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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Kuang, Wenting Jin, Shuo Wang, Danyang Zheng, Yuda Zou, Yongpan Wu, Kaishun Shenzhen University College of Computer Science and Software Engineering China Information Hub Hong Kong

ISBN: (纸本)9798350386226

The rising prevalence of mood disorders, including depression and anxiety, underscores the critical state of physical and mental health issues in contemporary society. Automatic emotion recognition technology emerges as a potential tool for monitoring mental health disorders, and offering valuable guidance through human-computer interfaces. However, existing technologies grapple with limitations, such as emotional concealment, potential privacy leakage, and specific device location restrictions. To overcome these limitations, we propose a mobile wearable emotion recognition system called EmoTracer incorporating ubiquitous multi-source sensors to measure physiology signals, and address challenges such as the intricate signal-emotion relationship, sparse data processing, and notable disparities among different subjects. We implement a real-time prototype and carry out comprehensive experiments to evaluate its performance. For six basic emotions, the results show that EmoTracer can achieve 95.6% accuracy in intra-subject emotion classification and 80.7% accuracy with 5 shots in cross-subject emotion classification. © 2024 IEEE.

关键词： Emotion Recognition

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DLICA: Deep Learning based novel Strategy for Intelligent Channel Adaption in Wireless SDN-IoT Environment 3

DLICA: Deep Learning based novel Strategy for Intelligent Ch...

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3rd International Conference on Communication, Computing and Digital Systems, C-CODE 2023

作者： Zabeehullah Arif, Fahim Abbas, Yawar Ahmad, Shahbaz Waseem, Muhammad Computer Software Engineering Military College of Signals Rawalpindi Pakistan Computer Science University of Engineering and Technology Lahore Pakistan

ISBN: (纸本)9798350332391

software-Defined Network (SDN) is an emerging networking paradigm and provides the cost-effective scalability and flexibility required for an IoT environment. Here, wireless SDN is supposed to efficiently use limited network resources as well as provide smart traffic routing in order to deliver a bunch of IoT-generated data to the remote destination (Cloud). As, with the increasing number of IoT nodes and a huge amount of data generation, there is an immense load on SDN-IoT channels. This paper will mainly focus on appropriate channels assignment to each SDN-IoT enabled switch in order to prevent network congestion and improve network performance. Traditional fixed channel assignment algorithms in the SDN-IoT environments have failed because of large-scale and unpredictable dynamic traffic flows. In this paper, we have proposed a novel deep learning-based model called DLICA for intelligent channel assignment in wireless SDN-IoT. Moreover, a deep learning-based future traffic load prediction model has also been proposed to adaptively assign channels according to the traffic load prediction. Simulations show that proposed deep learning models achieve excellent accuracy while predicting traffic load and appropriate channel assignment. Apart from high accuracy, the proposed models also improve network performance significantly as compared to traditional channel assignment algorithms. © 2023 IEEE.

关键词： Cost effectiveness

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Exploring Soft-Label Training for Implicit Discourse Relation Recognition 5

Exploring Soft-Label Training for Implicit Discourse Relatio...

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5th Workshop on Computational Approaches to Discourse, CODI 2024

作者： Costa, Nelson Filipe Kosseim, Leila Laboratory Department of Computer Science and Software Engineering Concordia University MontréalQC Canada

ISBN: (纸本)9798891760790

This paper proposes a classification model for single label implicit discourse relation recognition trained on soft-label distributions. It follows the PDTB 3.0 framework and it was trained and tested on the DiscoGeM corpus, where it achieves an F1-score of 51.38 on third-level sense classification of implicit discourse relations. We argue that training on soft-label distributions allows the model to better discern between more ambiguous discourse relations. © 2024 Association for Computational Linguistics.

关键词： Classification (of information)

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Medical Image Segmentation with U-Net for Breast Cancer and Lump Type Prediction

Medical Image Segmentation with U-Net for Breast Cancer and ...

