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UPB Scientific Bulletin, Series C: Electrical engineering and Computer Science 2025年第1期87卷 21-34页

作者： Contașel, Cristian Rughiniș, Răzvan Trancă, Dumitru Cristian Țurcanu, Dinu Dept. of Computer Science and Engineering National University of Science and Technology POLITEHNICA Bucharest Romania Dep. of Software Engineering and Automatics Technical University of Moldova Chișinău Moldova

This study provides an innovative architectural model for e-Health systems that aims to improve cyber resilience while maintaining high availability under fluctuating traffic loads. We examined typical cybersecurity incidents in the field of e-Health, their correlations with architectural defects, and frequent design patterns in currently operational systems. A testing approach based on our research finds those weaknesses and proposes viable fixes. In this paper, a comprehensive support strategy for transitioning from conventional monolithic architectures to microservices is presented. This change makes use of cloud computing's vertical and horizontal scalability to maximize resource utilization while ensuring system reliability. We also discuss deployment ideas for the new microservices, focusing on operational resilience and cybersecurity in e-Health environments. © 2025, Politechnica University of Bucharest. All rights reserved.

关键词： Electronic health record

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Network Traffic Anomaly Detection Based on DGBi-SA Model

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engineering Letters 2025年第3期33卷 612-619页

作者： Wang, Xin Dai, Hong Huang, Zheng Han, Yue University of Science and Technology Liaoning Liaoning CO Anshan114051 China School of Computer Science and Software Engineering University of Science and Technology Liaoning Liaoning CO Anshan114051 China

Network traffic anomaly detection plays a crucial role in today's network security and performance management. In response to the challenges in current network traffic data processing, such as insufficient structuring and dynamics, the paper proposes a network traffic anomaly detection approach based on DGBi-SA model, which integrates Dynamic Graph Convolutional Networks (DyGCN), Bi-directional Long Short-Term Memory (Bi-LSTM), and the Self-Attention mechanism. To solve the problem of insufficient structuring, the model employs DyGCN to capture network traffic's dynamic graph structural characteristics proficiently. Simultaneously, combining Bi-LSTM and the Self-Attention mechanism enhances the model's ability to capture temporal and spatial dependencies. The main objective of the research in the paper is to improve the accuracy and detection efficiency of network traffic prediction, thereby achieving effective detection and timely response to abnormal behaviors in complex network environments. Through a sequence of experiments conducted on the CICIDS-2017 dataset, the paper verifies that the DGBi-SA model can effectively solve the above problems. The experimental results indicate that the model outperforms existing methods in key performance indicators such as AUC, precision, recall, and F1 score, thereby proving that the DGBi-SA model has important application value and scientific significance in network traffic anomaly detection. © 2025, International Association of Engineers. All rights reserved.

关键词： Long short-term memory

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Intelligent control of additively manufactured beads on the basis of target morphologies and thermal field

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Frontiers of Mechanical engineering 2024年第6期19卷 87-98页

作者： Xuhui YANG Rui LI Kelong HU Aidong SUN Xiaoyao MA Mingxin LIU Mingtian WANG Runsheng LI Gang ZHAO Wenzheng ZHAI Hao SONG Zili LI Haiou ZHANG School of Mechanical Science and Engineering Huazhong University of Science and TechnologyWuhan 430074China School of Software Engineering Huazhong University of Science and TechnologyWuhan 430074China College of Mechanical and Electronic Engineering China University of PetroleumQingdao 266580China Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and TechnologyWuhan 430065China

Hybrid deposition with microrolling is a promising arc-based direct energy deposition technique to rapidly build complex parts,whose performance is comparable to that of their wrought *** forming conditions and bead morphologies pose difficulties in controlling the morphologies from a single weld bead to built part profiles,and these difficulties hinder the widespread application of the ***,a model that can automatically generate optimal process parameters on the basis of the infrared image,including thermal information and the point cloud information of the target weld bead,is *** show that the errors in critical parameters,namely,feed speed,travel speed,and rolling force,are below 0.4%,0.9%,and 2%,respectively,indicating that the proposed technique outperforms the compared ***,validation reveals that the actual depositing bead is similar(deviation below 0.05 mm)to the target *** proposed strategy provides an effective foundation for dynamic path planning and can considerably improve printing efficiency and precision.

