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2024 IEEE International Conference on systems, Man, and Cybernetics, SMC 2024

作者： Zhao, Shuo Gu, Qiliang Zhang, Jianqiang Song, Gongpeng Lu, Qin Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Shandong Branch of China Mobile Communication Group Design Institute Co. Jinan China

ISBN: (纸本)9781665410205

Chinese short text similarity computation stands as a pivotal task within natural language processing, garnering significant attention. However, existing models grapple with limitations in handling intricate semantic relationships, such as the challenge of discerning subtle semantic nuances in text, inadequacies in effectively integrating diverse levels of semantic information, and the struggle to capture polysemous meanings accurately. In addressing these issues, this paper introduces an innovative Chinese short text similarity computation model, SMGC-SBERT. This model addresses shortcomings of the existing models by employing a multi-module fusion strategy, thereby enabling a more precise measurement of semantic similarity between texts. Primarily, the model incorporates SAT embedding to acquire phrase-level semantic information and leverages the MS-BERT model to encode text, improving the model's comprehension of textual polysemy and obtaining richer semantic representation. Then, the fusion of module features, including multi-branch convolutional networks and mix pooling, enables the extraction of textual features from varied levels, bolstering the model's representational capacity. Additionally, to further reduce overfitting risks while improving accuracy and other performance, a multi-layer feature adjustment network is utilized for short text similarity calculation. The final resultant experimental findings showcase the superiority of the SMGC-SBERT model over other neural network models, demonstrating significant advancements across both Chinese-SNLI and CCKS2018_Task3 Chinese short text datasets. © 2024 IEEE.

关键词： Semantics

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A Metamodel for a DSL to Design Decision Support systems in Smart Tourism

A Metamodel for a DSL to Design Decision Support Systems in ...

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Decision Aid Sciences and Application (DASA), International Conference on

作者： Charaf Ouaddi Lamya Benaddi Adnane Souha Lahbib Naimi El mahi Bouziane Abdeslam Jakimi Department of Computer Sciences Software Engineering and Information Systems Engineering Team Faculty of Science and Technology Errachidia Moulay Ismail University Morocco

ISBN: (数字)9798350369106

ISBN: (纸本)9798350369113

The development of Decision Support systems (DSS) for several companies operating in sectors such as tourism, healthcare, or others, presents significant challenges due to the nature of their multi-component architecture. A typical DSS consists of a data component, a model component incorporating machine learning and deep learning techniques, and a user interface component, each one requires diverse technologies. This complexity increases development time and effort, hindering the efficient design of DSS tailored to specific domains like tourism, where real-time decision-making is essential. One promising approach to overcome these challenges is the use of Domain-Specific Languages (DSLs). DSLs allow developers to model and generate code more efficiently by providing high-level abstractions suited to the domain. By leveraging DSLs, it is possible to simplify the design process and automate the generation of code or parts of the DSS, thereby reducing the overall complexity and time required for development. This paper proposes a metamodel for designing DSS, specifically for the Smart Tourism sector. The metamodel serves as a foundation for the creation of DSL. The goal is to provide a solution to facilitate the development of DSS for the demands of tourism. The paper outlines the key concepts, approach, and results, presenting the first element of the proposed DSL which is the metamodel capturing the essential components and relationships for DSS in Smart Tourism.

关键词： Decision support systems Codes Source coding Medical services User interfaces Syntactics Real-time systems Complexity theory DSL Software engineering

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RAFA-Net: Region Attention Network for Food Items and Agricultural Stress Recognition

IEEE Transactions on AgriFood Electronics

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IEEE Transactions on AgriFood Electronics 2025年第1期3卷 121-133页

作者： Bera, Asish Krejcar, Ondrej Bhattacharjee, Debotosh Birla Institute of Technology and Science Department of Computer Science and Information Systems Pilani333031 India University of Hradec Kralove Center for Basic and Applied Science Faculty of Informatics and Management Hradec Kralove50003 Czech Republic Skoda Auto University Mlada Boleslav29301 Czech Republic Universiti Teknologi Malaysia Malaysia Japan International Institute of Technology Kuala Lumpur81310 Malaysia Jadavpur University Department of Computer Science and Engineering Kolkata700032 India

