High Performance Computing Joint Engineering Research Center for Health Big Data Intelligent Analysis TechnologyShenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China School of Computer Science and Technology University of Chinese Academy of SciencesBeijing 100049China Department of Statistics Bangabandhu Sheikh Mujibur Rahman Science and Technology UniversityGopalganj 8100Bangladesh Guangdong Provincial Key Laboratory for Genome Stability and Disease Prevention and Regional Immunity and Diseases Department of PathologyShenzhen UniversityShenzhen 518060China

Gastric cancer(GC)is one of the most common cancers and ranks the third in cancer mortality all over the *** goal of this study was to identify potential hub-genes,highlighting their functions,signaling pathways,and candidate drugs for the treatment of GC *** used publicly available next generation sequencing(NGS)data to identify differentially expressed(DE)*** top DE genes were mapped to STRING database to construct the protein-protein interaction(PPI)network and top hub genes were selected for further *** found a total of 1555 DE genes with 870 upregulated and 685 downregulated genes in *** selected the top 400(200 upregulated and 200 downregulated)genes to construct a PPI network and extracted the top 15 hub *** gene ontology(GO)term and kyoto encyclopedia of genes and genomes(KEGG)pathway enrichment analyses of the 15 hub genes exposed some important functions and signaling pathways that were significantly associated with GC *** survival analysis of the hub genes disclosed that the lower expressions of the three hub genes CDH2,COL4A1,and COL5A2 were associated with better survival of GC *** three genes might be the candidate biomarkers for the diagnosis and treatment of ***,we considered 3 key proteins(genomic biomarkers)(COL4A1,CDH2,and CO5A2)as the drug target proteins(receptors),performed their docking analysis with the 102 meta-drug agents,and found Everolimus,Docetaxel,Lanreotide,Venetoclax,Temsirolimus,and Nilotinib as the top ranked 6 candidate drugs with respect to our proposed target proteins for the treatment against GC ***,the proposed drugs might play vital role for the treatment against GC patients.

关键词： gastric cancer hub genes candidate genes molecular docking candidate drugs

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Mixture of experts for audio-visual learning 24

Mixture of experts for audio-visual learning

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Ying Cheng Yang Li Junjie He Rui Feng School of Computer Science Fudan University and Shanghai Key Laboratory of Intelligent Information Processing and Shanghai Collaborative Innovation Center of Intelligent Visual Computing School of Computer Science Fudan University and Shanghai Key Laboratory of Intelligent Information Processing

ISBN: (纸本)9798331314385

With the rapid development of multimedia technology, audio-visual learning has emerged as a promising research topic within the field of multimodal analysis. In this paper, we explore parameter-efficient transfer learning for audio-visual learning and propose the Audio-visual Mixture of Experts (AVMoE) to inject adapters into pre-trained models flexibly. Specifically, we introduce unimodal and cross-modal adapters as multiple experts to specialize in intra-modal and intermodal information, respectively, and employ a lightweight router to dynamically allocate the weights of each expert according to the specific demands of each task. Extensive experiments demonstrate that our proposed approach AVMoE achieves superior performance across multiple audio-visual tasks, including AVE, AVVP, AVS, and AVQA. Furthermore, visual-only experimental results also indicate that our approach can tackle challenging scenes where modality information is missing. The source code is available at https://***/yingchengy/AVMOE.

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Estimating Blood Glucose Levels Using Machine Learning Models with Non- ?nvasive Wearable Device Data

Estimating Blood Glucose Levels Using Machine Learning Model...

