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10th International Seminar on New Paradigm and Innovation of Natural sciences and itsApplication, ISNPINSA 2020

作者： Yani, F. Nurhayati, O.D. Widodo, A.P. Department of Information System Post Graduated School Diponegoro University Indonesia Department of Computer Engineering Faculty of Engineering Diponegoro University Indonesia Department of Physics Faculty of Science and Mathematics Diponegoro University Indonesia

It is currently thought that university websites play important roles in maintaining communication with stakeholders. The evidence shows that the usability aspect of the website positively correlates with governance support. This paper presents a new approach to identify, assess and evaluate usability aspect base on ten Heuristic Evaluation principles by implementing the ANP method. This method works by determining the weight of the best site criteria according to Webometric version ranking. The initial questionnaire was tested for validity and reliability using total item correlation analysis, the r-value was obtained in the range 0.706-0.919 (valid) and the reliability value in the range 0.861-0.939 (reliable). It was also found that by implementing expert judgment tests, some questionnaires are required more editorial adjustment. Interestingly, the layout of the websites hit the usability criteria. Another ten HE aspects run well while some light improvements were required for error prevention, recognition rather than recall, help users recognize, diagnose, and recover from errors aspects. The most remarkable result to emerge from the data was that visibility of system status, match between system and the real world, and consistency standards shown good result. © Published under licence by IOP Publishing Ltd.

关键词： Heuristic methods

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BAT: Mining Binary-API Topic for Multi-service Application Development

BAT: Mining Binary-API Topic for Multi-service Application D...

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International Conference on computer Supported Cooperative Work in Design

作者： Pengfei He Linlin Liu Dianlong You Limin Shen Zhen Chen School of Information Science and Engineering Yanshan University Qinhuangdao China National Science Libraries Chinese Academy of Sciences Beijing China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao China

With the growing popularity of API-driven multiservice application (mashup) development, the burgeoning web APIs have left developers drowning in the sea of web API selections. Matching developers with the most appropriate APIs is the key to improving user satisfaction and promoting more popular web applications. As a result, more and more researchers pay attention to web API recommender systems based on collaborative filtering. However, employing collaborative filtering to recommend APIs is challenging due to the severe sparsity of mashup and API interactions. To address this problem, we propose a probabilistic generative model, called the Binary-API Topic model (BAT), to parameterize mashups and APIs. Technically, BAT is equipped with a mechanism to extract binary-APIs and predict unknown pairwise interactions. To improve generality and capture more relevance from a limited number of interactions, we learn binary-API topics by directly modeling the generation of API co-occurrence patterns across the repository (all mashup collections from ***). The main advantage of BAT is that it preserves API co-occurrence patterns in model learning and exploits the rich global relevance. Finally, through extensive experiments, we demonstrate that BAT can achieve the highest performance on the sparse real-world data set.

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Demographical gender prediction of Twitter users using big data analytics: An application of decision marketing

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International Journal of Reasoning-based Intelligent systems 2021年第2期13卷 41-49页

作者： Roy, Sudipta Patel, Bhavya Bhattacharyya, Debnath Dhayal, Kushal Kim, Tai-Hoon Mittal, Mamta PRT2L Washington University in St. Louis Saint LouisMO63110 United States Department of Computer Science and Engineering Ganpat University MehsanaGujarat384012 India Evolutionary System Pvt. Ltd. Kataria Automobiles Rd. AhmedabadGujarat380051 India Department of Computer Science and Engineering K L Deemed to be University KLEF Guntur522502 India School of Economics and Management Beijing Jiaotong University Beijing100044 China Department of Computer Science and Engineering G.B. Pant Government Engineering College New Delhi India

Twitter text is difficult to analyse due to the non-standard and unstructured data. Twitter does not accumulate user gender information as do other popular social media platforms. The demographic feature prediction and additional informative content are important for advertising, custom-made marketing and authorised investigation from the social medium. The proposed statistical method with real-time analysis using big data technologies is able to predict the gender of Twitter users. Gender prediction is performed using the naive Bayes classifier to address systemic issues, and Apache Hive is used to solve data cleaning, storage and processing issues. The proposed method is a speedy, easy-to-implement with pre-processing, close to state-of-the-art document text categorisation method using big data technologies. Copyright © 2021 Inderscience Enterprises Ltd.

