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Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： Lamia Bendebane Zakaria Laboudi Asma Saighi Seif Eddine Bouziane Research Laboratory on Computer Science's Complex Systems (ReLa(CS)2) University of Oum El Bouaghi Algeria Laboratory of Artificial Intelligence and Autonomous Things (LIAOA) University of Oum El Bouaghi Algeria Department of Intelligent Systems Engineering National School of Artificial Intelligence Algiers Algeria

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Studying mental health through social media data has become an emerging area of research, notably for the detection of depression and anxiety. In this regard, many researches have been conducted, yielding very satisfactory results (e.g., [1]–[3]). However, most of these studies have addressed each of these two mental disorders separately. This is due to the overlap of symptoms associated with depression and anxiety that makes distinguishing between them significantly challenging. Based on this context, this work leverages pretrained large language models (LLMs) to develop efficient multi-class models for predicting both depression and anxiety. For this purpose, a small, multi-labeled Twitter dataset is first constructed. Then, a pre-trained BERT-based uncased model is fine-tuned for six epochs on the dataset, resulting in a model referred to as DAC-BERT. Finally, the DAC-BERT model is evaluated and compared against both hybrid deep learning models and other LLM-based models. The obtained results show that DAC-BERT model outperforms existing approaches, achieving accuracies of up to 97.20% on a multi-labeled dataset containing normal, depressive, and anxious tweets, and up to 96.50% on a dataset with only normal and depressive tweets. These findings highlight the promising potential of fine-tuning LLMs for predicting mental health disorders, particularly depression and anxiety.

关键词： Deep learning Sentiment analysis Social networking (online) Large language models Anxiety disorders Blogs Mental health Predictive models Depression Data models

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6th Place Solution to Google Universal Image Embedding

arXiv

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arXiv 2022年

作者： Gkelios, Socratis Kastellos, Anestis Chatzichristofis, Savvas A. Department of Electrical and Computer Engineering Democritus University of Thrace Greece Intelligent Systems Laboratory Department of Computer Science Neapolis University Pafos Cyprus

This paper presents the 6th place solution to the Google Universal Image Embedding competition on Kaggle. Our approach is based on the CLIP architecture;a powerful pre-trained model used to learn visual representations from natural language supervision. We also utilized the SubCenter ArcFace loss with dynamic margins to improve the distinctive power of class separability and embeddings. Finally, a diverse dataset has been created based on the test’s set categories and the leaderboard’s feedback. By carefully crafting a training scheme to enhance transfer learning, our submission scored 0.685 on the private leaderboard. Copyright © 2022, The Authors. All rights reserved.

关键词： Embeddings

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Advanced Clustering Techniques for Speech Signal Enhancement: A Review and Metanalysis of Fuzzy C-Means, K-Means, and Kernel Fuzzy C-Means Methods

arXiv

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arXiv 2024年

作者： Abdullah, Abdulhady Abas Ahmed, Aram Mahmood Rashid, Tarik Veisi, Hadi Rassul, Yassin Hussein Hassan, Bryar Fattah, Polla Ali, Sabat Abdulhameed Shamsaldin, Ahmed S. Artificial Intelligence and Innovation Centre University of Kurdistan Hewler Erbil Iraq School of Intelligent Systems College of Interdisciplinary Science and Technologies University of Tehran Iran Computer Science and Engineering Department University of Kurdistan Hewler KR Erbil Iraq Software and Informatics Engineering College of Engineering Salahaddin University-Erbil Iraq

