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1st International Conference in Advanced Innovation on Smart City, ICAISC 2023

作者： Alawadhi, Abdulwadood Almogahed, Abdullah Omar, Mohd. Hasbullah Universiti Utara Malaysia InterNetWorks Research Laboratory School of Computing Kedah Malaysia Taiz University Faculty of Engineering and Information Technolog Department of Software Engineering Taiz Yemen

ISBN: (纸本)9781665472753

Networks that use visible light communication (VLC) are suitable for meeting 5G and 6G criteria. The VLC is a new wireless high-speed communication technology. To meet the bandwidth requirements of bandwidth-hungry devices, VLC makes use of the illumination infrastructure. VLC is an excellence alternative to communication 6G technologies because of its immunology to electromagnetic interference, high data rate, high reuse factor and capacity to operate in an unlicensed band. Although the standard of IEEE 802.15.7 would provide detailed physical and medium access control layer standards, the emerging VLC technology confronts MAC issues. However, MAC-specific solutions in wireless networks need not resolve VLC network issues. Full-duplex capability, deafness, channel use, hidden nodes, and connectivity should all be considered in VLC MAC approaches. © 2023 IEEE.

关键词： Visible light communication

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Learning-Based UAV Swarm Video Analytics Orchestration in Disaster Response Management

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SN Computer Science 2025年第5期6卷 1-24页

作者： Gao, Tiying Goins, Dwight Ballotti, Chaise Liu, Jiaqing Qu, Chengyi Department of Computer Science Florida Atlantic University Boca Raton 33431 FL United States Department of Computing and Software Engineering Florida Gulf Coast University Fort Myers 33965 FL United States College of Information Science and Engineering Ritsumeikan University Osaka 603-8577 Japan

Unmanned Aerial Vehicles (UAV) are increasingly used in sectors such as smart cities, precision agriculture, disaster response, and last-mile logistics, with Multi-Access Edge computing (MEC) playing a key role in enhancing their capabilities. In disaster response management, UAV assist in locating survivors, tracking objects, mapping post-disaster areas, and delivering critical supplies to inaccessible regions. However, unstable network conditions in disaster environments pose significant challenges to maintaining reliable video transmission and real-time decision-making. In this paper, we propose a comprehensive orchestration framework that integrates both offline and online strategies to optimize UAV video transmission, multi-UAV networking, and network management. The offline strategy combines policy-based orchestration with batch reinforcement learning (RL) to prepare UAV for deployment by optimizing network settings and video properties. The online strategy leverages reinforcement learning to enable real-time trajectory prediction and adaptive multi-UAV networking, ensuring efficient communication and decision-making during missions. Our experimental results, conducted across various Disaster Response Scenarios (DRS), demonstrate that the DQN-based approach significantly improves network throughput and round-trip time (RTT) compared to traditional methods, e.g. heuristic-based and rule-based, achieving approximately 87% of the Oracle baseline. The proposed framework enhances both the efficiency and adaptability of UAV operations, providing a robust solution for disaster response management. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Disaster response management Reinforcement learning UAV video analytics Unmanned aerial systems

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QSroute : A QoS Aware Routing Scheme for software Defined Networking 8

QSroute : A QoS Aware Routing Scheme for Software Defined Ne...

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8th IEEE International Conference on software engineering and Computer Systems, ICSECS 2023

作者： Kamarudin, Imran Edzereiq Ameedeen, Mohamed Ariff Umar Ong, Muhamad Idaham Zabidi, Azlee Universiti Malaysia Pahang AL-Sultan Abdullah Faculty of Computing Department of Computer Systems and Networking Pekan Malaysia Department of Software Engineering Faculty of Computing Universiti Malaysia Pahang AL-Sultan Abdullah Pekan Malaysia

ISBN: (纸本)9798350310931

The increasing demand for bandwidth-intensive network applications, such as video streaming, multimedia, and Internet of Things (IoT) applications, necessitates improved resource management to protect the network without compromising Quality of Service (QoS). Meeting these challenges requires a centralized view of all available network resources. software Defined Networking (SDN), an emerging technology, provides a centralized view and control of network resources. This feature enables administrators to programmatically define and manage network behavior, including routing, making it more flexible and adaptable. This study proposes a QoS-aware routing scheme for SDN that considers available bandwidth, packet delay, and packet loss to determine the optimal routing path. Optimal paths are selected based on meeting predefined threshold criteria. The study concludes by discussing potential directions for future research in this field. © 2023 IEEE.

