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5th International Conference on Frontiers technology of Information and computer, ICFTIC 2023

作者： Wang, Qifan Shuai, Jiaqi Guo, Yan Wu, Junmin University of Science and Technology of China School of Software Suzhou China University of Science and Technology of China School of Computer Science and Technology Suzhou Institute for Advanced Research Suzhou China

ISBN: (纸本)9798350309034

The Ultra-Wideband (UWB) technology boasts notable attributes, including robust signal penetration capabilities, exceptional resistance to interference, precision in positioning, and admirable signal stability. These attributes render UWB particularly well-suited for high-speed wireless access within indoor environments. Nevertheless, the intricate nature of indoor settings poses an ongoing challenge for achieving precise positioning. Traditional trilateration-based positioning methods excel in vast, unobstructed indoor spaces but yield suboptimal results in confined, elongated areas such as underground mine tunnels. This paper proposes a positioning algorithm founded on two-point ranging for this kind of quasi-one-dimensional indoor space whose length-to-width ratio is enormous. And we use the Median Absolute Deviation (MAD) method for outlier elimination to mitigate location inaccuracies. The presented algorithm addresses the challenges associated with intricate hardware deployment and substantial Non-Line-of-Sight (NLOS) errors prevalent in the conventional method. Systematic experiments were undertaken to methodically assess the performance of the positioning system. © 2023 IEEE.

关键词： indoor positioning Two Way Ranging (TWR) underground mine tunnel UWB positioning

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Underwater Image Enhancement Method Based on CGAN and Parallel Attention Mechanism 3

Underwater Image Enhancement Method Based on CGAN and Parall...

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3rd International Conference on Neural Networks, Information and Communication Engineering, NNICE 2023

作者： Ma, Chi Yang, Le Hu, Hui Chen, Yuenai Bu, Anxu The School of Computer Science and Engineering Huizhou University Huizhou516007 China The School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China

ISBN: (纸本)9798350335972

Due to the absorption and scattering of light in water, the underwater image has some problems such as colour distortion, serious colour difference and ambiguity, which seriously affects the detection of underwater resources and object recognition. At present, the latest underwater image enhancement methods are all based on deep learning, but they do not take into account the diversity of channel information and pixel features. In order to achieve better enhancement effect, a parallel attention mechanism combining Channel Attention and Pixel Attention is introduced based on Conditional Generative Adversarial Network consisting of multi-scale generator and double discriminator. Compared with other latest methods, the enhancement effect of the proposed method is closer to the real image, and the necessity of each module of the proposed model is proved by ablation study. © 2023 IEEE.

关键词： Pixels

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ScDCMAC:adaptive feedback clustering of scRNA-seq data with deep gene correlation mining

ScDCMAC:adaptive feedback clustering of scRNA-seq data with ...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Pang, Shanchen Wang, Ruiqian Wu, Wenhao Li, Hengxiao Wu, Jiarui Zhang, Yuanyuan Wang, Shudong College of Computer Science and Technology Qingdao Institute of Software Qingdao China Qingdao University of Technology School of Information and Control Engineering Qingdao China

ISBN: (纸本)9798350386226

Cluster assignment is crucial for analyzing single-cell RNA sequencing (scRNA-seq) data, essential for studying cell diversity and biological functions at the single-cell level. Deep learning-based clustering methods have been extensively applied in scRNA-seq data analysis. However, current deep models frequently overlook the deep exploration of potential gene correlation, resulting in networks lacking guidance from gene correlation information when learning hidden layer features. This paper proposes an adaptive feedback fuzzy clustering method based on deep gene correlation mining(scDCMAC). The deep gene correlation mining network employs contrast learning to compute the contrast loss on two cell similarity matrices, constructed from denoised autoencoder hidden layer features and original input features, to uncover potential gene correlation. The adaptive feedback clustering network further denoises the hidden layer features using a self-attention mechanism, computes the affiliation matrix, and supervises the denoising autoencoder's dimensionality reduction using the affiliation matrix. Experiments on 13 real scRNA-seq datasets demonstrate that scDCMAC excels in clustering accuracy and stability, showing great potential in scRNA-seq sample analysis. © 2024 IEEE.

关键词： Dimensionality reduction

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Dual Cross-Stage Partial Learning for Detecting Objects in Dehazed Images 24

Dual Cross-Stage Partial Learning for Detecting Objects in D...

