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The Relation Between a Tensor and Its Associated Semi-Symmetric Form

Numerical Mathematics(Theory,Methods and Applications) 2022年第2期15卷 530-564页

作者： Hassan Bozorgmanesh Masoud Hajarian Anthony Theodore Chronopoulos Department of Applied Mathematics Faculty of Mathematical SciencesShahid Beheshti UniversityTehranIran Department of Computer Science University of TexasSan AntonioTexas 78249USA (Visiting Faculty)Department of Computer Engineering&Informatics University of PatrasRio 26500Greece

It is known that every tensor has an associated semi-symmetric *** purpose of this paper is to investigate the shared properties of a tensor and its semi-symmetric *** particular,a corresponding semi-symmetric tensor has smaller Frobenius norm under some conditions and can be used to get smaller bounds for eigenvalues and solutions of dynamical systems and tensor complementarity *** addition,every tensor has the same eigenvalues as its corresponding semi-symmetric form,also a corresponding semi-symmetric tensor inherits properties like being circulant,Toeplitz,Z-tensor,M-tensor,H-tensor and some ***,there are a two-way connection for properties like being positive definite,P-tensor,semi-positive,primitive and several others.

关键词： Tensor eigenvalue semi-symmetric tensor eigenvalue bound logarithmic norm tensor complementarity problem

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Distribution Matching for Multi-Task Learning of Classification Tasks: A Large-Scale Study on Faces & Beyond 38

Distribution Matching for Multi-Task Learning of Classificat...

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38th AAAI Conference on Artificial Intelligence, AAAI 2024

作者： Kollias, Dimitrios Sharmanska, Viktoriia Zafeiriou, Stefanos School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom School of Engineering and Informatics University of Sussex United Kingdom Department of Computing Imperial College London United Kingdom

ISBN: (纸本)1577358872

Multi-Task Learning (MTL) is a framework, where multiple related tasks are learned jointly and benefit from a shared representation space, or parameter transfer. To provide sufficient learning support, modern MTL uses annotated data with full, or sufficiently large overlap across tasks, i.e., each input sample is annotated for all, or most of the tasks. However, collecting such annotations is prohibitive in many real applications, and cannot benefit from datasets available for individual tasks. In this work, we challenge this setup and show that MTL can be successful with classification tasks with little, or non-overlapping annotations, or when there is big discrepancy in the size of labeled data per task. We explore task-relatedness for co-annotation and co-training, and propose a novel approach, where knowledge exchange is enabled between the tasks via distribution matching. To demonstrate the general applicability of our method, we conducted diverse case studies in the domains of affective computing, face recognition, species recognition, and shopping item classification using nine datasets. Our large-scale study of affective tasks for basic expression recognition and facial action unit detection illustrates that our approach is network agnostic and brings large performance improvements compared to the state-of-the-art in both tasks and across all studied databases. In all case studies, we show that co-training via task-relatedness is advantageous and prevents negative transfer (which occurs when MT model's performance is worse than that of at least one single-task model). Copyright © 2024, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Classification (of information)

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Cutting-edge approaches in intrusion detection systems: a systematic review of deep learning, reinforcement learning, and ensemble techniques

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Iran Journal of computer Science 2025年第2期8卷 303-333页

作者： Kalpani, Nethma Rodrigo, Nureka Seneviratne, Dilmi Ariyadasa, Subhash Senanayake, Janaka Department of Computer Science and Informatics Uva Wellassa University Badulla Sri Lanka School of Computing Engineering and Technology Robert Gordon University Aberdeen AB10 7GJ United Kingdom

The growing number of networked devices and complex network infrastructures necessitates robust network security measures. Network intrusion detection systems are crucial for identifying and mitigating malicious activities within network environments. Traditional intrusion detection systems (IDS) often struggle to adapt to new and evolving threats. To address these limitations, researchers are increasingly turning to advanced methodologies such as deep learning (DL), reinforcement learning (RL), and ensemble learning (EL), which offer enhanced capabilities in detecting sophisticated and previously unseen attacks. This systematic literature review (SLR) evaluates 33 technical studies from 2020 to 2024 on the application of DL, RL, and EL in IDS. The study reviews the DL and RL methods used in IDS, along with detection methods that employ EL techniques, to combine the strengths of these approaches. It highlights the advantages, disadvantages, and applicability of the proposed techniques, while also suggesting potential improvements. The aim of this SLR is to provide researchers and practitioners with a comprehensive understanding of current IDS methodologies leveraging DL, RL, and EL, enabling the development of more effective and resilient network security solutions. In addition, this review identifies gaps in the existing literature and outlines potential future research directions. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Deep learning Ensemble learning Intrusion detection systems Network security Reinforcement learning

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New Skills and Knowledge for Digital Entrepreneurs in the age of Artificial Intelligence 19

New Skills and Knowledge for Digital Entrepreneurs in the ag...

