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Real-Time Systems Symposium (RTSS)

作者： Chuanchao Gao Niraj Kumar Arvind Easwaran College of Computing and Data Science Energy Research Institute @ NTU Interdisciplinary Graduate Programme Nanyang Technological University Singapore

ISBN: (数字)9798331540265

ISBN: (纸本)9798331540272

Mobile-edge computing (MEC) has emerged as a promising paradigm for enabling Internet of Things (IoT) devices to handle computation-intensive jobs. Due to the imperfect parallelization of algorithms for job processing on servers and the impact of IoT device mobility on data communication quality in wireless networks, it is crucial to jointly consider server resource allocation and IoT device mobility during job scheduling to fully benefit from MEC, which is often overlooked in existing studies. By jointly considering job scheduling, server resource allocation, and IoT device mobility, we investigate the deadlineconstrained job offloading and resource management problem in MEC with both communication and computation contentions, aiming to maximize the total energy saved for IoT devices. For the offline version of the problem, where job information is known in advance, we formulate it as an Integer Linear Programming problem and propose an approximation algorithm, LHJS, with a constant performance guarantee. For the online version, where job information is only known upon release, we propose a heuristic algorithm, LBS, that is invoked whenever a job is released. Finally, we conduct experiments with parameters from real-world applications to evaluate their performance.

关键词： Wireless networks Computational modeling Approximation algorithms Scheduling data models Real-time systems Resource management Servers Internet of Things data communication

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Disentangled latent factors for muti-cause treatment effect estimation 6

Disentangled latent factors for muti-cause treatment effect ...

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2024 6th International Conference on Artificial Intelligence Technologies and Applications, ICAITA 2024

作者： Zhao, Zhuoyue Wang, Haotian Xu, Liyang Wang, Qian Tang, Yuhua Department of Intelligent Data Science National University of Defense Technology Changsha410073 China Intelligent Game and Decision Lab. Academy of Military Science Beijing100071 China Institute for Quantum Information State Key Laboratory of High-Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha410073 China

Existing methods estimate treatment effects from observational data and assume that covariates are all confounders. However, observed covariates may not directly represent confounding variables that influence both treatment and outcome. They always include variables that only affect the treatment or the outcome. In addition, for multi-dimensional binary treatments, disentangled methods are mainly designed for binary variables and ignore the impact of multi-cause treatment variables on the inference of latent factors. To address these two issues, based on variable decomposition and proxy inference, we propose the Disentangled Latent Factors for Multi-cause Treatment Estimation (DEMTE) algorithm. It utilizes an identifiable autoencoder to infer and disentangle latent factors based on the joint distribution of variables in observational data. DEMTE evaluates the treatment effect on the disentangled factors. Synthetic experiments and semi-synthetic experiments demonstrate the effectiveness of the inference and disentanglement techniques and our method achieves more accurate treatment effect estimation. © 2024 institute of Physics Publishing. All rights reserved.

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Scientific Research Transformation Needed for Comprehensive Artificial Intelligence Implementation 17

Scientific Research Transformation Needed for Comprehensive ...

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17th International Conference on Management of Large-Scale System Development, MLSD 2024

作者： Medennikov, Viktor Bogatyreva, Larisa Federal Research Center 'Computer Science and Control' of the Russian Academy of Sciences Information and Computing Systems Department Moscow Russia V.A. Trapeznikov Institute of Control Sciences of the Russian Academy of Sciences Laboratory of Modular Data Processing and Control Systems Moscow Russia

ISBN: (纸本)9798350375718

The paper deals with complementary changes in scientific research and real economy (exemplified by the farming industry) taking place when using the holistic approach to the industry's digital transformation resulting in the appearance of precise agrarian technologies that require a comprehensive, system-based combination of research and production resources that are able to ensure increased productivity in the industry. We demonstrate that among these new technologies, the artificial intelligence methods have a special significance. They have to, however, go through integration transformations to become standards for the proposed common digital farming management platform. © 2024 IEEE.