关键词： wire arc additive manufacturing deep learning thermal field point cloud stainless steel

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A Scalable and Generalized Deep Ensemble Model for Road Anomaly Detection in Surveillance Videos

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Computers, Materials & Continua 2024年第12期81卷 3707-3729页

作者： Sarfaraz Natha Fareed A.Jokhio Mehwish Laghari Mohammad Siraj Saif A.Alsaif Usman Ashraf Asghar Ali Department of Information Technology Quaid-e-Awam University of EngineeringScience&TechnologyNawabshah67480Pakistan Department of Software Engineering Sir Syed University of Engineering and TechnologyKarachi75000Pakistan Electrical Engineering Department College of EngineeringKing Saud UniversityRiyadh11421Saudi Arabia School of Business Torrens UniversitySydneyNSW 2007Australia School of Engineering and Information Technology The University of New SouthWales(UNSD)CanberraACT 2604Australia

Surveillance cameras have been widely used for monitoring in both private and public sectors as a security *** Circuits Television(CCTV)Cameras are used to surveillance and monitor the normal and anomalous ***-world anomaly detection is a significant challenge due to its complex and diverse *** is difficult to manually analyze because vast amounts of video data have been generated through surveillance systems,and the need for automated techniques has been raised to enhance detection *** paper proposes a novel deep-stacked ensemble model integrated with a data augmentation approach called Stack Ensemble Road Anomaly Detection(SERAD).SERAD is used to detect and classify the four most happening road anomalies,such as accidents,car fires,fighting,and snatching,through road surveillance videos with high *** SERAD adapted three pre-trained Convolutional Neural Networks(CNNs)models,namely VGG19,ResNet50 and *** stacking technique is employed to incorporate these three models,resulting in much-improved accuracy for classifying road abnormalities compared to individual ***,it presented a custom real-world Road Anomaly Dataset(RAD)comprising a comprehensive collection of road images and *** experimental results demonstrate the strength and reliability of the proposed SERAD model,achieving an impressive classification accuracy of 98.7%.The results indicate that the proposed SERAD model outperforms than the individual CNN base models.

关键词： Convolutional neural network transfer learning stack ensemble learning road anomaly detection data augmentation

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Puncturable ciphertext-policy attribute-based encryption scheme for efficient and flexible user revocation

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Science China(Information Sciences) 2023年第7期66卷 130-146页

作者： Dilxat GHOPUR Jianfeng MA Xindi MA Yinbin MIAO Jialu HAO Tao JIANG School of Cyber Engineering Xidian University Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Xi'an Satellite Control Center

With the rapid deployment of storage services, secure and efficient user authorization and revocation data shared through the cloud have become a grand challenge hindering cloud data *** previous direct and indirect user revocation schemes implemented large-scale user revocation, they faced heavy communication and computational costs. To address these challenges, this study presents a new encryption scheme that combines ciphertext-policy attribute-based encryption(CP-ABE) with puncturable encryption to achieve efficient and flexible user revocation. We design a proxy server to reduce the computational overhead in the decryption phase. Because the puncture process is performed on a semi-honest cloud, we use the digital signature method to verify the correctness of its operation. Furthermore, we prove the security of our scheme under the chosen-plaintext attack(CPA), and compare it with other schemes to highlight its advantages. Numerical analysis and experimental simulation results reveal that our scheme is more suitable than other schemes for use in a cloud environment for user revocation.

关键词： ciphertext-policy attribute-based encryption puncturable encryption user revocation outsource decryption digital signature

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A Review of AI-Assisted Impact Analysis for software Requirements Change: Challenges and Future Directions 25

A Review of AI-Assisted Impact Analysis for Software Require...

