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4th IEEE International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024

作者： Suresh, Devanolla Banu, E.Afreen Karthikeyan, R. Naik, Pramod Kumar Mohammed, Safaa Hallol Abdtawfeeq, Tareq Hafdhi Madanapalle Institute of Technology & Science Department of Computer Science & Technology Madanapalle India Cse Saveetha School of Engineering Chennai India Sri Ramachandra Institute of Higher Education and Research Artificial Intelligence and Data Analytics Department of Cse India Dayananda Sagar University Department of Cse Bengaluru India National University of Science and Technology Collage of Pharmacy Dhi Qar64001 Iraq Al-Farahidi University Baghdad Iraq

ISBN: (纸本)9798350360165

From a macro viewpoint, it is essential to comprehend the similarities across human illnesses in order to identify underlying mechanisms at the microscopic level. Previous studies have shown that employing machine learning methods or merging many data sources can improve performance in sickness similarity measurement. However, there is currently a gap in the development of a useful framework for information extraction and integration from a range of biological data using deep learning models. We introduce CoGO, a Contrastive learning system that predicts commonalities in illnesses based on gene networks and ontology structure. Gene ontology (GO) and gene interaction network domain information is integrated into CoGO using graph deep learning models. Graph deep learning models are first used to encode gene and GO word features from independent graph structure data. Gene and GO characteristics are then mapped via a nonlinear projection to a shared embedding space. Next, by using cross-view contrastive loss to increase the agreement of related gene-GO links, meaningful gene representations are generated. Finally, to calculate disease similarity, CoGO computes the cosine similarity of sickness representation vectors derived from related gene embeddings. © 2024 IEEE.

关键词： Contrastive Learning

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JH-Ranker: Enhancing Keigo Recognition in Japanese Sentences through Multi-Task Learning

JH-Ranker: Enhancing Keigo Recognition in Japanese Sentences...

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International Joint Conference on Neural Networks (IJCNN)

作者： Shih-Wei Guo Ya-Chun Wen Jui-Ling Yu Yao-Chung Fan Department of Computer Science and Engineering National Chung Hsing University Taichang Taiwan Department of Japanese Language and Literature Providence University Taichang Taiwan Department of Data Science and Big Data Analytics Providence University Taichang Taiwan

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

In the realm of Japanese language processing, the correct use and understanding of Keigo (honorific language) presents unique challenges due to its complex nature and context-dependent usage. This study introduces JH-Ranker, a novel multitask learning framework designed to enhance the recognition and understanding of Keigo in Japanese sentences. We are employing a multi-task learning approach by integrating Keigo Level, Contextual Field, and the Classification of Keigo Forms. This integration aims to foster a comprehensive understanding of Keigo usage from small data, leveraging the synergy between multiple tasks to achieve a deeper understanding with limited resources. Utilizing the BERT-base-japanese pre-trained model, JH-Ranker takes into account various Keigo Forms and contextual scenarios, going beyond mere syntactic analysis. Our methodology also emphasizes the importance of considering both the form and context of Keigo, addressing challenges such as label imbalance and overfitting in multi-label classification tasks. The experimental results demonstrate a significant improvement in Keigo recognition capabilities over existing baseline models, particularly in discerning the subtle nuances of Keigo Levels and Forms. This study contributes to the realm of natural language processing by presenting a refined and contextually sensitive method for Japanese Keigo. It demonstrates the language model’s adaptability and understanding in dealing with intricate honorific language scenarios.

关键词： Adaptation models Analytical models Neural networks Syntactics Multitasking Natural language processing Context modeling

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Meta-Posterior Consistency for the Bayesian Inference of Metastable Systems

arXiv

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arXiv 2024年

作者： Adams, Zachary P. Mukherjee, Sayan Freie Universität Berlin Department of Mathematics and Computer Science Center for Scalable Data Analytics and Artificial Intelligence Universität Liepzig Germany Center for Scalable Data Analytics and Artificial Intelligence Department of Computer Science Universität Liepzig Germany Max Planck Institute for Mathematics in the Natural Sciences Duke University Departments of Statistical Science Mathematics Computer Science and Biostatistics & Bioinformatics United States

The vast majority of the literature on learning dynamical systems or stochastic processes from time series has focused on stable or ergodic systems, for both Bayesian and frequentist inference procedures. However, most real-world systems are only metastable, that is, the dynamics appear to be stable on some time scale, but are in fact unstable over longer time scales. Consistency of inference for metastable systems may not be possible, but one can ask about metaconsistency: Do inference procedures converge when observations are taken over a large but finite time interval, but diverge on longer time scales? In this paper we introduce, discuss, and quantify metaconsistency in a Bayesian framework. We discuss how metaconsistency can be exploited to efficiently infer a model for a sub-system of a larger system, where inference on the global behavior may require much more data. We also discuss the relation between metaconsistency and the spectral properties of the model dynamical system in the case of uniformly ergodic *** Codes 62F15, 60J70 © 2024, CC BY.

