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7th International Conference on Software engineering and information Management, ICSIM 2024

作者： Sharma, Bibhya Reddy, Pritika Singh, Vineet School of Information Technology Engineering Mathematics and Physics The University of the South Pacific Fiji Department of Computing Science and Information Systems Fiji National University Fiji

ISBN: (纸本)9798400709197

Good Governance of any country is reflected through transparency and accountability of its activities and the availability of information and open data in a timely manner to its citizens. The Right to information (RTI) Act guides the government and its citizens to achieve the objectives availability, and effective communication between the government and society, hence leading to good governance. While there are about 135 countries globally who have adopted the RTI Act, the Act is yet to be fully enacted in Fiji and the advocacy and awareness about RTI is in the infancy stage. This study entails details of an RTI awareness initiative in Fiji through the design and implementation of an e-course. The RTI e-course was developed using Articulate Storyline 360 and integrated into a learning management platform - Moodle. The course was then piloted to the university students in Fiji, Tonga and Vanuatu. The effectiveness of the course was measured in terms of course completion and collected feedback using an online questionnaire. The results from the Cronbach Alpha test showed that the RTI e-course was valid and reliable. Furthermore, the participants were satisfied with the design, structure and content of the e-course. The RTI e-course is the first-ever course on RTI as such this can be used by South Pacific countries to create awareness and educate their populace on their right to information. The awareness created will assist the people of the South Pacific to be more aware of the activities carried out by the government and eventually improve civic engagement and participation of the South Pacific populace to economic development of their society. © 2024 ACM.

关键词： Transparency

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A survey for light field super-resolution

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High-Confidence Computing 2024年第1期4卷 118-129页

作者： Mingyuan Zhao Hao Sheng Da Yang Sizhe Wang Ruixuan Cong Zhenglong Cui Rongshan Chen Tun Wang Shuai Wang Yang Huang Jiahao Shen State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100191China Key Laboratory of Data Science and Intelligent Computing International Innovation InstituteBeihang UniversityHangzhou 311115China State Key Laboratory of Software Development Environment School of Computer Science and EngineeringBeihang UniversityBeijing 100191China

Compared to 2D imaging data,the 4D light field(LF)data retains richer scene’s structure information,which can significantly improve the computer’s perception capability,including depth estimation,semantic segmentation,and LF ***,there is a contradiction between spatial and angular resolution during the LF image acquisition *** overcome the above problem,researchers have gradually focused on the light field super-resolution(LFSR).In the traditional solutions,researchers achieved the LFSR based on various optimization frameworks,such as Bayesian and Gaussian *** learning-based methods are more popular than conventional methods because they have better performance and more robust generalization *** this paper,the present approach can mainly divided into conventional methods and deep learning-based *** discuss these two branches in light field spatial super-resolution(LFSSR),light field angular super-resolution(LFASR),and light field spatial and angular super-resolution(LFSASR),***,this paper also introduces the primary public datasets and analyzes the performance of the prevalent approaches on these ***,we discuss the potential innovations of the LFSR to propose the progress of our research field.

关键词： Light field super-resolution Convolutional neural network Transformer Sub-aperture image Epipolar-plane image

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Local Path Planning of Unmanned Surface Vehicle Based on Improved DWA Algorithm

Local Path Planning of Unmanned Surface Vehicle Based on Imp...

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International Conference on Guidance, Navigation and Control, ICGNC 2024

作者： Ji, Chenjia Zhang, Lanyong College of Intelligent Systems Science and Engineering Harbin Engineering University Harbin150001 China State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin150001 China

ISBN: (纸本)9789819622351

An improved dynamic window method is proposed to solve the obstacle avoidance problem of intelligent ships in offshore waters that cannot effectively avoid when encountering unknown dynamic obstacles. Firstly, an autonomous collision avoidance decision-making model was constructed to judge whether the unmanned boat had a collision hazard, and the encounter situation was reclassified. To ensure that the USV can safely pass through the moving obstacle vessel, a collision risk assessment function considering the velocity of dynamic obstacles is introduced. Moreover, a collision avoidance rule function is incorporated into the evaluation function of the collision avoidance algorithm to ensure that the USV adheres to collision avoidance regulations. Ultimately, the simulation results confirm that the enhanced dynamic window method can safely avoid collisions while adhering to collision avoidance rules. This paper proposes an effective solution for collision avoidance behavior of intelligent vessels in near shore waters when facing unknown dynamic obstacles, providing insightful recommendations for subsequent research in maritime sectors. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Unmanned surface vehicles

