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Image Emotion Classification Network Based on Multilayer Attentional Interaction,Adaptive Feature Aggregation

Computers, Materials & Continua 2023年第5期75卷 4273-4291页

作者： Xiaorui Zhang Chunlin Yuan Wei Sun Sunil Kumar Jha Engineering Research Center of Digital Forensics Ministry of EducationJiangsu Engineering Center of Network MonitoringSchool of Computer and SoftwareNanjing University of Information Science&TechnologyNanjing210044China Wuxi Research Institute Nanjing University of Information Science&TechnologyWuxi214100China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Nanjing University of Information Science&TechnologyNanjing210044China School of Automation Nanjing University of Information Science&TechnologyNanjing210044China Adani University AhmedabadGujaratIndia

The image emotion classification task aims to use the model to automatically predict the emotional response of people when they see the *** have shown that certain local regions are more likely to inspire an emotional response than the whole ***,existing methods perform poorly in predicting the details of emotional regions and are prone to overfitting during training due to the small size of the ***,this study proposes an image emotion classification network based on multilayer attentional interaction and adaptive feature *** perform more accurate emotional region prediction,this study designs a multilayer attentional interaction *** module calculates spatial attention maps for higher-layer semantic features and fusion features through amultilayer shuffle attention *** layer-by-layer up-sampling and gating operations,the higher-layer features guide the lower-layer features to learn,eventually achieving sentiment region prediction at the optimal *** complement the important information lost by layer-by-layer fusion,this study not only adds an intra-layer fusion to the multilayer attention interaction module but also designs an adaptive feature aggregation *** module uses global average pooling to compress spatial information and connect channel information from all ***,the module adaptively generates a set of aggregated weights through two fully connected layers to augment the original features of each ***,the semantics and details of the different layers are aggregated through gating operations and residual connectivity to complement the lost *** reduce overfitting on small datasets,the network is pre-trained on the FI dataset,and further weight fine-tuning is performed on the small *** experimental results on the FI,Twitter I and Emotion ROI(Region of Interest)datasets show that the proposed network exceeds existing image emotion classification methods

关键词： Attentionmechanism emotional region prediction image emotion classification transfer learning

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A Security Model of Multihospital FHIR Database Authorization Based on Secret Sharing and Blockchain

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IEEE Internet of Things Journal 2024年第6期11卷 10325-10335页

作者： Yang, Ching-Nung Li, Peng Cheng, Hsiang-Han Kuo, Hsin-Chuan Lu, Ming-Chan Xiong, Lizhi National Dong Hwa University Department of Computer Science and Information Engineering Hualien97401 Taiwan North China Electric Power University Department of Mathematics and Physics Hebei Key Laboratory of Physics and Energy Technology Baoding071003 China Nanjing University of Information Science and Technology School of Computer and Software Nanjing210044 China

Secret sharing (SS) is a threshold technology that shares a secret value by generating and distributing n shares in the way that a set of any k shares can recover the secret. On the other hand, blockchain is a decentralized system establishing a secure data structure for storing data in the form of a hash chain. Combining SS and blockchain is definitely a promising paradigm in the data security field. Recently, the fast healthcare interoperability resources (FHIRs) standard is defined for medical data exchange across various medical institutions. In this article, we combine SS, ElGamal's encryption/decryption, and blockchain to propose a multihospital FHIR database authorization (MFDA). To achieve a secure MFDA, the group-based SS (GSS) and the notion using GSS on exponent (referred to as EGSS) are proposed. The secrets of GSS and EGSS have the discrete logarithm problem (DLP) relation, on which the secrets can be used as ElGamal's private key and public key, respectively. Also, their shares will have DLP relation. By uploading public shares of EGSS on blockchain and using private shares of GSS in a private network, we can recover ElGamal's public key from a deployed smart contract on blockchain and recover ElGamal's private key from a qualified set. Meantime, when recovering private key, the private shares of GSS can be authenticated by public shares on blockchain to check tampering and corrupted of private shares during the communication in a private network. Theorems, examples, and experiments demonstrate that the proposed MFDA has efficient and secure database authorization. © 2014 IEEE.