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25th International Arab Conference on Information technology, ACIT 2024

作者： Samhan, Ahmad Alhajhassan, Suhair Dabaa't, Sara Abu Elrashidi, Ali Zarqa University Faculty of Information Technology Software Engineering Department Zarqa Jordan Applied Science Private University Faculty of Information Technology Software Engineering Department Amman Jordan University of Business and Technology Faculty of Engineering Electrical Engineering Department Jeddah Saudi Arabia

ISBN: (纸本)9798331540012

This paper presents a review study that explores the landscape and challenges of AI-assisted impact analysis in software requirements engineering. The growth of complex and change-prone software systems has highlighted the need for AI techniques - especially machine learning (ML) and natural language processing (NLP) - to be harnessed to improve impact analysis scalability and efficiency. Key challenges identified include data quality issues, scalability constraints, and ethical problems related to bias and accountability. To investigate these challenges and provide insights, a review was conducted across IEEE Xplore, ACM Digital Library, and Google Scholar, cross referencing between keywords that include concepts from the domains of "software requirements", "impact analysis", and "AI-Assisted approaches". Inclusion criteria focused on studies that presents empirical data on AI-assisted impact analysis. This study highlights research gaps and presents insights for overcoming obstacles using emerging technologies such as edge AI in order to enhance post-impact analysis. Findings show that while AI improves traditional impact analysis, further work is necessary to deliver effective, ethical and practical implementations within evolving software contexts. © 2024 IEEE.

关键词： AI Challenges Artificial Intelligence Edge AI Impact Analysis Natural Language Processing software engineering software Requirements

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Optimization Design of the Multi-Layer Cross-Sectional Layout of An Umbilical Based on the GA-GLM

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China Ocean engineering 2024年第2期38卷 247-254页

作者： YANG Zhi-xun YIN Xu FAN Zhi-rui YAN Jun LU Yu-cheng SU Qi MAO Yandong WANG Hua-lin Institute of marine machinery College of Mechanical and Electrical EngineeringHarbin Engineering UniversityHarbin 150001China State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial EquipmentDepartment of Engineering MechanicsDalian University of TechnologyDalian 116024China Ningbo Institube of Dalian University of Technology Ningbo 315040China

Marine umbilical is one of the key equipment for subsea oil and gas exploitation,which is usually integrated by a great number of different functional components with *** layout of these components directly affects manufacturing,operation and storage performances of the *** the multi-layer cross-sectional layout design of the umbilical,a quantifiable multi-objective optimization model is established according to the operation and storage *** the manufacturing factors,the multi-layering strategy based on contact point identification is introduced for a great number of functional ***,the GA-GLM global optimization algorithm is proposed combining the genetic algorithm and the generalized multiplier method,and the selection operator of the genetic algorithm is improved based on the steepest descent *** algorithm is used to find the optimal solution in the global space,which can converge from any initial layout to the feasible layout *** feasible layout solution is taken as the initial value of the generalized multiplier method for fast and accurate ***,taking umbilicals with a great number of components as examples,the results show that the cross-sectional performance of the umbilical obtained by optimization algorithm is better and the solution efficiency is ***,the multi-layering strategy is effective and *** design method proposed in this paper can quickly obtain the optimal multi-layer cross-sectional layout,which replaces the manual design,and provides useful reference and guidance for the umbilical industry.

关键词： umbilical cross-sectional layout multi-layers GA-GLM optimization

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Bidirectional attentional mechanism for Arabic chatbot

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International Journal of Information technology (Singapore) 2024年第5期16卷 3109-3120页

作者： Youness, Farida Elshenawy, Ayman Makour, Mohamed Ashraf Systems and Computer Engineering Department Faculty of Engineering Al-Azhar University Cairo Egypt Software Engineering and Information Technology Department Faculty of Engineering and Technology Egyptian Chinees University Cairo Egypt

A chatbot is an intelligent agent that developed based on Natural language processing (NLP) to interact with people in a natural language. The development of multiple deep NLP models has allowed for the creation of a vector representation of words and sentences that captures meaning in context. In this paper, a generative Arabic chatbot is trained with the Sequence to Sequence (Seq2Seq) model based on encoder-decoder architecture. It utilizes both the bi-directional encoder types of Gated Recurrent Units (GRU) and Long Short-Term Memory (LSTM). The chatbot was trained using preprocessed dataset of 12 k conversations that were translated from English to Arabic. An attention layer was provided to the RNNs to focus on the important data between the input and output sequences. The obtained results showed that the proposed chatbot performed well with long sentences and elicited positive responses. In addition to that BiLSTM with attention performs better than BiGRU without attention. To assess the model, the Bilingual Evaluation Understudy (BLEU) Score is employed. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management 2024.