Speech signal processing is a cornerstone of modern communication technologies, tasked with improving the clarity and comprehensibility of audio data in noisy environments. The primary challenge in this field is the effective separation and recognition of speech from background noise, crucial for applications ranging from voice-activated assistants to automated transcription services. The quality of speech recognition directly impacts user experience and accessibility in technology-driven communication. This review paper explores advanced clustering techniques, particularly focusing on the Kernel Fuzzy C-Means (KFCM) method, to address these challenges. Our findings indicate that KFCM, compared to traditional methods like K-Means (KM) and Fuzzy C-Means (FCM), provides superior performance in handling nonlinear and non-stationary noise conditions in speech signals. The most notable outcome of this review is the adaptability of KFCM to various noisy environments, making it a robust choice for speech enhancement applications. Additionally, the paper identifies gaps in current methodologies, such as the need for more dynamic clustering algorithms that can adapt in real time to changing noise conditions without compromising speech recognition quality. Key contributions include a detailed comparative analysis of current clustering algorithms and suggestions for further integrating hybrid models that combine KFCM with neural networks to enhance speech recognition accuracy. Through this review, we advocate for a shift towards more sophisticated, adaptive clustering techniques that can significantly improve speech enhancement and pave the way for more resilient speech processing systems. © 2024, CC BY.

关键词： Noise abatement

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IP SafeGuard - An AI-Driven Malicious IP Detection Framework

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IEEE Access 2025年 13卷 90249-90261页

作者： Siam, Abdullah Al Alazab, Moutaz Awajan, Albara Hasan, Md Rakibul Obeidat, Areej Faruqui, Nuruzzaman Daffodil International University Daffodil Smart City Birulia Department of Software Engineering Dhaka1216 Bangladesh Oryx Universal College with Liverpool John Moores University School of Computing and Data Sciences Cybersecurity Department Doha34110 Qatar Al-Balqa Applied University Faculty of Artificial Intelligence Department of Intelligent Systems Al-Salt19385 Jordan Department of Computer Science and Engineering Dhaka Bangladesh Amman Jordan

The rising frequency and sophistication of cyberattacks have made real-time malicious IP detection a critical challenge for modern Security Operations Center (SOC). Traditional solutions, such as static blacklists and manual IP reputation checks, are no longer sufficient in today's dynamic threat scenario. To overcome these constraints, we present IP SafeGuard, an AI-driven platform that incorporates multi-source threat intelligence, sophisticated feature engineering, and machine learning (ML)for real-time IP categorization. The framework collects data from AbuseIPDB, VirusTotal, and other sources to compute a Dynamic Threat Score (DTS) for each IP address. It leverages an XGBoost-based classification model to achieve high accuracy and low false-positive rates, even in skewed datasets. Experimental findings indicate the improved performance of IP SafeGuard, with an accuracy of 98.2%, a precision of 97.8%, and a recall of 98.5%. The average detection duration of 45 milliseconds makes it appropriate for real-time SOC integration, enabling automated incident response through Security Information and Event Management (SIEM) alerting and firewall blocking. The framework's modular design assures scalability and adaptability, making it a vital tool for high-volume situations. By overcoming the limits of old approaches and using the power of ML, IP SafeGuard considerably boosts the efficiency and efficacy of current cybersecurity systems. Future work involves expanding the system to enable new threat intelligence sources and studying federated learning for secure and privacy-preserving threat information exchange. © 2013 IEEE.

关键词： Federated learning

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computer-Vision-Based Non-Contact Paste Concentration Measurement*

Computer-Vision-Based Non-Contact Paste Concentration Measur...

引用

Automation in Manufacturing, Transportation and Logistics (ICaMaL), International Conference on

作者： Tailin Liang Zhaolin Yuan State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University Beijing China Department of Industrial and Systems Engineering Research Institute of Advanced Manufacturing The Hong Kong Polytechnic University Hong Kong China

ISBN: (数字)9798350378658

ISBN: (纸本)9798350378665

For monitoring the paste concentration, existing techniques, such as ultrasonic concentration meters and neutron meters, suffer from radiation hazards and low precision in high concentrations. This paper proposes a novel non-contact concentration measurement method based on deep learning. With the dataset collected by preparing the paste with different concentrations and taking photographs from the paste surface, a convolutional neural network is trained to extract the features from images and predict the concentration. The experiments indicate that the measurement accuracy is close to 88.79 % for the manual stirring paste dataset and 91.42 % for the automatic stirring paste dataset, which are sufficient in industrial applications. As a substitution of traditional concentration measurement instruments, the proposed vision-based concentration measurement method is non-contact and its accuracy is sufficient in industrial applications.

