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International Conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Gulnara Abitova Ayanbek Serikov Vladimir Nikulin Mira Rakhimzhanova Gulnur Shuteyeva Korlan Kulniyazova dep. computer engineeering Astana IT University Astana Kazakhstan dept. electical engineering and computer science Binghamton University Binghamton USA dept. system anslysis and managemnt Eurasian National University Astana Kazakhstan

This article presents a novel system designed for human resources (HR) companies, aiming to predict an individual's personality based on resume analysis through the integration of advanced information technology tools. At the core of this system is Airable, serving as the central database, which is seamlessly I integrated with various other tools such as *** for document handling, GitHub for code repository management, and OpenAI for advanced AI algorithms. The integration and automation of these diverse tools are skillfully orchestrated using Make, a no-code solution that streamlines the workflow and enhances efficiency. This innovative approach allows HR companies to leverage technology for more accurate personality predictions, thereby improving their recruitment processes and decision-making strategies.

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PUSH: a primal heuristic based on Feasibility PUmp and SHifting

arXiv

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arXiv 2022年

作者： Agasucci, Valerio Coppola, Corrado Grani, Giorgio Sapienza University of Rome Dep. of Computer Science Control and Management Engineering Rome Italy Sapienza University of Rome Dep. of Statistical Sciences Rome Italy

This work describes PUSH, a primal heuristic combining Feasibility Pump and Shifting. The main idea is to replace the rounding phase of the Feasibility Pump with a suitable adaptation of the Shifting and other rounding heuristics. The algorithm presents different strategies, dep.nding on the nature of the partial rounding obtained. In particular, we distinguish when the partial solution is feasible, infeasible with potential candidates and infeasible without candidates. We used a threshold to indicate the percentage of variables to round with our algorithm and which other to round to the nearest integer. Most importantly, our algorithm tackles directly equality constraints without duplicating rows. We select the parameters of our algorithm on the 19 instances provided for the Mip Competition 2022. Finally, we compared our approach to other start heuristics, like Simple Rounding, Rounding, Shifting and Feasibility Pump on the first 800 MIPLIB2017 instances ordered by the number of non *** Codes 90-08 Copyright © 2022, The Authors. All rights reserved.

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FC Portugal 3D Simulation Team: Team Description Paper 2020

arXiv

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arXiv 2023年

作者： Lau, Nuno Reis, Luís Paulo Simões, David Kasaei, Mohammadreza Abreu, Miguel Silva, Tiago Resende, Francisco DETI/UA Electronics Telecommunications and Informatics Dep. University of Aveiro Portugal DEI/FEUP Informatics Engineering Dep. Faculty of Engineering The University of Porto Portugal IEETA Institute of Electronics and Telematics Engineering of Aveiro Portugal LIACC Artificial Intelligence and Computer Science Lab. University of Porto Portugal

The FC Portugal 3D team is developed upon the structure of our previous Simulation league 2D/3D teams and our standard platform league team. Our research concerning the robot low-level skills is focused on developing behaviors that may be applied on real robots with minimal adaptation using model-based approaches. Our research on high-level soccer coordination methodologies and team playing is mainly focused on the adaptation of previously developed methodologies from our 2D soccer teams to the 3D humanoid environment and on creating new coordination methodologies based on the previously developed ones. The research-oriented development of our team has been pushing it to be one of the most competitive over the years (World champion in 2000 and Coach Champion in 2002, European champion in 2000 and 2001, Coach 2nd place in 2003 and 2004, European champion in Rescue Simulation and Simulation 3D in 2006, World Champion in Simulation 3D in Bremen 2006 and European champion in 2007, 2012, 2013, 2014 and 2015). This paper describes some of the main innovations of our 3D simulation league team during the last years. A new generic framework for reinforcement learning tasks has also been developed. This paper also includes general information related to the design of our agent architecture. The current research is focused on improving the above-mentioned framework by developing new learning algorithms to optimize low-level skills, such as running and sprinting. We are also trying to increase student contact by providing reinforcement learning assignments to be completed using our new framework, which exposes a simple interface without sharing low-level implementation details. © 2023, CC BY-NC-ND.