关键词： Marketing

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A 64-channel, 80 e-(rms), 10.5 Mcps, Energy-Resolving Photon-Counting Readout ASIC in 130-nm Technology for Silicon X-ray Imaging Detectors

A 64-channel, 80 e-(rms), 10.5 Mcps, Energy-Resolving Photon...

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IEEE Symposium on Nuclear science (NSS/MIC)

作者： T. Gao Y. Dou Y. Zhang T. Lu L. Sun W. Zhang X. Cao X. Liu J. Zhang W. Gao School of Computer Science Interdisciplinary Research Center of Bio-inspired System-on-chip Northwestern Polytechnical University Xi’an China Nuchip Medical Technology Co. Ltd. Shanting District Shanting China Ministry of Education Engineering Research Center of Embedded System Integrated Xi’an China

ISBN: (数字)9798350388152

ISBN: (纸本)9798350388169

A silicon-strip detector is a good candidate for energy-resolving photon-counting detectors in next-generation computed tomography (CT) imaging scanners with an energy range of 2 to 140 keV. A full-customized multichannel readout application-specific integrated circuit (ASIC) is required to read out the detector signals and generate the photon count with energy classification. The challenges of such an ASIC lie in low-noise analog front-ends to meet the energy detection limitation of less than 2 keV, as well as a high-performance multi-energy discriminator-counter circuit to achieve a count rate larger than 10 Mcps. In this paper, we propose a novel multichannel energy-resolving photon-counting readout ASIC in 130-nm technology for such application. The first prototype ASIC includes 64 readout channels, 2 testing channels, peripheral blocks, and a global digital part. Eight energy bins are implemented by employing eight comparators and counters in each channel. The die size is 4.3 mm × 3.5 mm. The simulated results are listed below. The input dynamic range is 0.08 fC ~ 7 fC. The best equivalent noise charge of the analog front end is 79 e- (rms) plus 22 e-/pF under the power dissipation of about 2.5 mW/channel. The count rate is up to 10.5 Mcps. The inconsistency between channels is less than 4 %. The chip has been submitted for fabrication. The samples will come back in June. For future work, the overall performance of the prototype chip will be evaluated and a 192-channel ASIC will be developed.

关键词： Semiconductor detectors Computed tomography Noise Prototypes Silicon Power dissipation X-ray imaging

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Revolutionizing electric robot charging infrastructure through federated transfer learning and data route optimization

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Cluster Computing 2025年第6期28卷 1-14页

作者： Hayawi, Kadhim Sajid, Junaid Malik, Asad Waqar Trabelsi, Zouheir College of Interdisciplinary Studies Computational Systems Zayed University Abu Dhabi United Arab Emirates School of Electrical Engineering and Computer Science (SEECS) National University of Sciences and Technology (NUST) Islamabad Pakistan Department of Electrical and Computer Engineering National Strategic Planning and Analysis Research Center Mississippi State University Starkville USA Department of Information System and Security United Arab Emirates University Al Ain United Arab Emirates

The growing prevalence of electric robots within industrial settings calls for innovative solutions to address the limitations in the existing charging infrastructure, ultimately improving the efficiency and sustainability of these robotic systems. However, internet connectivity, which may be required for prior scheduling and charging, is often unavailable due to security concerns, harsh conditions, operational control requirements, and regulatory compliance. Protecting sensitive data, ensuring reliability, maintaining production control, and adhering to industry-specific regulations drive the decision to isolate internal networks from the internet. In such scenarios, locating a suitable charging spot for ERs, particularly during urgent situations with critically low battery levels, becomes challenging. This paper proposes a scheduling algorithm that relies on an ER-to-ER mechanism for charging at available stations. Moreover, since the process relies on ad-hoc communication susceptible to potential malicious actions by intermediate entities, it poses a risk to the overall process. To mitigate this risk, we propose a machine learning-driven method to detect malicious connections, safeguarding the network’s stability. Moreover, to reduce network congestion resulting from routing requests, we have modified the Grey-wolf optimization algorithm specifically for industrial settings. This modification enables the efficient scheduling of charging requests at optimal stations, all while prioritizing the privacy of robots.

