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2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Ramamoorthy, Saravanan Kandaneri Sindhu, L. Valarmathi, K. Gobinath, C. Umaeswari, P. College of Non-Medicine University: Texila American University Guyana Sona College of Technology Department of Information Technology Salem India S. A. Engineering College Department of Information Technology Chennai India Reva University School of Computing and Information Technology Bengaluru India R.M.K. Engineering College Department of Computer Science and Business Systems Kavaraipettai India

ISBN: (纸本)9798350360660

From smart homes to industrial automation, the efficiency and connectivity of many applications have been greatly improved by the fast expansion of the Internet of Things (IoT) and Cyber-Physical Systems (CPS). Sophisticated cyberattacks are becoming more common in this networked ecosystem, thus strong security measures are needed. In order to protect CPS and IoT settings, this study presents a new intrusion detection method that makes use of Digital Twin technology. To build an adaptable and dynamic Intrusion Detection System (IDS), our suggested architecture combines Digital Twins with state-of-the-art machine learning methods. In order to accurately detect and categorize any threats, the IDS constantly learns from data in both the past and the present. When tested on benchmark datasets and in real-world Internet of Things (IoT) settings, the framework outperforms conventional intrusion detection system (IDS) solutions in terms of detection accuracy and false alarm rate reduction. To protect IoT and CPS infrastructures from ever-changing cyber threats, this scalable and resilient security solution is offered by this IDS that is powered by digital twins. We present ICNN-FCID, an Integrated Convolutional Neural Network for Fog computing Environment, a hybrid deep learning intrusion detection model for fog computing environments. © 2024 IEEE.

关键词： Network intrusion

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Readability-guided Idiom-aware Sentence Simplification (RISS) for Chinese 23

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23rd Chinese National Conference on Computational Linguistics, CCL 2024

作者： Zhang, Jingshen Chen, Xinglu Qiu, Xinying Wang, Zhimin Feng, Wenhe School of Information Science and Technology Guangzhou China Faculty of Chinese Language and Culture Guangzhou China Laboratory of Language Engineering and Computing Guangdong University of Foreign Studies Guangzhou China

ISBN: (纸本)9780000000002

Chinese sentence simplification faces challenges due to the lack of large-scale labeled parallel corpora and the prevalence of idioms. To address these challenges, we propose Readability-guided Idiom-aware Sentence Simplification (RISS), a novel framework that combines data augmentation techniques. RISS introduces two key components: (1) Readability-guided Paraphrase Selection (RPS), a method for mining high-quality sentence pairs, and (2) Idiom-aware Simplification (IAS), a model that enhances the comprehension and simplification of idiomatic expressions. By integrating RPS and IAS using multi-stage and multi-task learning strategies, RISS outperforms previous state-of-the-art methods on two Chinese sentence simplification datasets. Furthermore, RISS achieves additional improvements when fine-tuned on a small labeled dataset. Our approach demonstrates the potential for more effective and accessible Chinese text simplification. © 2024 China National Conference on Computational Linguistics.

关键词： Computational linguistics

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HxL3: Optimized Delivery Architecture for HTTP Low-Latency Live Streaming

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IEEE Transactions on Multimedia 2023年 25卷 2585-2600页

作者： Tashtarian, Farzad Bentaleb, Abdelhak Erfanian, Alireza Hellwagner, Hermann Timmerer, Christian Zimmermann, Roger Alpen-Adria-Universität Klagenfurt Christian Doppler Laboratory Athena Institute of Information Technology Klagenfurt9020 Austria Department of Computer Science and School of Computing Singapore117417 Singapore Bitmovin Klagenfurt9020 Austria

While most of the HTTP adaptive streaming (HAS) traffic continues to be video-on-demand (VoD), more users have started generating and delivering live streams with high quality through popular online streaming platforms. Typically, the video contents are generated by streamers and being watched by large audiences which are geographically distributed far away from the streamers' locations. The locations of streamers and audiences create a significant challenge in delivering HAS-based live streams with low latency and high quality. Any problem in the delivery paths will result in a reduced viewer experience. In this paper, we propose HxL3, a novel architecture for low-latency live streaming. HxL3 is agnostic to the protocol and codecs that can work equally with existing HAS-based approaches. By holding the minimum number of live media segments through efficient caching and prefetching policies at the edge, improved transmissions, as well as transcoding capabilities, HxL3 is able to achieve high viewer experiences across the Internet by alleviating rebuffering and substantially reducing initial startup delay and live stream latency. HxL3 can be easily deployed and used. Its performance has been evaluated using real live stream sources and entities that are distributed worldwide. Experimental results show the superiority of the proposed architecture and give good insights into how low latency live streaming is working. © 1999-2012 IEEE.