关键词： Stochastic systems

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Navigating Energy: Unveiling Power Consumption Patterns in Autonomous Vehicles 1

Navigating Energy: Unveiling Power Consumption Patterns in A...

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1st International Conference on Emerging Technologies for Dependable Internet of Things, ICETI 2024

作者： Saif, Abdu Shah, Nor Shahida Mohd Ameen, Ali Alnoamani, Aiman Al-Qatf, Majjed Alsamhi, Saeed Hamood Department of Communication and Computer Engineering Faculty of Engineering and IT Taiz University P.O. Box 6803 Taiz Yemen Faculty of Engineering Technology Universiti Tun Hussein Onn Malaysia Johor Malaysia Faculty of Computer Science Lincoln University College Kuala Lumpur Malaysia Insight Center for Data Analytics University of Galway Galway Ireland

ISBN: (纸本)9798331533557

Energy consumption is critical in self-driving cars and is set to change how transportation works. In this paper, we address the challenges of vehicle energy consumption by examining how the location of self-driving cars affects their energy use over time, looking at two- and three-dimensional aspects. Using models and real-world data, we examine the energy usage patterns of vehicle systems within MATLAB to reveal insights into their design and operation. In this paper, the power dynamics of cars that significantly contribute to the discussions of practical and sustainable self-driving transportation are introduced. The main goal is to demonstrate how power management may be revolutionized for self-driving car systems, ensuring maximum performance and dependability in today’s dynamic mobility scene. The findings show that enhancing autonomous vehicles’ design and operational strategies ensures their reliability and optimizes performance in today’s rapidly evolving transportation landscape. © 2024 IEEE.

关键词： Autonomous vehicles

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Artificial Neural Network Modeling for Predicting Thermal Conductivity of EG/Water-Based CNC Nanofluid for Engine Cooling Using Different Activation Functions

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Frontiers in Heat and Mass Transfer 2024年第2期22卷 537-556页

作者： Munirul Hasan Mustafizur Rahman Mohammad Saiful Islam Wong Hung Chan Yasser M.Alginahi Muhammad Nomani Kabir Suraya Abu Bakar Devarajan Ramasamy Faculty of Computing Universiti Malaysia Pahang Al-Sultan AbdullahPekanPahang26600Malaysia Faculty of Mechanical and Automotive Engineering Technology Universiti Malaysia Pahang Al-Sultan AbdullahPekanPahang26600Malaysia Department of Management and Information Technology St.Francis College179 Livingston StBrooklynNY11201USA Dell Technologies Data Analyticsand AutomationBukit TengahBukit Mertajam14000Malaysia Department of Computer Science Adrian CollegeAdrianMIUSA Department of Computer Science&Engineering United International University BangladeshUnited CityMadani AvenueBaddaDhaka1212Bangladesh

A vehicle engine cooling system is of utmost importance to ensure that the engine operates in a safe temperature *** most radiators that are used to cool an engine,water serves as a cooling *** performance of a radiator in terms of heat transmission is significantly influenced by the incorporation of nanoparticles into the cooling *** and uniformity of nanoparticle distribution are the two major factors for the practical use of *** shape and size of nanoparticles also have a great impact on the performance of heat *** researchers are investigating the impact of nanoparticles on heat *** study aims to develop an artificial neural network(ANN)model for predicting the thermal conductivity of an ethylene glycol(EG)/waterbased crystalline nanocellulose(CNC)nanofluid for cooling internal combustion *** implementation of an artificial neural network considering different activation functions in the hidden layer is made to find the bestmodel for the cooling of an engine using the *** of the model with different activation functions in artificial neural networks are analyzed for different nanofluid concentrations and *** artificial neural networks,Levenberg–Marquardt is an optimization approach used with activation functions,including Tansig and Logsig functions in the training *** findings of each training,testing,and validation phase are presented to demonstrate the network that provides the highest level of *** best result was obtained with Tansig,which has a correlation of 0.99903 and an error of 3.7959×10^(–8).It has also been noticed that the Logsig function can also be a good model due to its correlation of 0.99890 and an error of 4.9218×10^(–8).Thus ourANNwith Tansig and Logsig functions demonstrates a high correlation between the actual output and the predicted output.