关键词： Blockchain

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GIRP: Energy-Efficient QoS-Oriented Microservice Resource Provisioning via Multi-Objective Multi-Task Reinforcement Learning

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IEEE Transactions on Mobile Computing 2025年第7期24卷 5793-5807页

作者： Yuan, Honggang Wang, Ting Fu, Min Shi, Yuanming East China Normal University MoE Engineering Research Center of Software/Hardware Co-design Technology and Application Shanghai Key Laboratory of Trustworthy Computing Shanghai200062 China National University of Singapore Department of Electrical and Computer Engineering 117583 Singapore ShanghaiTech University School of Information Science and Technology Shanghai201210 China

Microservice architecture has revolutionized web service development by facilitating loosely coupled and independently developable components distributed as containers or virtual machines. While existing studies emphasize end-to-end latency, this paper investigates energy-efficient quality-of-service (QoS)-oriented microservice provisioning, focusing on both QoS satisfaction and power consumption (PC) conservation. We propose the Green and Intelligent Resource Provision (GIRP) architecture, integrating a data-driven energy-latency-aware resource allocation and scheduling manager to balance latency and PC. To reconcile the trade-offs involved, a dual-objective optimization problem is formulated to minimize latency and energy use by selecting proper servers, allocating CPU cores, and determining service replicas. To address challenges with discrete variables, dual objectives, and implicit mappings, we leverage a model-free deep deterministic policy gradient-based reinforcement learning algorithm. Specifically, we develop a multi-task agent via the Multi-gate Mixture-of-Experts model to simultaneously make two separate actions regarding CPU core numbers and service replica numbers, followed by a single-task agent to determine service scheduling. Extensive experiments on the DeathStarBenchmark testbed validate GIRP's effectiveness, demonstrating approximately 52% resource savings and a 43% reduction in PC compared to leading methods like Sinan, Firm, and heuristic-based algorithms. These results highlight GIRP's capability to optimize microservice orchestration by balancing end-to-end latency and power efficiency. © 2025 IEEE. All rights reserved.

关键词： Quality of service

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Fine-Tuning Cyber Security Defenses: Evaluating Supervised Machine Learning Classifiers for Windows Malware Detection

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Computers, Materials & Continua 2024年第8期80卷 2917-2939页

作者： Islam Zada Mohammed Naif Alatawi Syed Muhammad Saqlain Abdullah Alshahrani Adel Alshamran Kanwal Imran Hessa Alfraihi Department of Software Engineering International Islamic UniversityIslamabad25000Pakistan Information Technology Department Faculty of Computers and Information TechnologyUniversity of TabukTabuk71491Saudi Arabia Department of Computer Science and Artificial Intelligence College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia Department of Cybersecurity College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia Department of Computer Science University of PeshawarPeshawar25121Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

Malware attacks on Windows machines pose significant cybersecurity threats,necessitating effective detection and prevention *** machine learning classifiers have emerged as promising tools for malware ***,there remains a need for comprehensive studies that compare the performance of different classifiers specifically for Windows malware *** this gap can provide valuable insights for enhancing cybersecurity *** numerous studies have explored malware detection using machine learning techniques,there is a lack of systematic comparison of supervised classifiers for Windows malware *** the relative effectiveness of these classifiers can inform the selection of optimal detection methods and improve overall security *** study aims to bridge the research gap by conducting a comparative analysis of supervised machine learning classifiers for detecting malware on Windows *** objectives include Investigating the performance of various classifiers,such as Gaussian Naïve Bayes,K Nearest Neighbors(KNN),Stochastic Gradient Descent Classifier(SGDC),and Decision Tree,in detecting Windows *** the accuracy,efficiency,and suitability of each classifier for real-world malware detection *** the strengths and limitations of different classifiers to provide insights for cybersecurity practitioners and *** recommendations for selecting the most effective classifier for Windows malware detection based on empirical *** study employs a structured methodology consisting of several phases:exploratory data analysis,data preprocessing,model training,and *** data analysis involves understanding the dataset’s characteristics and identifying preprocessing *** preprocessing includes cleaning,feature encoding,dimensionality reduction,and optimization to prepare the data for *** training utilizes various

关键词： Security and privacy challenges in the context of requirements engineering supervisedmachine learning malware detection windows systems comparative analysis Gaussian Naive Bayes K Nearest Neighbors Stochastic Gradient Descent Classifier Decision Tree

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Material Composition of the Newly Discovered Zongzhuo Formation Sedimentary Mélange in the Dingri Area,Southern Tibet,and its Constraints on the Basin Controlling Dingri-Gamba Fault

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Acta Geologica Sinica(English Edition) 2024年第5期98卷 1171-1186页

作者： YAN Songtao DING Ailing DAI Xuejian LI Hu LIU Tao ZHU Lidong WU Qingsong Civil-Military Integration Center of China Geological Survey China Geological SurveyChengdu 610036China School of Software Engineering Chengdu University of Information TechnologyChengdu 610225China College of Earth Science Chengdu University of TechnologyChengdu 610059China Institute of Sedimentary Geology Chengdu University of TechnologyChengdu 610059China