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Recent Progress in All-Solution-Processed Organic Solar Cells

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Chinese Journal of Chemistry 2024年第2期42卷 190-198页

作者： Yixuan Xu Qian Wang Wentao Zou Xu Zhang Yanna Sun Yuanyuan Kan Ping Cai Ke Gao School of Materials Science and Engineering&Guangxi Key Laboratory of Information Materials Guilin University of Electronic TechnologyGuilinGuangxi 541004China Shandong Provincial Key Laboratory for Science of Material Creation and Energy Conversion Science Center for Material Creation and Energy ConversionInstitute of Frontier and Interdisciplinary ScienceShandong UniversityQingdaoShandong 266237China

All-solution-processed organic solar cells(OSCs)(from the bottom electrode to the top electrode)are highly attractive thanks to their low cost,lightweight and high-throughput ***,achieving highly efficient all-solution-processed OSCs remains a significant *** of the key issues is the lack of high-quality solution-processed electrode systems that can replace indium tin oxide(ITO)and vacuum-deposited metal *** this paper,we comprehensively review recent advances in all-solution-processed osCs,and classified the devices as the top electrode materials,including silver nanowires(AgNWs),conducting polymers and composite conducting *** correlation between electrode materials,properties of electrodes,and device performance in all-solution-processed OSCs is *** addition,the critical roles of the active layer and interface layer are also ***,the prospects and challenges of all-solution-processed OSCs are presented.

关键词： Organic solar cells All-solution-processed organic solar cells Solution-processed electrodes High performance Silver nanowires Conductive polymers Composite conducting materials

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A Note on Even-Sized Spectral Clustering 13

A Note on Even-Sized Spectral Clustering

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Joint 13th International Conference on Soft Computing and Intelligent systems and 25th International Symposium on Advanced Intelligent systems, SCIS and ISIS 2024

作者： Miyakawa, Hiroki Endo, Yasunori Graduate School of Science and Technology Degree Programs in Systems and Information Engineering Master's Program in Risk and Resilience Engineering University of Tsukuba Ibaraki Tsukuba Japan Institute of Systems and Information Engineering University of Tsukuba Ibaraki Tsukuba Japan

ISBN: (纸本)9798350373332

Clustering is a typical unsupervised learning method for classifying unsupervised data. One of the clustering meth-ods, even-sized clustering based on optimization (ECBO), is a clustering algorithm that imposes a constraint to equalize the size of each cluster. ECBO has been suggested to be effective in delivery and other problems. However, it is limited to Euclidean space. On the other hand, spectral clustering with a wide range of applicability for partitioning graph data has been proposed. In this paper, we propose even-sized spectral clustering, which imposes a size-equal constraint on spectral clustering, and show that it is an extension of the graph partitioning problem. We also verify the validity of the results through numerical examples. © 2024 IEEE.

关键词： Unsupervised learning

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CTNet:A Convolutional Transformer Network for Color Image Steganalysis

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Journal of Computer science & technology 2025年第2期40卷 413-427页

作者： Kang-Kang Wei Wei-Qi Luo Shun-Quan Tan Ji-Wu Huang School of Computer Science and Engineering Sun Yat-sen UniversityGuangzhou 510006China Guangdong Key Laboratory of Information Security Technology Sun Yat-sen UniversityGuangzhou 510006China College of Computer Science and Software Engineering Shenzhen UniversityShenzhen 518060China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen UniversityShenzhen 518060China Shenzhen Key Laboratory of Media Security Shenzhen UniversityShenzhen 518060China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen 518129China