关键词： Arabic chatbot Attention mechanism Deep learning RNN

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UNiFY-ESSL: UNIfied Framework for Yielding EEG-Based Emotion Recognition Model with Self-Supervised Learning

UNiFY-ESSL: UNIfied Framework for Yielding EEG-Based Emotion...

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2024 International Conference on Brain Computer Interface and Healthcare Technologies, iCon-BCIHT 2024

作者： Ahuja, Chirag Sethia, Divyashikha Delhi Technology University Department of Computer Engineering Delhi India Delhi Technology University Department of Software Engineering Delhi India

ISBN: (纸本)9798331540067

Electroencephalography (EEG) based emotion recognition shows promise in human-computer interaction and mental health monitoring, but faces challenges in cross-dataset generalization. This study introduces the Unified Framework for EEG-based Emotion Recognition with Self-Supervised Learning (UNiFY-ESSL), combining self-supervised learning (SSL) and advanced sampling techniques to integrate multiple EEG datasets. UNiFY-ESSL utilizes a unified 14-channel setup across all datasets (SEED, DEAP, and DREAMER), allowing consistent preprocessing and feature extraction. This approach adapts datasets with varying initial channel counts (62 for SEED, 32 for DEAP, and 14 for DREAMER) to a common configuration based on DREAMER's channels. The framework explores contrastive learning methods, specifically Simple Contrastive Learning (Sim-CLR) and Contrastive Predictive Coding (CPC), enhanced by a novel stratified sampling scheme. Experiments yield impressive results: SimCLR achieves F1-scores of 82.62%, 87.83%, and 89.05% for SEED, DEAP, and DREAMER respectively, while CPC attains 81.35%, 82.27%, and 91.23%. It demonstrates improved cross-dataset generalization, with a 1-2% performance gain on DREAMER and maintained performance on DEAP despite channel reduction. However, SEED experiences a 3% F1-score drop due to significant channel reduction. These findings highlight the potential of our sampling method in advancing EEG-based emotion recognition while underscoring the need for future work on efficient channel combination strategies, particularly for datasets with higher initial channel counts. © 2024 IEEE.

关键词： Self-supervised learning

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BGNN: Behavior-aware graph neural network for heterogeneous session-based recommendation

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Frontiers of Computer Science 2023年第5期17卷 103-118页

作者： Jinwei LUO Mingkai HE Weike PAN Zhong MING College of Computer Science and Software Engineering Shenzhen UniversityShenzhen 518060China National Engineering Laboratory for Big Data System Computing Technology Shenzhen UniversityShenzhen 518060China

Session-based recommendation(SBR)and multibehavior recommendation(MBR)are both important problems and have attracted the attention of many researchers and *** from SBR that solely uses one single type of behavior sequences and MBR that neglects sequential dynamics,heterogeneous SBR(HSBR)that exploits different types of behavioral information(e.g.,examinations like clicks or browses,purchases,adds-to-carts and adds-to-favorites)in sequences is more consistent with real-world recommendation scenarios,but it is rarely *** efforts towards HSBR focus on distinguishing different types of behaviors or exploiting homogeneous behavior transitions in a sequence with the same type of ***,all the existing solutions for HSBR do not exploit the rich heterogeneous behavior transitions in an explicit way and thus may fail to capture the semantic relations between different types of ***,all the existing solutions for HSBR do not model the rich heterogeneous behavior transitions in the form of graphs and thus may fail to capture the semantic relations between different types of *** limitation hinders the development of HSBR and results in unsatisfactory *** a response,we propose a novel behavior-aware graph neural network(BGNN)for *** BGNN adopts a dual-channel learning strategy for differentiated modeling of two different types of behavior sequences in a ***,our BGNN integrates the information of both homogeneous behavior transitions and heterogeneous behavior transitions in a unified *** then conduct extensive empirical studies on three real-world datasets,and find that our BGNN outperforms the best baseline by 21.87%,18.49%,and 37.16%on average correspondingly.A series of further experiments and visualization studies demonstrate the rationality and effectiveness of our *** exploratory study on extending our BGNN to handle more than two types of behaviors show that our BGNN can e

关键词： session-based recommendation graph neural network heterogeneous behaviors

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