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Pseudo-mutual gazing enhances interbrain synchrony during remote joint attention tasking

Brain and Behavior 2023年第10期13卷 e3181页

作者： Chuang, Chun-Hsiang Hsu, Hao-Che Research Center for Education and Mind Sciences College of Education National Tsing Hua University Hsinchu Taiwan Institute of Information Systems and Applications College of Electrical Engineering and Computer Science National Tsing Hua University Hsinchu Taiwan Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Computer Science and Engineering National Taiwan Ocean University Keelung Taiwan

Introduction: Mutual gaze enables people to share attention and increase engagement during social interactions through intentional and implicit messages. Although previous studies have explored gaze behaviors and neural mechanisms underlying in-person eye contact, the growing prevalence of remote communication has raised questions about how to establish mutual gaze remotely and how the brains of interacting individuals synchronize. Methods: To address these questions, we conducted a study using eye trackers to create a pseudo-mutual gaze channel that mirrors the gazes of each interacting dyad on their respective remote screens. To demonstrate fluctuations in coupling across brains, we incorporated electroencephalographic hyperscanning techniques to simultaneously record the brain activity of interacting dyads engaged in a joint attention task in player-observer, collaborative, and competitive modes. Results: Our results indicated that mutual gaze could improve the efficiency of joint attention activities among remote partners. Moreover, by employing the phase locking value, we could estimate interbrain synchrony (IBS) and observe low-frequency couplings in the frontal and temporal regions that varied based on the interaction mode. While dyadic gender composition significantly affected gaze patterns, it did not impact the IBS. Conclusion: These results provide insight into the neurological mechanisms underlying remote interaction through the pseudo-mutual gaze channel and have significant implications for developing effective online communication environments. © 2023 The Authors. Brain and Behavior published by Wiley Periodicals LLC.

关键词： cooperation and competition hyperscanning EEG interbrain synchrony joint attention mutual gaze phase locking value

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TOWARDS THE NEUROEVOLUTION OF LOW-LEVEL ARTIFICIAL GENERAL INTELLIGENCE

arXiv

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arXiv 2022年

作者： Pontes-Filho, Sidney Olsen, Kristoffer Yazidi, Anis Riegler, Michael A. Halvorsen, Pål Nichele, Stefano Department of Computer Science Oslo Metropolitan University Oslo Norway Department of Computer Science Norwegian University of Science and Technology Trondheim Norway Department of Informatics University of Oslo Oslo Norway AI Lab – OsloMet Artificial Intelligence Lab Oslo Norway NordSTAR Nordic Center for Sustainable and Trustworthy AI Research Oslo Norway Department of Holistic Systems Simula Metropolitan Centre for Digital Engineering Oslo Norway Department of Computer Science and Communication Østfold University College Halden Norway

In this work, we argue that the search for Artificial General Intelligence (AGI) should start from a much lower level than human-level intelligence. The circumstances of intelligent behavior in nature resulted from an organism interacting with its surrounding environment, which could change over time and exert pressure on the organism to allow for learning of new behaviors or environment models. Our hypothesis is that learning occurs through interpreting sensory feedback when an agent acts in an environment. For that to happen, a body and a reactive environment are needed. We evaluate a method to evolve a biologically-inspired artificial neural network that learns from environment reactions named Neuroevolution of Artificial General Intelligence (NAGI), a framework for low-level AGI. This method allows the evolutionary complexification of a randomly-initialized spiking neural network with adaptive synapses, which controls agents instantiated in mutable environments. Such a configuration allows us to benchmark the adaptivity and generality of the controllers. The chosen tasks in the mutable environments are food foraging, emulation of logic gates, and cart-pole balancing. The three tasks are successfully solved with rather small network topologies and therefore it opens up the possibility of experimenting with more complex tasks and scenarios where curriculum learning is *** Codes 68T05 © 2022, CC BY.

关键词： Sensory feedback

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Smart Education in Smart Cities: Challenges And Solution

Smart Education in Smart Cities: Challenges And Solution

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IEEE International Smart Cities Conference (ISC2)

作者： Afzal Badshah Moustafa M. Nasralla Ateeqa Jalal Haleem Farman Department of Computing Hamdard University Islamabad Campus Islamabad Pakistan Department of Communications and Engineering Smart Systems Engineering Lab College of Engineering Prince Sultan University Riyadh Saudi Arabia Department of Computer Science University of Science and Technology Bannu Pakistan

Traditional education faces many challenges. Integrating recent technologies (e.g., Artificial Intelligence (AI), Internet of Things (IoT), 5G, etc) into the education system, known as smart education, offers promising solutions. However, the integration of innovative solutions into the traditional system remains limited. The limitations, revealed during the recent COVID-19 pandemic, have now become imperative. This study addresses problems within the traditional system and proposes innovative solutions like smart pedagogy, assessment, classroom, and administration. It explores the shift to smart education and the associated challenges, including computational and social resistance. Innovative education yields concrete benefits, enhancing learner engagement, motivation, attendance, and learning outcomes. Embracing innovative technologies holds the potential to make education more effective and efficient in meeting the evolving needs of learners.

