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2nd International Conference Computational and Characterization Techniques in engineering and Sciences, IC3TES 2024

作者： Kumari, Nalli Vinaya Pradeep Ghantasala, G.S. Sharma R, Rajesh Ananthanagu, U. Vidyullatha, Pellakuri Pandiya, Dileep Kumar Jntuh Malla Reddy Engineering College for Women Department of Computer Science and Engineering Hyderabad India Alliance University Alliance College of Engineering and Design Department of Computer Science and Engineering Bengaluru India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Guntur India Zoominfo Technologies Principl Software Engineer Boston United States

ISBN: (纸本)9798350364699

Railway web page traffic manipulate is a complex and dynamic method that includes making real-time picks based mostly on various uncertain and difficult to understand factors, collectively with fluctuating passenger demand, diverse train schedules, and unpredictable weather conditions. Traditional decision-making strategies often battle to cope with the ones uncertainties, foremost to inefficiencies and potential safety risks. This paper proposes the software program of fuzzy properly judgment-based completely decision-making systems to optimize railway web page traffic manipulate. Fuzzy properly judgment, with its potential to model uncertainty and control difficult to understand data, offers a robust framework for boosting decision-making approaches in railway operations. By simulating various conditions and integrating real-time data, the proposed gadget can dynamically regulate train schedules, manipulate delays, and enhance everyday operational efficiency. The paper may present case studies demonstrating the effectiveness of fuzzy properly judgment in dealing with railway web page traffic, reducing delays, and improving passenger satisfaction. The findings advice that incorporating fuzzy properly judgment into railway web page traffic manipulates systems can purpose greater adaptive and resilient railway operations, ultimately contributing to greater steady and additional reliable rail networks. © 2024 IEEE.

关键词： Efficiency

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Demystifying AI Advances in Explainable AI (XAI) 3

Demystifying AI Advances in Explainable AI (XAI)

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3rd International Conference on Computational Modelling, Simulation and Optimization, ICCMSO 2024

作者： Kumar Pandiya, Dileep Ramrao Joshi, Vilas Tripathi, Kailash Nath ZoomInfo Technology Inc Principal Software Engineer BostonMA United States ISBM College of Engineering Department of Computer Engineering Maharashtra Pune India

ISBN: (纸本)9798350361391

In today's dynamic landscape, the integration of artificial intelligence (AI) has revolutionized operations across diverse domains. However, the enigmatic nature of many AI algorithms presents formidable obstacles, spanning from a dearth of confidence to potential ethical quandaries. Enter Explainable AI (XAI), a burgeoning field that serves as a beacon of clarity, providing to augment transparency and comprehension. This manuscript embarks on a comprehensive exploration of the strides made in XAI, meticulously dissecting its significance, fundamental methodologies, practical applications, hurdles, ethical dimensions, and trajectories for future advancement. By delving into these multifaceted dimensions, this endeavor aims to illuminate the trajectory towards closing the chasm between intricate AI frameworks and human cognition. © 2024 IEEE.

关键词： Advancements Artificial Intelligence Ethical implications Explainable AI (XAI) Transparency

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A Model for Detection of the Susceptible in Hospital Contact Network Based on Social Network Analysis 14th

A Model for Detection of the Susceptible in Hospital Contact...

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14th International Conference on computer Science and its Applications, CSA 2022 and the 16th KIPS International Conference on Ubiquitous Information Technologies and Applications, CUTE 2022

作者： Yang, Yixuan Park, Doo-Soon Kim, Seok-Hoon Peng, Sony Siet, Sophort Ugli, Sadriddinov Ilkhomjon Rovshan Vilakone, Phonexay Department of Software Convergence Soonchunhyang University Asan Korea Republic of Department of Computer Software Engineering Soonchunhyang University Asan Korea Republic of Department of Computer Engineering and Information Technology National University of Laos Vientiane Laos

ISBN: (纸本)9789819912513

COVID-19 can be modeled as a SEIR disease transmission model, and early detection and prevention of susceptible is a great way to stop the spread of the virus. The purpose of this paper is to conduct community detection on the hospital's contact network (including patients, nurses, doctors, and managers), obtain frequently contacted communities (susceptible communities), and analyze the independent contact networks of different roles to obtain high-influence communities and rank the node influence, if nodes with high influence are included in the susceptible community, we will recommend focusing on observing these nodes to prevent the spread of virus. In this paper, we introduce a model that combines social networks analysis with disease transmission model to analyze hospital contact networks, apply the classic overlapping community detection method CPM to community detection of contact networks, and utilize PageRank algorithm to calculate the node's influence. The experimental results show that the model can effectively solve our problem. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Hospitals

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Can end-user feedback in social media be trusted for software evolution: Exploring and analyzing fake reviews

Can end-user feedback in social media be trusted for softwar...

