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Human-computer Interaction, Optimization and Robotic Applications (HORA), International Congress on

作者： Noraziahtulhidayu Kamarudin Aiman Yusoff Abd Samad Hasan Basari Noor Zuraidin bin Mohd Safar Mohd Hatta Hussin Department of Multimedia Multimedia and Signal Processing Focus Group Faculty of Computer Science and Information Universiti Tun Hussein Onn Malaysia Department of Multimedia Faculty of Computer Science and Information Universiti Tun Hussein Onn Malaysia Department of Software Engineering Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Department of Information Security and Web Technology Faculty of Computer Science and Information Universiti Tun Hussein Onn Malaysia Johor Darul Takzim Pertubuhan Peladang Senggarang

During the application development process, it is important for a developer to analyze user requirements. This is crucial to the success of interactive systems and is an essential component of the design of information systems. User requirement analysis aids developers in understanding and analyzing users' needs and technology requirements. Throughout this case study, it is able to forecast the understanding, and technology acceptance, and at the same time improve the quality of work, reducing training and support costing and perennially improve customer or user satisfaction. The method of research selected is questionnaires that focused on collecting the data directly from Malaysian farmers through Google Forms as an online medium. The method was chosen because it is easy to implement and inexpensive due to the current situation of Covid-19. The questionnaire is designed with two sections where; a) includes the participant's demography to analyze information of the intended audience while b)focused on the knowledge of participants about recognition images. Results demonstrate the farmer's opinion and knowledge of the technology of image recognition in the agriculture sector. The quantitative research was undertaken using an online platform due to the Covid-19 pandemic. From the analysis, it is justified that the Technology Acceptance Model (TAM) together with the mobile farming image recognition app is useful in collecting farmers' feedback and positive results have an outcome on the challenges faced by the durian farmers.

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Data security and privacy computing in artificial intelligence

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Journal of Information and Intelligence 2024年第2期2卷 99-101页

作者： Dengguo Feng Hui Li Rongxing Lu Zheli Liu Jianbing Ni Hui Zhu Institute of Software Academy of SciencesBeijing 100190China State Key Laboratory of Integrated Services Networks School of Cyber EngineeringXidian UniversityXi’an 710071China Faculty of Computer Science University of New BrunswickFredericton E3B 5A3Canada College of Computer Science Nankai UniversityTianjin 300071China Department of Electrical and Computer Engineering Queen’s UniversityKingston K7L 3N6Canada

Artificial intelligence(AI)is a field of computer science dedicated to creating systems and algorithms that can perform tasks typically requiring human intelligence,such as learning,problem-solving,language understanding,and decision-making,contributing to a wide array of applications across diverse *** development of AI,such as machine learning and deep learning,has revolutionized data processing and *** transformation is rapidly changing human life and has allowed for many practical AI based applications,including biometric recognition,text/sentimental analysis,and attack detection in the fields of health care,finance,autonomous vehicles,personalized ***,the potential benefits of AI are hindered by issues,such as insecurity and privacy violations in data processing and communication.

关键词： such has finance

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High Impact Low Probability Weatherization Impact Analysis for Electricity Infrastructure

High Impact Low Probability Weatherization Impact Analysis f...

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IEEE Region 10 International Conference TENCON

作者： Lakshita Lakshita Nirmal-Kumar C. Nair Faculty of Electrical Computer and Software Engineering University of Auckland Auckland New Zealand

ISBN: (纸本)9781665495332

High impact low probability (HILP) weather events are known to have the capability to damage the power system network and lead to a complete outage. Such HILP events encourage the system to have continuous improvement for the resilience of the network. Enhancing the power system resilience has always been challenging for system operators to implement resilient strategies and techniques for pre-, during, and post-weather events. This paper introduces an approach to visualize the effect of wind gusts and the possibility of an outage from HILP, considering the contingencies of the power system network to analyze the risks of failure on the system. Furthermore, this paper also discussed the resilient strategy of utilizing battery storage to reduce the risk on the other components due to the overloading of critical components. Weather data analysis was conducted and incorporated with the power system network to visualize the weatherization effect on the Auckland Network, assuming the contingencies of the two busbars.

关键词： Wind Substations Loading Data visualization Power systems Batteries Risk management

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Geometrically Inspired Kernel Machines for Collaborative Learning Beyond Gradient Descent

arXiv

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arXiv 2024年

作者： Kumar, Mohit Valentinitsch, Alexander Fuchs, Magdalena Brucker, Mathias Bowles, Juliana Husakovic, Adnan Abbas, Ali Moser, Bernhard A. Faculty of Computer Science and Electrical Engineering University of Rostock Germany Software Competence Center Hagenberg GmbH HagenbergA-4232 Austria School of Computer Science University of St Andrews United Kingdom Primetals Technologies Austria GmbH LinzA-4031 Austria Institute of Signal Processing Johannes Kepler University Linz Austria