Deep convolutional neural networks (CNNs) have facilitated remarkable success in recognizing various food items and agricultural stress. A decent performance boost has been witnessed in solving the agro-food challenges by mining and analyzing region-based partial feature descriptors. Also, computationally expensive ensemble learning schemes fusing multiple CNNs have been studied in earlier works. This work proposes a region attention scheme for modeling long-range dependencies by building a correlation among different regions within an input image. The attention method enhances feature representation by learning the usefulness of context information from complementary regions. Spatial pyramidal pooling and average pooling pairs aggregate partial descriptors into a holistic representation. Both pooling methods establish spatial and channelwise relationships without incurring extra parameters. A context gating scheme is applied to refine the descriptiveness of weighted attentional features, which is relevant for classification. The proposed region attention network for food items and agricultural stress recognition method, dubbed RAFA-Net, has been experimented on three public food datasets, and has achieved state-of-the-art performances with distinct margins. The highest top-1 accuracy of RAFA-Net is 91.69%, 91.56%, and 96.97% on the UECFood-100, UECFood-256, and MAFood-121 datasets, respectively. In addition, better accuracies have been achieved on two benchmark agricultural stress datasets. The best top-1 accuracies on the Insect Pest (IP-102) and PlantDoc-27 plant disease datasets are 92.36%, and 85.54%, respectively;implying RAFA-Net's generalization capability. © 2023 IEEE.

关键词： Plant diseases

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Brain-Conditional Multimodal Synthesis: A Survey and Taxonomy

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第5期6卷 1080-1099页

作者： Mai, Weijian Zhang, Jian Fang, Pengfei Zhang, Zhijun South China University of Technology School of Automation Science and Engineering Guangzhou510640 China Southeast University School of Computer Science and Engineering Nanjing210096 China South China University of Technology Key Library of Autonomous Systems and Network Control Ministry of Education The School of Automation Science and Engineering Guangzhou510640 China Institute for Super Robotics Huangpu Guangzhou510555 China Nanchang University Jiangxi Thousand Talents Plan Nanchang330031 China Jishou University College of Computer Science and Engineering Jishou416000 China Guangdong Artificial Intelligence and Digital Economy Laboratory Pazhou Lab Guangzhou510335 China Shaanxi University of Technology Shaanxi Provincial Key Laboratory of Industrial Automation School of Mechanical Engineering Hanzhong723001 China Changsha Normal University School of Information Science and Engineering Changsha410100 China Guangdong University of Petrochemical Technology School of Automation Science and Engineering Institute of Artificial Intelligence and Automation Maoming525000 China

In the era of artificial intelligence generated content (AIGC), conditional multimodal synthesis technologies (e.g., text-to-image) are dynamically reshaping the natural content. Brain signals, serving as potential reflections of how the brain interprets external information, exhibit a distinctive one-to-many correspondence with various external modalities. This correspondence makes brain signals emerge as a promising guiding condition for multimodal synthesis (e.g., image, text, and audio), which is crucial for developing practical brain–computer interface systems and unraveling complex mechanisms underlying human perception. This survey comprehensively examines the emerging field of brain-conditional multimodal synthesis, termed AIGC-brain, to delineate the current landscape and future directions. To begin, related neuroimaging datasets and generative models are introduced as the foundation of AIGC-brain decoding and analysis. Next, we present a comprehensive taxonomy according to AIGC-brain methodologies, followed by task-specific representative work and implementation details to facilitate in-depth comparison and analysis. Quality assessments are then introduced for both qualitative and quantitative evaluation. Finally, this survey explores insights gained, outlining current challenges and prospects of AIGC-brain. As a pioneering survey, this article paves the way for future advances in AIGC-brain research. © 2020 IEEE.

关键词： Taxonomies

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Embedded Acoustic Noise Cancellation (ANC) Performances Analysis for Voice Communications using Linear Adaptive Approaches 23

Embedded Acoustic Noise Cancellation (ANC) Performances Anal...

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6th International Conference on Networking, Intelligent systems and Security, NISS 2023

作者： Wahbi, Azeddine Ouchatti, Abderrahmane Elghayyaty, Mohamed El Habti El Idrissi, Anas Roukhe, Ahmed Bensassi, Bahloul Hlou, Laamari Lab. of Elec. and Industrial Engineering Information Processing Computer Sciences and Logistic Faculty of Sciences Ain Chock Casablanca Hassan Ii University Maarif Casablanca Morocco Laboratory of Electronic Systems Information Processing and Energetics Faculty of Sciences Ibn Tofail University Kenitra Morocco Laboratory of Atomic Mechanical Photonics and Energy Faculty of Science University Moulay Ismail Meknes Morocco

ISBN: (纸本)9798400700194

ANC systems have emerged as the most widely adopted technologies in voice communications. This paper purpose a comparison between the adaptive approaches in time and frequency domain, which is used to model, design and implement these systems. All designs are developed by creating new embedded blocks using Simulink environment. Furthermore, the performance of the proposed embedded designs based adaptive algorithm is analyzed and evaluated on the basis of Signal to Noise Ratio (SNR) and computational complexity improvement, where these designs are applied on ANC to compensate linear distortion in the noise path. Moreover, the produced results showed that using embedded blocks based adaptive algorithm in frequency domain gives the best result than the rest of the other embedded designs. © 2023 ACM.

