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23rd International Symposium on Knowledge and systems Sciences, KSS 2024

作者： Yuan, Erbiao Yang, Guangfei Zhou, Yuhe Liu, Lian Institute of Systems Engineering Dalian University of Technology Dalian116024 China Operation Software and Simulation Research Institute Dalian Naval Academy Dalian116021 China

ISBN: (纸本)9789819601776

Decision-making requires a combination of intuition, perception and deep thinking skills, which is crucial in personal work and life. Improving decision-making performance is quite beneficial for enhancing people’s competitiveness. This paper predicts the decision-making performance and further analyzes the impact of indoor environment on it. Unlike previous studies, which were mostly empirical, our paper discusses the decision-making performance from a data-driven perspective. We collect data using Chinese chess game and indoor environmental sensors as experimental tools, and propose a hybrid model SA–LSTM, which integrates self-attention (SA) mechanism and long-short term memory (LSTM) network. The results demonstrate that our model can accurately predict the decision-making performance. Compared with other five models, SA–LSTM has lower error and better goodness of fit. Specifically, the root mean square error and mean absolute error of our model are reduced by 11% and 14% compared with the second best performing model LSTM. In addition, SA–LSTM model shows greater robustness. Further interpretability analysis suggests the impact of environmental features on the prediction of decision-making performance, and the SA mechanism enables a deeper exploration of their relationship, thereby emphasizing the significance of environmental variables and enhancing prediction accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Mean square error

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Sentiment Analysis of Covid-19 Twitter Data using Deep Learning Algorithm

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Procedia Computer Science 2024年 235卷 3397-3407页

作者： B. Valarmathi N. Srinivasa Gupta V. Karthick T. Chellatamilan K. Santhi Dhanush Chalicheemala Department of Software and Systems Engineering School of Information Technology and Engineering Vellore Institute of Technology Vellore Tamil Nadu India. Department of Manufacturing School of Mechanical Engineering Vellore Institute of Technology Vellore Tamil Nadu India. Assistant Systems Engineer Tata Consultancy Services Chennai Department of Analytics School of Computer Science and Engineering Vellore Institute of Technology Vellore Tamil Nadu India. Department of Information Technology School of Information Technology and Engineering Vellore Institute of Technology Vellore Tamil Nadu India

A key method used in the study of Natural Language Processing (NLP) is sentiment analysis, or emotion analysis, plays a pivotal role in text analysis. Its primary function is to discern and categorize the underlying emotions within textual content, classifying them as positive, neutral, or negative sentiments. A variety of textual inputs can be used with this process, including entire documents, individual sentences, and more. Understanding the sentiments expressed by individuals is of paramount importance to organizations in today’s digital age. Clients and customers now have the means to express their thoughts and emotions with greater ease and immediacy than ever before. This wealth of sentiment data can offer valuable insights that organizations can leverage to enhance their products, services, and overall customer experience. The present study focuses on the sentiment analysis of Twitter data related to the COVID-19 pandemic, employing the Long Short-Term Memory (LSTM) algorithm. To improve the accuracy of sentiment analysis, a pre-processing procedure is introduced, which involves the use of the Neat Text module in Python to clean the tweets. In this research endeavor, a dataset comprising 1,79,107 COVID-19-related tweets is subjected to sentiment analysis using the proposed pre-processing module and LSTM. The results demonstrate an impressive accuracy rate of 96% in accurately determining the sentiments conveyed in these COVID-19 tweets. In contrast, the existing algorithm, based on Artificial Neural Network (ANN), achieved a significantly lower accuracy rate of 76%. This research not only showcases the effectiveness of LSTM and pre-processing in sentiment analysis but also highlights the significance of sentiment analysis in gaining valuable insights from vast amounts of textual data, especially when important events like the COVID-19 pandemic are involved.

