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International Symposium on Soft Computing and Intelligent Systems (SCIS)

作者： Prabhat Kumar Mishra Arun Kumar Arulappan In-Ho Ra Thanga Mariappan L Gina Rose G Young-Seok Lee School of Computer Science Engineering and Information Systems VIT University Vellore India School of Computer & Software Kunsan National University Gunsan Republic of Korea Department of Computer Technology Anna University Chennai India

ISBN: (数字)9798350373332

ISBN: (纸本)9798350373349

The integration of Artificial Intelligence (AI) into educational technologies marks a significant shift in learning methodologies and operational dynamics within educational institutions. At the forefront is an AI-driven virtual mock interview platform designed to address the high Customer Acquisition Costs (CAC) in the edtech sector, especially for interview preparation services. This initiative harnesses a blend of AI technologies, including ADA 2 for creating context-aware embeddings and Machine Learning (ML), to transform the traditional mock interview process into a dynamic, cost-effective system. Central to the platform is its use of advanced Natural Language Processing (NLP) techniques and GPT-4 Large Language Model (LLM), automating the process of mock interviews and providing personalized feedback, ensuring a preparation journey that meets specific candidate needs and mirrors real interview scenarios. A key evaluation among 100 students from a cohort of 1800 demonstrated a 90% cost reduction for three mock interviews, reducing expenses from ₹3000 to just ₹300 per candidate. This cost efficiency significantly enhances access to quality interview preparation, improving student satisfaction and accessibility. Moreover, the platform provides valuable insights into student performance, setting a new standard in educational technology by offering an effective, personalized interview preparation experience. This project reflects a holistic approach to student development and the critical role of technology in addressing the evolving needs of learners

关键词： Costs Large language models Learning (artificial intelligence) Educational technology Transforms Machine learning Natural language processing Mirrors Interviews Standards

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Augmented Feature Representation with Parallel Convolution for Cross-domain Facial Expression Recognition 16th

Augmented Feature Representation with Parallel Convolution ...

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16th Chinese Conference on Biometric Recognition, CCBR 2022

作者： Yang, Fan Xie, Weicheng Zhong, Tao Hu, Jingyu Shen, Linlin Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China

ISBN: (纸本)9783031202322

Facial expression recognition (FER) has made significant progress in the past decade, but the inconsistency of distribution between different datasets greatly limits the generalization performance of a learned model on unseen datasets. Recent works resort to aligning feature distributions between domains to improve the cross-domain recognition performance. However, current algorithms use one output each layer for the feature representation, which can not well represent the complex correlation among multi-scale features. To this end, this work proposes a parallel convolution to augment the representation ability of each layer, and introduces an orthogonal regularization to make each convolution represent independent semantic. With the assistance of a self-attention mechanism, the proposed algorithm can generate multiple combinations of multi-scale features to allow the network to better capture the correlation among the outputs of different layers. The proposed algorithm achieves state-of-the-art (SOTA) performances in terms of the average generalization performance on the task of cross-database (CD)-FER. Meanwhile, when AFED or RAF-DB is used for the training, and other four databases, i.e. JAFFE, SFEW, FER2013 and EXPW are used for testing, the proposed algorithm outperforms the baselines by the margins of 5.93% and 2.24% in terms of the average accuracy. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Convolution

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An Investigation into Improved YOLOv8-based Target Detection Algorithms for UAV Aerial Imagery

An Investigation into Improved YOLOv8-based Target Detection...

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Image Processing, computer Vision and Machine Learning (ICICML), International Conference on

作者： Rui Ming Xinyu Liu Yuxuan Lin Qian Gong College of Computer and Control Engineering Minjiang University Fuzhou China College of Computer and Data Science/College of Software Fuzhou University Fuzhou China

In UAV imagery, the intricate backgrounds combined with the high quantity and compact distribution of minute targets have consistently made target detection a formidable challenge in the realm of computer vision. This study introduces an enhancement over the YOLOv8 algorithm, wherein a sophisticated multi-scale convolutional layer, integrating depth-separable convolution, attention mechanisms, and multi-scale processing techniques, replaces the original model's convolution. Moreover, we introduce an attention mechanism for a Bi-Level Routing within the core component of the base model, and adjustments are made to the original model's loss function. Lastly, to confirm the viability of the enhanced model proposed in this paper, we conducted a validation of the metrics using publicly accessible datasets. The findings illustrate that the improved model outlined in this research substantially enhances target recognition accuracy in UAV images. Furthermore, the model exhibits superior performance in mitigating issues of duplicate detection and target omission.