关键词： Quality of service

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DearLLM: Enhancing Personalized Healthcare via Large Language Models-Deduced Feature Correlations 39

DearLLM: Enhancing Personalized Healthcare via Large Languag...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Xu, Yongxin Jiang, Xinke Chu, Xu Qiu, Rihong Feng, Yujie Ding, Hongxin Zhao, Junfeng Wang, Yasha Xie, Bing School of Computer Science School of Software & Microelectronics Peking University Beijing China Key Laboratory of High Confidence Software Technologies Ministry of Education Beijing China National Engineering Research Center For Software Engineering Peking University Beijing China Center on Frontiers of Computing Studies Peking University Beijing China Peking University Information Technology Institute Binhai Tianjin China Nanhu Laboratory Jiaxing China Department of Computing The Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)157735897X

Exploring the correlations between medical features is essential for extracting patient health patterns from electronic health records (EHR) data, and strengthening medical predictions and decision-making. To constrain the hypothesis space of pure data-driven deep learning in the context of limited annotated data, a common trend is to incorporate external knowledge, especially knowledge priors related to personalized health contexts, to optimize model training. However, most existing methods lack flexibility and are constrained by the uncertainties brought about by fixed feature correlation priors. In addition, in utilizing knowledge, these methods overlook the knowledge informative for personalized healthcare. To this end, we propose DearLLM, a novel and effective framework that leverages feature correlations deduced by large language models (LLMs) to enhance personalized healthcare. Concretely, DearLLM captures and learns quantitative correlations between medical features by calculating the conditional perplexity of LLMs’ deduction based on personalized patient backgrounds. Then, DearLLM enhances healthcare predictions by emphasizing knowledge that carries unique patient information through a feature-frequency-aware graph pooling method. Extensive experiments on two real-world benchmark datasets show significant performance gains brought by DearLLM. Furthermore, the discovered findings align well with medical literature, offering meaningful clinical interpretations. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Electronic health record

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Multi-layer network embedding on scc-based network with motif

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Digital Communications and Networks 2024年第3期10卷 546-556页

作者： Lu Sun Xiaona Li Mingyue Zhang Liangtian Wan Yun Lin Xianpeng Wang Gang Xu Department of Communication Engineering Institute of Information Science TechnologyDalian Maritime UniversityDalian116026China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and TelecommunicationsChongqing400065China Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province DUT School of Software Technology&DUT-RU International School of Information Science and EngineeringDalian University of TechnologyDalian 116620China College of Information and Communication Engineering Harbin Engineering UniversityHarbin 150001China School of Information and Communication Engineering Hainan UniversityHaikou 570228China State Key Laboratory of Millimeter Waves School of Information Science and EngineeringSoutheast UniversityNanjing 210096China

Interconnection of all things challenges the traditional communication methods,and Semantic Communication and computing(SCC)will become new *** is a challenging task to accurately detect,extract,and represent semantic information in the research of SCC-based *** previous research,researchers usually use convolution to extract the feature information of a graph and perform the corresponding task of node ***,the content of semantic information is quite *** graph convolutional neural networks provide an effective solution for node classification tasks,due to their limitations in representing multiple relational patterns and not recognizing and analyzing higher-order local structures,the extracted feature information is subject to varying degrees of ***,this paper extends from a single-layer topology network to a multi-layer heterogeneous topology *** Bidirectional Encoder Representations from Transformers(BERT)training word vector is introduced to extract the semantic features in the network,and the existing graph neural network is improved by combining the higher-order local feature module of the network model representation network.A multi-layer network embedding algorithm on SCC-based networks with motifs is proposed to complete the task of end-to-end node *** verify the effectiveness of the algorithm on a real multi-layer heterogeneous network.