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19th European Conference on Innovation and Entrepreneurship, ECIE 2024

作者： Orozova, Daniela Angelova, Nadezhda Zlatev, Zlatin Department of Informatics and Mathematics Faculty of Economics Trakia University Stara Zagora Bulgaria Department of Electrical Engineering Automation Computer Systems and Communications Faculty of Technics and Technologies Trakia University Yambol Bulgaria

ISBN: (纸本)9781917204149

Digital technologies are becoming increasingly complex and integrated, leading to significant transformations in society and the economy. The article aims to explore and summarize the new opportunities and potential risks of the widespread use of artificial intelligence (AI) in all aspects of life, to define the new skills and necessary knowledge of digital entrepreneurs and to highlight the need for transformation in modern education. Recognizing that the relationship between technology and business is two-way and becoming stronger, revealing that well-prepared employees are a guarantee of success and prosperity of companies in various fields, we try to focus on the main groups of qualities, skills and basic knowledge of students in the age of artificial intelligence. The development of the Internet, expansion of connectivity through social networks, the advent of AI, 3D printing, and immersive technologies like Augmented Reality, and Virtual Reality, require new knowledge and skills, leading to new challenges in education. Qualified personnel in this modern world must have solid professional training and systemic thinking (knowledge, skills, accumulated information), developed cognitive abilities, and personal skills based on collecting and analyzing large amounts of diverse information from heterogeneous sources. Questions arise: how can multiple information sources be combined effectively, and how can the fusion of multiple sources provide additional information to support decision-making processes? Combining information obtained from the real world makes the results heterogeneous and more informative. It follows the need to develop machine learning methods to extract relevant information from increasingly complex data sets. The goal is to improve the accuracy of the applied classification algorithms by combining predictions from multiple models, as well as obtaining a more stable final classification evaluation, effective handling of noisy data, adaptation to c

关键词： Adversarial machine learning

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CRISP - DM Process in Smart Building Management 11

CRISP - DM Process in Smart Building Management

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11th International Conference on Electrical and Electronics engineering, ICEEE 2024

作者： Dursun, Mahir Sabyrzhan, Moldir Gazi University Faculty of Technology Dept. of Electrical and Electronics Engineering Ankara Turkey Graduate School of Informatics Gazi University Department of Computer Science Ankara Turkey

ISBN: (纸本)9798350362541

Smart Buildings are buildings that integrate and combine intelligence, management, control, materials, and construction as a single system to meet all the needs of the building, such as comfort, energy efficiency, and durability. In universities, laboratories are used less frequently than other facilities and classrooms. Courses and students may change in the same classroom, and the period of use of such classrooms is much more extended than in laboratories. However, classrooms may only be used for classes during long breaks or for short periods of non-use between exams. In addition, there is no provision for when the heating system will start to heat up before classes or when it will be turned off after classes. By planning heating and lightning, energy and costs will be saved. This paper presents data acquisition methods for preparing a dataset to create a machine learning (ML) model to save energy loss in university laboratories. The developed model will predict energy consumption in classrooms and laboratories based on weather conditions. The study is being conducted in two stages. The first stage describes how to collect data using electronic devices such as Internet of Things (IoT) controllers of Smart Building Management Systems. The second stage gave possibilities for data analysis and preparation using the phases of the Cross-Industry Standard Process for Data Mining (CRISP-DM). © 2024 IEEE.

关键词： Information management

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Leveraging LLMs for Translating and Classifying Mental Health Data 4

Leveraging LLMs for Translating and Classifying Mental Healt...

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4th Workshop on Multilingual Representation Learning, MRL 2024

作者： Skianis, Konstantinos Seza Dogruöz, A. Pavlopoulos, John Department of Computer Science and Engineering University of Ioannina Greece LT3 IDLab Universiteit Gent Belgium Department of Informatics Athens University of Economics and Business Greece Archimedes AthenaRC Greece

ISBN: (纸本)9798891761841

Large language models (LLMs) are increasingly used in medical fields. In mental health support, the early identification of linguistic markers associated with mental health conditions can provide valuable support to mental health professionals, and reduce long waiting times for patients. Despite the benefits of LLMs for mental health support, there is limited research on their application in mental health systems for languages other than English. Our study addresses this gap by focusing on the detection of depression severity in Greek through user-generated posts which are automatically translated from English. Our results show that GPT3.5-turbo is not very successful in identifying the severity of depression in English, and it has a varying performance in Greek as well. Our study underscores the necessity for further research, especially in languages with less resources. Also, careful implementation is necessary to ensure that LLMs are used effectively in mental health platforms, and human supervision remains crucial to avoid misdiagnosis. ©2024 Association for Computational Linguistics.

关键词： Electronic health record

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An Accurate and Wide-Range AFC for Modified Walsh-Hadamard Code Division Multiplexing 16

An Accurate and Wide-Range AFC for Modified Walsh-Hadamard C...

