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A Survey of Human Action Recognition and Posture Prediction

Tsinghua science and Technology 2022年第6期27卷 973-1001页

作者： Nan Ma Zhixuan Wu Yiu-ming Cheung Yuchen Guo Yue Gao Jiahong Li Beiyan Jiang Beijing Key Laboratory of Information Service Engineering the College of RoboticsBeijing Union UniversityBeijing 100101China Department of Computer Science Hong Kong Baptist UniversityHong Kong 999077China Beijing National Research Center for Information Science and Technology Tsinghua UniversityBeijing 100084China School of Software Tsinghua UniversityBeijing 100084China College of Robotics Beijing Union UniversityBeijing 100101China

Human action recognition and posture prediction aim to recognize and predict respectively the action and postures of persons in *** are both active research topics in computer vision community,which have attracted considerable attention from academia and *** are also the precondition for intelligent interaction and human-computer cooperation,and they help the machine perceive the external *** the past decade,tremendous progress has been made in the field,especially after the emergence of deep learning ***,it is necessary to make a comprehensive review of recent *** this paper,firstly,we attempt to present the background,and then discuss research ***,we introduce datasets,various typical feature representation methods,and explore advanced human action recognition and posture prediction ***,facing the challenges in the field,this paper puts forward the research focus,and introduces the importance of action recognition and posture prediction by taking interactive cognition in self-driving vehicle as an example.

关键词： human action recognition posture prediction computer vision human-computer cooperation interactive cognition

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Encryption Algorithm for Securing Non-Disclosure Agreements in Outsourcing Offshore software Maintenance

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computers, Materials & Continua 2022年第11期73卷 3827-3845页

作者： Atif Ikram Masita Abdul Jalil Amir Bin Ngah Nadeem Iqbal Nazri Kama Azri Azmi Ahmad Salman Khan Yasir Mahmood Assad Alzayed Faculty of Ocean Engineering Technology and Informatics University Malaysia TerengganuKuala TerengganuMalaysia Department of Computer Science&Information Technology Faculty of Information TechnologyThe University of LahoreLahore54000Pakistan Department of Software Engineering Faculty of Information TechnologyThe University of LahoreLahore54000Pakistan Advanced Informatics Department Razak Faculty of Technology and InformaticsUniversiti Teknologi Malaysia54100Kuala LumpurMalaysia Computer Science and Information Systems Department College of Business Studies Public Authority for Applied Education and Training(PAAET)AdailiyaKuwait

Properly created and securely communicated,non-disclosure agreement(NDA)can resolve most of the common disputes related to outsourcing of offshore software maintenance(OSMO).Occasionally,these NDAs are in the form of *** the work is done offshore,these agreements or images must be shared through the Internet or stored over the *** breach of privacy,on the other hand,is a potential threat for the image owners as both the Internet and cloud servers are not void of *** article proposes a novel algorithm for securing the NDAs in the form of *** an agreement is signed between the two parties,it will be encrypted before sending to the cloud server or travelling through the public network,the *** the image is input to the algorithm,its pixels would be scrambled through the set of randomly generated rectangles for an arbitrary amount of *** confusion effects have been realized through an XOR operation between the confused image,and chaotic ***,5D multi-wing hyperchaotic system has been employed to spawn the chaotic vectors due to good properties of chaoticity it *** machine experimentation and the security analysis through a comprehensive set of validation metric vividly demonstrate the robustness,defiance to the multifarious threats and the prospects for some real-world application of the proposed encryption algorithm for the NDA images.

关键词： Non-disclosure agreement encryption decryption secret key chaoticmap confusion diffusion

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QDICP: A Quantum Blockchain Model for Copyright Protection of Digital Images in Consumer Electronics

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IEEE Transactions on Consumer Electronics 2024年

作者： Qu, Zhiguo Fu, Xiyu Sun, Le Muhammad, Ghulam Nanjing University of Information Science and Technology School of Computer Science Nanjing210044 China Nanjing University of Information Science and Technology School of Software Nanjing210044 China King Saud University College of Computer and Information Sciences Department of Computer Engineering Riyadh Saudi Arabia