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OpenGait: Revisiting Gait Recognition Toward Better Practicality

OpenGait: Revisiting Gait Recognition Toward Better Practica...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Chao Fan Junhao Liang Chuanfu Shen Saihui Hou Yongzhen Huang Shiqi Yu Department of Computer Science and Engineering Southern University of Science and Technology Research Institute of Trustworthy Autonomous System Southern University of Science and Technology Department of Industrial and Manufacturing Systems Engineering The University of Hong Kong School of Artificial Intelligence Beijing Normal University WATRIX.AI

Gait recognition is one of the most critical long-distance identification technologies and increasingly gains popularity in both research and industry communities. Despite the significant progress made in indoor datasets, much evidence shows that gait recognition techniques perform poorly in the wild. More importantly, we also find that some conclusions drawn from indoor datasets cannot be generalized to real applications. Therefore, the primary goal of this paper is to present a comprehensive benchmark study for better practicality rather than only a particular model for better performance. To this end, we first develop a flexible and efficient gait recognition codebase named OpenGait. Based on OpenGait, we deeply revisit the recent development of gait recognition by re-conducting the ablative experiments. Encouragingly, we detect some unperfect parts of certain prior woks, as well as new insights. Inspired by these discoveries, we develop a structurally simple, empirically powerful, and practically robust baseline model, Gait-Base. Experimentally, we comprehensively compare Gait-Base with many current gait recognition methods on multiple public datasets, and the results reflect that GaitBase achieves significantly strong performance in most cases regardless of indoor or outdoor situations. Code is available at https://***/ShiqiYu/OpenGait.

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AsyncSC: An Asynchronous Sidechain for Multi-Domain Data Exchange in Internet of Things

arXiv

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arXiv 2024年

作者： Yang, Lingxiao Dong, Xuewen Wan, Zhiguo Gao, Sheng Tong, Wei Lu, Di Shen, Yulong Du, Xiaojiang The School of Computer Science and Technology Xidian University The Engineering Research Center of Blockchain Technology Application and Evaluation Ministry of Education China The Shaanxi Key Laboratory of Blockchain and Secure Computing Xi'An710071 China The Zhejiang Lab Hangzhou311121 China The School of Information Central University of Finance and Economics Beijing100081 China The School of Information Science and Engineering Zhejiang Sci-Tech University Hangzhou310018 China The School of Computer Science and Technology Xidian University China The Shaanxi Key Laboratory of Network and System Security Xi'An710071 China The School of Engineering and Science Stevens Institute of Technology Hoboken07030 United States

Sidechain techniques improve blockchain scalability and interoperability, providing decentralized exchange and cross-chain collaboration solutions for Internet of Things (IoT) data across various domains. However, current state-of-the-art (SOTA) schemes for IoT multi-domain data exchange are constrained by the need for synchronous networks, hindering efficient cross-chain interactions in discontinuous networks and leading to suboptimal data exchange. In this paper, we propose AsyncSC, a novel asynchronous sidechain construction. It employs a committee to provide Cross-Blockchain as a Service (C-BaaS) for data exchange in multi-domain IoT. To fulfill the need for asynchronous and efficient data exchange, we combine the ideas of aggregate signatures and verifiable delay functions to devise a novel cryptographic primitive called delayed aggregate signature (DAS), which constructs asynchronous cross-chain proofs (ACPs) that ensure the security of cross-chain interactions. To ensure the consistency of asynchronous transactions, we propose a multilevel buffered transaction pool that guarantees the transaction sequencing. We analyze and prove the security of AsyncSC, simulate an asynchronous communication environment, and conduct a comprehensive evaluation. The results show that AsyncSC outperforms SOTA schemes, improving throughput by an average of 1.21 to 3.96 times, reducing transaction latency by 59.76% to 83.61%, and maintaining comparable resource overhead. Copyright © 2024, The Authors. All rights reserved.

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A transactional-behavior-based hierarchical gated network for credit card fraud detection

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IEEE/CAA Journal of Automatica Sinica 2025年

作者： Xie, Yu Zhou, MengChu Liu, Guanjun Wei, Lifei Zhu, Honghao De Meo, Pasquale College of Information Engineering Shanghai Maritime University Shanghai201306 China School of Information and Electronic Engineering Zhejiang Gongshang University Hangzhou310018 China Helen and John C. Hartmann Department of Electrical and Computer Engineering New Jersey Institute of Technology NewarkNJ07102 United States Key Laboratory of Embedded System and Service Computing Ministry of Education Department of Computer Science Tongji University Shanghai201804 China College of Computer Science and Information Engineering Bengbu University Bengbu233030 China Department of Ancient and Modern Civilizations University of Messina Messina98166 Italy

