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Fine-Grained Cross-Modal Fusion Based Refinement for Text-to-Image Synthesis

Chinese Journal of Electronics 2023年第6期32卷 1329-1340页

作者： SUN Haoran WANG Yang LIU Haipeng QIAN Biao Department of Computer Science and Information Engineering Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Ministry of EducationHefei University of Technology

Text-to-image synthesis refers to generating visual-realistic and semantically consistent images from given textual descriptions. Previous approaches generate an initial low-resolution image and then refine it to be high-resolution. Despite the remarkable progress, these methods are limited in fully utilizing the given texts and could generate text-mismatched images, especially when the text description is complex. We propose a novel finegrained text-image fusion based generative adversarial networks(FF-GAN), which consists of two modules: Finegrained text-image fusion block(FF-Block) and global semantic refinement(GSR). The proposed FF-Block integrates an attention block and several convolution layers to effectively fuse the fine-grained word-context features into the corresponding visual features, in which the text information is fully used to refine the initial image with more details. And the GSR is proposed to improve the global semantic consistency between linguistic and visual features during the refinement process. Extensive experiments on CUB-200 and COCO datasets demonstrate the superiority of FF-GAN over other state-of-the-art approaches in generating images with semantic consistency to the given texts.

关键词： Visualization Fuses Convolution Semantics Linguistics Benchmark testing Generative adversarial networks

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ViGT: proposal-free video grounding with a learnable token in the transformer

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Science China(information Sciences) 2023年第10期66卷 196-212页

作者： Kun LI Dan GUO Meng WANG School of Computer Science and Information Engineering Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Intelligent Interconnected Systems Laboratory of Anhui Province Institute of Artificial Intelligence Hefei Comprehensive National Science Center

The video grounding(VG) task aims to locate the queried action or event in an untrimmed video based on rich linguistic descriptions. Existing proposal-free methods are trapped in the complex interaction between video and query, overemphasizing cross-modal feature fusion and feature correlation for VG. In this paper, we propose a novel boundary regression paradigm that performs regression token learning in a transformer. Particularly, we present a simple but effective proposal-free framework, namely video grounding transformer(ViGT), which predicts the temporal boundary using a learnable regression token rather than multi-modal or cross-modal features. In ViGT, the benefits of a learnable token are manifested as follows.(1) The token is unrelated to the video or the query and avoids data bias toward the original video and query.(2) The token simultaneously performs global context aggregation from video and query ***, we employed a sharing feature encoder to project both video and query into a joint feature space before performing cross-modal co-attention(i.e., video-to-query attention and query-to-video attention) to highlight discriminative features in each modality. Furthermore, we concatenated a learnable regression token [REG] with the video and query features as the input of a vision-language transformer. Finally, we utilized the token [REG] to predict the target moment and visual features to constrain the foreground and background probabilities at each timestamp. The proposed ViGT performed well on three public datasets:ANet-Captions, TACoS, and YouCookⅡ. Extensive ablation studies and qualitative analysis further validated the interpretability of ViGT.

关键词： video grounding temporal sentence grounding boundary regression token learning proposal-free

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SACNN-IDS: A self-attention convolutional neural network for intrusion detection in industrial internet of things

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CAAI Transactions on Intelligence Technology 2024年第6期9卷 1398-1411页

作者： Mimonah Al Qathrady Safi Ullah Mohammed S.Alshehri Jawad Ahmad Sultan Almakdi Samar M.Alqhtani Muazzam A.Khan Baraq Ghaleb Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajranSaudi Arabia Department of Computer Science Quaid-i-Azam UniversityIslamabadPakistan Department of Computer Science College of Computer Science and Information SystemsNajran UniversityNajranSaudi Arabia School of Computing Engineering and the Built EnvironmentEdinburgh Napier UniversityEdinburghUK ICESCO Chair Big Data Analytics and Edge Computing Quaid-i-Azam UniversityIslamabadPakistan