Compared with convolutional neural network(CNN),Transformer can obtain global receptive field features more effectively and has recently achieved great success in natural language processing and computer *** to the particularity of steganography,however,almost all existing steganalytic networks just employ CNN with local receptive fields to detect embedding *** this paper,we propose a novel convolutional Transformer network for color image ***,we firstly obtain various image residuals for each color channel of an input image in the pre-processing *** capture more comprehensive steganalytic features,the truncated residuals after channel concatenation will pass through a feature extraction module composed of a CNN group and a Transformer *** CNN group aims to extract local receptive fields features,while the Transformer group with multi-head self-attention as the key tries to extract global steganalytic ***,we employ a global covariance pooling(GCP)and two fully-connected(FC)layers with dropout for *** comparative experiments demonstrate that the proposed method can significantly improve the detection performances in color image steganalysis and achieve state-of-the-art *** the proposed method is originally designed for color images,it can also obtain competitive results for grayscale images compared with the current best *** addition,we provide numerous ablation studies to verify the rationality of the proposed network architecture.

关键词： steganalysis steganography convolutional neural network Transformer color image

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RMGNet: The Progressive Relationship-Mining Graph Neural Network for Text-to-Image Person Re-Identification

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IEEE Transactions on Circuits and systems for Video technology 2025年第6期35卷 5749-5761页

作者： Zhang, Xin Liu, Kun Wang, Xinwang Zhou, Zhong Chen, Haiyong Hebei University of Technology School of Artificial Intelligence Tianjin300130 China Southeast University School of Instrument Science and Engineering Jiangsu Nanjing210018 China Wuxi Institute of Technology School of Integrated Circuit Wuxi214121 China Zhongguancun Laboratory Beijing100086 China Beihang University State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beijing100191 China

The Text-to-Image Person Re-identification (TI-ReID) task objective is to precisely identify the person’s images with the textual description of the person. The mainstream research methods focus on cross-modal aligning local features, and overlook the learning of intra-modal and cross-modal relationships between different features. This renders the person features lacking in high-level semantic information. To resolve such issues, we propose the Progressive Relationship-Mining Graph Network (RMGNet), including the Intra-Modal Relationship-Mining (IMRM) and the Cross-Modal Relationship-Mining (CMRM) module. These modules are employed to model and mine semantic relationship information among different features. Specifically, the IMRM module models and mines the high-level semantic interrelationships inherent in the image and text features. The CMRM module introduces the nearest neighbor method to model cross-modal semantic relationships to enhance the cross-modal semantic correspondence capabilities of person features. On this basis, we design the Adaptive Corner Center (Acc) loss and the Coarse-to-Fine Learning (C2FL) strategy. These ensure the network receives consistent and effective metric learning supervision throughout the entirety of the training process. To validate the efficacy of the proposed method, extensive experiments are conducted on three prevalent datasets: CHUK-PEDES, ICFC-PEDES, and RSTPReid. The achieved mAP of 70.59%, 41.62%, and 49.58% surpassed those current state-of-the-art methods. © 1991-2012 IEEE.

关键词： Graph neural networks

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Electron modulation by atomic Ir site decoration in porous Co/N co-doped carbon for electrocatalytic hydrogen evolution

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Nano Research 2023年第2期16卷 2011-2019页

作者： Youkui Zhang Zitong Yan Yeli Gao Yaqin Fu Wenhao Li Yunxiang Lin Chuanqiang Wu Yujuan Pu Tao Duan Li Song State Key Laboratory of Environment-Friendly Energy Materials National Co-Innovation Center for Nuclear Waste Disposal and Environmental SafetySouthwest University of Science and TechnologyMianyang 621010China National Synchrotron Radiation Laboratory Chinese Academy of Sciences(CAS)Center for Excellence in NanoscienceUniversity of Science and Technology of ChinaHefei 230029China Institute of Physical Science and Information Technology School of Materials Science and EngineeringAnhui Key Laboratory of Information Materials and DevicesKey Laboratory of Structure and Functional Regulation of Hybrid Materials of Ministry of EducationAnhui UniversityHefei 230601China College of Chemistry and Chemical Engineering Chongqing UniversityChongqing 401331China