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作者： Khan, Javed Ali Ullah, Tahir Khan, Arif Ali Yasin, Affan Akbar, Muhammad Azeem Aurangzeb, Khursheed Department of Computer Science Faculty of Physics Engineering and Computer Science University of Hertfordshire Hatfield United Kingdom School of Computer & Technology Beijing Institute of Technology Beijing China M3S Empirical Software Engineering Research Unit University of Oulu Oulu Finland School of Software Northwestern Polytechnical University Shaanxi Xian China Software Engineering Department Lappeenranta-Lahti University of Technology Lappeenranta Finland Department of Computer Engineering College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia

End-user feedback in social media platforms, particularly in the app stores, is increasing exponentially with each passing day. software researchers and vendors started to mine end-user feedback by proposing text analytics methods and tools to extract useful information for software evolution and maintenance. In addition, research shows that positive feedback and high-star app ratings attract more users and increase downloads. However, it emerged in the fake review market, where software vendors started incorporating fake reviews against their corresponding applications to improve overall software ratings. For this purpose, we conducted an exploratory study to understand how end-users register and write fake reviews in the Google Play Store. We curated a research data set containing 68,000 end-user comments from the Google Play Store and a fake review generator, that is, the Testimonial generator (TG). Its purpose is to understand fake reviews on these platforms and identify the common patterns potential end-users and professionals use to report fake reviews by critically analyzing the end-user feedback. We conducted a detailed survey at the University of Science and Technology Bannu, Pakistan, to identify the intelligence and accuracy of crowd-users in manually identifying fake reviews. In addition, we developed a ground truth to be compared with the results obtained from the automated machine and deep learning (M&DL) classifier experiment. In the survey, 512 end-users participated and recorded their responses in identifying fake reviews. Finally, various M&DL classifiers are employed to classify and identify end-user reviews into real and fake to automate the process. Unlike humans, the M&DL classifiers performed well in automatically classifying reviews into real and fake by obtaining much higher accuracy, precision, recall, and f-measures. The accuracy of manually identifying fake reviews by the crowd-users is 44.4%. In contrast, the M&DL classifiers obtained an

关键词： Application programs

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Evaluating the Efficacy of Large Language Models in Automating Academic Peer Reviews 23

Evaluating the Efficacy of Large Language Models in Automati...

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23rd IEEE International Conference on Machine Learning and Applications, ICMLA 2024

作者： Weiminzhao Mahmoud, Qusay H. Ontario Tech University Department of Electrical Computer and Software Engineering OshawaONLlG 0CS Canada

ISBN: (纸本)9798350374889

This paper explores the application of large language models (LLMs) in automating the peer review process for academic papers, a critical area for enhancing the efficiency and consistency of scholarly publication. We utilized GPT-4-0125 to automatically generate reviews for 20 papers sourced from *** and analyzed the AI-generated peer reviews for quality and effectiveness. The analysis includes a detailed assessment of the text properties of the reviews, such as sentiment, revealing that LLM-generated reviews tend to be more uniformly positive than their human-written counterparts. In addition, we conducted a user survey in which participants attempted to distinguish between AI-generated and human-written reviews. The survey results indicated a low correct identification rate, suggesting that participants often could not discern the origin of the review, thereby highlighting the potential of LLMs to mimic human-like review qualities. However, the study also identifies limitations in LLM's performance, particularly concerning the variability in review quality, which appears to correlate with the model's vocabulary usage of the generated content. © 2024 IEEE.