关键词： Adaptive algorithms

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Coverless Image Steganography System Based on Maze Game Generation

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Intelligent Automation & Soft Computing 2023年第11期38卷 125-138页

作者： Al Hussien Seddik Saad Mohammed S.Reda GamalM.Behery Ahmed A.El-harby Mohammed Baz Mohamed Abouhawwash Ahmed Ismail Ebada Department of computer science Faculty of ScienceMinia UniversityShalabyMiniaEgypt Department of computer science Faculty of Computers and Artificial IntelligenceDamietta UniversityNew DamiettaDamiettaEgypt Department of Computer Engineering College of Computer and Information TechnologyTaif UniversityP.O.Box 11099Taif21994Saudi Arabia Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)College of EngineeringMichigan State UniversityEast LansingMI48824USA Department of Information Systems Faculty of Computers and Artificial IntelligenceDamietta UniversityNew DamiettaDamiettaEgypt

The trend of digital information transformation has become a topic of *** data are threatening;thus,protecting such data from attackers is considered an essential ***,a new methodology for data concealing has been suggested by researchers called coverless *** steganography can be accomplished either by building an image database to match its image subblocks with the secret message to obtain the stego image or by generating an *** paper proposes a coverless image steganography system based on pure image generation using secret message bits with a capacity higher than the other traditional *** system uses the secret message to generate the stego image in the form of one of the Intelligence Quotient(IQ)games,the ***,a full grid is generated with several specific rows and columns determined from the number of bits of the secret ***,these bits are fed to the full grid to form the maze game stego ***,the generated maze game stego image is sent to the *** experimental results,using the Bit Error Rate(BER),were conducted,and confirmed the strength of this system represented by a high capacity,perfect performance,robustness,and stronger hiding system compared with existing coverless steganography systems.

关键词： Coverless data hiding digital image steganography intelligence quotient games maze game

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Transforming Cybersecurity in the Digital Era: The Power of AI 2

Transforming Cybersecurity in the Digital Era: The Power of ...

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2nd International Conference on Cyber Resilience, ICCR 2024

作者： Mahfuri, Mahmoud Ghwanmeh, Sameh Almajed, Rsha Alhasan, Waseem Salahat, Mohammed Lee, Jin Hie Ghazal, Taher M. International University For Science And Technology Computer And Information Technology Department Damascus Syria American University In The Emirates Department Of Computer Scienc Dubai United Arab Emirates School Of Computer Science And Informatics De Montfort University Dubai Dubai United Arab Emirates College Of Engineering And Technology University Of Science And Technology Of Fujairah Fujairah United Arab Emirates Miracle Clinic Seoul Korea Republic of Khalifa University Centre For Cyber Physical Systems Computer Science Department United Arab Emirates Center For Cyber Security Faculty Of Information Science And Technology Selangor Bangi43600 Malaysia

ISBN: (纸本)9798350394962

In the midst of the digital revolution of the 21st century, cybersecurity has come to be a primary social situation, requiring revolutionary and Innovative solution. To find the proper answer for this pressing demand, AI (AI) has developed as an innovative catalyst that has fundamentally transformed the cybersecurity landscape. The power of AI is exemplified by its ability to process and interpret large and heterogeneous cybersecurity data sets optimizing critical functions such as threat detection, asset prioritization, and vulnerability management beyond human capabilities swiftly and accurately. This transformation is redefining our cybersecurity approach. This paper presents a full exploration of AI's extreme influence on cybersecurity, delving deep into how AI tools not only enhance but often surpass human-mediated processes. By unraveling the intricacies of integrating AI into the realm of cybersecurity, we vividly illustrate AI's potential to anticipate, detect, and proactively mitigate cyber threats, empowering organizations to bolster their digital security. However, it's vital to recognize the essential difficulties of AI. We underline the critical necessity for non-stop human administration and involvement to confirm that cybersecurity processes are in proportion and powerful. Additionally, we mention the probable ethical concerns and underscore the importance of strong authority structures to accelerate accountable and transparent AI usage in cybersecurity. This paper elucidates how AI is revolutionizing cybersecurity techniques, contributing to a more secure and greater secure digital future. It additionally lays the base for current investigation and discourse on the role of AI in cybersecurity - a conversation of growing importance in our unexpectedly advancing digital age. © 2024 IEEE.

关键词： Cybersecurity

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Simulated annealing for resource and power allocation in 5G and B5G D2D communications 6

Simulated annealing for resource and power allocation in 5G ...