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Smart Seed Classification System based on MobileNetV2 Architecture 2

Smart Seed Classification System based on MobileNetV2 Archit...

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2nd International Conference on Computing and Information Technology, ICCIT 2022

作者： Hamid, Yasir Wani, Sharyar Soomro, Arjumand Bano Alwan, Ali A. Gulzar, Yonis Abu Dhabi Polytechnic Information Security and Engineering Technology Abudhabi United Arab Emirates International Islamic University Malaysia Kulliyyah of Information and Communication Technology Department of Computer Science Gombak Malaysia College of Business Administration King Faisal University Department of Management Information Systems Al-Ahsa Saudi Arabia School of Theoretical and Applied Science Ramapo College of New Jersey MahwahNJ United States College of Business Administration King Faisal University Department of Management Information Systems Al-Ahsa Saudi Arabia

ISBN: (纸本)9781665436052

The agricultural transformation in the last decade using artificial intelligence has led to significant gains in productivity and profitability. The traditional machine learning approaches present inherent limitations in extracting features and information from image data. Deep learning techniques, particularly CNN's, help to overcome these limitations due to their multi-level architecture. Various deep learning applications in agriculture include crop disease identification, fruit classification, and germination rate monitoring. Seed image analysis is considered a significant task for the preservation of biodiversity and sustainability. This research uses MobileNetV2, a deep learning convolutional neural network (DCNNs) for seed classification. This model has been preferred due to its simple architecture and memory-efficient characteristics. A total of 14 different classes of seeds were used for the experimentation. The results indicate accuracies of 98% and 95% on training and test sets, respectively. © 2022 IEEE.

关键词： Seed

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Fairness Testing: A Comprehensive Survey and Analysis of Trends

arXiv

引用

arXiv 2022年

作者： Chen, Zhenpeng Zhang, Jie M. Hort, Max Harman, Mark Sarro, Federica The Department of Computer Science University College London London United Kingdom The Department of Informatics King’s College London London United Kingdom The Simula Research Laboratory Oslo Norway

Unfair behaviors of Machine Learning (ML) software have garnered increasing attention and concern among software engineers. To tackle this issue, extensive research has been dedicated to conducting fairness testing of ML software, and this paper offers a comprehensive survey of existing studies in this field. We collect 100 papers and organize them based on the testing workflow (i.e., how to test) and testing components (i.e., what to test). Furthermore, we analyze the research focus, trends, and promising directions in the realm of fairness testing. We also identify widely-adopted datasets and open-source tools for fairness testing. © 2022, CC BY.

关键词： Machine learning

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SecBCS: a secure and privacy-preserving blockchain-based crowdsourcing system

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science China(Information sciences) 2020年第3期63卷 20-33页

作者： Chao LIN Debiao HE Sherali ZEADALLY Neeraj KUMAR Kim-Kwang Raymond CHOO Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of EducationSchool of Cyber Science and Engineering Wuhan University State Key Laboratory of Cryptology College of Communication and Information University of Kentucky Department of Computer Science and Engineering Thapar University Department of Information Systems and Cyber Security and Department of Electrical and Computer Engineering University of Texas at San Antonio

A robust and scalable crowd management infrastructure is crucial in addressing operational challenges when deploying high-density sensors and actuators in a smart city. While crowdsourcing is widely used in crowd management, conventional solutions, such as Upwork and Amazon Mechanical Turk, generally depend on a trusted third-party platform. There exist several potential security concerns(e.g., sensitive leakage, single point of failure and unfair judgment) in such a centralized paradigm. Hence, a recent trend in crowdsourcing is to leverage blockchain(a decentralized ledger technology) to address some of the existing limitations. A small number of blockchain-based crowdsourcing systems(BCSs) with incentive mechanisms have been proposed in the literature, but they are generally not designed with security in mind. Thus, we study the security and privacy requirements of a secure BCS and propose a concrete solution(i.e., SecBCS)with a prototype implementation based on JUICE.