关键词： Adversarial machine learning

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Loop Subgraph-Level Greedy Mapping Algorithm for Grid Coarse-Grained Reconfigurable Array

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Tsinghua Science and Technology 2023年第2期28卷 330-343页

作者： Naijin Chen Fei Cheng Chenghao Han Jianhui Jiang Xiaoqing Wen School of Computer and Information Science Anhui Polytechnic UniversityWuhu 241000China School of Software Engineering Tongji UniversityShanghai 201804China Department of Computer Science and Networks Kyushu Institute of TechnologyFukuoka 820-8502Japan

To solve the problem of grid coarse-grained reconfigurable array task mapping under multiple constraints,we propose a Loop Subgraph-Level Greedy Mapping(LSLGM)algorithm using parallelism and processing element *** the constraint of a reconfigurable array,the LSLGM algorithm schedules node from a ready queue to the current reconfigurable cell array *** mapping a node,its successor’s indegree value will be dynamically *** its successor’s indegree is zero,it will be directly scheduled to the ready queue;otherwise,the predecessor must be dynamically *** the predecessor cannot be mapped,it will be scheduled to a blocking *** dynamically adjust the ready node scheduling order,the scheduling function is constructed by exploiting factors,such as node number,node level,and node *** with the loop subgraph-level mapping algorithm,experimental results show that the total cycles of the LSLGM algorithm decreases by an average of 33.0%(PEA44)and 33.9%(PEA_(7×7)).Compared with the epimorphism map algorithm,the total cycles of the LSLGM algorithm decrease by an average of 38.1%(PEA_(4×4))and 39.0%(PEA_(7×7)).The feasibility of LSLGM is verified.

关键词： Grid Coarse-Grained Reconfigurable Array(GCGRA) mapping loop subgraph scheduling

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Reference Trustable Decoding: A Training-Free Augmentation Paradigm for Large Language Models 38

Reference Trustable Decoding: A Training-Free Augmentation P...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Shi, Luohe Yao, Yao Li, Zuchao Zhang, Lefei Zhao, Hai National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan430072 China Department of Computer Science and Engineering Shanghai Jiao Tong University China

Large language models (LLMs) have rapidly advanced and demonstrated impressive capabilities. In-Context Learning (ICL) and Parameter-Efficient Fine-Tuning (PEFT) are currently two mainstream methods for augmenting LLMs to downstream tasks. ICL typically constructs a few-shot learning scenario, either manually or by setting up a Retrieval-Augmented Generation (RAG) system, helping models quickly grasp domain knowledge or question-answering patterns without changing model parameters. However, this approach involves trade-offs, such as slower inference speed and increased space occupancy. PEFT assists the model in adapting to tasks through minimal parameter modifications, but the training process still demands high hardware requirements, even with a small number of parameters involved. To address these challenges, we propose Reference Trustable Decoding (RTD), a paradigm that allows models to quickly adapt to new tasks without fine-tuning, maintaining low inference costs. RTD constructs a reference datastore from the provided training examples and optimizes the LLM's final vocabulary distribution by flexibly selecting suitable references based on the input, resulting in more trustable responses and enabling the model to adapt to downstream tasks at a low cost. Experimental evaluations on various LLMs using different benchmarks demonstrate that RTD establishes a new paradigm for augmenting models to downstream tasks. Furthermore, our method exhibits strong orthogonality with traditional methods, allowing for concurrent usage. Our code can be found at https://***/ShiLuohe/ReferenceTrustableDecoding. © 2024 Neural information processing systems foundation. All rights reserved.

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Multi-Agent Deep Q-Networks for Efficient Edge Federated Learning Communications in software-Defined IoT

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computers, Materials & Continua 2022年第5期71卷 3319-3335页

作者： Prohim Tam Sa Math Ahyoung Lee Seokhoon Kim Department of Software Convergence Soonchunhyang UniversityAsan31538Korea Department of Computer Science Kennesaw State UniversityMariettaGA 30060USA Department of Computer Software Engineering Soonchunhyang UniversityAsan31538Korea