The rational design of materials at atomic scale as efficient and stable electrocatalysts for hydrogen evolution reaction(HER)is critical for energy ***,we report a novel hybrid nanostructure with iridium(Ir)and cobalt(Co)atomic pair configuration anchored in porous nitrogen-doped carbon(pNC)nanosheets(denoted as IrCo-pNC)for electrocatalytic *** investigations and theoretical calculations reveal that the interaction between Ir and Co species in pNC promotes electron accumulation and depletion around isolated Ir and Co atoms,respectively,resulting in a local asymmetry electron density *** functional theory calculations also suggest that the electrons transfer from Co to adjacent Ir atom causing the down shift of the d-band center of Ir 5d in IrCo-pNC catalyst,thus optimizing the adsorption of hydrogen on Ir *** as-prepared IrCo-pNC exhibits significant HER performance with an overpotential of 21 mV to achieve a current density of 10 mA·cm^(−2)in 0.5 M *** work provides insight into the role of asymmetry electron density distribution in nanomaterials in regulating HER electrocatalysis.

关键词： electron modulation iridium decoration porous carbon electrocatalysis hydrogen evolution reaction

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Unveiling Veracity: An Integrated Semi-Supervised Algorithm for Fake News Detection 16

Unveiling Veracity: An Integrated Semi-Supervised Algorithm ...

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16th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2024

作者： Malik, Aman Behera, Dayal Kumar Swain, Amulya Ratna Hota, Jhalak Birla Institute of Technology and Science Department of Computer Science & Information Systems Hyderabad India Kiit University School of Computer Engineering Bhubaneswar India

ISBN: (纸本)9798350371154

In recent years, the pervasive dissemination of misinformation and deliberately falsified content, commonly referred to as 'fake news,' has become a critical challenge in the realm of information dissemination. With the exponential growth of digital media and online platforms, the ease and speed at which misleading information can be generated and spread have increased substantially. Addressing the formidable task of detecting fake news has thus become an imperative within the field of information technology. The supervised model gives promising results. However, the availability of labelled data is critical in the realm of fake news detection. Leveraging unlabeled data alongside labelled data is an effective approach, but it requires careful handling. This paper explores the application of Semi-Supervised Learning for fake news detection by integrating Label Propagation and various ML models as base classifiers. By leveraging both labelled and unlabeled data, Label Propagation with CatBoost Classifier seeks to overcome traditional limitations, offering a promising avenue for bolstering the reliability of automated systems in distinguishing between authentic and deceptive information in the vast landscape of digital content. © 2024 IEEE.

关键词： information dissemination

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A dynamic point cloud fast compression framework based on eliminating spatial and temporal redundancy

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Multimedia Tools and Applications 2025年 1-19页

作者： Su, Kainan Wang, Zunran Yang, Chenguang The Key Laboratory of Autonomous Systems and Networked Control College of Automation Science and Engineering South China University of Technology Guangzhou510641 China Huawei Technologies Co Ltd Shenzhen518028 China

Dynamic point clouds can be compressed by eliminating spatial and temporal redundancy, but few research studies have considered both simultaneously. Existing research can only distinguish the specific foreground and background of the scene, achieving the former with a limited compression ratio, while the latter relies on 3D motion estimation with high computing complexity. Therefore, it is difficult to meet the real-time requirement of building remote human-robot interaction system. Aiming at the shortcomings of the above methods, a dynamic point cloud fast compression framework based on eliminating spatial and temporal redundancy is proposed. On the one hand, for eliminating the spatial redundancy, the information of the recognized objects in point cloud data will be replaced with their categories and poses. On the other hand, we eliminate the temporal redundancy by using the motion vector of the point cloud macroblock. Specifically, in order to reduce cost time of the 3D motion estimation, we firstly introduce the 2D image ORB feature to calculate the motion vector from current frame to key frame, and then look for the similar macroblock in key frame. Thus, the information of the macroblocks similar to the key frame in the current frame is replaced by the index of the corresponding macroblocks of the key frame and the motion vector. Experimental results show that the proposed method has higher computational efficiency and compression quality than the existing compression algorithm. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Semantic Segmentation

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