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2024 International Conference on Decision Aid Sciences and Applications, DASA 2024

作者： Aygün, Elif Nur Kaya, Mehmet Celal Bayar University Department of Software Engineering Manisa Turkey Firat University Department of Computer Engineering Elaziǧ Turkey

ISBN: (纸本)9798350369106

Today, it is known that, like other types of cancer, breast cancer cases are increasing every year. It is an indisputable fact that early diagnosis and correct diagnosis, which medical literature pays particular attention to, have positive effects on patient health in this field. Therefore, it is a great necessity to identify breast cancer cases correctly and not to confuse malignant masses with benign masses. At this stage, a study was carried out to support the physician's decision, and segmentation was carried out on images containing potential breast cancer with the U-Net model. In addition, a two-class classification model is proposed to determine whether ultrasound images containing masses are benign or malignant. While the detection rate of the current model for benign masses is 99.9%, it reaches 86.6% for malignant masses. In addition, various data augmentation techniques were used hybridly to increase malignant mass images containing fewer samples, and the overall prediction success was increased to 91.13%. © 2024 IEEE.

关键词： Lung cancer

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Fine-Tuning GPT-4o-Mini for Programming Questions Generation

Fine-Tuning GPT-4o-Mini for Programming Questions Generation

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2024 International Conference on computer and Applications, ICCA 2024

作者： Hesham, Alaa Hamdy, Abeer The British University in Egypt Faculty of Informatics and Computer Science Department of Software Engineering Cairo Egypt

ISBN: (纸本)9798350367560

Creating programming questions that are both meaningful and educationally relevant is a critical task in computer science education. This paper introduces a fine-tuned GPT4o-mini model (C2Q). It is designed to generate meaningful questions by leveraging semantic feature extraction and well- crafted prompts. The approach addresses the limitations of traditional generative models, offering a deeper understanding of programming code and producing questions that are precise, diverse, and relevant to a given code snippets. The proposed framework incorporates essential code elements, such as control structures and method attributes, to generate questions that align with programming concepts. Evaluation metrics used were BLEU, ROUGE-1, and ROUGE-L to evaluate the model's performance. The findings reveal that the model achieves better structural coherence and conceptual relevance while focusing on contextual understanding over exact term matching. This work highlights the potential of the proposed approach to advance teaching and assessment methods in computer science. © 2024 IEEE.

关键词： Program processors

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Location Detection of an Object Non Line of Sight with Audio Signal 8

Location Detection of an Object Non Line of Sight with Audio...

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8th International Artificial Intelligence and Data Processing Symposium, IDAP 2024

作者： Olgun, Nevzat Calisan, Mucahit Afyon Kocatepe University Department of Software Engineering Afyonkarahisar Turkey Engineering and Architecture Faculty Department of Computer Engineering Bingöl Turkey

ISBN: (纸本)9798331531492

Locating objects Non-Line-of-Sight is an important challenge in many fields such as defense applications, autonomous vehicles, natural disasters, etc. With the advancement of signal processing techniques, there has been an increased interest in the detection of objects in hidden areas. In this respect, audio signals have a strong potential as a low-cost technology for object localization. This study presents an experimental application to detect the location of hidden objects Non-Line-of-Sight based on audio signals. The signals from the sound source strike the hidden object through a reflecting surface, such as a wall, and are received back from the reflecting surface as secondary signals. In this study, object detection can be performed with acoustic sound signals without physically intervening in an area beyond the line of sight and without locating any sensors in this area. The experimental results obtained from the study show that the position detection of an object located Non-Line-of-Sight can be done satisfactorily. © 2024 IEEE.

关键词： Audio signal processing

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FCA-based θ-iceberg core decomposition in graphs

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Journal of Ambient Intelligence and Humanized Computing 2024年第2期15卷 1423-1428页

作者： Hao, Fei Xinchang, Khamphaphone Park, Doo-Soon Key Laboratory of Modern Teaching Technology Ministry of Education Xi’an China School of Computer Science Shaanxi Normal University Xi’an710119 China Department of Computer Science and Engineering Soonchunhyang University Asan31538 Korea Republic of Department of Computer Software Engineering Soonchunhyang University Asan31538 Korea Republic of

Complex networking analysis is a powerful technique for understanding both complex networks and big graphs in ubiquitous computing. Particularly, there are several novel metrics, such as k-clique and k-core are proposed in order to study the relative importance of nodes in complex networks. Among of those metrics, k-core analysis is an effective approach for simplifying graphical structure. However, the relation between k and the scale of networks is not explored in most existing literature. Toward this end, this paper formulate a new research problem, θ-Iceberg Core decomposition in graphs, which is able to incorporate a parameter θ (0 © Springer-Verlag GmbH Germany, part of Springer Nature 2017.

