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arXiv 2024年

作者： Zhu, Hongze Xie, Guoyang Hou, Chengbin Dai, Tao Gao, Can Wang, Jinbao Shen, Linlin National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Computer Science City University of Hong Kong Hong Kong Department of Intelligent Manufacturing CATL Ningde China Fuzhou Fuyao Institute for Advanced Study Fuyao University of Science and Technology Fuzhou China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

High-resolution point clouds (HRPCD) anomaly detection (AD) plays a critical role in precision machining and high-end equipment manufacturing. Despite considerable 3D-AD methods that have been proposed recently, they still cannot meet the requirements of the HRPCD-AD task. There are several challenges: i) It is difficult to directly capture HRPCD information due to large amounts of points at the sample level;ii) The advanced transformer-based methods usually obtain anisotropic features, leading to degradation of the representation;iii) The proportion of abnormal areas is very small, which makes it difficult to characterize. To address these challenges, we propose a novel group-level feature-based network, called Group3AD, which has a significantly efficient representation ability. First, we design an Intercluster Uniformity Network (IUN) to present the mapping of different groups in the feature space as several clusters, and obtain a more uniform distribution between clusters representing different parts of the point clouds in the feature space. Then, an Intracluster Alignment Network (IAN) is designed to encourage groups within the cluster to be distributed tightly in the feature space. In addition, we propose an Adaptive Group-Center Selection (AGCS) based on geometric information to improve the pixel density of potential anomalous regions during inference. The experimental results verify the effectiveness of our proposed Group3AD, which surpasses Reg3D-AD by the margin of 5% in terms of object-level AUROC on Real3DAD. We provide the code and supplementary information on our website: https://***/M-3LAB/Group3AD. Copyright © 2024, The Authors. All rights reserved.

关键词： Anomaly detection

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Ontological Approach to the Development and Accreditation of the Educational Programs for the Training of IT Specialists of Ukraine 15

Ontological Approach to the Development and Accreditation of...

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15th IEEE International Scientific and Technical Conference on Computer Sciences and information Technologies, CSIT 2020

作者： Kovaliuk, Tetiana Kobets, Nataliya Pasichnyk, Volodymyr Kunanets, Nataliia Taras Shevchenko National University of Kyiv Software System and Technology Department Kyiv Ukraine Borys Grinchenko Kyiv University Department of Computer Science and Mathematics Kyiv Ukraine Lviv Polytechnic National University Information Systems and Networks Department Lviv Ukraine

ISBN: (纸本)9781728174433

The article discusses the concept of knowledge management, which can be used as a methodological basis for the development of new educational programs. The ontology of the educational program is built based on the content of the educational program for the subject area "Computer Science"of the bachelor's educational level. A formal model of the domain ontology is given. It is shown that the structure of ontologies is a conceptualized representation of the subject area, similar to the structure of a concept map, which is a successful method of meaningful learning. To build the ontology of accreditation of the educational program, domains of the discipline;educational program;institution domain;and accreditation domain are considered. The developed ontology can improve Educational Program development management and ensure successful accreditation © 2020 IEEE.

关键词： Ontology

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An efficient proposed yangon railway system simulation module

An efficient proposed yangon railway system simulation modul...

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2020 International Conference on Electronics, information, and Communication, ICEIC 2020

作者： Aung, Htain Lynn Sasithong, Pruk Ullah, Irfan Pengnoo, Manus Wuttisittikulkij, Lunchakorn Pirom, Wasin Poomrittigul, Suvit Wijayasekara, Sanika Chulalongkorn University Department of Electrical Engineering Bangkok Thailand Pathumwan Insitute of Technology Department of Software Engineering and Information System Bangkok Thailand General Sir John Kotelawala Defence University Department of Electrical Electronic and Telecommunication Engineering Ratmalana Sri Lanka

ISBN: (纸本)9781728162898

Public transportation is significant in an economical city like Yangon. In Yangon, there are four types of public transport facilities;mostly peoples can choose bus, train, taxi and water bus particularly. Railway system in Yangon city is not very developed. Most of the peoples have to depend on the buses system in the city. The massive amount of using buses in the city as the disadvantages;it can lead to traffic jam and air pollution. In this research article, we proposed an efficient software tool with user-friendly Graphical User Interface (GUI) that can help to upgrade the Yangon city railway system to the intelligent railway system and simulate the Yangon Circular Railway system to a greater extent. In the proposed software, we can easily visualize the train movement, modify the number of stations, the arrival rate, generate the simulation graph, modify the train, stations and speed, etc., to utilize the current resources. © 2020 IEEE.