关键词： Meters Deep learning Accuracy Ultrasonic variables measurement Transportation Manuals Feature extraction Surface texture Convolutional neural networks Surface treatment

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Multi-Unit Floor Plan Recognition and Reconstruction Using Improved Semantic Segmentation of Raster-Wise Floor Plans

arXiv

引用

arXiv 2024年

作者： Kratochvila, Lukas de Jong, Gijs Arkesteijn, Monique Bilík, Šimon Zemčík, Tomáš Horak, Karel Rellermeyer, Jan S. Department of Control and Instrumentation Faculty of Electrical Engineering and Communication Brno University of Technology Brno Czech Republic Department of Software Technology Faculty of Electrical Engineering Mathematics and Computer Science TU Delft Delft Netherlands Department of Management in the Built Environment Faculty of Architecture and the Built Environment TU Delft Delft Netherlands Computer Vision and Pattern Recognition Laboratory Department of Computational Engineering Lappeenranta-Lahti University of Technology LUT Lappeenranta Finland Dependable and Scalable Software Systems Institute of Systems Engineering Faculty of Electrical Engineering and Computer Science Leibniz University Hannover Hannover Germany

Digital twins have a major potential to form a significant part of urban management in emergency planning, as they allow more efficient designing of the escape routes, better orientation in exceptional situations, and faster rescue intervention. Nevertheless, creating the twins still remains a largely manual effort, due to a lack of 3D-representations, which are available only in limited amounts for some new buildings. Thus, in this paper we aim to synthesize 3D information from commonly available 2D architectural floor plans. We propose two novel pixel-wise segmentation methods based on the MDA-Unet and MACU-Net architectures with improved skip connections, an attention mechanism, and a training objective together with a reconstruction part of the pipeline, which vectorizes the segmented plans to create a 3D model. The proposed methods are compared with two other state-of-the-art techniques and several benchmark datasets. On the commonly used CubiCasa benchmark dataset, our methods have achieved the mean F1 score of 0.86 over five examined classes, outperforming the other pixel-wise approaches tested. We have also made our code publicly available to support research in the field. © 2024, CC BY.

关键词： Semantic Segmentation

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Fuzzy Granule Density-Based Outlier Detection with Multi-Scale Granular Balls

arXiv

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arXiv 2025年

作者： Gao, Can Tan, Xiaofeng Zhou, Jie Ding, Weiping Pedrycz, Witold The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China The National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China The School of Artificial Intelligence and Computer Science Nantong University Nantong226019 China The Faculty of Data Science City University of Macau Macau999078 China The Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland The Department of Electrical and Computer Engineering King Abdulaziz University Jeddah Saudi Arabia The Department of Computer Engineering Istinye University Istanbul Turkey

Outlier detection refers to the identification of anomalous samples that deviate significantly from the distribution of normal data and has been extensively studied and used in a variety of practical tasks. However, most unsupervised outlier detection methods are carefully designed to detect specified outliers, while real-world data may be entangled with different types of outliers. In this study, we propose a fuzzy rough sets-based multi-scale outlier detection method to identify various types of outliers. Specifically, a novel fuzzy rough sets-based method that integrates relative fuzzy granule density is first introduced to improve the capability of detecting local outliers. Then, a multi-scale view generation method based on granular-ball computing is proposed to collaboratively identify group outliers at different levels of granularity. Moreover, reliable outliers and inliers determined by the three-way decision are used to train a weighted support vector machine to further improve the performance of outlier detection. The proposed method innovatively transforms unsupervised outlier detection into a semi-supervised classification problem and for the first time explores the fuzzy rough sets-based outlier detection from the perspective of multi-scale granular balls, allowing for high adaptability to different types of outliers. Extensive experiments carried out on both artificial and UCI datasets demonstrate that the proposed outlier detection method significantly outperforms the state-of-the-art methods, improving the results by at least 8.48% in terms of the Area Under the ROC Curve (AUROC) index. The source codes are released at https://***/Xiaofeng-Tan/MGBOD. © 2025, CC BY.