Federated learning(FL)activates distributed on-device computation techniques to model a better algorithm performance with the interaction of local model updates and global model distributions in aggregation averaging ***,in large-scale heterogeneous Internet of Things(IoT)cellular networks,massive multi-dimensional model update iterations and resource-constrained computation are challenging aspects to be tackled *** paper introduces the system model of converging softwaredefined networking(SDN)and network functions virtualization(NFV)to enable device/resource abstractions and provide NFV-enabled edge FL(eFL)aggregation servers for advancing automation and ***-agent deep Q-networks(MADQNs)target to enforce a self-learning softwarization,optimize resource allocation policies,and advocate computation offloading *** gathered network conditions and resource states,the proposed agent aims to explore various actions for estimating expected longterm rewards in a particular state *** exploration phase,optimal actions for joint resource allocation and offloading decisions in different possible states are obtained by maximum Q-value ***-based virtual network functions(VNF)forwarding graph(VNFFG)is orchestrated to map VNFs towards eFL aggregation server with sufficient communication and computation resources in NFV infrastructure(NFVI).The proposed scheme indicates deficient allocation actions,modifies the VNF backup instances,and reallocates the virtual resource for exploitation *** neural network(DNN)is used as a value function approximator,and epsilongreedy algorithm balances exploration and *** scheme primarily considers the criticalities of FL model services and congestion states to optimize long-term *** results presented the outperformance of the proposed scheme over reference schemes in terms of Quality of Service(QoS)performance metrics,including packet

关键词： Deep Q-networks federated learning network functions virtualization quality of service software-defined networking

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Leaching Fraction (LF) of Irrigation Water for Saline Soils Using Machine Learning

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Intelligent Automation & Soft Computing 2023年第5期36卷 1915-1930页

作者： Rab Nawaz Bashir Imran Sarwar Bajwa Muhammad Waseem Iqbal Muhammad Usman Ashraf Ahmed Mohammed Alghamdi Adel ABahaddad Khalid Ali Almarhabi Department of Computer Science COMSATS University IslamabadVehari CampusPakistan Department of Computer Science and IT Islamia University BahawalpurPakistan Department of Software Engineering Superior University Lahore54000Pakistan Department of Computer Science GC Women UniversitySialkot53310Pakistan Department of Software Engineering College of Computer Science and EngineeringUniversity of Jeddah21493Saudi Arabia Department of Information System Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science College of Computing in Al-QunfudahUmm Al-Qura UniversityMakkah24381Saudi Arabia

Soil salinity is a serious land degradation issue in *** is a major threat to agriculture *** irrigation water is applied to leach down the salts from the root zone of the plants in the form of a Leaching fraction(LF)of irrigation *** the leaching process to be effective,the LF of irriga-tion water needs to be adjusted according to the environmental conditions and soil salinity level in the form of Evapotranspiration(ET)*** relationship between environmental conditions and ET rate is hard to be deﬁned by a linear relationship and data-driven Machine learning(ML)based decisions are required to determine the calibrated Evapotranspiration(ETc)***-assisted ETc is pro-posed to adjust the LF according to the ETc and soil salinity level.A regression model is proposed to determine the ETc rate according to the prevailing tempera-ture,humidity,and sunshine,which would be used to determine the smart LF according to the ETc and soil salinity *** proposed model is trained and tested against the Blaney Criddle method of Reference evapotranspiration(ETo)*** validation of the model from the test dataset reveals the accu-racy of the ML model in terms of Root mean squared errors(RMSE)are 0.41,Mean absolute errors(MAE)are 0.34,and Mean squared errors(MSE)are 0.28 mm *** applications of the proposed solution in a real-time environ-ment show that the LF by the proposed solution is more effective in reducing the soil salinity as compared to the traditional process of leaching.

关键词： Leaching fraction saline soil evapotranspiration machine learning calibrated evapotranspiration artiﬁcial intelligence blaney criddle method

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Verification of Musical Education with Face Images Using Convolutional Neural Network and Vision Transformers 5

Verification of Musical Education with Face Images Using Con...

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5th International Conference on Neural Networks and Neurotechnologies, NeuroNT 2024

作者： Polyak, Mark D. Senichenkova, Yana O. Saint Petersburg State University of Aerospace Instrumentation Department of Computer Technologies and Software Engineering Saint-Petersburg Russia

ISBN: (纸本)9798350363739

This research investigates whether machine learning techniques can be used to study the relationship between physical appearance and personality or other characteristics. Specifically, it explores the hypothesis that a person's profession may influence their facial features. The study focuses on musicians of a symphony orchestras as a specific example. A dataset of photographs was collected, including both symphony orchestra musicians and non-musicians. Three neural networks were used to solve the problem of classifying the images into two categories (musicians vs. non-musicians): VGG-Face, CLIP-ViT, and Google ViT. These neural networks extracted numerical representations (embeddings) from the images. The embeddings were then used to train support vector machines, a type of machine learning algorithm. The study compares the amount of information captured by the embeddings from each of the three neural network models. © 2024 IEEE.

关键词： Embeddings

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