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24th IEEE International Conference on Data Mining, ICDM 2024

作者： Zhao, Jinbiao Zhang, Zhao Ren, Jiahuan Zhang, Haijun Zhao, Zhongqiu Wang, Meng School of Computer Science and Information Engineering Hefei University of Technology Hefei China Yunnan Key Laboratory of Software Engineering Yunan China Harbin Institute of Technology Department of Computer Science Shenzhen China

ISBN: (纸本)9798331506681

Performing an object detection task after the restoration of a hazy image, or rather detecting with the network backbone directly, will result in the inclusion of information mixed with dehazing, which tends to interfere with detection performance. To address these issues, we propose a novel framework for detecting objects in dehazed images via Dual Cross-Stage Partial Learning (DCSP). Specifically, we introduce a Cross-Stage Partial (CSP) module for extracting clean feature information after dehazing. Secondly, to enhance data integrity, we employ a skip-input strategy to supplement information related to object detection features that may be lost during the dehazing task, while avoiding the gradient vanishing problem. In addition, CSP is also introduced to facilitate comprehensive learning of multiple feature representations. Finally, to avoid the inclusion of irrelevant dehazing information in detection, we apply a Ground-Truth Flow at detection network (at dark3), for fine feature information calibration. Additionally, we created a synthetic fog dataset to expand the training data for DCSP. Experimental results on both synthetic and real-world datasets demonstrate the effectiveness and accuracy of the proposed method. The code is available at https://***/zhaojinbiao/DCSP. © 2024 IEEE.

关键词： Demulsification

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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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Deep Reinforcement Learning-Based Distributed Collaborative Service Caching in Edge Computing 12

Deep Reinforcement Learning-Based Distributed Collaborative ...

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12th International Conference on Advanced Cloud and Big Data, CBD 2024

作者： Yang, Wei Chu, Yi Chen, Chao Jiao, Shengming Xu, Xiaolong Xue, Shengjun School of Software Nanjing University of Information Science and Technology Nanjing China Jiangsu Meteorological Information Center Nanjing China School of Computer Science and Technology Silicon Lake College Suzhou China

ISBN: (纸本)9798331511074

As the Internet of Things (IoT) continues to evolve, smart devices increasingly depend on edge computing for services characterized by low latency and high efficiency. The rapid proliferation of smart devices and the diversification of service requests impose greater demands on the quality of edge service provision. Traditional caching strategies based on service request volumes, which neglect the geographic distribution of devices and the randomness of requests, fail to meet the dual requirements of accuracy and response speed, resulting in inefficient resource allocation and caching. To tackle these challenges, this paper introduces ECPG, a novel distributed collaborative service caching strategy leveraging deep reinforcement learning. By modeling the service caching issue as a Markov Decision Process (MDP) and optimizing it through the Deep Deterministic Policy Gradient (DDPG) algorithm, this strategy facilitates independent yet collaborative caching decisions by devices based on real-time data and environmental conditions. This approach significantly improves service response times and overall system efficiency. Experimental results demonstrate ECPG's superior performance in reducing service latency and increasing cache hit rates, underscoring its practicality and efficacy in contemporary edge computing environments. © 2024 IEEE.

关键词： Deep reinforcement learning

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Defense Mechanisms Against Audio Adversarial Attacks: Recent Advances and Future Directions 1

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3rd EAI International Conference on Edge Computing and IoT, EAI ICECI 2022

作者： Li, Routing Xue, Meng School of Software Engineering Huazhong University of Science and Technology Wuhan China School of Computer Science Wuhan University Wuhan China

ISBN: (数字)9783031289903

ISBN: (纸本)9783031289897

With the popularity of speech and speaker recognition systems in recent years, voice interfaces are increasingly integrated into various Internet of Things (IoT) devices. However, studies have demonstrated that such systems are vulnerable to attacks using manipulated inputs. During the last few years, defense mechanisms have been studied and discussed from various aspects to protect voice systems from such attacks. Notwithstanding, there is lacking survey focus on the defense mechanism of audio adversarial examples. In this paper, we provide a comprehensive survey on state-of-the-art defense methods by illuminating their main concepts, reviewing the recent progress with a novel taxonomy, and discussing the future directions. It promises to bring awareness to the security problems in speech and speaker recognition systems and encourages people to propose more robust defenses against audio adversarial examples. © 2023, ICST Institute for computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Speech recognition

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What Are the Trends in Eco-Design Research When We Talk about Digital technology? - A Five Year Analytical Dive into Research Data

What Are the Trends in Eco-Design Research When We Talk abou...