关键词： COVID-19 Natural Language Processing Text Mining Long Short-Term Memory WordCloud Opinion mining Sentiment analysis Opinion extraction

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Neural-Network-Based Control for Discrete-Time Nonlinear systems with Input Saturation Under Stochastic Communication Protocol

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IEEE/CAA Journal of Automatica Sinica 2021年第4期8卷 766-778页

作者： Xueli Wang Derui Ding Hongli Dong Xian-Ming Zhang Department of Control Science and Engineering University of Shanghai for Science and TechnologyShanghai 200093China School of Software and Electrical Engineering Swinburne University of TechnologyMelbourne 3122VictoriaAustralia Institute of Complex Systems and Advanced Control Northeast Petroleum UniversityDaqing 163318China

In this paper,an adaptive dynamic programming(ADP)strategy is investigated for discrete-time nonlinear systems with unknown nonlinear dynamics subject to input *** save the communication resources between the controller and the actuators,stochastic communication protocols(SCPs)are adopted to schedule the control signal,and therefore the closed-loop system is essentially a protocol-induced switching system.A neural network(NN)-based identifier with a robust term is exploited for approximating the unknown nonlinear system,and a set of switch-based updating rules with an additional tunable parameter of NN weights are developed with the help of the gradient *** virtue of a novel Lyapunov function,a sufficient condition is proposed to achieve the stability of both system identification errors and the update dynamics of NN ***,a value iterative ADP algorithm in an offline way is proposed to solve the optimal control of protocol-induced switching systems with saturation constraints,and the convergence is profoundly discussed in light of mathematical ***,an actor-critic NN scheme is developed to approximate the control law and the proposed performance index function in the framework of ADP,and the stability of the closed-loop system is analyzed in view of the Lyapunov ***,the numerical simulation results are presented to demonstrate the effectiveness of the proposed control scheme.

关键词： Adaptive dynamic programming(ADP) constrained inputs neural network(NN) stochastic communication protocols(SCPs) suboptimal control

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iContinuum: An Emulation Toolkit for Intent-Based Computing Across the Edge-to-Cloud Continuum

iContinuum: An Emulation Toolkit for Intent-Based Computing ...

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IEEE International Conference on Cloud Computing, CLOUD

作者： Negin Akbari Adel N. Toosi John Grundy Hourieh Khalajzadeh Mohammad S. Aslanpour Shashikant Ilager Department of Software Systems and Cybersecurity Monash University Australia School of Info Technology Deakin University Australia Institute of Information Systems Engineering TU Wien Austria

ISBN: (数字)9798350368536

ISBN: (纸本)9798350368543

The Internet of Things (IoT) has led to a surge in smart devices, generating vast volumes of data. Cloud computing offers scalability but does not suffice for many real-time and privacy-sensitive IoT applications. This limitation has prompted a blend of both edge and cloud resources, creating the need for seamless integration, known as the “compute continuum“. Testing applications and resource management techniques within this continuum is vital but can be very complex. Simulation and emulation are preferred methods, with emulation providing more accurate representations of real-world environments. In this paper, we introduce iContinuum, a novel emulation toolkit facilitating an intent-based platform for edge-to-cloud testing and experimentation. Leveraging software-Defined Networking (SDN) and containerization, iContinuum enables experimentation and performance evaluation while aligning application requirements with actual performance. We present our detailed architecture, implementation, and evaluation of iContinuum, showcasing how our proposed toolkit bridges the gap between simulation and real-world deployment within compute continuum environments, and further demonstrate the effectiveness of Intent-Based Scheduling through a specific use case.

关键词： Cloud computing Scalability Emulation Real-time systems Internet of Things Resource management Surges

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SFedXL: Semi-Synchronous Federated Learning With Cross-Sharpness and Layer-Freezing

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IEEE Internet of Things Journal 2025年第12期12卷 18692-18707页

作者： Zhao, Mingxiong Zhao, Shihao Feng, Chenyuan Yang, Howard H. Niyato, Dusit Quek, Tony Q. S. Yunnan University National Pilot School of Software Kunming650500 China Engineering Research Center of Integration and Application of Digital Learning Technology Ministry of Education Beijing100039 China Engineering Research Center of Cyberspace Ministry of Education Kunming650504 China Yunnan University of Finance and Economics Yunnan Key Laboratory of Service Computing Kunming650221 China EURECOM Department of Communication Systems Biot06410 France University of Illinois Urbana-Champaign Institute Zhejiang University Zhejiang University Haining314400 China Nanyang Technological University School of Computer Science and Engineering Jurong West 639798 Singapore Singapore University of Technology and Design Tampines 487372 Singapore

