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作者： Kang, Dongwann Lee, Taemin Shin, Yong-Hyeon Seo, Sanghyun Department of Computer Science and Engineering Seoul National University of Science and Technology 232 Gongneung-ro Nowon-gu Seoul01811 Korea Republic of Department of Artificial Intelligience and Software Kangwon National University 346 Chungang-ro Kangwon-do Samcheok-si25913 Korea Republic of School of Art and Technology Chung-Ang University 4726 Seodong-daero Daedeok-myeon Anseong-si17546 Korea Republic of

This paper presents a method to generate stained-glass animation from video inputs. The method initially segments an input video volume into several regions considered as fragments of glass by mean-shift segmentation. However, the segmentation predominantly results in over-segmentation, causing several tiny segments in a highly textured area. In practice, assembling significantly tiny or large glass fragments is avoided to ensure architectural stability in stained glass manufacturing. Therefore, we use low-frequency components in the segmentation to prevent over-segmentation and subdivide segmented regions that are oversized. The subdividing must be coherent between adjacent frames to prevent temporal artefacts, such as flickering and the shower door effect. To temporally subdivide regions coherently, we obtain a panoramic image from the segmented regions in input frames, subdivide it using a weighted Voronoi diagram, and thereafter project the subdivided regions onto the input frames. To render stained glass fragment for each coherent region, we determine the optimal match glass fragment for the region from a dataset consisting of real stained-glass fragment images and transfer its color and texture to the region. Finally, applying lead came at the boundary of the regions in each frame yields temporally coherent stained-glass animation. © 2022 KSII.

关键词： Textures

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Towards Efficient Resume Understanding: A Multi-Granularity Multi-Modal Pre-Training Approach

Towards Efficient Resume Understanding: A Multi-Granularity ...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Feihu Jiang Chuan Qin Jingshuai Zhang Kaichun Yao Xi Chen Dazhong Shen Chen Zhu Hengshu Zhu Hui Xiong University of Science and Technology of China Career Science Lab BOSS Zhipin PBC School of Finance Tsinghua University Institute of Software Chinese Academy of Sciences Shanghai Artificial Intelligence Laboratory The Thrust of Artificial Intelligence The Hong Kong University of Science and Technology (GuangZhou) The Department of Computer Science and Engineering The Hong Kong University of Science and Technology Guangzhou HKUST Fok Ying Tung Research Institute

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

In the contemporary era of widespread online recruitment, resume understanding has been widely acknowledged as a fundamental and crucial task, which aims to extract structured information from resume documents automatically. Compared to the traditional rule-based approaches, the utilization of recently proposed pre-trained document understanding models can greatly enhance the effectiveness of resume understanding. The present approaches have, however, disregarded the hierarchical relations within the structured information presented in resumes, and have difficulty parsing resumes in an efficient manner. To this end, in this paper, we propose a novel model, namely ERU, to achieve efficient resume understanding. Specifically, we first introduce a layout-aware multi-modal fusion transformer for encoding the segments in the resume with integrated textual, visual, and layout information. Then, we design three self-supervised tasks to pre-train this module via a large number of unlabeled resumes. Next, we fine-tune the model with a multi-granularity sequence labeling task to extract structured information from resumes. Finally, extensive experiments on a real-world dataset clearly demonstrate the effectiveness of ERU.

关键词： Visualization Resumes Layout Transformers Feature extraction Encoding Data mining Labeling Recruitment

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Explainable Semantic Federated Learning Enabled Industrial Edge Network for Fire Surveillance

arXiv

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arXiv 2024年

作者： Dong, Li Peng, Yubo Jiang, Feibo Wang, Kezhi Yang, Kun School of Computer Science Hunan University of Technology and Business Changsha410205 China Xiangjiang Laboratory Changsha410205 China School of Intelligent Software and Engineering Nanjing University Suzhou China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha410081 China Department of Computer Science Brunel University London United Kingdom

