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24th International Electronics symposium, IES 2022

作者： Hamano, Amane Sasaki, Shiori Musashino University Faculty of Data Science Tokyo Japan

ISBN: (数字)9781665489713

ISBN: (纸本)9781665489713

This paper presents a method of multilayered analytical visualization for detecting time-series changes in urban vegetation and forest distribution by integrating distributed time-series geographic information data, socioeconomic indicator data, and natural disaster data. As the world's large areas have been deforested seriously in recent years, there has been a growing demand for methods that accurately assess the importance of forests and their effects and impacts. This study aims to establish a process to assess forests' role in urban disaster resilience. Specifically, the method is designed to analyze the relationship between 1) forest distribution and population density, 2) forest distribution and urban infrastructure development, and 3) forest distribution and disaster occurrence, using GIS, satellite data, demographic, highway, flood, and inundation data. From the results of the analysis, the relationship between forest-urban development and natural disasters will be evaluated. This paper demonstrates the feasibility of the proposed method for the realization of SDG 15 and the potential for the integrated use of various and distributed open data through the experiments conducted in the Kanto region of Japan. © 2022 ieee.

关键词： data integration

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Knowledge Graph Embedding (KGE) for Semantic Representation of Big data 2

Knowledge Graph Embedding (KGE) for Semantic Representation ...

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2nd ieee International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Sharma, Pratibha Chitkara University Institute of Engineering and Technology Chitkara University Centre for Interdisciplinary Research in Business and Technology Punjab India

ISBN: (纸本)9798350369960

This work presents a new approach to logically represent big data in rapidly expanding large data sets, namely medical visualization, using Knowledge Graph Embedding (KGE) Knowledge graphs are a great tool for capturing data communication complex due to structure and depth comprehension skills. These grids can be effectively transformed into continuous vector spaces, preserving the logical relationships between the data. The basis of this approach is to build an optimal knowledge map for medical image data, which is then subjected to sophisticated embedding techniques and then evaluate the efficiency of the resulting vector representation in many applications such as anomaly detection,. semantic similarity analysis and image classification. Furthermore, the use of embedded knowledge facilitates the interpretation of results, which is important for medical research. This work extends the application of KGE to other big data applications in addition to new opportunities for complex data representation in medical imaging © 2023 ieee.

关键词： Anomaly Detection Big data Knowledge Graph Embedding Medical Imaging Semantic Representation

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Loghub: A large Collection of System Log datasets for AI-driven Log Analytics 34

Loghub: A Large Collection of System Log Datasets for AI-dri...

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34th ieee International symposium on Software Reliability Engineering (ISSRE)

作者： Zhu, Jieming He, Shilin He, Pinjia Liu, Jinyang Lyu, Michael R. Chinese Univ Hong Kong CUHK Shenzhen Sch Data Sci Shenzhen Peoples R China Chinese Univ Hong Kong Dept Comp Sci & Engn Hong Kong Peoples R China CUHK Hong Kong Peoples R China

ISBN: (纸本)9798350315943

Logs have been widely adopted in software system development and maintenance because of the rich runtime information they record. In recent years, the increase of software size and complexity leads to the rapid growth of the volume of logs. To handle these large volumes of logs efficiently and effectively, a line of research focuses on developing intelligent and automated log analysis techniques. However, only a few of these techniques have reached successful deployments in industry due to the lack of public log datasets and open benchmarking upon them. To fill this significant gap and facilitate more research on AI-driven log analytics, we have collected and released loghub, a large collection of system log datasets. In particular, loghub provides 19 real-world log datasets collected from a wide range of software systems, including distributed systems, supercomputers, operating systems, mobile systems, server applications, and standalone software. In this paper, we summarize the statistics of these datasets, introduce some practical usage scenarios of the loghub datasets, and present our benchmarking results on loghub to benefit the researchers and practitioners in this field. Up to the time of this paper writing, the loghub datasets have been downloaded for roughly 90,000 times in total by hundreds of organizations from both industry and academia. The loghub datasets are available at https://***/logpai/loghub.