关键词： HTTP

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LDREG: LOCAL DIMENSIONALITY REGULARIZED SELF-SUPERVISED LEARNING 12

LDREG: LOCAL DIMENSIONALITY REGULARIZED SELF-SUPERVISED LEAR...

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12th International Conference on Learning Representations, ICLR 2024

作者： Huang, Hanxun Campello, Ricardo J.G.B. Erfani, Sarah Ma, Xingjun Houle, Michael E. Bailey, James School of Computing and Information Systems The University of Melbourne Australia Department of Mathematics and Computer Science University of Southern Denmark Denmark School of Computer Science Fudan University China Department of Computer Science New Jersey Institute of Technology United States

Representations learned via self-supervised learning (SSL) can be susceptible to dimensional collapse, where the learned representation subspace is of extremely low dimensionality and thus fails to represent the full data distribution and modalities. Dimensional collapse - also known as the "underfilling" phenomenon -is one of the major causes of degraded performance on downstream tasks. Previous work has investigated the dimensional collapse problem of SSL at a global level. In this paper, we demonstrate that representations can span over high dimensional space globally, but collapse locally. To address this, we propose a method called local dimensionality regularization (LDReg). Our formulation is based on the derivation of the Fisher-Rao metric to compare and optimize local distance distributions at an asymptotically small radius for each data point. By increasing the local intrinsic dimensionality, we demonstrate through a range of experiments that LDReg improves the representation quality of SSL. The results also show that LDReg can regularize dimensionality at both local and global levels. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

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Identification of Cyberbullying and Finding Target User's Intention on Public Forums 6

Identification of Cyberbullying and Finding Target User's In...

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6th International Conference on Contemporary computing and Informatics, IC3I 2023

作者： Krishnasamy, Lalitha Kiruthika, M. Manjunatha Swamy, C. Vigneshwaran, T. Sharon Roji Priya, C. Vijay Anand, D. School of Engineering and Technology Department of CSE Christ University Bengaluru India School of Computing Department of Computational Intelligence SRM Institute of Science and Technology Tamilnadu Kattankulathur India Kongu Engineering College Department of Information Technology Erode India

ISBN: (纸本)9798350304480

Numerous cybercriminals are active in the online realm, carrying out cyber-crimes according to predetermined and preplanned agendas. Cyberbullying, which was formerly limited to physical limits, has now expanded online as a result of technology advancements. One type of cyberbullying is denigration or insult. The cyberbullying cases are in exponential rise in social media as per the reports of Computer Emergency Team by Sri Lanka. Insulting words are changeable in dynamic and the same terminology may have numerous meanings depending on the context. Bullying cannot be defined just because a statement comprises such a term. As a result, when classifying comments, standard keyword detecting approaches are insufficient. Other languages also may have dealt with this issue by utilizing lexical databases like WordNet, which might give synonyms as well as homonyms for words. Because no adequate lexical database mainly for the English language has been built, recognizing a word like bullying is difficult. As a result, employed rules to solve the problem. Facebook comments containing profanity were gathered, outliers were eliminated, and the remaining messages were pre-processed. Five feature extraction rules were employed to assess insult in the text. Following that, used the Support Vector Machine (SVM) technique. Using an F1-score of 85%, the findings demonstrate that when compared to existing works, SVM performs better. The focus on English language cyberbully identification, which has never been addressed earlier, distinguishes this study. © 2023 IEEE.

