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ISC High Performance 2025 Research Paper Proceedings (40th International Conference)

作者： Rabab Alomairy Qinglei Cao Hatem Ltaief David Keyes Alan Edelman Computer Science & Artificial Intelligence Laboratory Massachusetts Institute of Technology USA Department of Computer Science Saint Louis University USA Division of Computer Electrical and Mathematical Sciences and Engineering King Abdullah University of Science and Technology KSA

ISBN: (数字)9783982633619

The Hamming distance, a fundamental measure of dissimilarity between data points, plays a crucial role in various fields, including error detection, machine learning, and genomic sequence alignment, where it is commonly used for identifying mismatches in nucleotide or protein sequences. This work introduces two implementations for computing Hamming distances for sequence alignment: synchronous and asynchronous matrix-based approaches. While most existing implementations rely on vector-based methods due to their simplicity and ease of use, they are not efficient for large-scale data. Our work focuses on enhancing performance by introducing matrix-based implementations that significantly improve computational efficiency and scalability. Our asynchronous implementation showcases Julia for sequential task flow and PaRSEC for parameterized task graph execution models on homogeneous and heterogeneous architectures. CPU computations use INT8 GEMM from oneMKL, while GPU implementations employ Tensor/Matrix Core INT8 GEMM from cuBLAS/hipBLAS and 1-bit TensorOps GEMM capabilities from CUTLASS. For constructing bitmask matrices on GPUs, we develop both a naive CUDA implementation using global memory and an optimized implementation utilizing shared memory at the warp level, with the optimized version achieving a 5X speedup over the naive approach. The results demonstrate significant performance improvements, with the asynchronous matrix-based implementation achieving up to 284X speedup over the vector-based approach on CPUs, while the asynchronous GPU-enabled implementation on A100 GPUs delivers a 15X speedup compared to the CPU matrix-based approach and a three orders of magnitude improvement over the CPU vector-based approach. Furthermore, the asynchronous implementation of PaRSEC scales well on up to 256 nodes of Summit and Frontier. These advancements highlight the scalability and efficiency of matrix-based Hamming distance computation, leveraging GPU acceleration and advan

关键词： Proteins Runtime Scalability Graphics processing units Genomics computer architecture Computational efficiency Hamming distances Resource management Bioinformatics

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BA-Audit: Blockchain-Based Public Auditing for Aggregated Data Sharing in Edge-Assisted IoT 1

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4th EAI International Conference on Security and Privacy in New Computing Environments, SPNCE 2021

作者： Liu, Mingxi Lu, Ning Yin, Jingli Cheng, Qingfeng Shi, Wenbo School of Computer Science and Engineering Northeastern University Shenyang China School of Computer Science and Technology Xidian University Xi’an China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China Strategic Support Force Information Engineering University Zhengzhou China

ISBN: (数字)9783030967918

ISBN: (纸本)9783030967901

With the massive placement of sensors leading to a surge in IoT data, it is wise for system administrators to use data aggregation strategies to collect and share data in edge-assisted IoT scenarios because traditional methods will exhaust bandwidth resources. Users need to verify the integrity of aggregated data to ensure security sharing. However, existing data auditing technologies are disabled because aggregate data has no metadata. This paper proposes a blockchain-based auditing scheme for aggregated data to achieve efficient and secure data sharing. First, we design a hash index balanced tree (HIBT) to reduce the computational expense of signature retrieval. Then, we propose an audit protocol to protect aggregated data immutability. Also, we provide security and performance analysis at the end of the paper. © 2022, ICST Institute for computer sciences, Social Informatics and Telecommunications engineering.

关键词： Blockchain

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Explainable AI-Based Humerus Fracture Detection and Classification from X-Ray Images 26

Explainable AI-Based Humerus Fracture Detection and Classifi...

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26th International Conference on computer and Information Technology, ICCIT 2023

作者： Barua, Koushick Mahmud, Tanjim Barua, Anik Sharmen, Nahed Basnin, Nanziba Islam, Dilshad Hossain, Mohammad Shahadat Andersson, Karl Hossain, Sazzad Rangamati Science and Technology University Department of Computer Science and Engineering Rangamati4500 Bangladesh Chattogram Maa-O-Shishu Hospital Medical College Dept. of Obstetrics and Gynecology Chittagong4100 Bangladesh Leeds Beckett University Department of Computer Science and Engineering United Kingdom Chattogram Veterinary and Animal Sciences University Dept. of Physical and Mathematical Sciences Chittagong4202 Bangladesh University of Chittagong Department of Computer Science and Engineering Chittagong4331 Bangladesh Luleå University of Technology Pervasive and Mobile Computing Laboratory Luleå971 87 Sweden Department of Computer Science and Engineering Dhaka1207 Bangladesh