In fire surveillance, Industrial Internet of Things (IIoT) devices require transmitting large monitoring data frequently, which leads to huge consumption of spectrum resources. Hence, we propose an Industrial Edge Semantic Network (IESN) to allow IIoT devices to send warnings through Semantic communication (SC). Thus, we should consider (1) Data privacy and security. (2) SC model adaptation for heterogeneous devices. (3) Explainability of semantics. Therefore, first, we present an eXplainable Semantic Federated Learning (XSFL) to train the SC model, thus ensuring data privacy and security. Then, we present an Adaptive Client Training (ACT) strategy to provide a specific SC model for each device according to its Fisher information matrix, thus overcoming the heterogeneity. Next, an Explainable SC (ESC) mechanism is designed, which introduces a leakyReLU-based activation mapping to explain the relationship between the extracted semantics and monitoring data. Finally, simulation results demonstrate the effectiveness of XSFL. Copyright © 2024, The Authors. All rights reserved.

关键词： Federated learning

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Evolution of Quantum Computing: A Systematic Survey on the Use of Quantum Computing Tools

arXiv

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arXiv 2022年

作者： Upama, Paramita Basak Faruk, Jobair Hossain Nazim, Mohammad Masum, Mohammad Shahriar, Hossain Uddin, Gias Barzanjeh, Shabir Ahamed, Sheikh Iqbal Rahman, Akond Department of Computer Science and Engineering Eastern University Bangladesh Department of Software Engineering and Game Development Kennesaw State University United States Department Computer Science Kennesaw State University United States Department of Information Technology Kennesaw State University United States Schulich School of Engineering University of Calgary Canada Department of Physics and Astronomy University of Calgary Canada Department of Mathematics Statistics and Computer Marquette University United States Department of Computer Science Tennessee Tech university United States

Quantum Computing (QC) refers to an emerging paradigm that inherits and builds with the concepts and phenomena of Quantum Mechanic (QM) with the significant potential to unlock a remarkable opportunity to solve complex and computationally intractable problems that scientists could not tackle previously. In recent years, tremendous efforts and progress in QC mark a significant milestone in solving real-world problems much more efficiently than classical computing technology. While considerable progress is being made to move quantum computing in recent years, significant research efforts need to be devoted to move this domain from an idea to a working paradigm. In this paper, we conduct a systematic survey and categorize papers, tools, frameworks, platforms that facilitate quantum computing and analyze them from an application and Quantum Computing perspective. We present quantum Computing Layers, Characteristics of Quantum computer platforms, Circuit Simulator, Open-source Tools Cirq, TensorFlow Quantum, ProjectQ that allow implementing quantum programs in Python using a powerful and intuitive syntax. Following that, we discuss the current essence, identify open challenges and provide future research direction. We conclude that scores of frameworks, tools and platforms are emerged in the past few years, improvement of currently available facilities would exploit the research activities in the quantum research community. © 2022, CC BY.

关键词： Circuit simulation

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Deep Learning-Based Probability Model for Traffic Information Estimation

Journal of Network Intelligence

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Journal of Network Intelligence 2022年第3期7卷 592-607页

作者： Sun, Zhaoshan Pan, Jeng-Shyang Pan, Tien-Szu Chen, Chi-Hua College of Computer Science and Mathematics Fujian University of Technology Fuzhou350118 China Collage of Computer Science and Engineering Shandong University of Science and Technology China Chaoyang University of Technology Taiwan Fujian University of Technology China Department of Electronic Engineering National Kaohsiung University of Science and Technology 415 Chien-Kung Road Kaohsiung807 Taiwan College of Computer and Data Science College of Software Fuzhou University Fuzhou350116 China

This study proposes a novel traffic information estimation method based on deep learning and cellular floating vehicle data (CFVD). In this paper, a probabilistic analysis model based on deep learning is proposed to consider the relationship between vehicle speed and communication behaviors. Then, a vehicle speed estimation method based on the proposed probabilistic analytical model is proposed to estimate vehicle speed. For traffic flow estimation, normal location update is adopted to estimate traffic flow. The estimated vehicle speed and the estimated traffic flow can be gathered to estimate traffic density. The proposed method is verified by the simulation tool and the experiment results revealed that the accuracies of estimated vehicle speed, estimated traffic flow, and estimated traffic density are 96.36%, 99.80%, and 96.45%, respectively. Thus, this method estimates traffic information accurately and helps improve the performance of the intelligent transportation system (ITS). © 2022, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Vehicles

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PRAD: Unsupervised KPI Anomaly Detection by Joint Prediction and Reconstruction of Multivariate Time Series 19

PRAD: Unsupervised KPI Anomaly Detection by Joint Prediction...