关键词： Semantic communication and computing Multi-layer network Graph neural network Motif

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Detecting Double Mixed Compressed Images Based on Quaternion Convolutional Neural Network

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Chinese Journal of Electronics 2024年第3期33卷 657-671页

作者： Hao WANG Jinwei WANG Xuelong HU Bingtao HU Qilin YIN Xiangyang LUO Bin MA Jinsheng SUN Department of Automation Nanjing University of Science and Technology Engineering Research Center of Digital Forensics Ministry of Education Department of Computer and Software Nanjing University of Information Science and Technology School of Management Nanjing University of Posts and Telecommunications Department of Computer Science and Engineering Guangdong Province Key Laboratory of Information Security Technology Sun Yat-sen University State Key Laboratory of Mathematical Engineering and Advanced Computing Qilu University of Technology

Detection of color images that have undergone double compression is a critical aspect of digital image *** the existence of various methods capable of detecting double Joint Photographic Experts Group(JPEG) compression,they are unable to address the issue of mixed double compression resulting from the use of different compression *** particular,the implementation of Joint Photographic Experts Group 2000(JPEG2000)as the secondary compression standard can result in a decline or complete loss of performance in existing *** tackle this challenge of JPEG+JPEG2000 compression,a detection method based on quaternion convolutional neural networks(QCNN) is *** QCNN processes the data as a quaternion,transforming the components of a traditional convolutional neural network(CNN) into a quaternion *** relationships between the color channels of the image are preserved,and the utilization of color information is ***,the method includes a feature conversion module that converts the extracted features into quaternion statistical features,thereby amplifying the evidence of double *** results indicate that the proposed QCNN-based method improves,on average,by 27% compared to existing methods in the detection of JPEG+JPEG2000 compression.

关键词： Color image forensics JPEG JPEG2000 Mixed double compression Quaternion convolutional neural network

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Fine-Grained Features-based Code Search for Precise Query-Code Matching 31

Fine-Grained Features-based Code Search for Precise Query-Co...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Zhang, Xinting Cheng, Mengqiu Wang, Mengzhen Gong, Songwen Cai, Yi Xie, Jiayuan Li, Qing Department of Computing The Hong Kong Polytechnic University Hong Kong School of Software Engineering South China University of Technology China Department of Mathematics The University of Hong Kong Hong Kong Guangdong Neusoft University China

ISBN: (纸本)9798891761964

Code search aims to quickly locate target code snippets from databases using natural language queries, which promotes code reusability. Existing methods can effectively obtain aligned token-level and query word-level features. However, these studies usually represent the semantics of code and query by averaging the features of each token and word respectively, which makes it difficult to accurately capture the code details that are closely related to the query. To address this issue, we propose a fine-grained code search model that consists of a cross-modal encoder, a mapping layer, and a classification layer. Specifically, we utilize a pre-trained model, GraphCodeBERT, in the cross-modal encoder to align features. In the mapping layer, we introduce a co-attention network to capture the fine-grained interactions between code and query, ensuring a model can precisely identify key code segments relevant to the query. Finally, in the classification layer, we incorporate instruction learning techniques that leverage contextual reasoning to improve the accuracy of query-code matching. Experimental results show that our proposed model significantly outperforms existing methods across multiple programming language datasets. © 2025 Association for Computational Linguistics.

关键词： Structured Query Language

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The Current and Future Role of Visual Question Answering in eXplainable Artificial Intelligence 31

The Current and Future Role of Visual Question Answering in ...

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Workshops at the 31st International Conference on Case-Based Reasoning, ICCBR-WS 2023

作者： Caro-Martínez, Marta Wijekoon, Anjana Díaz-Agudo, Belén Recio-García, Juan A. Department of Software Engineering and Artificial Intelligence Universidad Complutense de Madrid Spain School of Computing Robert Gordon University Aberdeen United Kingdom

Over the last few years, we have seen how the interest of the computer science research community on eXplainable Artificial Intelligence has grown in leaps and bounds. The reason behind this rise is the use of Artificial Intelligence in many daily life tasks, and the consequent necessity of people to understand the intelligent systems’ behaviour. Computer vision-related tasks are not an exception, for example, Visual Question Answering tasks. The Artificial Intelligence models that carry out this specific task make an effort to answer questions about what we can watch in a particular image. In this work, we review the existing work about eXplainable Artificial Intelligence on Visual Question Answering which is a problem on which there is still much work to be done. Moreover, we open the discussion about the challenges to overcome regarding this topic, like the future role of Visual Question Answering to address eXplainable Artificial Intelligence issues or difficulties. © 2023 CEUR-WS. All rights reserved.