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Masked Image Self-Learning and Knowledge Distillation for Source-Free Unsupervised Domain Adaptation in Crowd Counting

Masked Image Self-Learning and Knowledge Distillation for So...

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Image Processing, computer Vision and Machine Learning (ICICML), International Conference on

作者： Jia Zeng Chaoqun Ma Penghui Shao Anyong Qing Yang Wang School of Electrical Engineering Southwest Jiaotong University Chengdu China Department of Computer Science and Software Engineering Concordia University Montreal Canada

Unsupervised Domain Adaptation (UDA) techniques leverage labeled data from a source domain to adapt to unlabeled data from a target domain, offering a primary solution for addressing cross-domain challenges in crowd counting. However, in real-world scenarios, accessing source data during the model adaptation process within the target domain can be impractical due to privacy and security concerns. This gives rise to a practical yet challenging issue known as Source-Free Unsupervised Domain Adaptation (SFUDA), where model adaptation is limited to the utilization of an existing source model and unlabeled target data. To tackle this challenge, we propose an algorithm that combines mask image self-learning and knowledge distillation to address SFUDA in crowd counting effectively. Specifically, the student model uses masked images to self-learn at both feature and output levels, exploring clues and contextual relationships around masked image blocks to predict occluded areas. Furthermore, we employ knowledge distillation to boost the model’s performance within the teacher-student framework, encompassing feature extraction and density map generation stages. To mitigate cumulative errors, we stochastically recovery a subset of parameters from the source model. We conducted extensive experiments to compare our approach with a modified SFUDA algorithm, and the results demonstrate competitive performance across multiple datasets.

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Channel Estimation for RIS Assisted Wireless Communication Systems Based on the Channel Map

Channel Estimation for RIS Assisted Wireless Communication S...

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International Conference on computers, Communications, and Systems (ICCCS)

作者： Lei Xie Enzhi Zhou Weixi Zhou Junchuan Liang Xiaofeng Tang Zhicheng Dong College of Information Science and Technology Tibet University Lhasa China School of Computer and Software Engineering Xihua University Chengdu China College of Computer Science Sichuan Normal University Chengdu China Tibet Museum of Natural Science Lhasa China

ISBN: (数字)9798350350210

ISBN: (纸本)9798350350227

Reconfigurable Intelligent Surfaces (RIS) as a transformative technology in wireless communication, can significantly improve the wireless communication environment by adjusting the phase and amplitude of reflective elements. However, RIS assisted communication systems face a challenge where their numerous reflective elements necessitate significant pilot overhead for accurate channel estimation. In other words, the performance of channel estimation decreases when pilot resources are limited. We propose the channel map based channel estimation (CMCE) to address this issue. The CMCE scheme utilizes the signal processing capability of the RIS to acquire directional information of wireless channel paths at different user locations, and construct the channel map. Then the CMCE scheme optimizes the reflection coefficients of the RIS and focuses the reflected signals of the RIS in the receiving direction of the base station. The proposed scheme is simulated and compared to schemes without reflection coefficient optimization. The simulation results show that, under the same pilot overhead, the proposed scheme can significantly reduce the normalized mean square error (NMSE) of cascaded channel estimation.

关键词： Wireless communication Wireless sensor networks Base stations Accuracy Simulation Channel estimation Reconfigurable intelligent surfaces

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PATCH: Empowering Large Language Model with Programmer-Intent Guidance and Collaborative-Behavior Simulation for Automatic Bug Fixing

arXiv

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arXiv 2025年

作者： Zhang, Yuwei Jin, Zhi Xing, Ying Li, Ge Liu, Fang Zhu, Jiaxin Dou, Wensheng Wei, Jun Affiliated with Nanjing Institute of Software Technology University of Chinese Academy of Sciences Nanjing China Key Laboratory of System Software Chinese Academy of Sciences Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China Key Laboratory of High Confidence Software Technologies Peking University Ministry of Education School of Computer Science Peking University Beijing China School of Computer Science Wuhan University Wuhan China School of Intelligent Engineering and Automation Beijing University of Posts and Telecommunications Beijing China State Key Laboratory of Complex & Critical Software Environment School of Computer Science and Engineering Beihang University Beijing China

