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SSRN 2024年

作者： Devlen, Charles Wahab, Ahmed Anu Hou, Daqing Cheng, Nadia Huntley, Parker Electrical and Computer Engineering Clarkson University PotsdamNY United States Statistics University of Virginia CharlottesvilleVA United States College of Computing Georgia Tech AtlantaGA United States Software Engineering RIT RochesterNY United States Computing and Information Science RIT RochesterNY United States

Deep learning models, such as Siamese Neural Networks (SNN), have shown great potential in capturing the intricate patterns in behavioral data. However, the impacts of dataset breadth (i.e., the number of subjects) and depth (e.g., the amount of training samples per subject) on the performance of these models are often informally assumed, but remain under-explored. To this end, we have conducted extensive experiments using the concepts of "feature space" and "density" to guide and gain deeper understanding on the impact of dataset breadth and depth on three publicly available keystroke datasets (Aalto, CMU and Clarkson II), by varying the number of training subjects, number of samples per subject, amount of data in each sample, and number of triplets used in training. Our results show that when feasible, increasing dataset breadth enables the training of a well-trained model that effectively captures more inter-subject variability. In addition, the extent of depth’s impact from a dataset depends on the nature of the dataset: Free-text datasets are influenced by all three depth-wise factors;inadequate samples per subject, sequence length, and training triplets, as well as gallery sample size, may all lead to an under-trained model, while fixed-text datasets are less affected by these factors. © 2024, The Authors. All rights reserved.

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Practical Anomaly Detection over Multivariate Monitoring Metrics for Online Services

arXiv

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arXiv 2023年

作者： Liu, Jinyang Yang, Tianyi Chen, Zhuangbin Su, Yuxin Feng, Cong Yang, Zengyin Lyu, Michael R. The Chinese University of Hong Kong Hong Kong School of Software Engineering Sun Yat-sen University Zhuhai China Computing and Networking Innovation Lab Huawei Cloud Computing Technology Co. Ltd China

As modern software systems continue to grow in terms of complexity and volume, anomaly detection on multivariate monitoring metrics, which profile systems’ health status, becomes more and more critical and challenging. In particular, the dependency between different metrics and their historical patterns plays a critical role in pursuing prompt and accurate anomaly detection. Existing approaches fall short of industrial needs for being unable to capture such information efficiently. To fill this significant gap, in this paper, we propose CMAnomaly, an anomaly detection framework on multivariate monitoring metrics based on collaborative machine. The proposed collaborative machine is a mechanism to capture the pairwise interactions along with feature and temporal dimensions with linear time complexity. Cost-effective models can then be employed to leverage both the dependency between monitoring metrics and their historical patterns for anomaly detection. The proposed framework is extensively evaluated with both public data and industrial data collected from a large-scale online service system of Huawei Cloud. The experimental results demonstrate that compared with state-of-the-art baseline models, CMAnomaly achieves an average F1 score of 0.9494, outperforming baselines by 6.77% ∼ 10.68%, and runs 10× ∼ 20× faster. Furthermore, we also share our experience of deploying CMAnomaly in Huawei Cloud. © 2023, CC BY-NC-ND.

关键词： software reliability

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Data-Driven Goal Recognition in Transhumeral Prostheses Using Process Mining Techniques

arXiv

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arXiv 2023年

作者： Su, Zihang Yu, Tianshi Lipovetzky, Nir Mohammadi, Alireza Oetomo, Denny Polyvyanyy, Artem Sardiña, Sebastian Tan, Ying van Beest, Nick School of Computing and Information Systems The University of Melbourne Australia Department of Mechanical Engineering The University of Melbourne Australia School of Computing Technologies RMIT University Australia Software Systems Group Data61 CSIRO Australia

A transhumeral prosthesis restores missing anatomical segments below the shoulder, including the hand. Active prostheses utilize real-valued, continuous sensor data to recognize patient target poses, or goals, and proactively move the artificial limb. Previous studies have examined how well the data collected in stationary poses, without considering the time steps, can help discriminate the goals. In this case study paper, we focus on using time series data from surface electromyography electrodes and kinematic sensors to sequentially recognize patients’ goals. Our approach involves transforming the data into discrete events and training an existing process mining-based goal recognition system. Results from data collected in a virtual reality setting with ten subjects demonstrate the effectiveness of our proposed goal recognition approach, which achieves significantly better precision and recall than the state-of-the-art machine learning techniques and is less confident when wrong, which is beneficial when approximating smoother movements of prostheses. © 2023, CC BY-NC-ND.