This paper develops a novel mathematical framework for collaborative learning by means of geometrically inspired kernel machines which includes statements on the bounds of generalisation and approximation errors, and sample complexity. For classification problems, this approach allows us to learn bounded geometric structures around given data points and hence solve the global model learning problem in an efficient way by exploiting convexity properties of the related optimisation problem in a Reproducing Kernel Hilbert Space (RKHS). In this way, we can reduce classification problems to determining the closest bounded geometric structure from a given data point. Further advantages that come with our solution is that our approach does not require clients to perform multiple epochs of local optimisation using stochastic gradient descent, nor require rounds of communication between client/server for optimising the global model. We highlight that numerous experiments have shown that the proposed method is a competitive alternative to the state-of-the-art. © 2024, CC BY.

关键词： Stochastic systems

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Classroom monitoring system based on facial expression recognition

Classroom monitoring system based on facial expression recog...

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International Symposium on Distributed Computing and Applications to Business, engineering and Science (DCABES)

作者： Boxuan Zhang Dandan Wei Qianying Zhang Wenyu Si Xiang Li Quanyin Zhu Faculty of Computer&Software Engineering Huaiyin Institute of Technology Huaian China

ISBN: (纸本)9781665428903

Facial expressions are important information that reflects human emotions. Recognizing dynamic expressions of students in class, 8 kinds of emotions are selected for application: positive emotions: “happy”; negative emotions: “disgust, Sadness, doubts, contempt, anger”; neutral emotion: “focus, surprise”. [1] In this design, the classroom performance scoring system in normal hours is split into four functions: wireless network list acquisition and verification, face recognition, emotion analysis, and scoring record storage. On this basis, SVM and Softmax are used. Facial expression recognition and a data storage database is designed to realize the function of an intelligent scoring system for classroom performance points. To solve this problem, an expression recognition method combining pyramid convolutional neural network and attention mechanism is proposed.

关键词： Support vector machines Emotion recognition Databases Face recognition Wireless networks Memory Convolutional neural networks

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Research on Text Summarization Generator Method Based on Input Text Linguistic Features and Copy Mechanism

Research on Text Summarization Generator Method Based on Inp...

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International Conference on Intelligent System and Knowledge engineering, ISKE

作者： Liuyang Wang Yangxin Yu Faculty of Computer & Software Engineering Huaiyin Institute of Technology of China Huai’an China

ISBN: (纸本)9781665405546

The Sequence-to-Sequence (Seq2Seq) of neural network (NN) method based on recurrent neural network (RNN) and attention mechanism plays an important role in information extraction and automatic summarization. However, this method can’t make full use of the text linguistic feature information, and there is a problem of unregistered words in the generated results, which affects the accuracy and readability of the text summarization. To solve this problem, the text linguistic feature is used to improve the input characteristics, and the copy mechanism is introduced to alleviate the problem of unregistered words in the summarization generation process. On this basis, a new method named Copy-Generator Model (CGM) based on Seq2Seq model is proposed to improve the effect of text summarization. The results of experiments using LCSTS (Large Scale Chinese Short Text Summarization) as the data source show that the proposed method in this paper can effectively improve the accuracy of summarization and can be applied to automatic text summarization.

关键词： Knowledge engineering Vocabulary Recurrent neural networks Soft sensors Semantics Artificial neural networks Linguistics

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Incorporation, Transparency and Cognitive Extension: Why the Distinction Between Embedded and Extended Might Be More Important to Ethics Than to Metaphysics

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Philosophy and Technology 2022年第1期35卷 1-21页

作者： Farina, Mirko Lavazza, Andrea Institute for Humanities and Social Sciences Universitetskaya St 1 Republic of Tatarstan Innopolis 420500 Russian Federation Lab of Industrializing Software Production (LIPS) Faculty of Computer Science and Engineering Universitetskaya St 1 Republic of Tatarstan Innopolis 420500 Russian Federation Centro Universitario Internazionale Via Antonio Garbasso AR Arezzo 42 - 52100 Italy

We begin (Sect. 1) by introducing our readers to the Extended Mind Thesis and briefly discuss a series of arguments in its favour. We continue (Sect. 2) by showing of such a theory can be resisted and go on to demonstrate that a more conservative account of cognition (called Embedded Theory) can be developed. We acknowledge (Sect. 3) a stalemate between these two different accounts of cognition and notice a couple of issues (namely, dimensions of transparency and excess of metaphysics in the debate) that we argue have prevented further progress in the field. To overcome the stalemate, we propose (Sect. 4) to focus on the ethical implications of these theories. We link (Sect. 5) work conducted on the Extended Mind Thesis with contemporary transhumanist research and claim that extended may be ethically preferred over embedded because it is a more progressive and inclusive theory. © 2022, The Author(s), under exclusive licence to Springer Nature B.V.