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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and Computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable Computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy Computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Xiong, Zhiying Fan, Qilin Wang, Kai Li, Xiuhua Zhang, Xu Xiong, Qingyu Chongqing University School of Big Data and Software Engineering Chongqing China Chongqing University Chongqing Key Laboratory of Digital Cinema Art Theory and Technology Chongqing China Harbin Institute of Technology School of Computer Science and Technology Weihai China Harbin Institute of Technology Research Institute of Cyberspace Security Weihai China Haihe Laboratory of Information Technology Application Innovation Tianjin China University of Exeter Department of Computer Science Exeter United Kingdom

ISBN: (纸本)9798350346558

Detecting anomalies for key performance indicator (KPI) data is of paramount importance to ensure the quality and reliability of network services. However, building the anomaly detection system for KPI is challenging due to the scarcity of abnormal labels and highly dynamic or even unseen patterns. In this paper, we propose an unsupervised KPI anomaly detection framework named PRAD by jointly optimizing prediction-based and reconstruction-based modules. Specifically, PRAD employs two parallel graph attention networks (GATs) to learn metric-oriented and time-oriented relationships. A temporal convolutional attention network (TCAN) is used to capture complex long-term dependencies. Furthermore, to tackle the overfitting issue in the variational auto-encoder (VAE), PRAD designs a reconstruction-based module that combines VAE with a generative adversarial network (GAN). The experimental results on the two public datasets demonstrate that the proposed PRAD outperforms existing anomaly detection methods. © 2022 IEEE.

关键词： Anomaly detection

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A multi-agent system to support design pattern recommendation 20

A multi-agent system to support design pattern recommendatio...

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6th International Conference on engineering and MIS, ICEMIS 2020

作者： Saleh, Eiman M. Sallabi, Omar Darbi, Huweida A. Department of Software Engineering Benghazi University Benghazi Libya Department of Computer Science Benghazi University Benghazi Libya Department of Computer Science Omar Al-Mukhtar University Libya

ISBN: (纸本)9781450377362

A design pattern is a successful solution to recurring problems. It is a powerful tool to improve design quality and to reduce the time and cost of design. One of the major challenges confronting developers while using of design pattern is finding a suitable selection for them, thus it is considered as a challenging task for developers with less experience in the area of the design pattern. Therefore, the developers need a pattern recommendation to assist them by suggesting suitable design patterns. This paper presents the architecture Design Pattern Selection based on a Multi-Agent System (DPSA). The DPSA aims to obtain the appropriate recommendation to reduce development efforts, facilitate and assist the developers in selecting suitable patterns for their problems. DPSA's agents operate to support four different types of recommendations: single pattern, implement the pattern, pattern sequences, and recommending practical. © 2020 ACM.

关键词： Case based reasoning

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A novel cooperative game for reinforcing obesity awareness amongst children in UAE

A novel cooperative game for reinforcing obesity awareness ...