关键词： bag of words cyberbullying feature extraction sentiment analysis SVM

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Creative Research on Automatic Detection of Flying Objects 9

Creative Research on Automatic Detection of Flying Objects

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9th International Conference on Intelligent computing and Signal Processing, ICSP 2024

作者： Liu, Lu Lu, Yiyang Xu, Binrui Peng, Heyang Ren, Yuhan College of International Education SiChuan Normal University Chengdu China Faculty of Computing Harbin Institute of Technology Harbin China School of Information Science and Engineering Harbin Institute of Technology Harbin China Law School SiChuan Normal University Chengdu China

ISBN: (纸本)9798350376548

With the increasing development of China's modernisation and the growing demand for national defence security, it is very important to detect unidentified flying objects in the air and eliminate potential threats to ensure national security. In order to solve the dual problems of high cost of manual inspection and poor performance of the detection model with low accuracy, this paper proposes a way to automate the detection of flying objects by using the YOLOv5 target detection model, improving the loss function CIOU-Loss to EIOU-Loss by default to speed up the convergence speed of the model, and at the same time adopting a variety of novel data enhancement methods to improve the accuracy. This paper also compares the performance of the YOLOv5 model before and after the improvement in the training and flying objects detection process to demonstrate the effectiveness of the improvement. © 2024 IEEE.

关键词： Object tracking

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A Scalable and Lightweight Authentication Architecture for the Internet of Things (IoT) in Smart Home Applications 2

A Scalable and Lightweight Authentication Architecture for t...

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2nd Zimbabwe Conference on information Communication and technology, ZCICT 2023

作者： Gamundani, Attlee M. Phillips, Amelia Muyingi, Hippolyte N. Namibia University of Science and Technology Faculty of Computing & Informatics Cyber Security Department Windhoek Namibia University of the Cumberlands School of Computer and Information Sciences WilliamsburgKY United States Namibia University of Science and Technology Faculty of Computing & Informatics Windhoek Namibia

ISBN: (纸本)9798350318128

In the evolving landscape of smart home applications, ensuring robust and efficient authentication within the Internet of Things (IoT) environments is paramount. This paper presents a scalable, lightweight authentication architecture for IoT-driven smart homes. The proposed architecture, drawing inspiration from cloud computing mechanisms, introduces a trusted, smart home agent, integrating the Data-information-Knowledge-Wisdom (DIKW) model to facilitate efficient and secure data exchange within smart homes. Our solution addresses the challenges of minimal computation and processing overheads, essential in resource-constrained IoT devices, and focuses on device-level authentication to reduce computational costs. A comparative analysis with existing models demonstrates the superiority of our architecture in terms of computational efficiency, making it an ideal candidate for scalable deployments. The architecture successfully navigates the complexities of ensuring secure communication among autonomous IoT devices, balancing the dual needs of computational efficiency and robust security, thereby promising resilience against common security threats in the smart home ecosystem. © 2023 IEEE.

关键词： Authentication

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Adaptive iterated local search algorithm for dynamic patient admission scheduling problems

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Soft computing 2025年第7期29卷 3241-3251页

作者： Turky, Ayad Sabar, Nasser R. Song, Andy Hussain, Abir Liatsis, Panos Department of Computer Science College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates Department of Computer Science and Information Technology La Trobe University MelbourneVIC3086 Australia School of Computing Technologies RMIT University Melbourne Australia Department of Electrical Engineering University of Sharjah Sharjah United Arab Emirates Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi United Arab Emirates

Healthcare resource management is essential for ensuring the quality of patient care. However, it can be a complex and costly task. This work addresses the patient admission scheduling (PAS) problem, a complex aspect of healthcare resource management. PAS aims to allocate patients to hospital beds within a planning horizon, subject to a variety of healthcare constraints. The goal is to maximize management efficiency and patient comfort to improve medical treatment. In this work, we consider a practical variant of PAS known as dynamic PAS (DPAS). DPAS considers several factors and constraints, such as the daily registration of new patients, urgent patients, uncertainties in stay lengths, operating theatre resources, and the surgery scheduling process. An effective and efficient adaptive iterated local search (AILS) algorithm is proposed to solve DPAS. To enable the search to explore the search space efficiently, the proposed AILS adaptively integrates a number of components. Two adaptive perturbation strategies are devised to locate unexplored areas in the search space. To exploit the newly discovered areas effectively, we propose an adaptive local search mechanism as an intensification strategy to find a high-quality solution. The proposed AILS algorithm is compared to benchmark problems used by existing algorithms. The experimental results demonstrate the effectiveness and efficiency of the proposed approach. Specifically, out of 30 tested instances, AILS obtains 17 of the best-known results using less computational time. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Resource allocation