关键词： Graphical user interfaces

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Analytical Models for QoT Estimations in Super-C and Super-C + L Bands Optical Transmission systems

Analytical Models for QoT Estimations in Super-C and Super-C...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Baoluo Yan Yijun Sun Ziwen Zhou Hong Liu Hongya Wang Chen Huan Zhiyong Zhao Lipeng Feng Anxu Zhang Zhenhua Feng Hu Shi WDM System Department of Wireline Product R&D Institute ZTE Corp. Beijing China State Key Laboratory of Mobile Network and Mobile Multimedia Technology China School of Software Engineering Chongqing University of Posts and Telecommunications Chongqing China Optics Valley Laboratory School of Optical and Electronic Information and Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan China China Telecom Research Institute Beijing China

We propose a novel physical model-based QoT estimation with achieved OSNR estimation error ≤ 1.5 dB in primary paths and OSNR prediction error ≤ 1.76 dB in unestablished lightpaths for both super-C bands and super-C... 详细信息

ISBN: (纸本)9781957171258

关键词： Hole burning Optical amplifiers Optical networks Optical signal to noise ratio Stimulated scattering Variable optical attenuators

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Semantic Learning and Emulation Based Cross-platform Binary Vulnerability Seeker

arXiv

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arXiv 2022年

作者： Gao, Jian Jiang, Yu Liu, Zhe Yang, Xin Wang, Cong Jiao, Xun Yang, Zijiang Sun, Jiaguang The School of Software Tsinghua University Beijing National Research Center for Information Science and Technology Key Laboratory for Information System Security Ministry of Education Beijing100084 China The College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing211106 China The Department of Electrical and Computer Engineering Villanova University VillanovaPA19085 United States The Department of Computer Science Western Michigan University KalamazooMI49008 United States

Clone detection is widely exploited for software vulnerability search. The approaches based on source code analysis cannot be applied to binary clone detection because the same source code can produce significantly different binaries due to different operating systems, microprocessor architectures and compilers. In this paper, we present BinSeeker, a cross-platform binary seeker that integrates semantic learning and emulation. With the help of the labeled semantic flow graph, BinSeeker can quickly identify M candidate functions that are most similar to the vulnerability from the target binary. The value of M is relatively large so this semantic learning procedure essentially eliminates those functions that are very unlikely to have the vulnerability. Then, semantic emulation is conducted on these M candidates to obtain their dynamic signature sequences. By comparing signature sequences, BinSeeker produces top-N functions that exhibit most similar behavior to that of the vulnerability. With fast filtering of semantic learning and accurate comparison of semantic emulation, BinSeeker seeks vulnerability precisely with little overhead. The experiments on six widely used programs with fifteen known CVE vulnerabilities demonstrate that BinSeeker outperforms three state-of-the-art tools Genius, Gemini and CACompare. Regarding search accuracy, BinSeeker achieves an MRR value of 0.65 in the target programs, whereas the MRR values by Genius, Gemini and CACompare are 0.17, 0.07 and 0.42, respectively. If we consider ranking a function with the targeted vulnerability in the top-5 as accurate, BinSeeker achieves the accuracy of 93.33 percent, while the accuracy of the other three tools is merely 33.33, 13.33 and 53.33 percent, respectively. Such accuracy is achieved with 0.27s on average to determine whether the target binary function contains a known vulnerability, and the time for the other three tools are 1.57s, 0.15s and 0.98s, respectively. Compared to the time used to manu

关键词： Semantics

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When to Update Your Model: Constrained Model-based Reinforcement Learning

arXiv

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arXiv 2022年

作者： Ji, Tianying Luo, Yu Sun, Fuchun Jing, Mingxuan He, Fengxiang Huang, Wenbing Department of Computer Science and Technology Tsinghua University China Science & Technology on Integrated Information System Laboratory Institute of Software Chinese Academy of Sciences China JD Explore Academy *** Inc China Gaoling School of Artificial Intelligence Renmin University of China China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China