Bug fixing holds significant importance in software development and maintenance. Recent research has made substantial strides in exploring the potential of large language models (LLMs) for automatically resolving software bugs. However, a noticeable gap in existing approaches lies in the oversight of collaborative facets intrinsic to bug resolution, treating the process as a single-stage endeavor. Moreover, most approaches solely take the buggy code snippet as input for LLMs during the patch generation stage. To mitigate the aforementioned limitations, we introduce a novel stage-wise framework named PATCH. Specifically, we first augment the buggy code snippet with corresponding dependence context and intent information to better guide LLMs in generating the correct candidate patches. Additionally, by taking inspiration from bug management practices, we decompose the bug-fixing task into four distinct stages: bug reporting, bug diagnosis, patch generation, and patch verification. These stages are performed interactively by LLMs, aiming to simulate the collaborative behavior of programmers during the resolution of software bugs. By harnessing these collective contributions, PATCH effectively enhances the bug-fixing capability of LLMs. We implement PATCH by employing the powerful dialogue-based LLM ChatGPT. Our evaluation on the widely used bug-fixing benchmark BFP demonstrates that PATCH has achieved better performance than state-of-the-art LLMs. Copyright © 2025, The Authors. All rights reserved.

关键词： computer software maintenance

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Boosting Fine-Tuning Via Conditional Online Knowledge Transfer

SSRN

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SSRN 2023年

作者： Liu, Zhiqiang Li, Yuhong Huang, Chengkai Liu, Yanxia School of software Engineering South China University of Technology China School of Computer Science and Engineering The University of New South Wales Australia

Fine-tuning is an effective technique to enhance network performance in scenarios with limited labeled data. To achieve this, recent methods exploit the knowledge mined in the source model (e.g., feature maps) to construct an extra regularization signal (RS), collaboratively supervising the target model along with target labels. However, these RSs are generated independently from the target information or are generated from the rough assistance of the target information, resulting in biased supervision different from the target task. In this paper, we propose a Conditional Online Knowledge Transfer (COKT) framework that finely utilizes the target information to construct robust and target-related RS. Specifically, we train a target-dominant RS branch that online supervises the target model in a knowledge distillation manner. The target information dominates the RS branch from three aspects: sample-wise conditional attention, residual feature fusion, and target task loss. With such a target-oriented framework, we can effectively exploit target-related prior knowledge of the source model. Extensive experiments demonstrate that COKT significantly outperforms the fine-tuning baselines, especially for dissimilar target tasks and small datasets. Moreover, different from most of the fine-tuning methods that are restricted to the vanilla fine-tuning scenario, COKT can be easily extended to cross-model and multimodel fine-tuning scenarios. © 2023, The Authors. All rights reserved.

关键词： Distillation

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Beyond Subspace Isolation: Many-to-Many Transformer for Light Field Image Super-resolution

arXiv

引用

arXiv 2024年

作者： Hu, Zeke Zexi Chen, Xiaoming Chung, Vera Yuk Ying Shen, Yiran School of Computer Science University of Sydney DarlingtonNSW2008 Australia School of Computer Science and Engineering Beijing Technology and Business University Beijing102488 China School of Software Shandong University Jinan250100 China

The effective extraction of spatial-angular features plays a crucial role in light field image super-resolution (LFSR) tasks, and the introduction of convolution and Transformers leads to significant improvement in this area. Nevertheless, due to the large 4D data volume of light field images, many existing methods opted to decompose the data into a number of lower-dimensional subspaces and perform Transformers in each subspace individually. As a side effect, these methods inadvertently restrict the self-attention mechanisms to a One-to-One scheme accessing only a limited subset of LF data, explicitly preventing comprehensive optimization on all spatial and angular cues. In this paper, we identify this limitation as subspace isolation and introduce a novel Many-to-Many Transformer (M2MT) to address it. M2MT aggregates angular information in the spatial subspace before performing the self-attention mechanism. It enables complete access to all information across all sub-aperture images (SAIs) in a light field image. Consequently, M2MT is enabled to comprehensively capture long-range correlation dependencies. With M2MT as the foundational component, we develop a simple yet effective M2MT network for LFSR. Our experimental results demonstrate that M2MT achieves state-of-the-art performance across various public datasets, and it offers a favorable balance between model performance and efficiency, yielding higher-quality LFSR results with substantially lower demand for memory and computation. We further conduct in-depth analysis using local attribution maps (LAM) to obtain visual interpretability, and the results validate that M2MT is empowered with a truly non-local context in both spatial and angular subspaces to mitigate subspace isolation and acquire effective spatial-angular representation. Copyright © 2024, The Authors. All rights reserved.

