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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Chen, Guang-Ze Jin, Junwei Sun, Hai-Wei Philip Chen, C.L. Faculty of Science and Technology University of Macau Department of Mathematics 99999 China School of Artificial Intelligence and Big Data Henan University of Technology Zhengzhou450001 China School of Computer Science and Engineering South China University of Technology and Pazhou Lab Guangzhou510641 510335 China

ISBN: (纸本)9781665410205

The Broad Learning System (BLS) has been established as an effective flat network alternative to Deep Neural Networks (DNNs), delivering high efficiency while achieving competitive accuracy. Despite its advantages, the incremental learning methods of BLS face challenges in stability and computation when expanding with new nodes or input. We introduce two novel incremental learning algorithms based on factorization updates for BLS that optimize node and input additions to overcome these limitations. Our node addition algorithm utilizes QR decomposition and Cholesky factorization, using the update of the Cholesky factor instead of pseudo-inverse computations. For input addition, we propose an iterative Cholesky factor update algorithm. Our algorithms demonstrate not only faster computation compared to the existing BLS but also improved testing accuracy on the MNIST or Fashion-MNIST dataset. This work presents a significant step forward in the practical application and scalability of BLS in various data-dense environments. © 2024 IEEE.

关键词： Adversarial machine learning

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Space-Time Clustering of Seismicity in Algeria 6th

Space-Time Clustering of Seismicity in Algeria

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6th conference on Computing Systems and Applications, CSA 2024

作者： Mazari, Oussama Sohaib Sebaa, Abderrazak Martínez-Álvarez, Francisco LIMED Laboratory Faculty of Exact Sciences University of Bejaia Bejaia06000 Algeria Laboratoire LITAN École supérieure en Sciences et Technologies de l’Informatique et du Numérique. RN 75 Amizour Bejaia06300 Algeria Data Science and Big Data Lab Pablo de Olavide University Seville41013 Spain

ISBN: (纸本)9783031718472

Each year, earthquakes pose a significant threat to human life, attributed to their sudden and unpredictable nature. Over time, a heightened awareness of this phenomenon has driven increased attention from researchers and experts. This paper seeks to demonstrate the applicability of the k-means algorithm to seismic data, focusing on the identification of seismic zones in Algeria. Initially, we conducted a comprehensive review of existing literature on clustering seismic data, revealing an unexplored niche in the context of Algeria’s seismicity. Subsequently, we introduce our dataset comprising 5876 seismic events. A detailed explanation of the k-means algorithm is provided, with a breakdown of each parameter. Visualization of our findings, including determining the optimal value for k using Elbow and Silhouette scores, is presented and thoroughly discussed. In conclusion, we identify and delineate the seismic zones in Algeria, highlighting the four most critical regions encapsulating these zones. This study contributes to a better understanding of seismic patterns in Algeria, potentially aiding in the development of more effective earthquake preparedness and mitigation strategies. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： K-means clustering

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CCLNet: Causal and Contrastive Learning Framework for Enhanced Pulmonary Embolism Detection

CCLNet: Causal and Contrastive Learning Framework for Enhanc...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Wang, Zhipeng Ge, Ruiquan Yu, Jianxun Qin, Feiwei Tian, Yuan Li, Nannan Min, Wenwen Elazab, Ahmed Wang, Changmiao Hangzhou Dianzi University HDU-ITMO Joint Institute Hangzhou China Hangzhou Dianzi University School of Computer Science Hangzhou China Dalian Polytechnic University School of Management Dalian China Chinese University of Hong Kong Second Affiliated Hospital Shenzhen China Macau University School of Science and Technology China Yunnan University School of Information Science and Engineering Yunnan China Shenzhen University School of Biomedical Engineering Shenzhen China Shenzhen Research Institute of Big Data Medical Big Data Lab Shenzhen China

ISBN: (纸本)9798350386226

The fusion of multimodal medical data is crucial for helping doctors make accurate treatment decisions. For example, combining Computed Tomography Pulmonary Angiography (CTPA) with Electronic Health Records (EHR) can significantly improve the accuracy of Pulmonary Embolism (PE) detection, thereby increasing patient survival rates. Although multimodal learning has advantages in PE diagnosis, the heterogeneity of multimodal data poses a significant challenge to accurate diagnosis. The natural semantic and structural differences between data modalities make it difficult to effectively integrate their information. In addition, within a single modality, the existence of redundant and irrelevant information introduces unnecessary variability, making the data more complex, and making stable diagnosis challenging. To address these issues, we propose a new framework called CCLNet, which includes a contrastive learning component for addressing inter-modality heterogeneity and a causal learning component for handling intra-modality heterogeneity. Specifically, we achieve precise alignment between visual and tabular modalities by using global-level information to soften labels during contrastive learning. In addition, by using causal intervention methods to eliminate the influence of heterogeneous factors within the modality, we can accurately reveal the causal relationship between features and targets, thereby improving the accuracy and stability of the model. Experimental results demonstrate that our method performs excellently, achieving the best results. Our code is available at https://***/LeavingStarW/CLPE. © 2024 IEEE.