ISBN: (纸本)9798350359015

The human skeletal framework relies heavily on bones, and one such crucial component is the 'Humerus.' Positioned in the upper arm, extending from the shoulder to the elbow junction, the Humerus provides essential structural support for muscles and facilitates upper-body movement, particularly in the arms and hands. Consequently, Humerus fractures significantly impact daily life, causing disruptions and limitations. This paper presents a thorough exploration of an Explainable AI-based Humerus Fracture Detection and Classification system, employing various deep learning models. Leveraging a dataset of 1266 X-ray images, encompassing fractured and non-fractured humerus bones from the publicly available 'MURA' dataset, our research evaluates the effectiveness of Convolutional Neural Networks (CNN), VGG16, VGG19, DenseNet121, and DenseNet169 in detecting fractures. After 30 epochs of training, we assessed their performance using critical metrics: accuracy, precision, recall, and F1 score. Notably, DenseNet121 and DenseNet169 exhibited superior accuracy, precision, and recall, laying a robust foundation for automated humerus fracture diagnosis. We also introduced two ensemble models, "Ensemble-1 (VGG16 and VGG19)"and "Ensemble-2 (DenseNet121 and DenseNet169),"which delivered substantial improvements in accuracy, precision, recall, and F1 score, showcasing the potential of ensemble techniques in clinical settings. Furthermore, we enhanced model interpretability and transparency by incorporating Saliency Maps and GRAD-CAM (Gradient-weighted Class Activation Mapping) for Explainable AI (XAI). This visualization allowed us to identify regions of interest in X-ray images contributing to the model's predictions, providing valuable insights for medical practitioners. © 2023 IEEE.

关键词： Convolutional neural network Explainable AI (XAI) Humerus X-ray diagnosis

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Fw-fuzz: A code coverage-guided fuzzing framework for network protocols on firmware

Fw-fuzz: A code coverage-guided fuzzing framework for networ...

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作者： Gao, Zicong Dong, Weiyu Chang, Rui Wang, Yisen Cyberspace Security Department State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China College of Computer Science and Technology Zhejiang University Hangzhou China

Fuzzing is an effective approach to detect software vulnerabilities utilizing changeable generated inputs. However, fuzzing the network protocol on the firmware of IoT devices is limited by inefficiency of test case generation, cross-architecture instrumentation, and fault detection. In this article, we propose the Fw-fuzz, a coverage-guided and crossplatform framework for fuzzing network services running in the context of firmware on embedded architectures, which can generate more valuable test cases by introspecting program runtime information and using a genetic algorithm model. Specifically, we propose novel dynamic instrumentation in Fw-fuzz to collect the running state of the firmware program. Then Fw-fuzz adopts a genetic algorithm model to guide the generation of inputs with high code coverage. We fully implement the prototype system of Fw-fuzz and conduct evaluations on network service programs of various architectures in MIPS, ARM, and PPC. By comparing with the protocol fuzzers Boofuzz and Peach in metrics of edge coverage, our prototype system achieves an average growth of 33.7% and 38.4%, respectively. We further verify six known vulnerabilities and discover 5 0-day vulnerabilities with the Fw-fuzz, which prove the validity and utility of our framework. The overhead of our system expressed as an additional 5% of memory growth. © 2020 John Wiley & Sons, Ltd.

关键词： Firmware

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Modelization and analysis of dynamic heterogeneous redundant system

Modelization and analysis of dynamic heterogeneous redundant...

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作者： Wang, Wei Li, Guangsong Gai, Keke Tang, Yazhe Yang, Benchao Si, Xueming State Key Laboratory of Mathematical Engineering and Advanced Computing Information Engineering University Zhengzhou China School of Computer Science and Technology Beijing Institute of Technology Beijing China School of Computer Science and Technology Xi'an Jiaotong University Xi'an China Shanghai Key Laboratory of Data Science Fudan University Shanghai China

With the development and popularization of Internet technology, network security has become the focus of attention. Vulnerabilities and back doors are regarded as two of the main reasons of network security problems. Dynamic heterogeneous redundant (DHR) architecture is a typical framework of cyberspace mimic defense. It can make use of untrusted hardware and software components to construct a high reliable and high security information system. In this article, a mathematical model is set up for the DHR architecture, and the system security is characterized by vulnerability consistency rate and success rate of system attack. The antiattack ability of the DHR system is analyzed using the mathematical model. © 2020 John Wiley & Sons, Ltd.