关键词： Artificial neural network activation function thermal conductivity nanocellulose

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Parm: Efficient Training of Large Sparsely-Activated Models with Dedicated Schedules

Parm: Efficient Training of Large Sparsely-Activated Models ...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Xinglin Pan Wenxiang Lin Shaohuai Shi Xiaowen Chu Weinong Sun Bo Li Data Science and Analytics Thrust The Hong Kong University of Science and Technology (Guangzhou) Department of Computer Science Hong Kong Baptist University School of Computer Science and Technology Harbin Institute of Technology Shenzhen Department of Computer Science and Engineering The Hong Kong University of Science and Technology

ISBN: (数字)9798350383508

ISBN: (纸本)9798350383515

Sparsely-activated Mixture-of-Expert (MoE) layers have found practical applications in enlarging the model size of large-scale foundation models, with only a sub-linear increase in computation demands. Despite the wide adoption of hybrid parallel paradigms like model parallelism, expert parallelism, and expert-sharding parallelism (i.e., MP+EP+ESP) to support MoE model training on GPU clusters, the training efficiency is hindered by communication costs introduced by these parallel paradigms. To address this limitation, we propose Parm, a system that accelerates MP+EP+ESP training by designing two dedicated schedules for placing communication tasks. The proposed schedules eliminate redundant computations and communications and enable overlaps between intra-node and inter-node communications, ultimately reducing the overall training time. As the two schedules are not mutually exclusive, we provide comprehensive theoretical analyses and derive an automatic and accurate solution to determine which schedule should be applied in different scenarios. Experimental results on an 8-GPU server and a 32-GPU cluster demonstrate that Parm outperforms the state-of-the-art MoE training system, DeepSpeed-MoE, achieving 1.13× to 5.77× speedup on 1296 manually configured MoE layers and approximately 3× improvement on two real-world MoE models based on BERT and GPT-2.

关键词： Training Schedules Costs Processor scheduling Computational modeling Prototypes Graphics processing units

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Causal Reasoning in Large Language Models using Causal Graph Retrieval Augmented Generation

Causal Reasoning in Large Language Models using Causal Graph...

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Conference on Human System Interactions, HSI

作者： Chamod Samarajeewa Daswin De Silva Evgeny Osipov Damminda Alahakoon Milos Manic Centre for Data Analytics and Cognition La Trobe University Australia Department of Computer Science Electrical and Space Engineering Luleå University of Technology Sweden Department of Computer Science Virginia Commonwealth University Richmond USA

ISBN: (数字)9798350362916

ISBN: (纸本)9798350362923

Large Language Models (LLMs) are leading the Generative Artificial Intelligence transformation in natural language understanding. Beyond language understanding, LLMs have demonstrated capabilities in reasoning tasks, including commonsense, logical, and mathematical reasoning. However, their proficiency in causal understanding has been limited due to the complex nature of causal reasoning. Several recent studies have discussed the role of external causal models for improved causal understanding. Building on the success of Retrieval-Augmented Generation (RAG) for factual reasoning in LLMs, this paper introduces a novel approach that utilizes Causal Graphs as external sources for establishing causal relationships between complex vectors. This method is empirically evaluated using two benchmark datasets across the metrics of Context Relevance, Answer Relevance, and Grounding, in its ability to retrieve relevant context with causal alignment. The retrieval effectiveness is further compared with traditional RAG methods that are based on semantic proximity.