Industrial Internet of Things(IIoT)is a pervasive network of interlinked smart devices that provide a variety of intelligent computing services in industrial *** IIoT nodes operate confidential data(such as medical,transportation,military,etc.)which are reachable targets for hostile intruders due to their openness and varied *** Detection systems(IDS)based on Machine Learning(ML)and Deep Learning(DL)techniques have got significant ***,existing ML and DL-based IDS still face a number of obstacles that must be *** instance,the existing DL approaches necessitate a substantial quantity of data for effective performance,which is not feasible to run on low-power and low-memory *** and fewer data potentially lead to low performance on existing *** paper proposes a self-attention convolutional neural network(SACNN)architecture for the detection of malicious activity in IIoT networks and an appropriate feature extraction method to extract the most significant *** proposed architecture has a self-attention layer to calculate the input attention and convolutional neural network(CNN)layers to process the assigned attention features for *** performance evaluation of the proposed SACNN architecture has been done with the Edge-IIoTset and X-IIoTID *** datasets encompassed the behaviours of contemporary IIoT communication protocols,the operations of state-of-the-art devices,various attack types,and diverse attack scenarios.

关键词： convolutional neural network deep learning industrial internet of things intrusion detection self-attention

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Solving Unequal Area Facility Layout Problems with Differential Evolution and Particle Swarm Optimization 7

Solving Unequal Area Facility Layout Problems with Different...

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7th information systems International Conference, ISICO 2023

作者： Muhayat, Hanifan Utamima, Amalia Laboratory of Data Engineering and Business Intelligence Department of Information Systems Institut Teknologi Sepuluh Nopember Surabaya60111 Indonesia

The unequal area facility layout problem (UAFLP) is a problem in industrial engineering that aims to minimize material handling costs between a group of unequal-shaped facilities. In this study, Differential Evolution (DE) with continuous variables and Particle Swarm Optimization (PSO) are employed to solve the UAFLP. This study also compares the performance of the DE method (never used to solve UAFLP) and the PSO method (frequently used to solve facility layout problems). The experimental results demonstrate that PSO is effective in solving small-sized datasets, while DE works well in solving datasets with larger areas. © 2023 The Authors. Published by Elsevier B.V.

关键词： Particle swarm optimization (PSO)

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Solving Preemptive Single Machine Scheduling with Lovebird Algorithm and Constructive Heuristics for Equal-Length Jobs 7

Solving Preemptive Single Machine Scheduling with Lovebird A...

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7th information systems International Conference, ISICO 2023

作者： Fernanda, Nita Utamima, Amalia Laboratory of Data Engineering and Business Intelligence Department of Information Systems Institut Teknologi Sepuluh Nopember Surabaya60111 Indonesia

Scheduling is essential in manufacturing to maintain machinery and increase revenue. Machines that are routinely maintained will boost production, whereas scheduling based on total weighted completion time (TWCT) can improve the performance of production equipment. The Lovebird Algorithm (LBA), Constructive Heuristics (CH), and combination of LBA-CH are used in this study to schedule a single machine with equal processing times. LBA, a population-based algorithm, represents job parts as birds for jobs that can be preempted. The experiments, which used ten jobs divided into dozens of job parts, show that LBA-CH can effectively reduce TWCT compared to CH by 18.16%. © 2023 The Authors. Published by Elsevier B.V.

关键词： Scheduling algorithms

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Federated Learning for Weld Quality Prediction

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Journal of Dynamics, Monitoring and Diagnostics 2024年第4期3卷 237-245页

作者： Shubhranil Chakraborty Sayani Guha Debasish Mishra Surjya K.Pal Department of Electronics and Communication Engineering Jalpaiguri Government Engineering CollegeIndia Data Science and Information Systems IFMR GSBKrea UniversityIndia Department of Mechanical Engineering Indian Institute of Technology KharagpurIndia