关键词： Image enhancement

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ETHACA - A Container-Based Cyber Range for Research and Education 29

ETHACA - A Container-Based Cyber Range for Research and Educ...

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29th IEEE International Workshop on computer Aided Modeling and Design of Communication Links and Networks, CAMAD 2024

作者： Chouliaras, Nestoras Kantzavelou, Ioanna Pantziou, Grammati Maglaras, Leandros Chouliara-Sidera, Panagiota Adamakos, Antonis Chalatsakos, Panagiotis University of West Attica Department of Informatics and Computer Engineering Athens Greece School of Computing Edinburgh Napier University EdinburghEH10 5DT United Kingdom University of Thessaly Department of Computer Science and Biomedical Infromatics Lamia Greece Senior Software Engineer at *** Athens Greece Open University of Cyprus Faculty of Pure and Applied Sciences Nicosia Cyprus

ISBN: (纸本)9798350377644

The advent of Industry 4.0 and the Industrial Internet of Things (IIoT) has revolutionized manufacturing and production systems, enhancing efficiency and communication through the integration of advanced technologies such as IoT devices and AI. However, this increased digitization has also led to heightened cybersecurity risks, with a notable rise in the frequency and complexity of cyber-attacks. Cyber Ranges emerges as a critical solution in addressing these challenges, offering a controlled environment for simulating cyber-attack scenarios, training cybersecurity teams, and developing robust defense strategies. The escalating demand for cybersecurity professionals has highlighted the crucial need for effective training solutions. This article presents an open-source Cyber Range constructed on container-based architecture, meticulously engineered for efficient operation with minimal resource consumption. In response to the scarcity of cybersecurity experts, this platform prioritizes extensive scalability and robust isolation between exercises, ensuring secure learning environments, and also integrates a complex developed exercise series aimed at strengthening cybersecurity learning experiences. These exercises encompass a variety of scenarios and challenges, catering to professionals at different skill levels. Each exercise replicates real-world cyber threats, offering practical, hands-on learning opportunities. Additionally, a comprehensive questionnaire accompanies these exercises, designed to gather feedback on participants' experiences. © 2024 IEEE.

关键词： Cyber attacks

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Mobile Based Solution to Weight Loss Planning for Children (with Obesity) in Sri Lanka 3

Mobile Based Solution to Weight Loss Planning for Children (...

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3rd International Conference on Advancements in Computing, ICAC 2021

作者： Rajapakse, R.M.M.P.K. Mudalige, J.M.A.I. Perera, L.A.D.Y.S. Warakagoda, R.N.A.M.S.C.B. Siriwardana, Samanthi Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka

ISBN: (纸本)9781665408622

Obesity is a condition where there is excess fat in the body, and it is one of the world's most extreme and dangerous dietary diseases. Genetic factors, lack of physical activity, unhealthy eating patterns, or a combination of these factors are the most common causes of obesity. This is important because it influences every part of a child's life. More, in particular, this disorder leads to poor health and negative social standing with perceptions. Nowadays, children are paying keen interest in technology and related devices. Therefore, in this research, we are planning to give a mobile-based solution with a smart band that is used to monitor the child. In this solution, we are mainly focusing on Sri Lankan children with obesity who are aged between 5-10. In our solution, there are four main sections which are, monitoring child activities, recognizing the activities, and getting relevant data, then based on those data and previous activity completion levels, this solution will suggest activities for losing weight, provide specific diet plans for each child considering the health conditions and predict the probability of having main obesity-related diseases by using previously gathered data. © 2021 IEEE.

关键词： Deep learning

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