关键词： Agricultural economics

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Human-Generative AI Collaborative Problem Solving Who Leads and How Students Perceive the Interactions

arXiv

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arXiv 2024年

作者： Zhu, Gaoxia Sudarshan, Vidya Kow, Jason Fok Ong, Yew Soon National Institute of Education Nanyang Technological University Singapore College of Computing and Data Science Nanyang Technological University Singapore

This research investigates distinct human-generative AI collaboration types and students’ interaction experiences when collaborating with generative AI (i.e., ChatGPT) for problem-solving tasks and how these factors relate to students’ sense of agency and perceived collaborative problem solving. By analyzing the surveys and reflections of 79 undergraduate students, we identified three human-generative AI collaboration types: even contribution, human leads, and AI leads. Notably, our study shows that 77.21% of students perceived they led or had even contributed to collaborative problem-solving when collaborating with ChatGPT. On the other hand, 15.19% of the human participants indicated that the collaborations were led by ChatGPT, indicating a potential tendency for students to rely on ChatGPT. Furthermore, 67.09% of students perceived their interaction experiences with ChatGPT to be positive or mixed. We also found a positive correlation between positive interaction experience and a sense of positive agency. The results of this study contribute to our understanding of the collaboration between students and generative AI and highlight the need to study further why some students let ChatGPT lead collaborative problem-solving and how to enhance their interaction experience through curriculum and technology design. © 2024, CC BY.

关键词： Students

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A Novel Dense-Net Deep Neural Network Method for Classify Tuberculosis Stages Using Chest X-Ray Images with Enhanced Features 1

A Novel Dense-Net Deep Neural Network Method for Classify Tu...

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1st International Conference on data, Computation and Communication, ICDCC 2024

作者： Siddiqui, Ebtasam Ahmad Patel, Rajneesh Kumar Poornima Institute of Engineering & Technology Artificial Intelligence & Data Science Jaipur India School of Computing Science & Engineering VIT Bhopa University India

ISBN: (纸本)9798331532956

Although chest X-ray (CXR) imaging is critical for the detection of potentially lethal disorders, interpretation may arise because to similarities between distinct lung conditions. In response, researchers proposed CBAMWDnet, a new deep learning model for tuberculosis (TB) diagnosis based on computed tomography (CXR) images. To successfully collect geographical and contextual data, the model includes the Convolutional Block Attention Module (CBAM) into its architecture, which is based on the Wide Dense Net (WDnet). Pre-processing using an enhanced Gabor filter improves image quality prior to applying the model. We test the model using three well-known datasets: Shenzhen, Montgomery, and Indiana. The results indicate that the Shenzhen dataset performs the best. CBAMWDnet beats previous techniques by better classifying tuberculosis (TB) into normal, primary, latent, and active stages. This complex classification engine improves both accuracy and dependability. © 2024 IEEE.

关键词： Deep neural networks

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Learning a Mini-Batch Graph Transformer via Two-Stage Interaction Augmentation 27

Learning a Mini-Batch Graph Transformer via Two-Stage Intera...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Li, Wenda Chen, Kaixuan Liu, Shunyu Zheng, Tongya Huang, Wenjie Song, Mingli State Key Laboratory of Blockchain and Security Zhejiang University China School of Software Technology Zhejiang University China Institute of Blockchain and Data Security China Big Graph Center School of Computer and Computing Science Hangzhou City University China College of Computer Science and Technology Zhejiang University Hangzhou China