关键词： Contrastive Learning

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On the Effectiveness of Function-Level Vulnerability Detectors for Inter-Procedural Vulnerabilities 24

On the Effectiveness of Function-Level Vulnerability Detecto...

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44th ACM/IEEE International Conference on Software Engineering, ICSE 2024

作者： Li, Zhen Wang, Ning Zou, Deqing Li, Yating Zhang, Ruqian Xu, Shouhuai Zhang, Chao Jin, Hai Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security Cluster and Grid Computing Lab National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hong Kong Jin YinHu Laboratory Wuhan China School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China University of Colorado Colorado Springs Department of Computer Science Colorado Springs Colorado United States Institute for Network Sciences and Cyberspace Tsinghua University Beijing China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9798400702174

Software vulnerabilities are a major cyber threat and it is important to detect them. One important approach to detecting vulnerabilities is to use deep learning while treating a program function as a whole, known as function-level vulnerability detectors. However, the limitation of this approach is not understood. In this paper, we investigate its limitation in detecting one class of vulnerabilities known as inter-procedural vulnerabilities, where the to-be-patched statements and the vulnerability-triggering statements belong to different functions. For this purpose, we create the first Inter -Procedural Vulnerability dataset (InterPVD) based on C/C++ open-source software, and we propose a tool dubbed VulTrigger for identifying vulnerability-triggering statements across functions. Experimental results show that VulTrigger can effectively identify vulnerability-triggering statements and inter-procedural vulnerabilities. Our findings include: (i) inter-procedural vulnerabilities are prevalent with an average of 2.8 inter-procedural layers;and (ii) function-level vulner-ability detectors are much less effective in detecting to-be-patched functions of inter-procedural vulnerabilities than detecting their counterparts of intra-procedural vulnerabilities. © 2024 ACM.

关键词： Open source software

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Resource abstraction and data placement for distributed hybrid memory pool

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Frontiers of Computer science 2021年第3期15卷 47-57页

作者： Tingting CHEN Haikun LIU Xiaofei LIAO Hai JIN National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab/Gluster and Grid Computing LabSchool of Gomputing Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China

Emerging byte-addressable non-volatile memory(NVM)technologies offer higher density and lower cost than DRAM,at the expense of lower performance and limited write *** have been many studies on hybrid NVM/DRAM memory management in a single physical ***,it is still an open problem on how to manage hybrid memories efficiently in a distributed *** paper proposes Alloy,a memory resource abstraction and data placement strategy for an RDMA-enabled distributed hybrid memory pool(DHMP).Alloy provides simple APIs for applications to utilize DRAM or NVM resource in the DHMP,without being aware of the hardware details of the *** propose a hotness-aware data placement scheme,which combines hot data migration,data replication and write merging together to improve application performance and reduce the cost of *** evaluate Alloy with several micro-benchmark workloads and public benchmark *** results show that Alloy can significantly reduce the DRAM usage in the DHMP by up to 95%,while reducing the total memory access time by up to 57%compared with the state-of-the-art approaches.

关键词： load balance distributed hybrid memory clouds

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Low-Latency Cooperative Spectrum Sensing via Truncated Vertical Federated Learning

Low-Latency Cooperative Spectrum Sensing via Truncated Verti...

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2022 IEEE GLOBECOM Workshops, GC Wkshps 2022

作者： Zhang, Zezhong Zhu, Guangxu Cui, Shuguang Shenzhen China The School of Science and Engineering Shenzhen China Shenzhen Research Institute of Big Data Shenzhen China Pengcheng Lab Shenzhen China

ISBN: (纸本)9781665459754

In recent years, the exponential increase in the demand of wireless data transmission rises the urgency for accurate spectrum sensing approaches to improve spectrum efficiency. The unreliability of conventional spectrum sensing methods by using measurements from a single secondary user (SU) has motivated research on cooperative spectrum sensing (CSS). In this work, we propose a vertical federated learning (VFL) framework to exploit the distributed features across multiple SUs without compromising the data privacy. However, the repetitive training process in VFL faces the issue of high communication latency. To accelerate the training process, we propose a truncated vertical federated learning (T-VFL) algorithm, where the training latency is highly reduced by integrating the standard VFL algorithm with a channel-aware user scheduling policy. The convergence performance of T-VFL is provided via mathematical analysis and justified by simulation results. Moreover, to guarantee the convergence performance of the T-VFL algorithm, we conclude three design rules on the neural architectures used under the VFL framework, whose effectiveness is proved through simulations © 2022 IEEE.

关键词： data privacy

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Diffusion-Based Method with TTS Guidance for Foreign Accent Conversion

Diffusion-Based Method with TTS Guidance for Foreign Accent ...