Designing and analyzing model-based RL (MBRL) algorithms with guaranteed monotonic improvement has been challenging, mainly due to the interdependence between policy optimization and model learning. Existing discrepancy bounds generally ignore the impacts of model shifts, and their corresponding algorithms are prone to degrade performance by drastic model updating. In this work, we first propose a novel and general theoretical scheme for a non-decreasing performance guarantee of MBRL. Our follow-up derived bounds reveal the relationship between model shifts and performance improvement. These discoveries encourage us to formulate a constrained lower-bound optimization problem to permit the monotonicity of MBRL. A further example demonstrates that learning models from a dynamically-varying number of explorations benefit the eventual returns. Motivated by these analyses, we design a simple but effective algorithm CMLO1 (Constrained Model-shift Lower-bound Optimization), by introducing an event-triggered mechanism that flexibly determines when to update the model. Experiments show that CMLO surpasses other state-of-the-art methods and produces a boost when various policy optimization methods are employed. © 2022, CC BY-NC-SA.

关键词： Constrained optimization

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Enhancing Pulmonary Nodule Classification performance with TriCaps-RL: A Capsule Network Reinforcement Learning Approach

Journal of Network Intelligence

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Journal of Network Intelligence 2024年第3期9卷 1441-1459页

作者： Zheng, Guang-Yuan Zhang, Fu-Quan Cheng, Chen Shan, Hong-Tao Soomro, Nouman Qadeer Liu, Yi College of Information Technology Shanghai Jian Qiao University Shanghai201306 China School of Mathematics and Computer Science Yanan University Shaanxi 716000 China School of Computer and Data Science Minjiang University No.200 Xiyuangong Road Fuzhou University Town Fuzhou350108 China Digital Media Art Key Laboratory of Sichuan Province Sichuan Conservatory of Music Chengdu610021 China Fuzhou Technology Innovation Center of intelligent Manufacturing information System Minjiang University Fuzhou350108 China Fujian Province University Fuzhou350300 China College of Business Shanghai Jian Qiao University Shanghai201306 China College of Electronic and Electrical Engineering Shanghai University of Engineering Science Shanghai201620 China Software Department Mehran University of Engineering & Technology Sindh 76062 Pakistan

Distinguishing the manifestations of pulmonary nodules poses a significant challenge in the medical field, demanding the expertise of experienced radiologists. This complexity results in the high cost and inadequacy of sample labeling. In response, this study introduces a reinforcement learning approach, denoted as TriCaps-RL, for the classification of pulmonary nodules based on CT signs. This approach initially employs the Q-value loss to train a single CapsNet. When the performance of the CapsNet plateaus and further enhancement becomes challenging, the loss function is progressively replaced by a triplet metric to augment performance. Consequently, a more nuanced differentiation among various types of pulmonary nodules is achieved. The proposed method can improve its performance through interactions with radiologists during their CT image reading process, thereby mitigating the shortage of radiologists and addressing the time-consuming issue of training sample labeling in medical image research. This paper delves into the fine-grained classification of pulmonary nodules using deep reinforcement learning based on CT imaging signs. In comparison to previous studies that primarily concentrate on the benign/malignant classification of lung nodules, this fine-grained classification of nodules founded on CT imaging signs proves to be more valuable for medical practitioners in making accurate diagnostic decisions. © 2024, Taiwan Ubiquitous information CO LTD. All rights reserved.

关键词： Lung cancer

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Exploring The Use of Blockchain technology in Cybersecurity and Data Science

Exploring The Use of Blockchain Technology in Cybersecurity ...

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Advance Computing and Innovative Technologies in Engineering (ICACITE), International Conference on

作者： Robaisya Rahmat Mohd Saleh Abbas MohdNorazmi Nordin YusmiMohd Yunus Sufri Muhammad Albert Feisal Ismail Universiti Tunku Abdul Rahman Sungai Long Selangor Malaysia INTI International University Nilai Negeri Sembilan Malaysia Faculty of Education Universiti Kebangsaan Malaysia Bangi Selangor Malaysia Institut Jantung Negara College Kuala Lumpur Malaysia Department of Software Engineering and Information System FCSiT Universiti Putra Malaysia Serdang Selangor Malaysia Faculty of Technology Management & Technopreneuurship Universiti Teknikal Malaysia Melaka Malaysia