The study of sedimentary mélanges holds pivotal importance in understanding orogenic processes and unveiling geodynamic *** this study,we present findings on zircon U-Pb isotopes and whole-rock elemental data concerning the recently uncovered Zongzhuo Formation sedimentary mélanges within the Dingri *** observations reveal the predominant composition of the Zongzhuo Formation,characterized by a matrix of sandstone-mudstone mixed with sand-conglomerates within native blocks exhibiting soft sediment ***,exotic blocks originating from littoral-neritic seas display evidence of landslide *** study identifies the depositional environment of the Zongzhuo Formation in Dingri as a slope turbidite fan,with its provenance traced back to the passive continental ***,this contrasts with the Zongzhuo Formation found in the Jiangzi-Langkazi *** on existing data,we conclude that the Zongzhuo Formation in the Dingri area was influenced by the Dingri-Gamba fault and emerged within a fault basin of the passive continental margin due to Neo-Tethys oceanic subduction during the Late Cretaceous *** provenance can be attributed to the littoral-neritic sea of the northern Tethys Himalaya *** study holds significant implications for understanding the tectonic evolution of Tethys Himalaya and for reevaluating the activity of the Dingri-Gamba fault,as it controls the active deposition of the Zongzhuo Formation.

关键词： sedimentary mélange provenance analysis Zongzhuo Formation Dingri-Gamba fault Tethys Himalaya

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Success Factors of Enterprise Resource Planning: A State-of-the-Art Mini Review

Success Factors of Enterprise Resource Planning: A State-of-...

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2024 International Conference on Artificial Intelligence, Metaverse and Cybersecurity, ICAMAC 2024

作者： BaniMustafa, Ahmed Bulkrock, Olla Faculty of Computer Information Science Higher Colleges of Technology Abu Dhabi United Arab Emirates Software Engineering Department Princess Sumaya University for Technology Amman Jordan

ISBN: (纸本)9798350353488

Enterprise Resource Planning Systems (ERP) are vital for today’s businesses. However, the successful implementation of ERP systems faces several challenges, which can determine its success or failure. This paper provides a brief state-of-the-art review of the success factors of ERP implementation using various applications that span different contexts. The paper discusses and compares the aims defined and the techniques applied in these studies, as well as the data used and the results obtained from each of these studies. This review paper reviews published work to tackle these challenges, covering the literature published in 2020-2023. A special effort was also made to focus on high-quality papers published in top journals with high-impact factors and indexed in Scopus and Web of Science. The review process is repeatable, as special keywords were used to search the literature using publishers’ databases and three specialized tools. The paper concludes by discussing the future of the successful implementation of ERP systems. The paper argues that ERP systems will continue to have a major impact on the success of today’s businesses. Therefore, they must carefully consider various factors that influence their successful implementation. © 2024 IEEE.

关键词： information use

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Perpendicularly assembled oriented electrospinning nanofibers based piezoresistive pressure sensor with wide measurement range

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Nano Research 2024年第7期17卷 6493-6501页

作者： Xiaoxue Bi Zhigang Duan Xiaojuan Hou Shuo Qian Mengjiao Yuan Jiajun Hu Jie Zhang Yuchen Lu Yanli Liu Jian He Zhiling Peng Xiujian Chou School of Mechanical and Electrical Engineering North University of ChinaTaiyuan 030051China Science and Technology on Electronic Test and Measurement Laboratory North University of ChinaTaiyuan 030051China School of Software North University of ChinaTaiyuan 030051China School of Information and Communication Engineering North University of ChinaTaiyuan 030051China

With the rapid development of wearable electronics,flexible pressure sensors have attracted wide attention in human–computer interaction and intelligent ***,it is a challenge to achieve a sensor with high sensitivity,wide measurement range,and wearing ***,we develop an oriented electrospinning thermoplastic polyurethane/polyacrylonitrile(TPU/PAN)nanofibers(OETPN)based piezoresistive pressure sensor(PONPS)in which the active layer and the electrode are assembled *** interdigital electrode is fabricated by spraying silver nanowires(AgNWs)on the OETPN through a mask *** active layer is composed of OETPN coated with MXene,encapsulated on the electrode by polyurethane(PU)*** porous structure of nanofibers membrane broadens the measurement range of the *** oriented nanofibers as active layer can improve the sensitivity in low pressure,because oriented nanofibers without interweaving nanofibers are more compressible than disordered *** prepared using the spraying method creates nanoscale microstructure and increases *** perpendicular assembly has greater response between the active layer and the electrode than the parallel assembly to improve the *** sensor exhibits high sensitivity(6.71 kPa^(−1),0.02–2 kPa)and wide measurement range(0.02–700 kPa).The sensor can detect weak signals such as radial artery.A pressure array constructed precisely represents the distribution of *** intelligent throat is created by combining machine learning algorithms with the *** can detect and recognize subtle throat vibrations while speaking,achieving recognition accuracy up to 100%using support vector machine(SVM)for five words with different *** fabricated sensor shows promising prospects in personal healthcare and intelligent robots.