关键词： Reinforcement learning

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Cybersecurity Threats in the Era of AI: Detection of Phishing Domains Through Classification Rules 2

Cybersecurity Threats in the Era of AI: Detection of Phishin...

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2nd International engineering Conference on Electrical, Energy, and Artificial Intelligence, EICEEAI 2023

作者： Aljaidi, Mohammad Alsarhan, Ayoub Al-Fraihat, Dimah Al-Arjan, Ahmed Igried, Bashar El-Salhi, Subhieh M. Khalid, Muhammad Al-Na'amneh, Qais Zarqa University Faculty of Information Technology Department of Computer Science Zarqa13110 Jordan The Hashemite University Department of Information Technology Jordan Isra University Faculty of Information Technology Department of Software Engineering Amman11622 Jordan The Hashemite University Faculty of Prince AL-Hussein Bin Abdullah Ii of Information Technology Department of Information Technology Jordan The Hashemite University Department of Computer Science and Applications Zarqa Jordan The Hashemite University P. O Box 330127 Zarqa13133 Jordan University of Hull Department of Computer Sciences and Technology Kingston upon Hull United Kingdom Applied Science Private University Dep. of Cyber Security Amman Jordan

ISBN: (纸本)9798350373363

Phishing is a cyber threat where attackers create deceptive websites or emails to exploit individuals for fraudulent purposes such as providing sensitive information like usernames, passwords, or financial details. Hence, detecting phishing emails is of paramount importance to protect sensitive information, preventing financial losses, protecting against identity theft, and maintaining trust. This study employs machine learning techniques to classify and categorize websites that are used for phishing attacks. To achieve this, we proposed a classification model for the detection of phishing domains based on classification rules. These rules were formulated based on analyzing and distributing them into three classifiers: domain name features, search engine opinion (SEO) information of the domain, and security scans by security vendors. A total of eleven classification rules were formulated which were distributed as follows: six rules for the first classifier, four rules for the second classifier, and one rule for the third classifier. Each classifier feeds its results to the main classifier, which then evaluates the received results based on its rules. Domain names from 222 confirmed phishing URLs were used to test the suggested model. The model demonstrated exceptional performance, achieving an impressive accuracy rate of 99.1% during testing. © 2023 IEEE.

关键词： Websites

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Reconfigurable Intelligent Surfaces and DF-relay Improved Spectral Efficiency in Cognitive Radio Networks 97

Reconfigurable Intelligent Surfaces and DF-relay Improved Sp...

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97th IEEE Vehicular Technology Conference, VTC 2023-Spring

作者： El Mettiti, Abderahmane Saber, Mohamed Chehri, Abdellah Chaibi, Hasna Badaoui, Abdel Saadane, Rachid Faculty of Sciences University Mohamed v Ehtp LRIT-GSCM Morocco SIRC/LAGeS Ehtp Laboratory Engineering System Casablanca Morocco Royal Military College Dep. Math. and Computer Science Kingston Canada Supmti Genuis Laboratory Rabat Morocco Mohammed v University Laboratory Lastimi Casablanca Morocco

ISBN: (纸本)9798350311143

Cognitive radio (CR) is considered a primary technology for spectrum usage efficiency and dynamic spectrum management. Spectrum sensing is an essential cognitive radio cycle function. Recently, reconfigurable intelligent surfaces (RIS) technology has emerged as a promising new enabler of 6G wireless communication, with the ability to control signal propagation and increase signal coverage and spectrum management. This paper investigates how integrating RISs with CR can improve spectral efficiency. First, we optimize the RIS parameters by identifying the optimal transmit powers and number of RIS antennas. Then, we compare the novel RIS technology with the traditional decode-and-forward (DF) relaying technology. Next, we investigate how adopting RIS can enhance spectral efficiency. Finally, we demonstrate how integrating the novel technology of reconfigurable intelligent surfaces with CR can significantly increase spectrum detection, the most crucial step in the cognitive radio process. Simulation studies indicate that RIS is more energy-efficient than SISO and DF-relay communications in various setups. Moreover, by boosting spectrum sensing capability in CR situations, RISs can significantly enhance the efficiency of frequency spectrum usage. © 2023 IEEE.