Blockchain technology has demonstrated to be extremely efficient at processing distributed transactions in a secured manner. It consists of a wide range of applications. B. To handle smart contacts and Bitcoin cash. Blockchain systems may allow automatic data exchange and thinking about, resulting in increased effectiveness and lower costs. This is made possible by the adoption of contract technology and decentralized ledgers. Additionally, blockchain technologies can enhance integrity and security of data, enabling more precise and reliable analysis of information. Ethereum along with information science integration may make it easier to create decentralized apps, opening up novel industries and income sources. However, there are still issues with adaptability, interconnection, and complexity in the combination of blockchain technology and data science. For this type of technology to achieve all of its potential advantages, it is imperative that studies and advancements in this field persist. Blockchain applications for data science are currently being investigated. This essay examines how blockchain technology is used in cybersecurity and data science.

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A Review of Deep-Learning-Based Models for Afaan Oromo Fake News Detection on Social Media Networks 3rd

A Review of Deep-Learning-Based Models for Afaan Oromo Fake ...

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3rd International Conference on Advancements in Smart Computing and information Security, ASCIS 2024

作者： Arega, Kedir Lemma Tune, Kula Kekeba Beyene, Asrat Mulatu Tariku, Wegderes Bune, Nurhussen Menza ICT Infrastructures and Administrator Team Leader Ambo University Ambo Ethiopia Department of Software Engineering Addis Ababa Science and Technology University Addis Ababa Ethiopia Department of Electrical and Computer Engineering Addis Ababa Science and Technology University Addis Ababa Ethiopia Department of Information Systems Mizan Tepi University Tepi Ethiopia Project Management System Support Expert Ethio Telecom Addis Ababa Ethiopia

ISBN: (纸本)9783031862953

This study explores modern fake news detection techniques in the Afan Oromo language, aiming to contribute to advancing research in this field. It explores deep learning methods for transformers, including Bidirectional Encoder Representations, Attention, and Generative Adversarial Networks, and their applications in various industries such as machine translation, email spam detection, information extraction, summarization, medical, and question-answering. The paper investigates false news identification, its categories, characteristics, and taxonomy, and compares machine learning and deep learning algorithms for fake news, liars, and corpus datasets. It proposes that future studies use picture data and deeper learning network topologies to decrease erroneous outcomes. The research endeavor aims to illuminate and advance the understanding of the complex difficulties and possibilities that exist in this particular sector, thereby acting as a crucial resource for decision-makers, academics, and professionals, while also encouraging the creation of more effective and trustworthy methodologies that can adequately address and alleviate the dissemination of false information shared on various social media channels. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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MedSteGAN: Medical Embedding-Optimized Steganography with Improved GAN

SSRN

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SSRN 2023年

作者： Peng, Yuxiang Fu, Chong Zheng, Yu Tian, Yunjia Cao, Guixing Chen, Junxin School of Computer Science and Engineering Northeastern University Liaoning Shenyang110819 China Engineering Research Center of Security Technology of Complex Network System Ministry of Education China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Liaoning Shenyang110819 China Department of Information Engineering The Chinese University of Hong Kong Ma Liu Shui Shatin New Territories Hong Kong Institute of Telecommunication Satellite China Academy of Space Technology Beijing100094 China School of Software Dalian University of Technology Liaoning Dalian116621 China

Medical diagnosing systems contain sensitive information, especially the resulting certificate of diagnosis that involves the patient’s privacy, required to be protected carefully. An effective way is to secretly embed privacy-sensitive information into medical-generated images, eventually being viewed as arbitrary images of nobody/nothing in public. The current research line of medical steganography targets simple data-hiding methods, yet remains barren in optimal embedding and stronger robustness. To fill this gap, we propose an end-to-end steganographic framework for medical images. Our idea is to integrate medical-image adaptive steganography with a generative adversarial network (GAN), named MedSteGAN. To improve both robustness, we design an optimized U-shape gen- erator that extracts features more efficiently utilizing larger kernel convolutions and the ReLU6 function. Also, deconvolutions are replaced by upsampling and convolution layers to avoid the checkerboard effect. To enhance security and image quality, we employ visual quality loss and mean squared error (MSE) loss based on the multi-scale structural similarity index metric (MS- SSIM) and the high-pass filters, respectively. Extensive experiments demonstrate that MedSteGAN outperforms state-of-the-art steganographic schemes in terms of robustness and image quality. © 2023, The Authors. All rights reserved.

关键词： Image quality

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