关键词： crowdsourcing blockchain secure privacy-preserving smart contract

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6G Mobile Communications systems: Requirements, Specifications, Challenges, Applications, and Technologies 1

6G Mobile Communications Systems: Requirements, Specificatio...

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1st IEEE International Maghreb Meeting of the Conference on sciences and Techniques of Automatic Control and computer Engineering, MI-STA 2021

作者： Yaklaf, Sliman Ka. A. Tarmissi, Khaled S. Shashoa, Nasar Aldian Ambark Azzaytuna University Faculty of Engineering Electrical and Electronics Engineering Department Tarhuna Libya Umm Al-Qura University College of Computers and Information Systems Computer Science Department Makkah Saudi Arabia Libyan Academy for Postgraduate Studies Department of Electrical and Computer Eng. Tripoli Libya

ISBN: (纸本)9781665418560

The Fifth Generation (5G) Wireless networks were planned to be free from the limitations of previous generations. It has become necessary for society to move beyond 5G and design a new architecture that can incorporate the technological needs of both the individual and society. Therefore, if Sixth Generation (6G) mobile communications are to be developed, many aspects must be improved, the most evident being: latency, higher data rate, quality of service (QoS) and system capacity. When looking at the previously mentioned aspiration and requirements of future networks, the need for the development of 6G networks becomes clear. Nevertheless, many challenges which society must overcome for such networks to be implemented exist. The cost of solving such problems pales in comparison to the potential benefits of 6G networks. This paper discusses the challenges of implementing 6G networks. © 2021 IEEE.

关键词： Internet of Everything

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Sensor-based authentication in smartphone: A systematic review

Journal of Engineering Research

引用

Journal of Engineering Research 2025年第2期13卷 741-750页

作者： Moceheb Lazam Shuwandy A.S. Jouda M.A. Ahmed Mahmood M. Salih Z.T. Al-qaysi A.H. Alamoodi Salem Garfan O.S. Albahri B.B. Zaidan A.S. Albahri Computer Science Department College of Computer Science and Mathematics Tikrit University (TU) Tikrit Iraq Salahaddin Health Office Ministry of Health Tikrit Iraq MEU Research Unit Middle East University Amman Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan Applied Science Research Center Applied Science Private University Amman Jordan Faculty of Computing and Meta-Technology (FKMT) Universiti Pendidikan Sultan Idris (UPSI) Perak Malaysia Victorian Institute of Technology Australia Computer Techniques Engineering Department Mazaya University College Nasiriyah Iraq SP Jain School of Global Management Lidcombe Sydney NSW 2141 Australia International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Douliu Taiwan ROC Technical College Imam Ja'afar Al-Sadiq University Baghdad Iraq Iraqi Commission for Computers and Informatics (ICCI) Baghdad Iraq

With the widespread usage of smartphones, users not just using their smartphone for calls and messaging only, but they are using it for a variety of purposes such as banking transactions, e-mailing, chatting, online shopping, video conferencing, health monitoring, and many more. Most of these activities store personal and sensitive data of the user on the device, reaching this confidential information by unauthorized person may cause huge losses and bad consequences. Therefore, securing the smartphone’s accessibility from unauthorized people became an extremely essential. With the advanced development in the area of sensor-based smartphone authentication methods, different methods were developed to be easily used by users through specific criteria. However, many aspects haven’t been discovered yet in the area of sensor-based smartphone authentication methods. On the basis of that, this study aims to review the area of sensor-based smartphone authentication methods systematically to provide a comprehensive understanding of this new area and to discuss the current challenges and issues. To conduct this systematic review, four scientific digital databases were utilized; scienceDirect, IEEE Xplore, Web of science, and Scopus. The total number of studies found was n = 256 articles, only n = 46 articles were included based on our inclusion criteria. To provide a comprehensive understanding of the included studies, taxonomy was drawn. Also, this study provides a discussion on the current motivations, challenges and issues, and recommendations in sensor-based smartphone authentication methods. This review enlightens, encourage, and direct researchers to develop authentication solutions and reduce the current gaps in this area.

关键词： Password Authentication Privacy MHealth Touch Sensor Microphone Sensor

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Real-Time Distributed Service De-Identification And Internet Problem Mitigation

Real-Time Distributed Service De-Identification And Internet...