关键词： Granulation

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Secure Communication in Dynamic RDARS-Driven systems

引用

IEEE Wireless Communications Letters 2025年

作者： Pei, Ziqian Wang, Jintao Zhang, Pingping Shi, Zheng Yang, Guanghua Ma, Shaodan University of Macau Department of Electrical and Computer Engineering State Key Laboratory of Internet of Things for Smart City Macau999078 China Jinan University School of Intelligent Systems Science and Engineering Zhuhai519070 China

In this letter, we investigate a dynamic reconfigurable distributed antenna and reflection surface (RDARS)-driven secure communication system, where the working mode of the RDARS can be flexibly configured. We aim to maximize the secrecy rate by jointly designing the active beamforming vectors, reflection coefficients, and the channel-aware mode selection matrix. To address the non-convex binary and cardinality constraints introduced by dynamic mode selection, we propose an efficient alternating optimization (AO) framework that employs penalty-based fractional programming (FP) and successive convex approximation (SCA) transformations. Simulation results demonstrate the potential of RDARS in enhancing the secrecy rate and show its superiority compared to existing reflection surface-based schemes. © 2012 IEEE.

关键词： Secure communication

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Examining the Use of Non-fungible Tokens (NFTs) as a Trading Mechanism for the Metaverse 1

引用

29th European systems, software and Services Process Improvement, EuroSPI 2022

作者： Yilmaz, Murat Hacaloğlu, Tuna Clarke, Paul Department of Computer Engineering Engineering Faculty Gazi University Ankara Turkey Metaverse Research and Development Laboratory Gazi University Ankara Turkey Department of Information Systems Engineering Atilim University Ankara Turkey School of Computing Dublin City University Dublin Ireland Lero the Science Foundation Ireland Research Centre for Software Limerick Ireland

ISBN: (数字)9783031155598

ISBN: (纸本)9783031155581

The notion of a metaverse seems hard to define but encourages the impression that it can be considered as a new virtual metaphysical landscape that somehow goes beyond our geographical locations and understanding (i.e., independent of time and space). Based on virtual reality, augmented reality, and blockchain, it is envisioned as an independent but extended world that is planned to be a digital virtuality entrenched not only in our old habits such as gaming and entertainment but also in virtual asset trade. In particular, trading is a pillar of the virtual economy, and auction houses will be crucial for Metaverse trading. This exploratory study examines the possibility of using an auction environment to improve the trading capabilities in a virtual universe. We investigate the cases of creating a virtual auction house with the potential of social trading of virtual assets with crypto coins and bartering. To this end, we built a virtual auction house and tested it initially using a set of scenarios. Our preliminary findings suggest that creating a virtual trading environment would be beneficial as an environment for buying and selling virtual assets and exploring their consequences. © 2022, Springer Nature Switzerland AG.

关键词： Augmented reality

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Di-Net: Decomposed Implicit Garment Transfer Network for Digital Clothed 3d Human

SSRN

引用

SSRN 2024年

作者： Zhong, Xiaojing Su, Yukun Wu, Zhonghua Lin, Guosheng Wu, Qingyao School of Software Engineering South China University of Technology China School of Computer Science and Engineering Nanyang Technological University Singapore Key Laboratory of Big Data and Intelligent Robot Ministry of Education China Peng Cheng Laboratory China Tencent Wechat Department China SenseTime Research

3D virtual try-on tasks aim to generate realistic try-on results for full- body garments, allowing them to be observed from arbitrary perspectives. Recent methods often represent the 3D human form with a fixed topol- ogy, limiting garment representation and leading to higher computational demands. Furthermore, the challenge of flexible transfer over various body parts remains. To address these problems, we introduce DI-Net(Decomposed Implicit Garment Transfer Network), where garment transfer is decomposed into 2D image layout transfer and 3D texture layout transfer. We propose a complementary warping module that leverages dense correspondence learn- ing and sparse flow learning to align the body appearance and garments with the target pose for image layout transfer. Subsequently, the transferred garments are lifted to the 3D human mesh by constructing a pixel-aligned implicit function and predicting the texture based on geometric priors. By applying different semantic masks for garment transfer, we efficiently control the texture of the generated mesh. Experimental results show the effective- ness and superiority of our method in the 3D virtual try-on task. © 2024, The Authors. All rights reserved.

关键词： Textures

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