关键词： Cognitive radio

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Research Reviews on Network-based Congestion Control

Research Reviews on Network-based Congestion Control

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International Conference on Information Networking

作者： Yeunwoong Kyung Jaewook Lee Seokwon Jang Haneul Ko Seungmin Oh Jaehwan Lee Youngjun Kim Div. of Info. & Comm. Eng Kongju National University Cheonan Korea Dep. of Info. and Comm. Engineering Pukyong National University Busan Korea Mobile Core Network Research Section Electronics and Telecomm. Research Institute Daejeon Korea Dep. of Electronic Eng Kyung Hee University Yongin-si Korea Dep. of Computer Science and Eng Kongju National University Cheonan Korea School of Computer Science and Eng Kyungnam University Changwon Korea

ISBN: (数字)9798350330946

ISBN: (纸本)9798350330953

Congestion control has been considered as a principal technology for efficient end-to-end traffic transmission. Previous studies on congestion control are mainly focused on the control of the transmission rate or window size at the host. However, such host-based congestion controls have difficulties on short time-scale variable network conditions and increased host complexity. To address this problem, network-based congestion controls that utilize network information have been considered which determine and control the congestion using the network information. In this paper, we review the network-based congestion control methods.

关键词： Data analysis 5G mobile communication Reviews Network architecture Complexity theory

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Camera-LiDAR Fusion based Object Segmentation in Adverse Weather Conditions for Autonomous Driving

Camera-LiDAR Fusion based Object Segmentation in Adverse Wea...

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International Baltic Electronics Conference, BEC

作者： Junyi Gu Mauro Bellone Artjom Lind Dep. of Mechanical and Industrial Engineering Tallinn University of Technology Tallinn Estonia FinEst Centre for Smart Cities Tallinn University of Technology Tallinn Estonia Institute of Computer Science University of Tartu Tartu Estonia

ISBN: (数字)9798350365115

ISBN: (纸本)9798350365122

This paper presents the results of a series of machine learning experiments that focus on the domain adaption between different training datasets for autonomous driving. The experiments use a neural network model obtained through transfer learning from the Waymo Open Dataset and then tested with our custom dataset, which was recorded at the TalTech campus in different weather conditions. In this work, we developed a set of tools to extract and process the sensory data from the iseAuto shuttle. The camera and LiDAR sensors of the iseAuto shuttle were calibrated, and the point clouds data of the LiDAR sensor was projected onto the camera plane. We present, here, our publicly available iseAuto dataset which was used for a classification task under adverse weather and low illumination conditions. The iseAuto dataset contains 8000 frames of camera and LiDAR data for traffic object detection and segmentation. We provide ground truth annotations of 2400 frames, in which the object contours were manually labeled out. In addition, we provided the manual-labeled ground-truth segmentations of cars and humans in images, which can be used by the community to test the accuracy of the segmentation of their models. An additional focus of this paper is to demonstrate that with a few custom annotated data, using transfer learning and semi-supervised learning, it is possible to obtain reasonable accuracy on noisy real-world data. The current performance in vehicle segmentation ranges from 65% to 85% in intersection over union (IoU) and between 43% and 60% IoU for pedestrian segmentation in challenging scenarios such as nighttime and rainy weather.