This study introduces a monitoring system that can accurately predict the quality of friction stir *** study involved four different sets of welding experiments using varying materials and tool *** goal was to create a universal monitoring system capable of predicting weld quality across these diverse experimental ***-time welding data was collected using instruments such as load cells and a power *** processing methods were then used to analyze this data and extract essential information about welding ***,federated learning(FL)was used to develop the universal monitoring *** method involves collective learning and model training,leading to a global model trained on decentralized data from the different experimental *** approach proved to be effective in forecasting weld quality,achieving a mean absolute error(MAE)of 10.44 for all welding ***,it offered benefits such as reduced latency and enhanced user data protection,while maintaining the accuracy of the global *** artificial neural network model was also developed for comparison with FL,achieving a MAE of 13.85 for the welding ***,this study demonstrates the effectiveness of training global and more reliable models,with multiple devices sharing their knowledge bases to train the global model effectively.

关键词： deep learning federated learning friction stir welding Industry 4.0 manufacturing neural network quality

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Optimizing beyond boundaries: empowering the salp swarm algorithm for global optimization and defective software module classification

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Neural Computing and Applications 2024年第30期36卷 18727-18759页

作者： Kassaymeh, Sofian Al-Betar, Mohammed Azmi Rjoubd, Gaith Fraihat, Salam Abdullah, Salwani Almasri, Ammar Software Engineering Department Faculty of Information Technology Aqaba University of Technology Aqaba 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 Irbid Jordan Concordia Institute for Information Systems Engineering Concordia University Montreal Canada Artificial Intelligence Department Faculty of Information Technology Aqaba University of Technology Aqaba Jordan Data Mining and Optimization Research Group Center for Artificial Intelligence Technology Universiti Kebangsaan Malaysia Bangi Malaysia Management Information Systems Department Al-Balqa Applied University Amman Jordan

This work presents a new version of the salp swarm optimizer (SSA), called "mSSA," that uses complex mathematical expressions to dynamically manipulate the crucial control parameter (c1) during optimization. These expressions are carefully designed to modulate the shift in search strategy from exploratory to exploitative, improving the flexibility and speed of convergence of the algorithm. To evaluate the performance of the developed mSSA variants, a thorough examination is carried out on twenty-three benchmark test functions alongside their application to the complex task of software module classification. The process of classifying defective software modules involves developing a multilayer perceptron (MLP) classifier that is suited to the particular complexity and heterogeneity of the task. Selecting the best optimizer is made easier by systematically evaluating the different mSSA versions as MLP classifier trainers. Based on metrics like classification accuracy, convergence speed, and avoidance of local minima, a comparative analysis in opposition to six previously published metaheuristic optimizers shows that mSSA3, when combined with the developed MLP classifier, outperforms both other mSSA variations and state-of-the-art metaheuristic optimizers in terms of overall performance. The excellent classification accuracy, swift convergence, and ability to avoid local minima of mSSA3 highlight its superiority and establish it as a cutting-edge method in the application of metaheuristic algorithms. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Global optimization

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Feedback digitalization preferences in online and hybrid classroom: Experiences from lockdown and implications for post-pandemic education

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Journal of Research in Innovative Teaching and Learning 2023年第1期18卷 56-75页

作者： Sedrakyan, Gayane Borsci, Simone Abdi, Asad van den Berg, Stéphanie M. Veldkamp, Bernard P. van Hillegersberg, Jos Department of Industrial Engineering and Business Information Systems University of Twente Enschede Netherlands Department of Cognition Data and Education University of Twente Enschede Netherlands