关键词： Natural language processing systems

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Enhancing Collaborative and Geometric Multi-Kernel Learning Using Deep Neural Network

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Computers, Materials & Continua 2022年第9期72卷 5099-5116页

作者： Bareera Zafar Syed Abbas Zilqurnain Naqvi Muhammad Ahsan Allah Ditta Ummul Baneen Muhammad Adnan Khan Department of Mechatronics and Control Engineering University of Engineering and TechnologyLahore54890Pakistan Department of Information Sciences Division of Science and TechnologyUniversity of EducationLahore54000Pakistan Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Department Software Gachon UniversitySeongnam13120Korea

This research proposes a method called enhanced collaborative andgeometric multi-kernel learning (E-CGMKL) that can enhance the CGMKLalgorithm which deals with multi-class classification problems with non-lineardata distributions. CGMKL combines multiple kernel learning with softmaxfunction using the framework of multi empirical kernel learning (MEKL) inwhich empirical kernel mapping (EKM) provides explicit feature constructionin the high dimensional kernel space. CGMKL ensures the consistent outputof samples across kernel spaces and minimizes the within-class distance tohighlight geometric features of multiple classes. However, the kernels constructed by CGMKL do not have any explicit relationship among them andtry to construct high dimensional feature representations independently fromeach other. This could be disadvantageous for learning on datasets with complex hidden structures. To overcome this limitation, E-CGMKL constructskernel spaces from hidden layers of trained deep neural networks (DNN).Due to the nature of the DNN architecture, these kernel spaces not onlyprovide multiple feature representations but also inherit the compositionalhierarchy of the hidden layers, which might be beneficial for enhancing thepredictive performance of the CGMKL algorithm on complex data withnatural hierarchical structures, for example, image data. Furthermore, ourproposed scheme handles image data by constructing kernel spaces from aconvolutional neural network (CNN). Considering the effectiveness of CNNarchitecture on image data, these kernel spaces provide a major advantageover the CGMKL algorithm which does not exploit the CNN architecture forconstructing kernel spaces from image data. Additionally, outputs of hiddenlayers directly provide features for kernel spaces and unlike CGMKL, do notrequire an approximate MEKL framework. E-CGMKL combines the consistency and geometry preserving aspects of CGMKL with the compositionalhierarchy of kernel spaces extracted from DNN hidde

关键词： CGMKL multi-class classification deep neural network multiplekernel learning hierarchical kernel spaces

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Development of Pattern-Based Test Case Catalog to Increase Reuse and Variety in software Testing

Development of Pattern-Based Test Case Catalog to Increase R...

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Intelligent Cybernetics Technology & Applications (ICICyTA), International Conference on

作者： Alwan Kemal Dana Sulistiyo Kusumo Rosa Reska Riskiana Software Engineering Department School of Computing Telkom University Bandung Indonesia

ISBN: (数字)9798331506490

ISBN: (纸本)9798331506506

In software development, the quality control process is a critical element that aims to oversee and verify product quality. Poor quality control processes are frequently a key factor in software project failures. software testing is key to identifying defects, but designing an effective test suite requires a huge effort that can affect cost, effectiveness, maintenance time, and development schedules. The Directorate of the Center for Excellence in Information Technology (PUTI) as one of the IT support units within Telkom University, is currently facing the same challenges in managing the BaSO application, which is an application for making official letters online. This application requires further testing to ensure that each function runs properly in various browser and device environments. In this case, the reuse and pattern approach was proposed as a solution, and based on this approach, a test case catalog was created. To assess the design system's impact and effectiveness, an experimental study was conducted for the PUTI QA team to test the BaSO application. The experimental results show that the use of a test case catalog during the test case development process can increase the reusability of test cases, with an average precision value of about 50%. In addition, the obtained test case variation value also increased by 35.5%. The findings highlight the significant impact of using a test case catalog, thus contributing to a more effective software testing process.

关键词： software testing Schedules Process control Quality control Feature extraction software Product design Quality assessment Pattern recognition software development management

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