关键词： Point cloud compression Laser radar Accuracy Transfer learning Object segmentation Semisupervised learning Cameras Sensors Autonomous vehicles Meteorology

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Review of Cloud AI for Real-Time Fraud Detection

Review of Cloud AI for Real-Time Fraud Detection

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International Conference on Autonomous Robots and Agents, ICARA

作者： Gulnara A.Abitova Miras Abalkanov Gulnara A. Abitova Gulnur Shuteyeva Elvira Aitmukhanbetova Korlan Kulniyazova Dep. Computer Engineeering Astana IT University Astana Kazakhstan Dept. Electical Engineering and Computer Science State Univerity of New York at Binghamton Binghamton USA Dept. System Anslysis and Managemnt Eurasian National University Astana Kazakhstan

ISBN: (数字)9798350394245

ISBN: (纸本)9798350394252

The paper presents a comprehensive analysis of the integration of Cloud AI in real-time fraud detection, highlighting significant advancements and practical applications. It is identifying a key challenge such as data privacy and the need for scalable solutions, while also exploring the potential of innovative AI algorithms and strategic partnerships to enhance fraud detection capabilities. Through detailed case studies, the work is demonstrated the effective application of Cloud AI across various sectors. The study concludes that Cloud AI, despite not being a complete solution, plays a crucial role in strengthening fraud detection systems. Our findings suggest that strategic implementation of Cloud AI can lead to more adaptive and efficient systems, thereby significantly contributing to the economic sector's resilience against fraudulent activities. This paper underscores the transformative impact of Cloud AI in fraud detection, offering insights into its future potential and laying the groundwork for further advancements in this field.

关键词： Economics Training Data privacy Technological innovation Scalability Real-time systems Fraud

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Zero-Shot Classification by Large-Margin Distance Learning 10

Zero-Shot Classification by Large-Margin Distance Learning

引用

10th International Conference on computer and Knowledge engineering, ICCKE 2020

作者： Reza Zarei, Mohammad Taheri, Mohammad Shiraz University Dep. of Computer Science and Engineering Shiraz Iran

ISBN: (纸本)9781728185668

Zero-Shot Learning (ZSL) has increasingly attained a lot of attentions due to the scalability it provides to recognition models for classifying instances from new classes of which no training data have been seen before. This scalability is achieved by providing semantic information about new classes, which could be obtained remarkably easier, with a lower cost, in comparison with gathering a new training set. In other words, ZSL can be seen as a subset of transfer learning. In this paper, a distance function is learnt in order to discriminate the classes with a customized large-margin loss function. We also propose a nonlinear prototype learning approach by defining a theoretical-based, but simple mapping function to generate the class prototypes from associated semantic information. The instances are compared with the generated prototypes for classification considering the learnt distance function. The evaluations on five widely-used ZSL datasets, illustrate the effectiveness and superiority of the proposed method in comparison with the state-of-the-art ZSL approaches, despite of its simplicity. © 2020 IEEE.

关键词： Distance education

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Prediction of Electrical Characteristics of a-IGZO TFT Based on Transfer Learning-Based Variational Autoencoder

Prediction of Electrical Characteristics of a-IGZO TFT Based...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Khean-Thye Bea Shih-Shin Hu Wei-Hsuan Lin Da-Zheng Lin Hsin-Hui Hu Xiu-Zhi Chen Ting-Ru Lin Yen-Lin Chen Kun-Ming Chen Wai Khuen Cheng Dep. of Electronic Engineering National Taipei University of Technology Taipei Taiwan Dep. of Computer Science and Information Engineering National Taipei University of Technology Taipei Taiwan Taiwan Semiconductor Research Institute Hsinchu Taiwan Faculty of Information and Communication Technology Universiti Tunku Abdul Rahman Kampar Malaysia

In this study, we proposed a transfer-learning based variational autoencoder model for predicting the electrical characteristics in the parameter tuning process of a-IGZO TFT structure design. The result achieve a high R2 score of 0.9704 with a low-computing-power hardware-friendly method that reduced time consumption significantly compared to prior approaches. The findings have practical implications for mitigating the time-consuming nature of TCAD simulations, and the method can expand to various types of input data while ensuring high performance and generalization. We demonstrated significant improvement in generalization and accuracy through a k-fold validation.

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