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International Symposium on Chinese Spoken Language Processing

作者： Qibing Bai Shuai Wang Zhijun Liu Mingyang Zhang Wei Rao Yannan Wang Haizhou Li School of Data Science Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen Tencent Ethereal Audio Lab Tencent Machine Learning Lab (MLL) University of Bremen

ISBN: (数字)9798331516826

ISBN: (纸本)9798331516833

Accent conversion (AC) aims to alter the accent of spoken language while preserving the original content and speaker characteristics. While any accent can be selected as a target, foreign accent conversion (FAC) that focuses on L2 speakers is particularly noteworthy due to its wide-ranging applications. Compared to general voice conversion tasks, which focus on speaker conversion, research related to accent conversion is relatively scarce, and the audio quality is often limited. In this article, we introduce a diffusion decoder into the conventional TTS-guided accent conversion framework and propose a phoneme-level acoustic-linguistic alignment strategy. Subjective evaluations on the Chinese-accent source speech confirm that the proposed method outperforms the baseline in terms of speech naturalness, accentedness, and speaker similarity 1 1 Audio samples are available at ***..

关键词： Decoding

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Layered Structure Aware Dependent Microservice Placement Toward Cost Efficient Edge Clouds 42

Layered Structure Aware Dependent Microservice Placement Tow...

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42nd IEEE International Conference on Computer Communications, INFOCOM 2023

作者： Zeng, Deze Geng, Hongmin Gu, Lin Li, Zhexiong China University of Geosciences School of Computer Science Wuhan China Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Wuhan China

ISBN: (纸本)9798350334142

Although the containers are featured by light-weightness, it is still resource-consuming to pull and startup a large container image, especially in relatively resource-constrained edge cloud. Fortunately, Docker, as the most widely used container, provides a unique layered architecture that allows the same layer to be shared between microservices so as to lower the deployment cost. Meanwhile, it is highly desirable to deploy dependent microservices of an application together to lower the operation cost. Therefore, the balancing of microservice deployment cost and the operation cost should be considered comprehensively to achieve minimal overall cost of an on-demand application. In this paper, we first formulate this problem into a Quadratic Integer Programming form (QIP) and prove it as a NP-hard problem. We further propose a Randomized Rounding-based Microservice Deployment and Layer Pulling (RR-MDLP) algorithm with low computation complexity and guaranteed approximation ratio. Through extensive experiments, we verify the high efficiency of our algorithm by the fact that it significantly outperforms existing state-of-the-art microservice deployment strategies. © 2023 IEEE.

关键词： Containers

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Teaching resource recommendation for OBE based on kernel canonical correlation analysis 12

Teaching resource recommendation for OBE based on kernel can...

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12th International Conference on Information Technology in Medicine and Education, ITME 2022

作者： Changqian, Wu Shaozi, Li Yaojin, Lin Xiehua, Yu MinNan Science and Technology Key Laboratory of Big Data and Artificial Intelligence University José Rizal University University of Fujian Province Quanzhou China Xiamen University School of Informatics Xiamen China Minnan Normal University School of Computer Science Lab of Data Science and Intelligence Application Zhangzhou China MinNan Science and Technology University Key Laboratory of Big Data and Artificial Intelligence University of Fujian Province Quanzhou China

ISBN: (纸本)9798350310153

data mining is used to extract information from OBE teaching resources, then the teaching resources and user personality database are established to improve the effectiveness of resources recommendation on the online learning platform. The properties of teaching resources and user personality traits are analyzed using the kernel canonical correlation analysis algorithm, and teaching materials with a high correlation coefficient are chosen to recommend to users. The simulation results demonstrate that this proposed method has higher accuracy and more accurate recommended resources when compared to the widely used teaching resource recommendation techniques. © 2022 IEEE.

关键词： data mining

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Fine-Grained Quantitative Emotion Editing for Speech Generation

Fine-Grained Quantitative Emotion Editing for Speech Generat...

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Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)

作者： Sho Inoue Kun Zhou Shuai Wang Haizhou Li School of Data Science Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen China Speech Lab Institute for Intelligent Computing Alibaba Group Singapore

ISBN: (数字)9798350367331

ISBN: (纸本)9798350367348

It remains a significant challenge how to quantitatively control the expressiveness of speech emotion in speech generation. In this work, we propose an approach for quantitative manipulation of the emotion rendering for emotion editing in speech generation. We apply a hierarchical emotion distribution extractor, i.e. Hierarchical ED, that quantifies the intensity of emotions at different levels of granularity. Hierarchical ED is subsequently integrated into the FastSpeech2 framework, guiding the model to learn emotion intensity at phoneme, word, and utterance levels. During synthesis, users can manually edit the emotional intensity of the generated voices. Both objective and subjective evaluations demonstrate the effectiveness of the proposed network in terms of fine-grained quantitative emotion editing.

关键词： Asia Information processing Rendering (computer graphics) Text to speech Speech processing

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