关键词： Network security

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Strong nonlinear optical processes with extraordinary polarization anisotropy in inversionsymmetry broken two-dimensional PdPSe

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Light(science & Applications) 2024年第8期13卷 1524-1533页

作者： Song Zhu Ruihuan Duan Xiaodong Xu Fangyuan Sun Wenduo Chen Fakun Wang Siyuan Li Ming Ye Xin Zhou Jinluo Cheng Yao Wu Houkun Liang Junichiro Kono Xingji Li Zheng Liu Qi Jie Wang School of Electrical and Electronic Engineering Nanyang Technological University639798 SingaporeSingapore School of Material Science and Engineering Nanyang Technological University639798 SingaporeSingapore CINTRA CNRS/NTU/THALES UMI 3288Research Techno PlazaNanyang Technological University637371 SingaporeSingapore School of Materials Science and Engineering Harbin Institute of Technology150001 HarbinChina Department of Chemistry National University of Singapore117543 SingaporeSingapore GPL Photonics Lab State Key Laboratory of Applied OpticsChangchun Institute of OpticsFine Mechanics and PhysicsChinese Academy of Sciences130033 ChangchunChina School of Electronics and Information Engineering Sichuan University610064 ChengduSichuanChina School of Physical and Mathematical Sciences Nanyang Technological University637371 SingaporeSingapore Departments of Electrical and Computer Engineering Physics and Astronomyand Materials Science and NanoEngineeringRice UniversityHoustonTXUSA

Nonlinear optical activities,especially second harmonic generation(SHG),are key phenomena in inversion-symmetrybroken two-dimensional(2D)transition metal dichalcogenides(TMDCs).On the other hand,anisotropic nonlinear optical processes are important for unique applications in nano-nonlinear photonic devices with polarization functions,having become one of focused research topics in the field of nonlinear ***,the strong nonlinearity and strong optical anisotropy do not exist simultaneously in common 2D ***,we demonstrate strong secondorder and third-order susceptibilities of 64 pm/V and 6.2×10^(-19)m^(2)/V^(2),respectively,in the even-layer PdPSe,which has not been discovered in other common TMDCs(e.g.,MoS2).Strikingly,it also simultaneously exhibited strong SHG anisotropy with an anisotropic ratio of~45,which is the largest reported among all 2D materials to date,to the best of our *** addition,the SHG anisotropy ratio can be harnessed from 0.12 to 45(375 times)by varying the excitation wavelength due to the dispersion ofχ^(2)*** an illustrative example,we further demonstrate polarized SHG imaging for potential applications in crystal orientation identification and polarization-dependent spatial *** findings in 2D PdPSe are promising for nonlinear nanophotonic and optoelectronic applications.

关键词： materials anisotropy nonlinear

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Identifying Influential Communicators based on Correlation and Minimum Distance in Social Media Data Streams 9

Identifying Influential Communicators based on Correlation a...

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9th IEEE Smart World Congress, SWC 2023

作者： Zhu, Jie Shi, Lei-Lei Liu, Lu Han, Zi-Xuan Jiang, Liang Panneerselvam, John Jiangsu University School of Computer Science and Telecommunication Engineering China Jiangsu University Jiangsu Key Laboratory of Security Technology for Industrial Cyberspace China University of Leicester School of Computing and Mathematical Sciences United Kingdom Jiangsu University of Science and Technology School of Ocean China

ISBN: (纸本)9798350319804

Twitter has become a crucial social networking platform for users to receive and exchange information about current events. Identifying the most influential users in social networks is pivotal for the efficient propagation of information. However, traditional influencer identification models often fall short of assessing users' influence comprehensively and neglect overlapping influence, which greatly restrains their efficiency and accuracy in identifying key users. To address such issues, we propose a method for identifying key users based on correlation and minimum distance (CM-UDM). This method can evaluate users' influence based on their individual, global, local, and propagation path characteristics in social networks. Additionally, we propose a key user decision graph in order to address the problem of overlapping users' influence. We have conducted experiments using one classical dataset and three real Twitter datasets, where the performance of our proposed method in identifying highly influential users is demonstrated. © 2023 IEEE.

关键词： Influence maximization Influence overlap Twitter

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An Adaptive Community Detection Algorithm based on Density Peaks 9

An Adaptive Community Detection Algorithm based on Density P...

