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

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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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Citizen Engagement in Smart Government: Content Analysis of Mohammed Bin Rashid Tweets 14

Citizen Engagement in Smart Government: Content Analysis of ...

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14th International Conference on Innovations in Information Technology, IIT 2020

作者： Sawafi, Aysha M. Al Awad, Mamoun A. UAE University College of IT Department of Computer Science and Software Engineering Al Ain United Arab Emirates

ISBN: (纸本)9781728181844

Citizen engagement is one of the main concepts of smart governments. In this paper we will measure the citizen engagement in different topics within the official Twitter account of Mohammed Bin Rashid. We have streamed the account tweets and classified them into five categories. The categories were identified after descriptive analysis of the total tweets. We used Retweetability rate to measure citizen engagement and Pearson Chi-Square test to measure the relationship between citizen retweets in response to Mohammed Bin Rashid tweets in four categories, namely, political engagement, achievements, development, and share values. Our results showed that there is a correlation between tweet contents and number of retweets in the development category only. Additionally, we found that other categories did not motivate citizen engagement. © 2020 IEEE.

关键词： Statistical tests

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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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Design and Implementation of a Simple Online Court Case Management System Based on the Android Platform 8

Design and Implementation of a Simple Online Court Case Mana...

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8th International Symposium on Digital Forensics and Security, ISDFS 2020

作者： Ali, Chawan Taha Hama Varol, Asaf University of Sulaimani Computer Science Sulaimani Iraq Firat University Department of Software Engineering Elazig Turkey

ISBN: (数字)9781728169392

ISBN: (纸本)9781728169392

The main purpose of this research is to create a new application in Android Operating System for a complainant who cannot visit his/her lawyer regularly or unable to go out due to fear of his/her life. Thanks to this research, a new application was established to make a bridge between a complainant and his/her lawyer. Everything is done automatically after necessary data entered into the application. So, costs are reduced and wasting of the time is diminished. © 2020 IEEE.

关键词： Android (operating system)

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Innovative, Integrated and Interactive (3I) LMS for Learners and Trainers 2

Innovative, Integrated and Interactive (3I) LMS for Learners...

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2nd International Conference on Advancements in Computing, ICAC 2020

作者： Fernando, K.J.L. Jayalath, W.J.D.L.D.D. Ranasinghe, A.D.R.N. Bandara, P.K.B.P.S. De Silva, Hansi Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka

ISBN: (纸本)9781728184128

3I-LMS is meant to conquer the insurmountable restraints to class/lecture room education. What are insurmountable restraints to physical classroom education and how does 3I-LMS conquer them. Firstly, the lockdown and physical distancing warranted by the spread of Covid-19. In order to overcome the restraints of the pandemic 3I-LMS would be the obvious answer. 3I-LMS, the medium for delivery and reception, is online for both learners and trainers. Secondly, ineffectiveness resulting in frail in pass rates, fast diminishing memory, faulty study techniques are overcome. 3I-LMS makes studying satisfied, gamified, engaged and enjoying. The outcome would be lasting memory, effective study techniques, higher recall rates leading to improved pass rates. Thirdly, inefficiencies such as slow access to relevant lessons, notes, slow answer evaluation, feedback, clarification to routine questions are also resolved by 3I-LMS. 3I-LMS provides for keyword search, relevant subject wise notes, instant answers for routine questions thus contributing to improved efficiencies. Additionally, 3I-LMS has unique and innovative features to assist assignment completion, emanate milestone alerts, monitoring emotions and time utilization. 3I-LMS provides for utmost security. Thus, the solution deploys face recognition and keystroke dynamics to combat impersonation, copying and unauthorized referencing. © 2020 IEEE.

关键词： Face recognition

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