ISBN: (纸本)9781643685489

Mini-batch Graph Transformer (MGT), as an emerging graph learning model, has demonstrated significant advantages in semi-supervised node prediction tasks with improved computational efficiency and enhanced model robustness. However, existing methods for processing local information either rely on sampling or simple aggregation, which respectively result in the loss and squashing of critical neighbor information. Moreover, the limited number of nodes in each mini-batch restricts the model’s capacity to capture the global characteristic of the graph. In this paper, we propose LGMformer, a novel MGT model that employs a two-stage augmented interaction strategy, transitioning from local to global perspectives, to address the aforementioned bottlenecks. The local interaction augmentation (LIA) presents a neighbor-target interaction Transformer (NTIformer) to acquire an insightful understanding of the co-interaction patterns between neighbors and the target node, resulting in a locally effective token list that serves as input for the MGT. In contrast, global interaction augmentation (GIA) adopts a cross-attention mechanism to incorporate entire graph prototypes into the target node representation, thereby compensating for the global graph information to ensure a more comprehensive perception. To this end, LGMformer achieves the enhancement of node representations under the MGT paradigm. Experimental results related to node classification on the ten benchmark datasets demonstrate the effectiveness of the proposed method. Our code is available at https://***/l-wd/LGMformer. © 2024 The Authors.

关键词： Contrastive Learning

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IoT -Weather Integration for Enhanced Cricket Tactics with Gradient Boosting Algorithm 8

IoT -Weather Integration for Enhanced Cricket Tactics with G...

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8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Ranganathan, Chitra Sabapathy Ramesh, S. Preetha, J. Rakesh, Nitin Mohankumar, N. Sujatha, S. Mphasis Corporation Tamil Nadu Chennai India School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of Computing Technologies Tamil Nadu Chennai India Kongunadu College of Engineering and Technology Department of Artificial Intelligence & Data Science Tamil Nadu Trichy India Nagpur Campus Maharashtra Pune India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331505462

From a strategic perspective, the pitch's state and the wind's direction are the most important factors in cricket. In this paper, an innovative method that uses the IoT to report the state of the pitch and the weather in real time is presented. The system gives updates on the state of the field and the weather continuously. The device can gather data on the pitch's hardness and humidity using sensors strategically placed around the field. It may also record information about the weather, including temperature, humidity, wind speed, and cloud cover. Cricket teams and players can have a deep understanding of the present playing conditions based on the real-time transmission and analysis. Teams may improve their results by making data-driven adjustments to their strategies and lineups. Teams may also be able to make more educated decisions on their approaches to batting, bowling, and fielding. The technology has the potential to alter the game's dynamics while also increasing audience participation by shedding light on cricket's many nuanced aspects. © 2024 IEEE.

关键词： Cricket (Sports)

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DLP-LORA: EFFICIENT TASK-SPECIFIC LORA FUSION WITH A DYNAMIC, LIGHTWEIGHT PLUGIN FOR LARGE LANGUAGE MODELS

arXiv

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arXiv 2024年

作者： Zhang, Yuxuan Li, Ruizhe Department of Computing Science University of Aberdeen United Kingdom Aberdeen Institute of Data Science and Artificial Intelligence South China Normal University China

Recent advancements in Large Language Models (LLMs) have achieved robust performance across diverse tasks, but fine-tuning these models for specific domains remains resource-intensive. Parameter-Efficient Fine-Tuning (PEFT) methods like Low-Rank Adaptation (LoRA) address this challenge by fine-tuning a small subset of parameters. However, existing methods for fusing multiple LoRAs lack dynamic fusion based on contextual inputs and often increase inference time due to token-level operations. We propose DLP-LoRA, a Dynamic Lightweight Plugin that employs a mini-MLP module with only 5M parameters to dynamically fuse multiple LoRAs at the sentence level using top-p sampling strategies. This approach reduces inference time to less than twice that of single LoRA inference by leveraging parallel computation. Evaluations across 26 tasks—including multiple-choice questions and question answering—demonstrate that DLP-LoRA achieves an average accuracy of 92.34% on multiple-choice datasets and significant improvements in BLEU and ROUGE scores on QA datasets, outperforming different LLMs backbones under composite task settings. DLP-LoRA effectively balances performance and efficiency, making it a practical solution for dynamic multi-task adaptation in LLMs. Our code is available at https://***/MeCuping/DLP-LoRA. © 2024, CC BY.