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International KES Conference on Human Centred Intelligent Systems, KES-HCIS 2020

作者： Alnaqbi, Fatema Alzahmi, Sarah Alharmoozi, Ayesha Alshehhi, Fatema Zia, Muhammad Talha Ouhbi, Sofia Belkacem, Abdelkader Nasreddine Department of Computer Science and Software Engineering CIT United Arab Emirates University Al Ain United Arab Emirates Department of Computer and Network Engineering CIT United Arab Emirates University Al Ain United Arab Emirates Department of Information Technology CIT United Arab Emirates University Al Ain United Arab Emirates

ISBN: (纸本)9789811557835

This paper presents a funny cooperative game that makes kids interact with their parents to indirectly educate both of them about the importance of making their own choices of eating unhealthy and healthy food. The game-based learning and cultivation of an informed decision-making approach throughout our proposed game design was utilized to achieve the obesity awareness objectives. This paper describes the design, implementation, and evaluation of our proposed cooperative game "ObeseGo" that was developed to enhance the obesity awareness for kids at an early age and educate them regarding their choices concerning eating a balanced meal to be able to adopt a healthy lifestyle. A survey questionnaire was conducted to evaluate our game. The results of the survey allowed us to analyze the impact and usefulness of the proposed game and to plan for improvement in future work. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020.

关键词： Surveys

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CTSSeg: Consistent Teacher-Student model for magnetic resonance image Segmentation

CTSSeg: Consistent Teacher-Student model for magnetic resona...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Chenbin Zhang Qingyuan He Kun Yan Meng Ma Defeng Liu Ping Wang School of Computer Science Peking University Beijing China Department of Radiology Peking University Third Hospital Beijing China School of Software and Microelectronics Peking University Beijing China National Engineering Research Center for Software Engineering Peking University Beijing China Center for Reproductive Medicine Peking University Third Hospital Beijing China School of Software and Microelectronics Peking University and the KeyLaboratory of High Confidence Software Technologies (PKU) Ministry of Education

Segmentation of magnetic resonance images is an essential way of measuring the volume of tissues and lesions, which can improve the efficiency of diagnosis. The mainstream image segmentation methods are based on deep learning, which requires a large amount of labeled data. However, labeling magnetic resonance images is expensive and time-consuming. Therefore, we propose a consistent teacher-student model for magnetic resonance image segmentation, which is abbreviated as CTSSeg. Specifically, the CTSSeg includes a student network and a teacher network, where the student network learns supervised from labeled data, while the teacher network utilizes unlabeled data to improve the student network via contrastive learning and pseudo-label learning. We evaluate the proposed CTSSeg on the Atrial Segmentation Challenge dataset and a local clinical dataset. The experimental results show that our method can make full use of both labeled and unlabeled data and yield state-of-the-art performance.

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Network intrusion detection in software defined networking with self-organized constraint-based intelligent learning framework

引用

Measurement: Sensors 2022年 24卷

作者： Bhardwaj, Anurag Tyagi, Ritu Sharma, Neha Khare, Akhilendra Punia, Manbir Singh Garg, Vikash Kumar Senior Software Engineer Apisero INC Delhi India City College Jayanagar Bengaluru India Chitkara University Institute of Engineering & Technology Chitkara University Punjab Rajpura India Department of Computer Science & Engineering Galgotias University Uttar Pradesh Greater Noida India Department of IT Homi Bhabha Cancer Hospital Punjab Sangrur India Department of Computer Science & Engineering SLIET Punjab Longowal India

With the advent of internet and communication system, a huge number of opportunities have been presented to humans, however, its vision will not be easy and comfortable. Instead the current network systems are filled up with slew of problems that must be addressed on timely basis. One of the prominent concerns in IoT is security. A number of security methods have been proposed but they are still in their lowest levels and needs upgrade. One such solution is software defined network (SDN). Although SDN is anticipated to provide a favorable atmosphere for different security activities in IoT, there is still so much progress to be made because SDN cannot solve security challenges on its own. Furthermore, the SDN inherently imposes additional security flaws. Because of the large susceptibility area in SDN-based IoT systems, a wide range of faults like DOS, DDOS, U2R etc., are directed against them. In addition to this, majority of the current IDS were based on machine learning techniques, however, the problem with such methods was that they generated high false rates and were not producing effective results when datasets were non-linear. Moreover, overfitting caused by the noisy data also hindered the performance of ML based systems. Therefore, the objective is to review and provide an effective and efficient intrusion detection techniques that solves the issues addressed. © 2022 The Authors

关键词： Denial-of-service attack

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