引用

Artificial Intelligence of Things (ICAIoT), International Conference on

作者： Zahraa A. Jaaz Inteasar Yaseen Khudhair Hala Shaker Mehdy Jamal Fadhil Tawfeq Ade Ghani Mostafa Satea Alhamdany College of Computing & Informatics Universiti Tenaga Nasional Malaysia Department of Computer AlNahrain University Baghdad Iraq Department of Computer Science University of Diyala Baghdad Iraq Department of Computer Science Al-Mustansiriya University Baghdad Iraq Department of Medical Instrumentation Technical Engineering Medical Technical College Al-Farahidi University Baghdad Iraq Department of Medical Instrumentation Technical Engineering Al-Mustaqbal University College Babylon Iraq Department of Computer Techniques Engineering AlSafwa University College Karbala Iraq

Distributed denial-of-service (DDoS) attacks are increasing in size, and complexity, and becoming more frequent where volumetric or high-rate attacks are the most prevalent type of DDoS penetrations. Cyber-attack and breaches against the networks of the Internet of Things (IoT) have dire consequences for the data and services that are used in the applications and devices while at the same time increasing the security challenges for its departments due to the increased resources required. A DDoS attack is an endeavor aimed at making a network, online service, or a whole organization unavailable by bombarding to saturate its traffic from multiple sources. The software-defined paradigm has been utilized as an effective mitigation technique for DDoS in IoT. Most researchers and experts have embraced this technique; therefore, in this study, we will present the conventional framework of software-defined detection and mitigation techniques for IoT. Furthermore, we will advance an algorithm for detecting and alleviating DDoS attacks that can be easily interfaced with software-defined frameworks (SD-IOT) for IoT. This algorithm will be based on neural networks for the packet in and out traffic while monitoring for DDoS attacks. The technique was successfully tested and revealed 89.047% accuracy for identifying actual DDoS attack datasets.

关键词： Neural networks Organizations Denial-of-service attack Real-time systems Complexity theory Internet of Things Security

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Hybrid Rider Optimization with Deep Learning Driven Biomedical Liver Cancer Detection and Classification

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Computational Intelligence and Neuroscience 2022年 2022卷 6162445页

作者： Al Duhayyim, Mesfer Mengash, Hanan Abdullah Marzouk, Radwa Nour, Mohamed K Mahgoub, Hany Althukair, Fahd Mohamed, Abdullah Department of Computer Science College of Sciences and Humanities-Aflaj Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia Department of Information Systems College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Computer Sciences College of Computing and Information System Umm Al-Qura University Mecca Saudi Arabia Department of Computer Science College of Science & Art at Mahayel King Khalid University Abha Saudi Arabia Department of Computer Science Faculty of Computers and Information Menoufia University Shibin Al Kawm Egypt Department of Electrical Engineering and Computer Sciences College of Engineering University of CA Berkeley United States Research Center Future University in Egypt New Cairo11845 Egypt

Biomedical engineering is the application of the principles and problem-solving methods of engineering to biology along with medicine. Computation intelligence is the study of design of intelligent agents which are systems acting perceptively. The computation intelligence paradigm offers more advantages to the enhancement and maintenance of the field of biomedical engineering. Liver cancer is the major reason of mortality worldwide. Earlier-stage diagnosis and treatment might increase the survival rate of liver cancer patients. Manual recognition of the cancer tissue is a time-consuming and difficult task. Hence, a computer-aided diagnosis (CAD) is employed in decision making procedures for accurate diagnosis and effective treatment. In contrast to classical image-dependent "semantic"feature evaluation from human expertise, deep learning techniques could learn feature representation automatically from sample images using convolutional neural network (CNN). This study introduces a Hybrid Rider Optimization with Deep Learning Driven Biomedical Liver Cancer Detection and Classification (HRO-DLBLCC) model. The proposed HRO-DLBLCC model majorly focuses on the identification of liver cancer in the medical images. To do so, the proposed HRO-DLBLCC model employs preprocessing in two stages, namely, Gabor filtering (GF) based noise removal and watershed transform based segmentation. In addition, the proposed HRO-DLBLCC model involves NAdam optimizer with DenseNet-201 based feature extractor to generate an optimal set of feature vectors. Finally, the HRO algorithm with recurrent neural network-long short-term memory (RNN-LSTM) model is applied for liver cancer classification, in which the hyperparameters of the RNN-LSTM model are tuned by the use of HRO algorithm. The HRO-DLBLCC model is experimentally validated and compared with existing models. The experimental results assured the promising performance of the HRO-DLBLCC model over recent approaches. © 2022 Mesfer Al Duhayyi

关键词： Diseases

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