The task of detecting fraud in credit card transactions is crucial to ensure the security and stability of a financial system, as well as to enforce customer confidence in digital payment systems. Historically, credit card companies have used rule-based approaches to detect fraudulent transactions, but these have proven inadequate due to the complexity of fraud strategies and have been replaced by much more powerful solutions based on machine learning or deep learning algorithms. Despite significant progress, the current approaches to fraud detection suffer from a number of limitations: for example, it is unclear whether some transaction features are more effective than others in discriminating fraudulent transactions, and they often neglect possible correlations among transactions, even though they could reveal illicit behaviour. In this paper, we propose a novel credit card fraud detection (CCFD) method based on a transaction behaviour-based hierarchical gated network. First, we introduce a feature-oriented extraction module capable of identifying key features from original transactions, and such analysis is effective in revealing the behavioural characteristics of fraudsters. Second, we design a transaction-oriented extraction module capable of capturing the correlation between users' historical and current transactional behaviour. Such information is crucial for revealing users' sequential behaviour patterns. Our approach, called transactional-behaviour-based hierarchical gated network model (TbHGN), extracts two types of new transactional features, which are then combined in a feature interaction module to learn the final transactional representations used for CCFD. We have conducted extensive experiments on a real-world credit card transaction dataset with an increase in average F1 between 1.42% and 6.53% and an improvement in average AUC between 0.63% and 2.78% over the state of the art. © 2025 Institute of Electrical and Electronics Engineers Inc.. All right

关键词： Deep learning

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Optimization Method of Design Freeform Progressive Addition Lenses Based On The Coincident Degree of Weight Distributions For Power-Deviation and Astigmatism

SSRN

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SSRN 2023年

作者： Xiang, Huazhong Wang, Yaqiong Ding, Qihui Zhan, Xiaodie Zheng, Zexi Chen, Jiabi Wang, Cheng Zhang, Dawei Zhuang, Songlin School of Medical Instrument and Food Engineering University of Shanghai for Science and Technology 516 Jun Gong Road Shanghai200093 China School of Mechanical Engineering University of Shanghai for Science and Technology Shanghai200093 China Engineering Research Center of Optic Instrument and System Ministry of Education University of Shanghai for Science and Technology Shanghai200093 China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China

The optimizing method of the freeform progressive addition lenses (PAL) was proposed based on the coincident degree of weight distributions for power-deviation and astigmatism. In the design phase, according to the existing surface shape, the power-deviation and astigmatism for freeform PALs were evaluated. Comparing with the prescription of patients, the coincident degrees between the obtained distributions and the prescription of the power, astigmatism of the PAL were calculated in the multi-viewing-axis condition. The normalization processing of coincident degrees were carried out, and the corresponding threshold value of weight distributions and optimizing the allocation of coincident degree were formed. Correspondingly, based on a minimization error function model, two PALs were designed, simulated, machined, and evaluated by commercial software. The results show and the optimized method were reducing the peripheral astigmatism and improving the optical properties of the PALs. This study puts forward a new powerful approach for optimizing the freeform PAL and advancing the design procedure optimization of PALs in optometry. © 2023, The Authors. All rights reserved.

关键词： Optical properties

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ESNet: evolution and succession network for high-resolution salient object detection 24

ESNet: evolution and succession network for high-resolution ...

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Proceedings of the 41st International Conference on Machine Learning

作者： Hongyu Liu Runmin Cong Hua Li Qianqian Xu Qingming Huang Wei Zhang Institute of Information Science Beijing Jiaotong University & Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing China School of Control Science and Engineering Shandong University & Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan China School of Computer Science and Technology Hainan University Hainan China Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China

Preserving details and avoiding high computational costs are the two main challenges for the High-Resolution Salient Object Detection (HRSOD) task. In this paper, we propose a two-stage HRSOD model from the perspective of evolution and succession, including an evolution stage with Low-resolution Location Model (LrLM) and a succession stage with High-resolution Refinement Model (HrRM). The evolution stage achieves detail-preserving salient objects localization on the low-resolution image through the evolution mechanisms on supervision and feature; the succession stage utilizes the shallow high-resolution features to complement and enhance the features inherited from the first stage in a lightweight manner and generate the final high-resolution saliency prediction. Besides, a new metric named Boundary-Detail-aware Mean Absolute Error (MAEBD) is designed to evaluate the ability to detect details in high-resolution scenes. Extensive experiments on five datasets demonstrate that our network achieves superior performance at real-time speed (49 FPS) compared to state-of-the-art methods. Our code is publicly available at: https://***/rmcong/ESNet_ICML24.

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