关键词： Measurement Limiting Large language models Semantics Benchmark testing Cognition Vectors

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FEDERATED INVERSE PROBABILITY TREATMENT WEIGHTING FOR INDIVIDUAL TREATMENT EFFECT ESTIMATION

arXiv

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arXiv 2025年

作者： Yin, Changchang Chen, Hong-You Chao, Wei-Lun Zhang, Ping Department of Computer Science and Engineering The Ohio State University ColumbusOH United States Department of Computer Science and Engineering Department of Biomedical Informatics Translational Data Analytics Institute The Ohio State University ColumbusOH United States

Individual treatment effect (ITE) estimation is to evaluate the causal effects of treatment strategies on some important outcomes, which is a crucial problem in healthcare. Most existing ITE estimation methods are designed for centralized settings. However, in real-world clinical scenarios, the raw data are usually not shareable among hospitals due to the potential privacy and security risks, which makes the methods not applicable. In this work, we study the ITE estimation task in a federated setting, which allows us to harness the decentralized data from multiple hospitals. Due to the unavoidable confounding bias in the collected data, a model directly learned from it would be inaccurate. One well-known solution is Inverse Probability Treatment Weighting (IPTW), which uses the conditional probability of treatment given the covariates to re-weight each training example. Applying IPTW in a federated setting, however, is non-trivial. We found that even with a well-estimated conditional probability, the local model training step using each hospital’s data alone would still suffer from confounding bias. To address this, we propose FED-IPTW, a novel algorithm to extend IPTW into a federated setting that enforces both global (over all the data) and local (within each hospital) decorrelation between covariates and treatments. We validated our approach on the task of comparing the treatment effects of mechanical ventilation on improving survival probability for patients with breadth difficulties in the intensive care unit (ICU). We conducted experiments on both synthetic and real-world eICU datasets and the results show that FED-IPTW outperform state-of-the-art methods on all the metrics on factual prediction and ITE estimation tasks, paving the way for personalized treatment strategy design in mechanical ventilation usage. © 2025, CC BY-NC-SA.

关键词： Inverse problems

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Mining fuzzy local periodic activity pattern for Smart home applications

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Knowledge-Based Systems 2024年 293卷

作者： Iqbal, Mohammad Prayitno, Adila Sekarrati Dwi Pao, Hsing-Kuo Mukhlash, Imam Department of Mathematics Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Sukolilo East Java Surabaya60111 Indonesia Department of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei106 Taiwan

Perceiving resident movements from ubiquitous sensor devices can aid health and safety management in smart home environment. One can find resident movement patterns to get notification about what should be aware of to prepare for possible accidents. The goal of this work is to propose a method to search for periodic residents’ activity patterns given smart home applications. Specifically, the proposed method is aimed to capture fuzzy local periodic activity patterns (FLPAP) from the smart home data. A FLPAP is an activity pattern that periodically occurs for a particular duration in fuzzy time intervals. We construct an algorithm to help to find the FLPAP called Fuzzy Local Periodic Activity Pattern Miner (FLoPMiner). On the side, a parameter called k-scale period is well-chosen to reorganize the data to make it suitable for the need of periodic activity pattern mining. The proposed method was demonstrated for its effectiveness on a public smart home dataset. After all, we successfully retrieved periodic resident activity patterns, which can be applied to self-health management systems, especially when focusing on a precise scale period. Overall, the proposed method shows descent pattern representation against the state-of-the-art methods of the same category. © 2024 Elsevier B.V.

关键词： Automation

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Towards Graph-Based Semi-Supervised Learning on Audio Embeddings for Label Classification

Towards Graph-Based Semi-Supervised Learning on Audio Embedd...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Rishabh Chandaliya Mohan Timilsina John Breslin Martin Serrano Department of Computer Science University of Galway Ireland Insight SFI Research Centre for Data Analytics Galway Ireland

The increasing importance of audio-based healthcare diagnostics, particularly in chronic respiratory problems, has prompted the development of novel approaches for disease classification using the full spectrum analysis of cough sounds. This paper presents an innovative study exploring the potential of employing supervised and semi-supervised learning methodologies for disease categorization based on cough audio samples. Specifically, this study focuses on scenarios characterized by a shortage of annotated data related to chronic diseases. The objectives of our study involved the utilization of standard machine learning algorithms for direct classification based on embeddings, as well as the integration of Graph Neural Networks (GNNs) on the KNN graph. Preliminary results indicate that GNN models consistently outperformed traditional classifiers. For instance, with just 1 % of the data, GAT and GCN achieved AUC PR values of 0.84 and 0.87, respectively, surpassing all traditional methods. The superiority mentioned above was sustained even when the data fraction was augmented to 3% and 5%. The usefulness of graph neural networks (GNNs) was further supported by comprehensive performance measures, wherein the graph convolutional network (GCN) exhibited exceptional preci-sion and PR (precision-recall). In summary, the AudioVecDiagnosis framework presents a promising opportunity for further investigation in audio-based healthcare diagnostics. It provides an optimum approach for situations with a limited availability of labeled data.

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