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6th International Conference on Pattern Analysis and Intelligent systems, PAIS 2024

作者： Benbraika, Mohamed Kamel Bourekkache, Samir Kraa, Okba Himeur, Yassine Telli, Khaled Ouamane, Abdelmalik University of El-Oued Computer Science Department El-Oued Algeria Mohamed Khider University Smart Computer Science Laboratory Biskra Algeria Mohamed Khider University Energy Systems Modeling Laboratory Biskra Algeria University of Dubai College of Engineering And Information Technology United Arab Emirates Mohamed Khider University Laboratory of LI3C Biskra Algeria

ISBN: (纸本)9798350350265

Device-to-device connection is a captivating new approach that holds great potential for the next generation of mobile networks, among others 5G and beyond. One of the reasons for this is that it has the capacity to boost system throughput, reduce the burden on the network core, and enhance spectral efficiency. Therefore, in order to make the most of these advantages, it is of the utmost importance to enhance the allocation of resources and power in order to reduce the amount of interference caused by co-channels. This paper proposes utilizing a based simulated annealing (SAA) technique to enhance combined radio spectrum and energy allocation. The SAA is made up of a phase that involves heating, which is then followed by slow and steady cooling. This is a method that is very often used in the field of metallurgy. Doing so results in an ordered and therefore stronger metal with more flexibility and less energy. The simulation outcomes show that the proposed approach surpasses both the random solution and Genetic approach (GA) in terms of time to stabilize to an optimal solution, throughput, and obtaining a feasible solution. © 2024 IEEE.

关键词： Genetic algorithms

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Swallowing Assessment using High-Resolution Cervical Auscultations and Transformer-based Neural Networks 46

Swallowing Assessment using High-Resolution Cervical Auscult...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Anwar, Ayman Khalifa, Yassin Lucatorto, Erin Coyle, James L. Sejdic, Ervin University of Toronto Faculty of Applied Science & Engineering Department of Electrical and Computer Engineering TorontoON Canada University of Pittsburgh Center for Research Computing and Information Technology Analytics PittsburghPA United States Cairo University Faculty of Engineering Systems and Biomedical Engineering Department Giza Egypt University of Pittsburgh School of Health and Rehabilitation Sciences Department of Communication Science and Disorders PittsburghPA United States University of Toronto North York General Hospital Faculty of Applied Science & Engineering Department of Electrical and Computer Engineering TorontoON Canada

ISBN: (纸本)9798350371499

Swallowing assessment is a crucial task to reveal swallowing abnormalities. There are multiple modalities to analyze swallowing kinematics, such as videofluoroscopic swallow studies (VFSS), which is the gold standard method, and high-resolution cervical auscultation (HRCA), which is a noninvasive technique that uses a triaxial accelerometer attached to the patient's neck. Deep learning models play an essential role in data driven analysis of swallowing landmarks using VFSS and/or HRCA as input data. Most of these models utilize convolutional and recurrent neural networks. Here, we investigate the ability of transformers to analyze swallowing kinematics;specifically upper esophageal sphincter opening and laryngeal vestibule closure using HRCA signals. We tested the model using an independent test dataset to assess the generalizability of the proposed network. The proposed network achieved an average detection accuracy higher than 90% and 85% for both segmentation tasks, which outperform the hybrid neural networks from the literature, and the model obtained high-performance measures for the independent dataset, showing the transformers' ability to generalize on unseen data. © 2024 IEEE.

关键词： Recurrent neural networks

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A Decision Support System for Enhancing Transportation Services Using Aspect-Based Sentiment Analysis

A Decision Support System for Enhancing Transportation Servi...

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Decision Aid Sciences and Application (DASA), International Conference on

作者： Lamya Benaddi Charaf Ouaddi Lahbib Naimi Adnane Souha Abdeslam Jakimi Brahim Ouchao Department of Computer Sciences Software Engineering and Information Systems Engineering Team Faculty of Science and Technology Errachidia Moulay Ismail University Morocco

ISBN: (数字)9798350369106

ISBN: (纸本)9798350369113

This paper proposes an innovative decision support system based on sentiment analysis, specifically designed for the transportation sector. The system employs an aspect-based sentiment analysis approach, which accurately identifies and classifies customer opinions on key transportation service aspects such as comfort, driver behavior, punctuality, and vehicle condition. By integrating the BERT model, known for its advanced natural language processing capabilities, our system provides an in-depth and reliable analysis of customer sentiments. This analysis enables managers and decision-makers to better understand the strengths and weaknesses of the provided transportation services and to precisely target areas that require improvement. By offering actionable insights, our approach aims to enhance the user experience, improve service quality, and increase overall customer satisfaction.

关键词： Decision support systems Sentiment analysis Social networking (online) Reviews Soft sensors Transportation User interfaces User experience Driver behavior Usability

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