The widespread, unauthorized use of digital images in Consumer Electronics (CE) often leads to serious copyright infringement issues. However, current digital image copyright protection schemes have the drawbacks of the large cost of computing and storage resources, low efficiency, and susceptibility to quantum attacks. In response to these challenges, this paper proposes a novel quantum blockchain model for digital image copyright protection in CE called QDICP. In QDICP, a new fundamental data structure of quantum blockchain is first designed. Secondly, a quantum delegated proof-of-stake consensus algorithm called F-QDPoS with filtering capabilities is proposed. Furthermore, quantum identity authentication is introduced to verify the authenticity of node identity in the network, which enhances its robustness on quantum attacks. Experimental and theoretical analyses demonstrate that QDICP exhibits good security, effectively defending against intercept-resend attacks, Entanglement-Measure attacks, third-party attacks, and Impersonation attacks. QDICP can not only ensure the integrity and consistency of the stored copyright information but also effectively alleviate the storage pressure on blockchain. Moreover, F-QDPoS can effectively prevent external eavesdropping and further improve the block authentication and on-chain efficiency of the quantum blockchain network. © 1975-2011 IEEE.

关键词： Quantum entanglement

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Optimal Bounds for Distinct Quartics

arXiv

引用

arXiv 2024年

作者： Charalampopoulos, Panagiotis Gawrychowski, Pawel Ghazawi, Samah School of Computing and Mathematical Sciences Birkbeck University of London United Kingdom Institute of Computer Science University of Wroclaw Poland Department of Software Engineering Braude College of Engineering Karmiel Israel Department of Computer Science University of Haifa Israel

A fundamental concept related to strings is that of repetitions. It has been extensively studied in many versions, from both purely combinatorial and algorithmic angles. One of the most basic questions is how many distinct squares, i.e., distinct strings of the form UU, a string of length n can contain as fragments. It turns out that this is always O(n), and the bound cannot be improved to sublinear in n [Fraenkel and Simpson, JCTA 1998]. Several similar questions about repetitions in strings have been considered, and by now we seem to have a good understanding of their repetitive structure. For higher-dimensional strings, the basic concept of periodicity has been successfully extended and applied to design efficient algorithms—it is inherently more complex than for regular strings. Extending the notion of repetitions and understanding the repetitive structure of higher-dimensional strings is however far from complete. Quartics were introduced by Apostolico and Brimkov [TCS 2000] as analogues of squares in two dimensions. Charalampopoulos, Radoszewski, Rytter, Waleń, and Zuba [ESA 2020] proved that the number of distinct quartics in an n × n 2D string is O(n2 log2 n) and that they can be computed in O(n2 log2 n) time. Gawrychowski, Ghazawi, and Landau [SPIRE 2021] constructed an infinite family of n×n 2D strings with Ω(n2 log n) distinct quartics. This brings the challenge of determining asymptotically tight bounds. Here, we settle both the combinatorial and the algorithmic aspects of this question: the number of distinct quartics in an n × n 2D string is O(n2 log n) and they can be computed in the worst-case optimal O(n2 log n) time. As expected, our solution heavily exploits the periodic structure implied by occurrences of quartics. However, the two-dimensional nature of the problem introduces some technical challenges. Somewhat surprisingly, we overcome the final challenge for the combinatorial bound using a result of Marcus and Tardos [JCTA 2004] for permutation

关键词： Optimization

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Artificial Intelligence For Realtime Face Recognition Attendance Using college Classrooms and Buses

Artificial Intelligence For Realtime Face Recognition Attend...

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Electrical Electronics and Computing Technologies (ICEECT), International Conference on

作者： Ahila A Hosanna Princye P Poonguzhali A Kavivendhan M. Deepa A. Arthy R. Saravanakumar Department of Electronics and Communication Engineering Sri Sairam College of Engineering Anekal Bangalore India Department of Computer Science and Business Systems Francis Xavier Engineering College Tirunelveli Tamil Nadu India Software Analyst Iconix Software Solution Tirunelveli Tamil Nadu India

ISBN: (数字)9798350378092

ISBN: (纸本)9798350378108

Teachers take attendance by having pupils sign in or check-in classes and transportation. Student absences often result from individual mistakes. This article examines a technology that records data from classroom photographs of every student's face. This research uses an Adaptive Boost Classifier, Random Forest (RF), and Deep Convolutional Neural Networks (DCNNs). The model performs well on the DCNN model with 88 and 92% accuracy and on the ResNet50 pre-trained model with 97.21% accuracy. After detecting each student's face, they recorded their present status in an Excel document. It kept the best system implementation approach based on performance.