关键词： oriented nanofibers perpendicular assembly parallel assembly intelligent throat machine learning algorithms

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Predictive Caching of File System for Database Workload Management 1

Predictive Caching of File System for Database Workload Mana...

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1st International Conference on AIML-Applications for engineering and technology, ICAET 2025

作者： Jadhav, Rohini Jadhav, Veena Pawar, Prajakta Kannaujia, Abhishek Kumar, Abhishek Gohil, Jayesh College of Engineering Department of Information Technology Pune India College of Engineering Department of Computer Engineering Pune India Bharati Vidyapeeth College of Engineering Department of Computer Engineering Lavale Pune India Veritas Principal Software Engineering India

ISBN: (纸本)9798350355611

The proactive caching technique known as 'predictive caching' attempts to improve file system performance by anticipating and pre-fetching data that is likely to be requested in the future. Conventional caching strategies, such LRU (Least Recently Used) and LFU (Least Frequently Used), base their decision on which material to keep in the cache on historical access patterns. Although these methods work well in many situations, they could not completely take advantage of the temporal and geographical proximity of file system access patterns, which could result in less-than-ideal cache use. Utilizing machine learning algorithms, statistical analysis, or heuristics, predictive caching makes predictions about future access patterns based on past data, system status, file properties, or user activity. So we propose a predictive caching technique which reduce access latency and boost system performance by pre-fetching and storing pertinent data ahead of time in anticipation of incoming data accesses. © 2025 IEEE.

关键词： Dynamic Data File Access Pattern Predictive Caching Random Forest Classifier

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Research on Low Overhead Fast Repair Array Code Algorithm

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International Journal of Network Security 2025年第3期27卷 594-604页

作者： Xiong, Pan Hong, Tieyuan Wang, Yuheng Tang, Dan Cai, Hongliang Zhang, Junyan School of Software Engineering Chengdu University of Information Technology Sichuan Province Engineering Technology Research Center of Support Software of Informatization Application Chengdu610225 China

With the growth of exponential data, disk data storage is becoming increasingly important. In the storage system, single-disk and dual-disk loss are more and more serious. To ensure the efficiency and speed of data restoration is crucial to the reliability of the storage system. The Row Diagonal Parity Code (RDP) indicates that the combined recovery algorithm can reduce the total number of disks that are read when a single-disk fault is rectified by 25%. However, when a two-disk fault is rectified, all disk elements must work together. In order to improve the efficiency and speed of single and double disk fault repair, the RDP Code is extended, and a Rapid Rows Diagonal Parity Code (RRDP code) with local repair property is proposed. On the premise of global horizontal coding of RDP code, RRDP code is computed by XOR to generate a local horizontal check column, and it participates in global horizontal redundancy calculation. The local horizontal redundancy check column shortens the repair chain by constructing the"missing diagonal" and then it has the function of local repair. Through theoretical analysis, RRDP code has decent and fast decoding complexity and I/O reading cost of single and dual disk fault repair under the premise of constant coding cost, which greatly saves the fault repair cost. © (2025), (International Journal of Network Security). All rights reserved.

关键词： Restoration

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Empowering Deaf Children with Sinhala Sign Language, Emotion Detection, and Sound Recognition 5

Empowering Deaf Children with Sinhala Sign Language, Emotion...

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5th International Conference on Advancements in Computing, ICAC 2023

作者： Fernando, W.H.K. Perera, P.L.D.N. Fernando, M.I.N. Gunasekara, P.T.E.W. Fernando, Harinda De Zoysa, Rivoni Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Computer System Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Computer Science Software Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

This study describes a broad endeavor to use cutting-edge technologies to empower deaf primary school students in Sri Lanka. Three key elements make up the study: a sound recognition and classification system, an Android software for translating gestures in Sinhala Sign Language (SSL), and a mobile app for emotion recognition and text-tospeech in Sinhala. By promptly alerting caregivers to potential dangers, the sound recognition technology secures the security of deaf children at home, providing an invaluable layer of security. The Android software bridges the communication gap and improves family interactions by translating SSL gestures captured by the device's camera into textual representations. This promotes successful communication between deaf children and their parents. Convolutional Neural Networks (CNNs) are used in the mobile application to detect and understand emotions based on facial expressions. This enables non-verbal youngsters to communicate successfully through Sinhala textto-speech output with adaptive tone modulation. This research aims to build a caring and inclusive atmosphere, enabling increased independence and enhanced communication skills in deaf children by combining these advances. The suggested solutions could significantly improve the lives of deaf kids, providing them with opportunities for better learning, social interaction, and emotional health. © 2023 IEEE.

关键词： Emotion Recognition

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