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16th International Conference on Advanced Technologies for Communications, ATC 2023

作者： Kojima, Toshiharu Iwabuchi, Koki The University of Electro-Communications Graduate School of Informatics and Engineering Department of Computer and Network Engineering 1-5-1 Chofugaoka Tokyo Chofu182-8585 Japan

ISBN: (纸本)9798350301328

This paper proposes a novel automatic frequency control (AFC) for modified Walsh-Hadamard code division multiplexing (MWHCDM). While MWHCDM is suitable for helicopter satellite communications, where rotor blades periodically block the transmission channel, it is affected by a time-varying Doppler shift due to helicopter maneuvers. The conventional AFC has a problem where its pull-in range is significantly narrower than the fluctuation range of the Doppler shift. The proposed AFC utilizes the structure of MWHCDM signals, and its operating range covers the fluctuation range with enough margin. computer simulation shows that the proposed AFC outperforms the conventional one on both operating/pull-in range and frequency estimation accuracy. © 2023 IEEE.

关键词： Frequency estimation

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Customized toolbox in VR Design 26

Customized toolbox in VR Design

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26th Pan-Hellenic Conference on informatics, PCI 2022

作者： Drampalou, Georgia Kourniatis, Nikolaos Voyiatzis, Ioannis Department of Informatics and Computer Engineering University of West Attica Greece Department of Civil Engineering University of West Attica Greece

ISBN: (纸本)9781450398541

Designing in an environment of Virtual Reality is a field that gains ground. Although more and more applications are constantly released, the majority of designers have not yet been introduced into Virtual Reality. A widely used tool for designing is Rhino which has been released by McNeel and Rhino Developer encourages the users to create plug-ins for custom use of the software. In parallel, Meta's developing tools offer their users the chance to customize their own applications. Towards the direction of Virtual Reality integration, both have already released new products for their users. RhinoVR is an open source plug-in for Rhino, while at the same time Meta provides Oculus hardware and platform solutions for users to turn their concept into reality. What is yet to be developed is a model that encourages the customization of a VR design platform, focusing mainly on developing a personalized toolbox for designing. © 2022 ACM.

关键词： Virtual reality

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Machine Learning in e-Commerce: Trends, Applications, and Future Challenges

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IEEE Access 2025年 13卷 99048-99067页

作者： Dritsas, Elias Trigka, Maria University of West Attica Department of Informatics and Computer Engineering Egaleo Park Campus Athens12243 Greece

The rapid evolution of e-commerce has been significantly influenced by the integration of machine learning (ML) and data science techniques. The present survey provides a comprehensive overview of how ML methods are applied across various functional domains in e-commerce, including personalized recommendations, dynamic pricing, fraud detection, customer segmentation, and behavioral analysis. We categorize and evaluate a wide range of ML paradigms, namely supervised, unsupervised, reinforcement, and hybrid learning, as well as emerging approaches such as neurosymbolic artificial intelligence (AI), federated learning (FL), and quantumML(QML).Key challenges related to scalability, interpretability, cold-start problems, data sparsity, and privacy are critically analyzed. Additionally, we highlight underexplored areas, such as continual learning (CL) and multi-agent architectures in commerce. The survey incorporates comparative tables, real-world use cases, and a taxonomy of methods to support both academic and industrial perspectives. Ultimately, by analyzing trends and gaps in the literature, we provide a forward-looking research roadmap that bridges ML innovations with the evolving demands of e-commerce ecosystems. © 2013 IEEE.

关键词： Marketplaces

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Pulmonary Diseases Decision Support System Using Deep Learning Approach

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computers, Materials & Continua 2022年第10期73卷 311-326页

作者： Yazan Al-Issa Ali Mohammad Alqudah Hiam Alquran Ahmed Al Issa Department of Computer Engineering Yarmouk UniversityIrbid21163Jordan Department of Biomedical Systems and Informatics Engineering Yarmouk UniversityIrbid21163Jordan Department of Biomedical Engineering Jordan University of Science and TechnologyIrbid 22110Jordan Pediatrician Mediclinic HospitalAl Ain14444UAE

Pulmonary diseases are common throughout the world,especially in developing *** diseases include chronic obstructive pulmonary diseases,pneumonia,asthma,tuberculosis,fibrosis,and recently *** general,pulmonary diseases have a similar footprint on chest radiographs which makes them difficult to discriminate even for expert *** recent years,many image processing techniques and artificial intelligence models have been developed to quickly and accurately diagnose lung *** this paper,the performance of four popular pretrained models(namely VGG16,DenseNet201,DarkNet19,and XceptionNet)in distinguishing between different pulmonary diseases was *** the best of our knowledge,this is the first published study to ever attempt to distinguish all four cases normal,pneumonia,COVID-19 and lung opacity from ChestX-Ray(CXR)*** models were trained using Chest-X-Ray(CXR)images,and statistically tested using 5-fold cross *** individual models,XceptionNet outperformed all other models with a 94.775%accuracy and Area Under the Curve(AUC)of Receiver Operating Characteristic(ROC)of 99.84%.On the other hand,DarkNet19 represents a good compromise between accuracy,fast convergence,resource utilization,and near real time detection(0.33 s).Using a collection of models,the 97.79%accuracy achieved by Ensemble Features was the highest among all surveyed methods,but it takes the longest time to predict an image(5.68 s).An efficient effective decision support system can be developed using one of those approaches to assist radiologists in the field make the right assessment in terms of accuracy and prediction time,such a dependable system can be used in rural areas and various healthcare sectors.

关键词： Pulmonary diseases deep learning lung opacity classification majority voting ensemble features

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