关键词： Log datasets log analytics log intelligence benchmarks anomaly detection

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Attribute-Aware Deep Hashing With Self-Consistency for large-Scale Fine-Grained Image Retrieval

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ieee TRANSACTIONS ON PATTERN analysis AND MACHINE INTELLIGENCE 2023年第11期45卷 13904-13920页

作者： Wei, Xiu-Shen Shen, Yang Sun, Xuhao Wang, Peng Peng, Yuxin Southeast Univ Sch Comp Sci & Engn Nanjing 210096 Peoples R China Southeast Univ Key Lab New Generat Artificial Intelligence Techn Nanjing 210096 Peoples R China Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Peoples R China Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 610056 Peoples R China Peking Univ Wangxuan Inst Comp Technol Natl Key Lab Multimedia Informat Proc Beijing 100871 Peoples R China

Our work focuses on tackling large-scale fine-grained image retrieval as ranking the images depicting the concept of interests (i.e., the same sub-category labels) highest based on the fine-grained details in the query. It is desirable to alleviate the challenges of both fine-grained nature of small inter-class variations with large intra-class variations and explosive growth of fine-grained data for such a practical task. In this paper, we propose attribute-aware hashing networks with self-consistency for generating attribute-aware hash codes to not only make the retrieval process efficient, but also establish explicit correspondences between hash codes and visual attributes. Specifically, based on the captured visual representations by attention, we develop an encoder-decoder structure network of a reconstruction task to unsupervisedly distill high-level attribute-specific vectors from the appearance-specific visual representations without attribute annotations. Our models are also equipped with a feature decorrelation constraint upon these attribute vectors to strengthen their representative abilities. Then, driven by preserving original entities' similarity, the required hash codes can be generated from these attribute-specific vectors and thus become attribute-aware. Furthermore, to combat simplicity bias in deep hashing, we consider the model design from the perspective of the self-consistency principle and propose to further enhance models' self-consistency by equipping an additional image reconstruction path. Comprehensive quantitative experiments under diverse empirical settings on six fine-grained retrieval datasets and two generic retrieval datasets show the superiority of our models over competing methods. Moreover, qualitative results demonstrate that not only the obtained hash codes can strongly correspond to certain kinds of crucial properties of fine-grained objects, but also our self-consistency designs can effectively overcome simplicity bias in fi

关键词： Codes visualization Image retrieval Image reconstruction Task analysis Atmospheric modeling Decorrelation Attribute-Aware large-scale fine-grained image retrieval learning-to-hash self-consistency simplicity bias

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BACKGROUND SUBTRACTION APPROACH TO UNSUPERVISED CELL SEGMENTATION: TOWARD EXCLUDING SPURIOUS FEATURES IN DEGRADED CYTOLOGY SLIDES 20

BACKGROUND SUBTRACTION APPROACH TO UNSUPERVISED CELL SEGMENT...

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20th ieee International symposium on Biomedical Imaging (ISBI)

作者： Takeda, Keita Mitate, Eiji Sakai, Tomoya Nagasaki Univ Grad Sch Engn Nagasaki Japan Kanazawa Med Univ Dept Oral & Maxillofacial Surg Kanazawa Japan

ISBN: (纸本)9781665473583

Automation and augmentation of the cytological diagnosis by data-driven techniques require a large number of microscopic slide images. The slide images with different imaging and storage conditions may contain latent image features that are spuriously correlated with cell classes. This paper focuses on the fact that image features other than cells can induce inference in deep learning of cytology images. To address this problem without the cost of cell annotation, this paper proposes a novel image processing method that subtracts from the whole image the background pattern discolored by the degradation of the slides. In the proposed method, a deep neural network model learns to separate sparse cells from the background in an unsupervised manner. To demonstrate the capability of the proposed method to exclude the background features, it is experimentally shown that it achieves cell detection with a comparable performance to that of supervised methods.

关键词： Spurious correlation data cleansing robust principal component analysis oral cytology

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SmartMLVs: LLM-enabled Multiple Linked Views Generation for Interactive visualization

SmartMLVs: LLM-enabled Multiple Linked Views Generation for ...