关键词： Problem oriented languages

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Personalized Care for Multiple Sclerosis with IoT Wearables and Deep Learning

Personalized Care for Multiple Sclerosis with IoT Wearables ...

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2024 Asian Conference on Intelligent Technologies, ACOIT 2024

作者： Vetriselvi, D. Sampathrajan, Rajeshkumar Deepa, A. Sujatha, V. Deepa, P. Pandi Murugan, S. Department of Computing Technologies School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chennai India McKinsey & Company Fort WorthTX United States Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Tamil Nadu Chennai India Department of Electronics and Communication Engineering Shree Sathyam College of Engineering and Technology Tamil Nadu Salem India Department of Artificial Intelligence and Data Science Agni College of Technology Tamil Nadu Chennai India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India

ISBN: (纸本)9798350374933

This research work presents a new method for improving individualized treatment for patients with Multiple Sclerosis (MS) by combining Internet of Things (IoT) wearables with deep learning (DL) methods, namely Long Short-Term Memory (LSTM) networks. MS is a long-term autoimmune disorder that affects the central nervous system. It is marked by unexpected episodes of worsening symptoms and periods of improvement, highlighting the need for tailored therapy. The widespread use of IoT devices presents unique possibilities for the ongoing monitoring of many physiological and activity measurements important to the development and symptoms of MS. Using LSTM networks, which are very proficient in analyzing temporal sequences, create a prediction model that may anticipate exacerbations of MS symptoms and customize therapies appropriately. Through the analysis of longitudinal data obtained from IoT devices, our model acquires knowledge about unique patterns of illness development and treatment response. This knowledge allows us to create customized care plans for individuals. These paths include proactive activities such as adjusting medication, making lifestyle changes, and seeking early medical advice. The goal is to reduce the worsening of symptoms, improve the quality of life, and optimize treatment results for individuals with MS. The proposed method shows potential for transforming the treatment of MS by offering proactive and patient-centered therapy adaptable to individual requirements and the dynamics of the illness. © 2024 IEEE.

关键词： Patient treatment

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RVISA: Reasoning and Verification for Implicit Sentiment Analysis

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IEEE Transactions on Affective computing 2025年

作者： Lai, Wenna Xie, Haoran Xu, Guandong Li, Qing Hong Kong Polytechnic University Department of Computing Hong Kong Lingnan University School of Data Science Hong Kong University of Technology Sydney School of Computer Science Data Science Institute SydneyNSW2007 Australia Education University of Hong Kong Hong Kong

Under the context of the increasing social demand for fine-grained sentiment analysis (SA), implicit sentiment analysis (ISA) poses a significant challenge owing to the absence of salient cue words in expressions. Thus, reliable reasoning is required to understand how sentiment is evoked, enabling the identification of implicit sentiments. In the era of large language models (LLMs), encoder-decoder (ED) LLMs have emerged as popular backbone models for SA applications, given their impressive text comprehension and reasoning capabilities across diverse tasks. In comparison, decoder-only (DO) LLMs exhibit superior natural language generation and in-context learning capabilities. However, their responses may contain misleading or inaccurate information. To accurately identify implicit sentiments with reliable reasoning, this study introduces a two-stage reasoning framework named Reasoning and Verification for Implicit Sentiment Analysis (RVISA), which leverages the generation ability of DO LLMs and reasoning ability of ED LLMs to train an enhanced reasoner. The framework involves three-hop reasoning prompting to explicitly furnish sentiment elements as cues. The generated rationales are then used to fine-tune an ED LLM into a skilled reasoner. Additionally, we develop a straightforward yet effective answer-based verification mechanism to ensure the reliability of reasoning learning. Evaluation of the proposed method on two benchmark datasets demonstrates that it achieves state-of-the-art performance in ISA. © 2010-2012 IEEE.

关键词： Decoding

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