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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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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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Predicting Cross-Architecture Performance of Parallel Programs

Predicting Cross-Architecture Performance of Parallel Progra...

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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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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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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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DuMo: Dual Encoder Modulation Network for Precise Concept Erasure 39

DuMo: Dual Encoder Modulation Network for Precise Concept Er...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Han, Feng Chen, Kai Gong, Chao Wei, Zhipeng Chen, Jingjing Jiang, Yu-Gang Shanghai Key Lab of Intell. Info. Processing School of Computer Science Fudan University China Shanghai Collaborative Innovation Center on Intelligent Visual Computing China

ISBN: (纸本)157735897X

The exceptional generative capability of text-to-image models has raised substantial safety concerns regarding the generation of Not-Safe-For-Work (NSFW) content and potential copyright infringement. To address these concerns, previous methods safeguard the models by eliminating inappropriate concepts. Nonetheless, these models alter the parameters of the backbone network and exert considerable influences on the structural (low-frequency) components of the image, which undermines the model’s ability to retain non-target concepts. In this work, we propose our Dual encoder Modulation network (DuMo), which achieves precise erasure of inappropriate target concepts with minimum impairment to non-target concepts. In contrast to previous methods, DuMo employs the Eraser with PRior Knowledge (EPR) module which modifies the skip connection features of the U-NET and primarily achieves concept erasure on details (high-frequency) components of the image. To minimize the damage to non-target concepts during erasure, the parameters of the backbone U-NET are frozen and the prior knowledge from the original skip connection features is introduced to the erasure process. Meanwhile, the phenomenon is observed that distinct erasing preferences for the image structure and details are demonstrated by the EPR at different timesteps and layers. Therefore, we adopt a novel Time-Layer MOdulation process (TLMO) that adjusts the erasure scale of EPR module’s outputs across different layers and timesteps, automatically balancing the erasure effects and model’s generative ability. Our method achieves state-of-the-art performance on Explicit Content Erasure (detecting only 34 nude parts), Cartoon Concept Removal (with an average LPIPS_da of 0.428, 0.113 higher than SOTA at 0.315), and Artistic Style Erasure (with an average LPIPS_da of 0.387, 0.088 higher than SOTA at 0.299), clearly outperforming alternative methods. © 2025, Association for the Advancement of Artificial Intel

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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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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 Rochester NY 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： Health Informatics Information Extraction Medical Records Informatics

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A Novel Krill Herd Based Random Forest Algorithm for Monitoring Patient Health

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computers, Materials & Continua 2023年第5期75卷 4553-4571页

作者： Md.Moddassir Alam Md Mottahir Alam Muhammad Moinuddin Mohammad Tauheed Ahmad Jabir Hakami Anis Ahmad Chaudhary Asif Irshad Khan Tauheed Khan Mohd Department of Health Information Management and Technology University of Hafr Al-BatinSaudi Arabia Department of Electrical and Computer Engineering Faculty of EngineeringKing AbdulazizJeddah21589Saudi Arabia Center of Excellence in Intelligent Engineering Systems King Abdul Aziz UniversityJeddahSaudi Arabia College of Medicine King Khalid UniversityAbhaSaudi Arabia Department of Physics College of ScienceJazan UniversityP.O.Box.114Jazan45142Saudi Arabia Department of Biology College of ScienceImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11623Saudi Arabia Computer Science Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Math and Computer Science Augustana CollegeIL61201United States

Artificial Intelligence(AI)is finding increasing application in healthcare *** learning systems are utilized for monitoring patient health through the use of IoT sensor,which keep track of the physiological state by way of various health ***,early detection of any disease or derangement can aid doctors in saving patients’***,there are some challenges associated with predicting health status using the common algorithms,such as time requirements,chances of errors,and improper *** propose an Artificial Krill Herd based on the Random Forest(AKHRF)technique for monitoring patients’health and eliciting an optimal prescription based on their health *** begin with,various patient datasets were collected and trained into the system using IoT *** a result,the framework developed includes four processes:preprocessing,feature extraction,classification,and result ***,preprocessing removes errors,noise,and missing values from the dataset,whereas feature extraction extracts the relevant ***,in the classification layer,we updated the fitness function of the krill herd to classify the patient’s health status and also generate a *** found that the results fromthe proposed framework are comparable to the results from other state-of-the-art techniques in terms of sensitivity,specificity,Area under the Curve(AUC),accuracy,precision,recall,and F-measure.

关键词： Healthcare system health monitoring clinical decision support internet of things artificial intelligence machine learning diagnosis

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