Federated learning (FL) emerges as a potential solution for enabling multiple terminal devices to collaboratively accomplish computational tasks within an autonomous aerial vehicle (AAV) swarm. However, traditional FL approaches, predicated on synchronous data aggregation, are not feasible for a AAV swarm owing to the inherently variable and dynamic nature of their communication networks compared with terrestrial systems. Furthermore, the data procured by AAVs is often highly heterogeneous, attributable to disparities in deployment environments and device attributes. Considering the distinct flight paths and unique operational conditions encountered by different AAVs, a considerable amount of data remains unlabeled. To tackle the challenges associated with asynchronous operations and the prevalence of unlabeled data, we introduce a novel framework termed semi-synchronous FL with cross-sharpness and layer-freezing (SFedXL), tailored for a AAV swarm. In particular, we devise a cross-sharpness model training strategy aimed at optimizing the utilization of both labeled and unlabeled datasets. Additionally, we propose an innovative semi-synchronous model aggregation protocol, complemented by client-specific layer-freezing and client cluster scheduling, designed to expedite the training process. Our simulation results indicate that the proposed algorithm surpasses current FL methods in terms of object recognition accuracy and communication efficiency, albeit with a tradeoff of increased local computation latency. © 2014 IEEE.

关键词： Federated learning

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PD-Refiner: An Underlying Surface Inheritance Refiner with Adaptive Edge-Aware Supervision for Point Cloud Denoising 24

PD-Refiner: An Underlying Surface Inheritance Refiner with A...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Zhang, Chengwei Zhang, Xueyi Yue, Xianghu Lao, Mingrui Jiang, Tao Wang, Jiawei Zhang, Fubo Chen, Longyong National Key Laboratory of Microwave Imaging Aerospace Information Research Institute Chinese Academy of Sciences Beijing China Laboratory for Big Data and Decision National University of Defense Technology Hunan Changsha China Department of Electrical and Computer and Engineering National University of Singapore Singapore Singapore National Key Laboratory of Information Systems Engineering National University of Defense Technology Hunan Changsha China School of Software Engineering Tongji University Shanghai China

ISBN: (纸本)9798400706868

Point clouds from real-world scenarios inevitably contain complex noise, significantly impairing the accuracy of downstream tasks. To tackle this challenge, cascading encoder-decoder architecture has become a conventional technical route to iterative denoise. However, circularly feeding the output of denoiser as its input again involves the re-extraction of underlying surface, leading to unstable denoising process and over-smoothed geometric details. To address these issues, we propose a novel denoising paradigm dubbed PD-Refiner that employs a single encoder to model the underlying surface. Then, we leverage several lightweight hierarchical Underlying Surface Inheritance Refiners (USIRs) to inherit and strengthen it, thereby avoiding the re-extraction from the intermediate point cloud. Furthermore, we design adaptive edge-aware supervision to improve the edge awareness of the USIRs, allowing for the adjustment of the denoising preferences from global structure to local details. The results demonstrate that our method not only achieves state-of-the-art performance in terms of denoising stability and efficacy, but also enhances edge clarity and point cloud uniformity. © 2024 ACM.

关键词： edge-aware supervision

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The Dilemma of Privacy Protection in the Metaverse

SSRN

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SSRN 2024年

作者： Rahartomo, Argianto Merino, Leonel Ghafari, Mohammad Institute for Software and Systems Engineering Technische Univertität Clausthal Goslar Germany School of Design School of Engineering Pontificia Universidad Católica de Chile Santiago Chile

In a world where every click and tap leaves a digital footprint, the abundant sensitive data involved in the metaverse could compromise user privacy to a great extent. We contacted developers, consulted legal frameworks, and analyzed APIs’ documentation in the metaverse. We found that privacy is a major concern, but developer awareness and existing support are limited. Developers lack strategies to identify sensitive data that are exclusive to the metaverse. The API documentation contains guidelines for collecting sensitive information, but it omits instructions to identify and protect it. Legal frameworks include definitions that are subject to individual interpretation. These findings highlight the urgent need to build a transparent and common ground for privacy definitions, identify sensitive data, and implement usable protection measures. © 2024, The Authors. All rights reserved.