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Pacific (formerly Asia-Pacific APVIS) visualization symposium

作者： Tian Qiu Fen Wang Shaohua Huang Meng Guo Yuheng Zhao Jincheng Li Siming Chen Fudan University Henan Institute of Advanced Technology Zhengzhou University Tongji University Beijing Normal University

ISBN: (数字)9798331505813

ISBN: (纸本)9798331505820

Automating the generation of multiple linked view visualization is imperative for improving data analysis efficiency. large Language Models (LLMs) offer substantial potential for enabling this automation, yet they encounter notable challenges in understanding complex queries and producing relevant interactive visualizations. To tackle these challenges, we introduce SmartMLVs, a system designed to harness LLMs for automatic interactive multiple linked views generation with human guidance. First, we analyze the challenges LLMs may encounter when designing visualizations in place of experts. To address these challenges, we gather the essential domain knowledge required for visual analysis process and propose a framework consisting of decomposition, visualization and linking. The decomposition process applies a human-AI interaction method to clarify user requirements. For each decomposed question, the generation process handles chart type selection, data processing and visualization generation. Finally, the linking process adds interactions for views and provides users with data insights. For better human-AI collaboration, we design a system for data exploration. Our system applies the entire framework, supporting users’ interactive exploration with multiple linked views, and can iteratively generate linked views based on user feedback. We examine the effectiveness of our method through usage scenarios and evaluations.

关键词： Knowledge engineering data analysis Automation large language models Visual analytics data visualization Collaboration Prompt engineering

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Emulating Side Channel Attacks on gem5: lessons learned 8

Emulating Side Channel Attacks on gem5: lessons learned

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8th ieee European symposium on Security and Privacy (EuroS and P)

作者： Bossuet, Lilian Grosso, Vincent Lara-Nino, Carlos Andres UJM St Etienne CNRS Inst Opt Grad SchLab H Curien UMR 5516 F-42023 St Etienne France

ISBN: (纸本)9798350327205

Side channel attacks (SCA) have the potential of disrupting the trust of the users on computing platforms and cryptographic algorithms. The main challenge in the design of countermeasures against such threats is that an evaluation of their effectiveness can only be performed after they have been implemented. By that point, significant resources would have been invested in the creation of a prototype. Moreover, the large volume of combinations from all the potential target algorithms and computing systems complicates a systematical analysis. It is necessary to find strategies to simplify and systematize the study of SCAs and their countermeasures. gem5 is a cycle-accurate simulator which offers the possibility to emulate a broad range of computing architectures. Beyond the functional verification, this tool computes multiple physical statistics from the simulated system. In this paper, we discuss the lessons learned from using gem5 to simulate SCAs on an ARM system. Our work shows that while there is a correlation between the data and the reported statistics, there are significant challenges that must be addressed to improve the use of gem5 for the emulation of physical phenomena.

关键词： Covert Channels gem5 Power analysis SCA

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Qiwu: Exploiting Ciphertext-Level SIMD Parallelism in Homomorphic Encryption Programs 25

Qiwu: Exploiting Ciphertext-Level SIMD Parallelism in Homomo...

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23rd ACM/ieee International symposium on Code Generation and Optimization, CGO 2025

作者： Zhang, Zhongcheng Liu, Ying Zhang, Yuyang Chen, Zhenchuan Zhao, Jiacheng Feng, Xiaobing Cui, Huimin Xue, Jingling SKLP ICT CAS UCAS Zhongguancun Laboratory Beijing China SKLP ICT CAS UCAS Beijing China SKLP ICT CAS Beijing China UNSW Sydney Australia