关键词： Formal concept analysis

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Beyond Agile: NLP-Driven Quality Attributes Retrieval Using ChatGPT in software Development Strategies

Beyond Agile: NLP-Driven Quality Attributes Retrieval Using ...

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2024 International Conference on engineering and Computing, ICECT 2024

作者： Aamana Ain, Qurat Ul Nisa, Sardar Un NUML Department of Software Engineering Rawalpindi Pakistan NUML Department of Computer Science Islamabad Pakistan

ISBN: (纸本)9798350349719

This study explores Agile software Development (ASD) methodologies' effectiveness in achieving quality software products. It investigates the underexplored application of ChatGPT as a Quality Attribute Extractor in ASD. Utilizing hard prompt techniques, ChatGPT extracts quality attributes from user stories without fine-Tuning, enhancing understanding through few-shot and zero-shot Prompt engineering. This innovative approach bridges NLP advancements with agile methodologies, facilitating efficient quality assessment in software development processes. © 2024 IEEE.

关键词： Bridge approaches

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CLaC-2 at CheckThat! 2024: A Zero-Shot Model for Check-Worthiness and Subjectivity Classification 25

CLaC-2 at CheckThat! 2024: A Zero-Shot Model for Check-Worth...

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25th Working Notes of the Conference and Labs of the Evaluation Forum, CLEF 2024

作者： Gruman, Shayne Kosseim, Leila Laboratory Department of Computer Science and Software Engineering Concordia University MontréalQC Canada

In this paper, we describe our approach to CLEF 2024 Lab 2 CheckThat! Task 1 (Check-worthiness) and Task 2 (Subjectivity), which aims to evaluate how consistent Large Language Models (LLMs) can distinguish between objective truths worth fact checking and subjective opinions. Our approach was based on prompt engineering utilizing Google's pre-trained LLM Gemini. To optimize performance, we including a random subset of the training data in the input prompt while also augmenting the test data through paraphrasing. We achieved and F1 score of 0.564 for Task 1 and 0.445 for Task 2 which ranked us 24th and 14th respectively. This work contributes to understanding the limitation of LLMs and highlights one of their major pitfalls, subjective reasoning. © 2024 Copyright for this paper by its authors.

关键词： Zero-shot learning

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Optimal Feature-Centric Approach for EEG-Based Human Emotion Identification 5

Optimal Feature-Centric Approach for EEG-Based Human Emotion...

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5th International Conference on Advancements in Computational Sciences, ICACS 2024

作者： Ashraf, Hira Saeed, Sanay Muhammad Umar Khan, Sheharyar Noorani, Sadam Hussain Arsalan, Aamir Rauf, Usman University of Engineering & Technology Department of Computer Engineering Taxila Pakistan Fatima Jinnah Women University Department of Software Engineering Rawalpindi Pakistan

ISBN: (纸本)9798350394764

Emotions are intrinsic to human nature, playing a vital role in human cognition. Emotions are closely intertwined with rational decision-making, perception, human interaction, and human intelligence. EEG has emerged as a promising tool for investigating the neural aspects of emotions. The primary objective of this study is to identify distinguishing features in EEG data and determine suitable classification techniques for categorizing of human emotion EEG-based approach for detecting mental states i.e., relaxation, neutrality, and concentration. To validate this research, a publicly available dataset EEG Brainwave mental state is utilized. Twenty-five temporal domain features were extracted from this dataset. Correlation-based feature selection technique is applied to select the significant features for the classification of three mental states. The selected EEG features are classified using four distinct classifiers. It is evident from the results, that the Random Forest achieves the highest classification accuracy of 98.61%. The proposed framework outperforms prior emotion classification methods in terms of electrode count and accuracy. © 2024 IEEE.

关键词： Feature Selection

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