关键词： Adaptation models Accuracy Face recognition Transportation Forestry Convolutional neural networks Pupils System implementation Random forests Residual neural networks

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AI Methods Used for Spam Detection in Social Systems - An Overview

AI Methods Used for Spam Detection in Social Systems - An Ov...

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International Conference on Social Networks Analysis, Management and Security (SNAMS)

作者： Ayman Hussien Odeh Mohammad Al Hattab Department of Software Engineering and Computer Science College of Engineering Al Ain University Al Ain UAE Department of Networok and Communication and Computer Engineering College of Engineering Al Ain University Al Ain UAE

Over the past few years, AI has emerged as a formidable asset in the battle against spam. AI approaches have demonstrated their potential in improving spam detection in social systems. The importance of AI methods lies in their flexibility and ability to identify patterns, rendering them highly suitable for addressing the ever-changing characteristics of spam. In this paper, we provide an overview of all AI methods and algorithms used for spam detection in social systems, and evaluation of their performance in terms of used metrics such as accuracy, recall, precision, and F1-score. Also, we provide a comparative analysis of the used AI methods, challenges and limitations, emerging trends and future research directions, and privacy concerns in AI-driven spam detection in social systems. This paper emphasizes the significance of AI Methods in the realm of spam detection and investigates a range of AI-driven strategies that have been suggested and put into practice to combat spam with efficacy.

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New Ontology structure for intelligent controlling of traffic signals 26

New Ontology structure for intelligent controlling of traffi...

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26th International Conference on Knowledge-Based and Intelligent Information and engineering Systems, KES 2022

作者： Mohammad, Mahmud Abdulla Manguri, Kamaran H. Abdulsamad, Taib Shamsadin Faeq Al-Talabani, Abdulbasit K. Abdulrahman, Akam Aziz Computer Science Department College of Basic Education University of Raparin Kurdistan Region Ranya Iraq College of Science and Technology University of Human Development Kurdistan Region Sulaymaniyah Iraq Department of Software Engineering Koya University Kurdistan Region Koya Iraq

This article proposes a novel ontology design for intelligent controlling of traffic signals, considering the investigated factors, crowded factors, road factors, visibility conditions, and emergency situations. Essentially, the proposed method uses video-based knowledge and key feature from a monocular video camera only, capturing footage from either a traffic signals perspective or the top of the road lane. The key factors and entities in the traffic scene are formed into an ontology, which has been evaluated using synthetic datasets to interpret challenging cases. Semantic features related to the key factors in the scene are obtained and fed to the ontology. The experimental results indicate that the proposed method is capable of controlling traffic signals more efficiently than the fixed intervals protocol. © 2022 The Authors. Published by Elsevier B.V.

关键词： Semantics

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Energy-Efficient Client Sampling for Federated Learning in Heterogeneous Mobile Edge Computing Networks

Energy-Efficient Client Sampling for Federated Learning in H...

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IEEE International Conference on Communications (ICC)

作者： Jian Tang Xiuhua Li Hui Li Min Xiong Xiaofei Wang Victor C. M. Leung School of Big Data & Software Engineering Chongqing University Chongqing China TKLAN College of Intelligence & Computing Tianjin University Tianjin China College of Computer Science & Software Engineering Shenzhen University Shenzhen China Department of Electrical & Computer Engineering The University of British Columbia Vancouver Canada

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

To address network congestion and data privacy concerns, federated learning (FL) that combines multiple clients and a parameter server has been widely used in mobile edge computing (MEC) networks to process the abundant data generated by mobile clients. However, the existing client sampling methods do not adequately consider the data heterogeneity and system heterogeneity. Parameter server selects inappropriate clients to participate in the FL training process. This inevitably leads to slower convergence of the global model and higher energy consumption. In this paper, we design a client sampling model with the goal of selecting suitable clients to improve the energy efficiency of FL in heterogeneous MEC networks. Then we propose an energy-efficient client sampling strategy by quantifying the communication capability, computation capability and data quality of clients. Based on the quantization results, clients are assigned with a corresponding sampled probability. Simulation results show that our proposed strategy can effectively accelerate the convergence of the global model and reduce the energy consumption compared with the baseline schemes.