关键词： Embedded mind Ethics Extended mind Morality Transhumanism

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Low level Syntax Elements Study in Intra HEVC/H.265 Video Codec 7

Low level Syntax Elements Study in Intra HEVC/H.265 Video Co...

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7th International Conference on Image and Signal Processing and their Applications, ISPA 2022

作者： Menasri, Wahiba Meddah, Karim University of Sciecnes and Technology Houari Boumediene Usthb Laboratory of Image Processing and Radiation Algiers Algeria University Yahya Fares of Medea Nouveau Pôle Urbain Faculty of Technology Medea Algeria Polytechnique Montréal Motce Laboratory Department of Computer and Software Engineering MontrealQC Canada Usthb Laboratory of Instrumentation Department of Electrical Engineering Algiers Algeria

ISBN: (纸本)9781665480420

Ultra High Definition Television (UHDTV) imposes extremely high throughput requirement on video encoders based on High Efficiency Video Coding (H.265/HEVC). HEVC adopt many advanced developed techniques in order to compress and decompress the video sequence for ensuring the real time requirements with keeping the quality. The decoded video is obtained according to the syntax elements language specified by the HEVC standard. Consequently, HEVC adopt 74 syntax elements at the low level which are divided into four partitions: including quad tree partitioning, intra and inter prediction, transform level, quantization and loop filtering. This work propose a profound study of HEVC low level syntax elements. The four partitions of syntax elements are defined, detailed and specified (role). After that, the encoding process and order of each syntax element is given by diagram blocks followed by an examples of encoding process syntax elements for 4x4 and 8x8 transform blocks (references blocks) by using Matlab ***, performance comparison of different benchmarks is given in order to highlights the decoding efficiency of HEVC compared it predecessor AVC (Advenced Video Coding). This work give the only detailed study that demonstrate the main functionality of the all intra syntax elements encoding process in CABAC HEVC. © 2022 IEEE.

关键词： MATLAB

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A Deep Learning Approach Towards Student Performance Prediction in Online Courses: Challenges Based on a Global Perspective

A Deep Learning Approach Towards Student Performance Predict...

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Information Technology (ACIT)

作者： Abdallah Moubayed MohammadNoor Injadat Nouh Alhindawi Ghassan Samara Sara Abuasal Raed Alazaidah School of Computing and Augmented Intelligence Arizona State University Gilbert Arizona USA Electrical and Computer Engineering Department Lebanese American University Byblos Lebanon Electrical and Computer Engineering Department Western University London Ontario Canada Data Science and Artificial Intelligence Department Faculty of Information Technology Zarqa University Zarqa Jordan Faculty of Science and Information Technology Jadara University Irbid Jordan Computer Science Department Faculty of Information Technology Zarqa University Zarqa Jordan Software Engineering Department Faculty of Information Technology Zarqa University Zarqa Jordan

Analyzing and evaluating students' progress in any learning environment is stressful and time consuming if done using traditional analysis methods. This is further exasperated by the increasing number of students due to the shift of focus toward integrating the Internet technologies in education and the focus of academic institutions on moving toward e-Learning, blended, or online learning models. As a result, the topic of student performance prediction has become a vibrant research area in recent years. To address this, machine learning and data mining techniques have emerged as a viable solution. To that end, this work proposes the use of deep learning techniques (CNN and RNN-LSTM) to predict the students' performance at the midpoint stage of the online course delivery using three distinct datasets collected from three different regions of the world. Experimental results show that deep learning models have promising performance as they outperform other optimized traditional ML models in two of the three considered datasets while also having comparable performance for the third dataset.

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Resilience based Criticality Analysis for Seismic Performance Assessment of Underground Cables

Resilience based Criticality Analysis for Seismic Performanc...

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IEEE Region 10 International Conference TENCON

作者： Ebad Ur Rehman Nirmal–Kumar C Nair Faculty of Electrical Computer and Software Engineering University of Auckland Auckland New Zealand

ISBN: (纸本)9781665495332

The concept of resilience in power systems has gained focused attention due to increasing outages, including large-scale blackouts, attributed directly or indirectly due to extreme weather and natural geo-physical triggered events. The resilience of electricity infrastructure ground assets and power system equipment can be improved by strengthening the foundations or re-engineering the vulnerable impact spots of equipment. However, understanding the criticality and thereby assessing resilience of underground cables is challenging due to the underground dynamics and the infrastructure spanning across a large geographical area. The cables go through different terrains and depths and the effects of earthquakes vary on these cables depending upon the magnitude. To address this problem, this piece of research explores the potential use of geospatial information software's to map underground cables to study the performance of underground cables by evaluating the repair rates and developing fragility curves for underground cables damaged based on utility data during an earthquake event. Based on the observed case-study, a resilience-based criticality framework is proposed for underground cables. This helps provide actionable and accepted metrics, particularly for short term and long-term resilience of underground cables in a distribution network.

关键词： Measurement Power cables Earthquakes Power system dynamics Maintenance engineering software Communication cables

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