关键词： Sensitive data

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Contemporary software Modernization: Strategies, Driving Forces, and Research Opportunities

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ACM Transactions on software engineering and Methodology 2025年第5期34卷

作者： Assunção, Wesley K.G. Marchezan, Luciano Arkoh, Lawrence Egyed, Alexander Ramler, Rudolf Department of Computer Science North Carolina State University RaleighNC United States Institute of Software Systems Engineering Johannes Kepler University Linz Linz Austria Software Competence Center Hagenberg GmbH Hagenberg Austria

software modernization is a common activity in software engineering, since technologies advance, requirements change, and business models evolve. Differently from conventional software evolution (e.g., adding new features, enhancing performance, or adapting to new requirements), software modernization involves re-engineering entire legacy systems (e.g., changing the technology stack, migrating to a new architecture style, or programming paradigms). Given the pervasive nature of software today, modernizing legacy systems is paramount to provide customers with competitive and innovative products and services, while keeping companies profitable. Despite the prevalent discussion of software modernization in gray literature, and the many papers in the literature, there is no work presenting a "big picture" of contemporary software modernization, describing challenges, and providing a well-defined research agenda. The goal of this work is to describe the state of the art in software modernization in the past 10 years. We collect the state of the art by performing a rapid review (searching five digital libraries), identifying potential 3,460 studies, leading to a final set of 126. We analyzed these studies to understand which strategies are employed, the driving forces that lead organizations to modernize their systems, and the challenges that need to be addressed. The results show that studies in the last 10 years have explored eight strategies for modernizing legacy systems, namely cloudification, architecture redesign, moving to a new programming language, targeting reuse optimization, software modernization for new hardware integration, practices to leverage automation, database modernization, and digital transformation. Modernization is triggered by 14 driving forces, with the most common ones being reducing operational costs, improving performance and scalability, and reducing complexity. In © 2025 Copyright held by the owner/author(s). Publication rights licensed to

关键词： Legacy systems

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Neurosymbolic Architectural Reasoning: Towards Formal Analysis through Neural software Architecture Inference

Neurosymbolic Architectural Reasoning: Towards Formal Analys...

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Neuro-Symbolic software engineering (NSE), IEEE/ACM International Workshop on

作者： Steffen Herbold Christoph Knieke Andreas Rausch Christian Schindler Faculty of Computer Science and Mathematics Universität Passau Passau Germany Institute for Software and Systems Engineering Technische Universität Clausthal Germany

ISBN: (数字)9798331514600

ISBN: (纸本)9798331514617

Formal analysis to ensure adherence of software to defined architectural constraints is not yet broadly used within software development, due to the effort involved in defining formal architecture models. Within this paper, we outline neural architecture inference to solve the problem of having a formal architecture definition for subsequent symbolic reasoning over these architectures, enabling neurosymbolic architectural reasoning. We discuss how this approach works in general and outline a research agenda based on six general research question that need to be addressed, to achieve this vision.

关键词： Codes software architecture Large language models Conferences Neural networks Computer architecture Cognition software Distance measurement software development management

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AI LipReader-Transcribing Speech from Lip Movements

AI LipReader-Transcribing Speech from Lip Movements

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Emerging Smart Computing and Informatics (ESCI), Conference on

作者： D Ajitha Disha Dutta Falguni Saha Parus Giri Rohan Kant Department of Software Systems School of Computer Science and Engineering Vellore Institute of Technology Vellore Vellore India Department of Database Systems School of Computer Science and Engineering Vellore Institute of Technology Vellore India Department of Information Security School of Computer Science and Engineering Vellore Institute of Technology Vellore Vellore India

Lip Reading AI is a discipline that is rapidly changing and has numerous applications in security, accessibility and human-computer interaction. This paper proposes a model which combines Convolutional Neural Networks (CNNs) to capture spatial capabilities, Long Short-Term Memory (LSTM) networks to examine temporal dependencies, and an adaptive interest mechanism. Meticulous preprocessing of the MIRACL VC-l dataset addressing challenges including one of a kind lip moves and occlusions accompanied with the aid of transitioning this study effortlessly to LRS2 dataset to complement lexemic versatility is one of its key function. The effects verify its robustness throughout unique datasets with superior overall performance towards cutting-edge techniques. Ablation checks suggest the crucial significance of every element in phrases of improving lip analyzing accuracy. Our proposed model version additionally suggests flexibility in restricted and naturalistic language situations.

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