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2023 International Symposium on World Ecological Design, ISWED 2023

作者： Zhuang, Nan Ma, Yanni Zhai, Shuo Zhao, Xin Weng, Shitong Ma, Zhenyuan Shi, Shang Ying, Fangtian School of Software Technology Zhejiang University China College of Computer Science and Technology Zhejiang University China Polytechnic Institute Zhejiang University China

ISBN: (纸本)9781643685069

The convergence of digital technology with eco-design offers an integrative approach, merging environmental sustainability with technological innovation in design. In the realm of eco-design, there is a growing emphasis on the application of digital technology. However, the focal areas and trends of eco-design studies utilizing digital technology remain underexplored. This leads to a deficiency in our deep understanding and prediction of the evolution and transformation of this domain. To comprehend the progression in this domain, we curated and scrutinized a dataset of 1,359 papers related to digital technology and eco-design presented at the CHI conference over the past five years. Through keywords analysis from the past half-decade, we pinpointed prevailing research areas in eco-design utilizing digital technology, such as virtual reality, sustainability, health, and human-computer interaction. We also gathered essential information about the papers for descriptive statistical analysis, including the count, authors' nationalities and gender distribution, and the scale of the authorship teams. Coupled by the visual insights from keyword clouds, we discerned trends in eco-design research in the backdrop of digital technology. Our findings underscore that the eco-design domain, when integrated with digital technology, persistently captivates academic interest, with comparatively smaller teams often embarking on projects in this research sphere. Predominantly, researchers from the United States and male participants lead these endeavors. Moreover, there's an escalating emphasis on people-centered design, striving to engineer solutions that harmonize environmental interactions with health promotions. This study's outcomes furnish insights for a more profound exploration in the realm of digital technology and eco-design, offering recommendations to further propel sustainable progress and innovative design. © 2024 The Authors.

关键词： Ecodesign

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Autonomous Aspect-Image Instruction A2II: Q-Former Guided Multimodal Sentiment Classification 30

Autonomous Aspect-Image Instruction A2II: Q-Former Guided Mu...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Feng, Junjia Lin, Mingqian Shang, Lin Gao, Xiaoying State Key Laboratory for Novel Software Technology Nanjing University China Department of Computer Science and Technology Nanjing University China School of Engineering and Computer Science Victoria University of Wellington New Zealand

ISBN: (纸本)9782493814104

Multimodal aspect-oriented sentiment classification (MABSC) task has garnered significant attention, which aims to identify the sentiment polarities of aspects by combining both language and vision information. However, the limited multimodal data in this task has become a big gap for the vision-language multimodal fusion. While large-scale vision-language pretrained models have been adapted to multiple tasks, their use for MABSC task is still in a nascent stage. In this work, we present an attempt to use the instruction tuning paradigm to MABSC task and leverage the ability of large vision-language models to alleviate the limitation in the fusion of textual and image modalities. To tackle the problem of potential irrelevance between aspects and images, we propose a plug-and-play selector to autonomously choose the most appropriate instruction from the instruction pool, thereby reducing the impact of irrelevant image noise on the final sentiment classification results. We conduct extensive experiments in various scenarios and our model achieves state-of-the-art performance on benchmark datasets, as well as in few-shot settings. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Sentiment analysis

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SparseCoder: Identifier-Aware Sparse Transformer for File-Level Code Summarization 31

SparseCoder: Identifier-Aware Sparse Transformer for File-Le...

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31st IEEE International Conference on software Analysis, Evolution and Reengineering, SANER 2024

作者： Wang, Yanlin Huang, Yanxian Guo, Daya Zhang, Hongyu Zheng, Zibin School of Software Engineering Sun Yat-sen University China School of Computer Science and Technology Sun Yat-sen University China School of Big Data and Software Engineering Chongqing University China

ISBN: (纸本)9798350330663

Code summarization aims to generate natural language descriptions of source code, facilitating programmers to understand and maintain it rapidly. While previous code summarization efforts have predominantly focused on method-level, this paper studies file-level code summarization, which can assist programmers in understanding and maintaining large source code projects. Unlike method-level code summarization, file-level code summarization typically involves long source code within a single file, which makes it challenging for Transformer-based models to understand the code semantics for the maximum input length of these models is difficult to set to a large number that can handle long code input well, due to the quadratic scaling of computational complexity with the input sequence length. To address this challenge, we propose SparseCoder, an identifier-aware sparse transformer for effectively handling long code sequences. Specifically, the SparseCoder employs a sliding window mechanism for self-attention to model short-term dependencies and leverages the structure message of code to capture long-term dependencies among source code identifiers by introducing two types of sparse attention patterns named global and identifier attention. To evaluate the performance of SparseCoder, we construct a new dataset FILE-CS for file-level code summarization in Python. Experimental results show that our SparseCoder model achieves state-of-the-art performance compared with other pre-trained models, including full self-attention and sparse models. Additionally, our model has low memory overhead and achieves comparable performance with models using full self-attention mechanism. Furthermore, we verify the generality of SparseCoder on other code understanding tasks, i.e., code clone detection and code search, and results show that our model outperforms baseline models in both tasks, demonstrating that our model can generate better code representations for various downstream tasks. Our

关键词： Semantics

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