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21st International Conference on Informatics, Management, and Technology in Healthcare, ICIMTH 2023

作者： Aziz, Sarah Ahmed, Arfan Abd-Alrazaq, Alaa Qidwai, Uvais Farooq, Faisal Sheikh, Javaid AI Center for Precision Health Weill Cornell Medicine-Qatar Doha Qatar Department of Computer Science and Engineering Qatar University Qatar Center for Digital Health and Precision Medicine Qatar Computing Research Institute Doha Qatar

ISBN: (纸本)9781643684000

In 2019 alone, Diabetes Mellitus impacted 463 million individuals worldwide. Blood glucose levels (BGL) are often monitored via invasive techniques as part of routine protocols. Recently, AI-based approaches have shown the ability to predict BGL using data acquired by non-invasive Wearable Devices (WDs), therefore improving diabetes monitoring and treatment. It is crucial to study the relationships between non-invasive WD features and markers of glycemic health. Therefore, this study aimed to investigate accuracy of linear and non-linear models in estimating BGL. A dataset containing digital metrics as well as diabetic status collected using traditional means was used. Data consisted of 13 participants data collected from WDs, these participants were divided in two groups young, and Adult Our experimental design included Data Collection, Feature Engineering, ML model selection/development, and reporting evaluation of metrics. The study showed that linear and non-linear models both have high accuracy in estimating BGL using WD data (RMSE range: 0.181 to 0.271, MAE range: 0.093 to 0.142). We provide further evidence of the feasibility of using commercially available WDs for the purpose of BGL estimation amongst diabetics when using Machine learning approaches. © 2023 The authors and IOS Press.

关键词： Wearable technology

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Demystifying Mental Health Reports Through an LLM-based Approach

Demystifying Mental Health Reports Through an LLM-based Appr...

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

作者： Srinivasan, Shyama Sastha Krishnamoorthy Bahadur, Arhaan Singh, Suruchi Kedia Gupta, Swati Jain, Vanya Deb, Koushik Sinha Kumar, Mohan Singh, Pushpendra Center of Excellence in Human-Centered Computing IIIT-Delhi Delhi New Delhi India Department of Computer Science IIIT-Delhi Delhi New Delhi India Department of Psychiatry All India Institute of Medical Sciences New Delhi Delhi New Delhi India Department of Computer Science Golisano College of Computing and Information Sciences Rochester Institute of Technology RochesterNY United States

ISBN: (纸本)9798400713958

Medical diagnoses typically contain strong jargon that is inaccessible to people with lower health literacy. Mental health is one such domain where comprehensibility is exacerbated further. Even with increasing awareness about mental health, support from practitioners or authentic knowledge sources is required to understand diagnostic information. Through this work, we utilize locally trained and finetuned Large Language Models (LLMs) to examine comprehension improvement of mental health diagnostic reports. Through a survey (n=105) with participants over 18 and a demonstration to practicing professionals, we examine the outputs of our AI-driven approach. Our approach offers two key benefits: (1) it simplifies complex psychiatric information for laypersons, providing understandable explanations;(2) it enables doctors to access concise summaries and information via tailored retrieval techniques. We outline this tool’s development, explain the methodology, evaluate its potential for information gain and healthcare delivery, and propose directions for future research. © 2025 Copyright held by the owner/author(s).

关键词： mHealth

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A scheduling policy to save 10% of communication time in parallel fast Fourier transform

A scheduling policy to save 10% of communication time in par...

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作者： Aseeri, Samar A. Gopal Chatterjee, Anando Verma, Mahendra K. Keyes, David E. Extreme Computing Research Center King Abdullah University of Science and Technology Thuwal Saudi Arabia Computer Center Indian Institute of Technology Kanpur Kanpur India Department of Physics Indian Institute of Technology Kanpur Kanpur India

The fast Fourier transform (FFT) has applications in almost every frequency related study, for example, in image and signal processing, and radio astronomy. It is also used as a Poisson operator inversion kernel in partial differential equations in fluid flows, in density functional theory, many-body theory, and others. The three-dimensional (Figure presented.) FFT has large time complexity (Figure presented.). Hence, parallelization is used to compute such FFTs. Popular libraries perform slab division or pencil decomposition of (Figure presented.) data. None of the existing libraries achieve perfect inverse scaling of time with (Figure presented.) cores because FFT requires all-to-all communication and clusters hitherto do not have physical all-to-all connections. Dragonfly, one of the popular topologies for the interconnect, supports hierarchical connections among the components. We show that if we align the all-to-all communication of FFT with the physical connections of Dragonfly topology we will achieve a better scaling and reduce communication time. © 2021 John Wiley & Sons Ltd.

关键词： Fast Fourier transforms

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