Purpose: This research aims to explore digital feedback needs/preferences in online education during lockdown and the implications for post-pandemic education. Design/methodology/approach: An empirical study approach was used to explore feedback needs and experiences from educational institutions in the Netherlands and Germany (N = 247) using a survey method. Findings: The results showed that instruments supporting features for effortless interactivity are among the highly preferred options for giving/receiving feedback in online/hybrid classrooms, which are in addition also opted for post-pandemic education. The analysis also showed that, when communicating feedback digitally, more inclusive formats are preferred, e.g. informing learners about how they perform compared to peers. The increased need for comparative performance-oriented feedback, however, may affect students' goal orientations. In general, the results of this study suggest that while interactivity features of online instruments are key to ensuring social presence when using digital forms of feedback, balancing online with offline approaches should be recommended. Originality/value: This research contributes to the gap in the scientific literature on feedback digitalization. Most of the existing research are in the domain of automated feedback generated by various learning environments, while literature on digital feedback in online classrooms, e.g. empirical studies on preferences for typology, formats and communication channels for digital feedback, to the best of the authors’ knowledge is largely lacking. The findings and recommendations of this study extend their relevance to post-pandemic education for which hybrid classroom is opted among the highly preferred formats by survey respondents. © 2023, Gayane Sedrakyan, Simone Borsci, Asad Abdi, Stéphanie M. van den Berg, Bernard P. Veldkamp and Jos van Hillegersberg.

关键词： Digital feedback Feedback communication instruments Feedback digitization Online feedback Use of technology for feedback

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A Transmission and Transformation Fault Detection Algorithm Based on Improved YOLOv5

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Computers, Materials & Continua 2023年第9期76卷 2997-3011页

作者： Xinliang Tang Xiaotong Ru Jingfang Su Gabriel Adonis School of Information Science and Engineering Hebei University of Science and TechnologyShijiazhuang050000China Department of Computer Science and Information Systems Birkbeck Institute for Data AnalyticsLondonB100ABWC1E 7HXUK

On the transmission line,the invasion of foreign objects such as kites,plastic bags,and balloons and the damage to electronic components are common transmission line *** these faults is of great significance for the safe operation of power ***,a YOLOv5 target detection method based on a deep convolution neural network is *** this paper,Mobilenetv2 is used to replace Cross Stage Partial(CSP)-Darknet53 as the *** structure uses depth-wise separable convolution to reduce the amount of calculation and parameters;improve the detection *** the same time,to compensate for the detection accuracy,the Squeeze-and-Excitation Networks(SENet)attention model is fused into the algorithm framework and a new detection scale suitable for small targets is added to improve the significance of the fault target area in the *** pictures of foreign matters such as kites,plastic bags,balloons,and insulator defects of transmission lines,and sort theminto a data *** experimental results on datasets show that themean Accuracy Precision(mAP)and recall rate of the algorithm can reach 92.1%and 92.4%,*** the same time,by comparison,the detection accuracy of the proposed algorithm is higher than that of other methods.

关键词： Transmission line YOLOv5 multi-scale integration SENet

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A Novel Metaverse-as-a-Service Architecture from an Operator View

A Novel Metaverse-as-a-Service Architecture from an Operator...

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2023 IEEE International Conference on Metaverse Computing, Networking and Applications, MetaCom 2023

作者： Ahsani, Vesal Rahimi, Ali Letafati, Mehdi Khalaj, Babak Hossein Sharif University of Technology Department of Electrical Engineering and Sharif Center for Information Systems and Data Science Tehran Iran

ISBN: (纸本)9798350333336

In this research paper, the authors present a comprehensive overview of three key elements of Metaverse-as-a-service (MaaS) platforms: privacy and security, edge computing, and blockchain technology. The study begins by examining security concerns for wireless access to the Metaverse. It then delves into privacy and security issues within the Metaverse from data-centric, learning-centric, and human-centric perspectives. The authors propose novel and less-explored methods to aid mobile network operators and Metaverse service providers in creating secure and private MaaS platforms across various layers of the Metaverse, from access to social interactions. The article also explores the potential of edge computing to enhance various aspects of the Metaverse and the challenges associated with its implementation. Furthermore, the study examines the 10 main challenges of MaaS platforms and how blockchain technology can address them. Finally, the authors present future visions and recommendations, such as content-centric security and zero-trust Metaverse, as well as unresolved challenges in blockchain technology to provide insights for network designers in the Metaverse era. © 2023 IEEE.

关键词： Edge computing

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