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A multi-objective grey wolf optimizer for energy planning problem in smart home using renewable energy systems

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Sustainable Operations and Computers 2024年第1期5卷 88-101页

作者： Makhadmeh, Sharif Naser Al-Betar, Mohammed Azmi Al-Obeidat, Feras Alomari, Osama Ahmad Abasi, Ammar Kamal Tubishat, Mohammad Elgamal, Zenab Alomoush, Waleed Department of Data Science and Artificial Intelligence University of Petra Amman11196 Jordan College of Engineering and Information Technology Ajman University Ajman United Arab Emirates Department of Information Technology Al-Huson University College Al-Balqa Applied University P.O. Box 50 Al-Huson Irbid Jordan Department of Computer Science and Information Technology College of Engineering Abu Dhabi University Abu Dhabi United Arab Emirates College of Technological Innovation Zayed University Abu Dhabi United Arab Emirates School of Computing Skyline University College Sharjah United Arab Emirates School of Information Technology Skyline University College Sharjah P.O. Box 1797 United Arab Emirates Abu Dhabi United Arab Emirates

This paper presents the energy planning problem (EPP) as an optimization problem to find the optimal schedules to minimize energy consumption costs and demand and enhance users’ comfort levels. The grey wolf optimizer (GWO), One of the most powerful optimization methods, is adjusted and adapted to address EPP optimally and achieve its objectives efficiently. The GWO is adapted due to its high performance in addressing NP-complex hard problems like the EPP, where it contains efficient and dynamic parameters that enhance its exploration and exploitation capabilities, particularly for large search spaces. In addition, new energy and real-world resources based on solar renewable energy systems (RESs) are combined with the proposed GWO to enhance its performance and ensure the optimisation of EPP objectives. Furthermore, EPP is presented as a multi-objective planning problem to optimize all objectives simultaneously. To efficiently investigate the proposed method performance, the results obtained by the GWO with the RESs are compared in three stages: comparison with original methods without RESs, comparison with methods using RESs, and comparison with state-of-the-art. The obtained results proved the robust performance of the proposed method in handling EPP and optimizing its objectives. © 2024 The Author(s)

关键词： Multiobjective optimization

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Performance evaluation of different deep learning models used for the purpose of healthy and diseased leaves classification of Cherimoya (Annona Cherimola) plant

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Neural computing and Applications 2025年第6期37卷 4531-4544页

作者： Chouhan, Siddharth Singh Singh, Uday Pratap Jain, Sanjeev School of Computing Science and Engineering VIT Bhopal University Bhopal-Indore Highway Kothrikalan Madhya Pradesh Sehore466114 India Department of Mathematics Central University of Jammu Jammu and Kashmir Samba181143 India Department of Computer Science and Information Technology Central University of Jammu Jammu and Kashmir Samba181143 India

Controlling plant leaves disease helps in upholding their health. This augments the overall strength of the plant productivity both in terms of higher quality and quantity. Recently, with the expansion of high-end computing devices, Artificial Intelligence (AI) techniques have allied in almost all applications. In agriculture, AI has been used in crop surveillance, soil health monitoring, nutrient deficiency estimation, flower and fruits quality assessment, weed estimation, and crop health diagnosis among all. Therefore, in this work, five deep learning approaches, namely EfficientNet, MobileNetV2, BiT, EANet, and Swin Transformers, have been used for the classification of healthy and diseased leaves of Cherimoya plant. EANet model with precision = 0.9929%, recall = 0.9536%, F1-score = 0.9846%, training accuracy = 0.9775%, and testing accuracy = 0.9689% achieved superior performance among all. We believe that the proposed work can be very useful in providing an accurate and timely plant disease diagnosing system. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Fruits

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