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9th IEEE Smart World Congress, SWC 2023

作者： Fan, Xiang Shi, Lei-Lei Liu, Lu Han, Zi-Xuan Jiang, Liang Yuan, Bo Jiangsu University School of Computer Science and Telecommunication Engineering China Jiangsu University Jiangsu Key Laboratory of Security Technology for Industrial Cyberspace China University of Leicester School of Computing and Mathematical Sciences United Kingdom Jiangsu University of Science and Technology School of Ocean China

ISBN: (纸本)9798350319804

Community detection is an important task for analyzing the topological function and structure of a network, as well as for various application scenarios such as advertising, marketing, and influence maximization. Label propagation is a widely used community detection algorithm for complex networks due to its simplicity and fast computation. However, this algorithm has several limitations in terms of stability and the quality of discovered communities. To address these shortcomings, we propose a novel community detection algorithm based on density peaks (ACDDP) in this paper. Firstly, we improve the local density and minimum distance of nodes by using an adaptive selection of community center nodes. Secondly, we specify the node update order to enhance the robustness and performance of the algorithm. The experimental results on real networks demonstrate that our proposed algorithm achieves higher accuracy in community division while ensuring robustness compared to other popular algorithms. © 2023 IEEE.

关键词： Adaptive Selection Community detection Density Peaks

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Effects of Crystallinity and Defects of Layered Carbon Materials on Potassium Storage:A Review and Prediction

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Electrochemical Energy Reviews 2022年第2期5卷 401-433页

作者： Xiaoxu Liu Tianyi Ji Hai Guo Hui Wang Junqi Li Hui Liu Zexiang Shen Shaanxi Key Laboratory of Green Preparation and Functionalization for Inorganic Materials School of Material Science and EngineeringShaanxi University of Science and TechnologyXi’an 710021ShaanxiChina Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological UniversitySingapore 637371Singapore College of Computer Science and Technology Dalian Minzu UniversityDalian 116650LiaoningChina

Layered carbon materials(LCMs)are composed of basic carbon layer units,such as graphite,soft carbon,hard carbon,and *** they have been widely applied in the anode of potassium-ion batteries,the potassium storage mechanisms and performances of various LCMs are isolated and difficult to relate to each *** importantly,there is a lack of a systematic understanding of the correlation between the basic microstructural unit(crystallinity and defects)and the potas-sium storage *** this review,we explored the key structural factors affecting the potassium storage in LCMs,namely,the crystallinity and defects of carbon layers,and the key parameters(L_(a),L_(c),d_(002),I_(D)/I_(G))that characterize the crystallinity and defects of different carbon materials were extracted from various databases and literature sources.A structure–property database of LCMs was thus built,and the effects of these key structural parameters on the potassium storage properties,including the capacity,the rate and the working voltage plateau,were systematically *** on the structure–prop-erty database analysis and the guidance of thermodynamics and kinetics,a relationship between various LCMs and potas-sium storage properties was ***,with the help of machine learning,the key structural parameters of layered carbon anodes were used for the first time to predict the potassium storage performance so that the large amount of research data in the database could more effectively guide the scientific research and engineering application of LCMs in the future.

关键词： Layered carbon materials Crystallinity and defects Potassium storage properties Machine learning

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Adaptive energy-preserving algorithms for guiding center system

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Plasma science and Technology 2023年第4期25卷 11-22页

作者：朱贝贝刘健张嘉炜祝爱卿唐贻发 Department of Applied Mathematics School of Mathematics and PhysicsUniversity of Science and Technology BeijingBeijing 100083Peopleʼs Republic of China School of Nuclear Science and Technology University of Science and Technology of ChinaHefei 230026Peopleʼs Republic of China Advanced algorithm Joint Lab Shandong Computer Science CenterQilu University of TechnologyJinan 250014Peopleʼs Republic of China The State Key Laboratory of Scientific and Engineering Computing The Institute of Computational Mathematics and Scientific/Engineering ComputingAcademy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190Peopleʼs Republic of China School of Mathematical Sciences University of Chinese Academy of SciencesBeijing 100049Peopleʼs Republic of China

We develop two types of adaptive energy preserving algorithms based on the averaged vector field for the guiding center dynamics,which plays a key role in magnetized *** adaptive scheme is applied to the Gauss Legendre’s quadrature rules and time stepsize respectively to overcome the energy drift problem in traditional energy-preserving *** new adaptive algorithms are second order,and their algebraic order is carefully *** results show that the global energy errors are bounded to the machine precision over long time using these adaptive algorithms without massive extra computation cost.

关键词： guiding center system energy-preserving methods adaptive energy conservation

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