ISBN: (纸本)9798400712753

Fully Homomorphic Encryption (FHE), particularly the CKKS scheme, enables computation on encrypted data, facilitating secure task offloading to untrusted servers. CKKS allows packing multiple complex values into a single ciphertext, crucial for fixed-point arithmetic in machine learning, while leveraging SIMD parallelism at the plaintext level. However, operations such as reductions can degrade performance by creating a large number of bubbles (or gaps) in intermediate ciphertexts, leading to wasted computational resources. We introduce Qiwu, a ciphertext-level vectorization approach that enhances performance by fusing multiple ciphertexts containing bubbles. Qiwu uses a DSL to specify zero bubbles in input ciphertexts and nonzero bubbles in output ciphertexts, employs data-flow analysis to track them, and formulates a fusion plan guided by a cost-benefit assessment. Implemented in an existing FHE compiler, Qiwu was evaluated on four applications (including three machine learning tasks) and three kernels. It achieves speedups of up to 18.0× on CPUs, averaging 3.4× (geometric mean), compared to the state-of-the-art compiler that exploit only plaintext-level parallelism. © 2025 Copyright held by the owner/author(s).

关键词： data flow analysis

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Performance Debugging and Tuning of Flash-X with data analysis Tools 4

Performance Debugging and Tuning of Flash-X with Data Analys...

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ieee/ACM Workshop on Programming and Performance visualization Tools (ProTools)

作者： Huck, Kevin Wu, Xingfu Dubey, Anshu Georgiadou, Antigoni Harris, J. Austin Klosterman, Tom Trappett, Matthew Weide, Klaus Univ Oregon Eugene OR 97403 USA Argonne Natl Lab 9700 S Cass Ave Argonne IL 60439 USA Oak Ridge Natl Lab Oak Ridge TN USA

ISBN: (纸本)9781665475648

State-of-the-art multiphysics simulations running on large scale leadership computing platforms have many variables contributing to their performance and scaling behavior. We recently encountered an interesting performance anomaly in Flash-X, a multiphysics multicomponent simulation software, when characterizing its performance behavior on several large-scale HPC platforms. The anomaly was tracked down to the interaction between the use of dynamic allocation of scratch data and data locality in the cache hierarchy. In this paper we present the details of unexpected performance variability of Flash-X, its extensive analysis using the performance measurement tool TAU to collect the data and Python data analysis libraries to explore the data, and our insights from this experience. In this process, we discovered and removed or mitigated two additional performance limiting bottlenecks for performance tuning.

关键词： Multiphysics application, adaptive mesh refinement, performance, data analysis

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Minilag Filter for Jitter Elimination of Pose Trajectory in AR Environment 22

Minilag Filter for Jitter Elimination of Pose Trajectory in ...

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22nd ieee International symposium on Mixed and Augmented Reality (ISMAR)

作者： Song, Xiuqiang Xie, Weijian Li, Jiachen Wang, Nan Zhong, Fan Zhang, Guofeng Qin, Xueying Shandong Univ Sch Software Jinan Peoples R China Shandong Univ Sch Comp Sci & Technol Jinan Peoples R China Zhejiang Univ State Key Lab CAD & CG Hangzhou Peoples R China SenseTime Res Hong Kong Peoples R China Tetras AI Singapore Singapore Minist Educ Engn Res Ctr Digital Media Technol Beijing Peoples R China

ISBN: (纸本)9798350328387

In AR applications, the jitter of virtual objects can weaken the sense of integration with the real environment. This jitter is often caused by noise in the pose obtained by 3D tracking or localization methods, especially in monocular vision systems without IMU support. Filtering the pose is an effective method to eliminate jitter, however, it can also cause significant lag in the filtered pose, seriously degrading the AR experience. Existing filters struggle to simultaneously reduce jitter while maintaining low lag. In this paper, we propose a novel Minilag filter, which achieves excellent pose smoothing while significantly reducing the lag through backtracking update and compensation strategies, and has excellent real-time performance. We represent the rotation in the pose in the Lie algebra and filter it in locally Euclidean space, ensuring that the filtering of rotation is consistent with that of vectors. We also analyze the noise distribution and characteristics in the tracked pose, providing a theoretical basis for setting filter parameters. We evaluated the proposed filter using both objective mathematical metrics and a user study, and the experimental results demonstrate that our method achieves state-of-the-art performance.

关键词： Human-centered computing visualization Mathematics of Computing Mathematical analysis

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