关键词： Training Energy consumption Multi-access edge computing Federated learning Computational modeling Simulation Sampling methods

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Using transformers and Bi-LSTM with sentence embeddings for prediction of openness human personality trait

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PeerJ computer science 2025年 11卷 1-42页

作者： Naz, Anam Khan, Hikmat Ullah Alsahfi, Tariq Alhajlah, Mousa Alshemaimri, Bader Daud, Ali Department of Information Technology University of Sargodha Punjab Sargodha Pakistan Department of Information Systems and Technology College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia Applied of Computer Science College King Saud University Riyadh Saudi Arabia Software Engineering Department College of Computing and Information Sciences King Saud University Riyadh Saudi Arabia Faculty of Resilience Rabdan Academy Abu Dhabi United Arab Emirates

Understanding human personality traits is significant as it helps in decision making related to consumers’ behavior, career counselling, team building and top candidates’ selection for recruitment. Among various traits, openness is essential as it shows both diverse aspects of sensitive nature or intuitive nature. The individuals having a sensing nature tends to be more practical and prefer to focus on concrete information whereas the users having intuitive trait type is characterized by a focus on abstract ideas, creative thinking and future-oriented perspectives. In this research work, we aim to explore diverse natural language processing (NLP) based features and apply state of the art deep learning algorithms for openness trait prediction. Using standard Myers-Briggs Type Indicator (MBTI) dataset, we propose the use of the latest deep features of sentence embeddings which captures contextual semantics of the content to be used with deep learning models. For comparison, we explore textual features of Frequency-Inverse Document (TF-IDF) and parts of speech (POS) tagging with machine learning models and deep features of word2vec and global vectors for word representation (GloVe) with deep learning models. The comprehensive empirical analysis reveals that TF-IDF used with gradient boosting achieves high accuracy of 90% whereas, the deep feature of sentence embeddings when used and with deep model bidirectional long short-term memory (Bi-LSTM) achieves 90.5% accuracy. The best results have been achieved using the latest Transformer-based DistilBERT, which achieves the highest accuracy of 92% outperforming the existing studies in relevant literature. Subjects Human-computer Interaction, Artificial Intelligence, Data Mining and Machine Learning, Natural Language and Speech © 2025 xxxx

关键词： Deep learning

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Usability evaluation of pandemic health care mobile applications

Usability evaluation of pandemic health care mobile applicat...

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International Symposium of Geoscience, Oil and Gas engineering, Sustainable and Environmental Technology 2020, GEOSOSTEK 2020

作者： Mubeen, M. Iqbal, M.W. Junaid, M. Sajjad, M.H. Naqvi, M.R. Khan, B.A. Saeed, M.M. Tahir, M.U. Department of Software Engineering The Superior College Lahore54700 Pakistan Department of Computer Science The Superior College Lahore54700 Pakistan

This study was conducted to evaluate the usability of health care apps and its significance in the field of health care. As health care centers are the settings working 24/7 for providing health services to patients. For this study a systematic review, comparison and functionality assessment of selected COVID19 mobile apps was performed. The assessment was performed on two digital platforms, App store on Apple iPhone and Google play store on Android smartphones. Further online search of keywords on Google was also conducted in order to ensure the reliability and validity of apps on these stores. And only those apps were selected which were in English because of the language proficiency of authors. The mobile apps selected were having five features of assessment, a questionnaire, Privacy statement, Precautionary measures if test positive for COVID19, after plans of Pandemic and Region based. The sample was a blend of participants from different fields including students, doctors, patients and software developers. This is quantitative study;a questionnaire was distributed among the selected participants who were familiar with the use of health care mobile apps. The data was analyzed on the basis of gender and age. The results were made on the basis of responses towards effectiveness, efficiency and satisfaction of users towards mobile phone apps. The results show that apps were found 64.5% informative for participants as they helped in fast communication of doctors and patients while sitting at the other corner of world. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Surveys

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