关键词： Data mining

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Optimizing Uncertainty Estimation on Scientific Visualizations Using Learning Models

Optimizing Uncertainty Estimation on Scientific Visualizatio...

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2024 Workshops of the International Conference for High Performance computing, Networking, Storage and Analysis, SC Workshops 2024

作者： Almeida, Clara J. Guerrero-Pantoja, David Pautsch, Erik Rizzi, Silvio Thiruvathukal, George K. Pantoja, Maria University of California Santa Cruz Robotics and Electrical Engineering Santa Cruz United States University of California Santa Cruz Computer Science and Game Design Santa Cruz United States Loyola University Chicago Computer Science Chicago United States Argonne National Laboratory Argonne Leadership Computing Facility Lemont United States Computer Science and Software Engineering California Polytechnics State University San Luis Obispo United States

ISBN: (纸本)9798350355543

Scientific visualizations (SciVis) translate numerical and spatial data as images, enabling scientists to better understand the phenomena described by the data and gain insights that may be overlooked by statistical methods alone. In recent years, machine learning, particularly Deep Learning (DL), has driven significant advancements in SciVis. By combining classical numerical techniques with DL methods, we can create models that potentially achieve the accuracies needed for real-world industrial applications and simulations. However, DL models often exhibit undue confidence, particularly when faced with inputs that differ significantly from the data they were trained on, known as out-of-distribution (OOD) inputs. This can lead to misclassifications where the model still produces high confidence scores, reflected in elevated softmax output values, even though its prediction is incorrect. Ideally, in such cases, the model should indicate uncertainty, effectively saying, "I do not know."In this work, we enhance the trustworthiness of DL models by quantifying their uncertainty and enabling selective classification, where the model abstains from making predictions when uncertainty is high. Instead of providing a single prediction, our model outputs a prediction distribution, allowing users to determine whether human intervention or other actions are necessary for handling high-uncertainty outputs. This paper evaluates uncertainty quantification across three different tasks: predicting airfoil pressure and velocity using a Reynolds-Averaged Navier-Stokes (RANS) model on a custom airfoil dataset, image classification using the ImageNet1K dataset, and digit recognition using the MNIST handwritten digits dataset. © 2024 IEEE.

关键词： deep learning ensemble learning evidential learning scientific visualization uncertainty

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Fed-CVLC: Compressing Federated Learning Communications with Variable-Length Codes

Fed-CVLC: Compressing Federated Learning Communications with...

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IEEE Annual Joint Conference: INFOCOM, IEEE Computer and Communications Societies

作者： Xiaoxin Su Yipeng Zhou Laizhong Cui John C. S. Lui Jiangchuan Liu College of Computer Science and Software Engineering Shenzhen University Shenzhen China School of Computing Faculty of Science and Engineering Macquarie University Sydney Australia Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) Shenzhen University Shenzhen China Department of Computer Science and Engineering The Chinese University of Hong Kong HKSAR School of Computing Science Simon Fraser University Canada

ISBN: (数字)9798350383508

ISBN: (纸本)9798350383515

In Federated Learning (FL) paradigm, a parameter server (PS) concurrently communicates with distributed participating clients for model collection, update aggregation, and model distribution over multiple rounds, without touching private data owned by individual clients. FL is appealing in preserving data privacy; yet the communication between the PS and scattered clients can be a severe bottleneck. Model compression algorithms, such as quantization and sparsification, have been suggested but they generally assume a fixed code length, which does not reflect the heterogeneity and variability of model updates. In this paper, through both analysis and experiments, we show strong evidences that variable-length is beneficial for compression in FL. We accordingly present Fed-CVLC (Federated Learning Compression with Variable-Length Codes), which fine-tunes the code length in response of the dynamics of model updates. We develop optimal tuning strategy that minimizes the loss function (equivalent to maximizing the model utility) subject to the budget for communication. We further demonstrate that Fed-CVLC is indeed a general compression design that bridges quantization and sparsification, with greater flexibility. Extensive experiments have been conducted with public datasets to demonstrate that Fed-CVLC remarkably outperforms state-of-the-art baselines, improving model utility by 1.50%-5.44%, or shrinking communication traffic by 16.67%-41.61%.

关键词： Training Data privacy Codes Quantization (signal) Federated learning Data models Servers

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A Systematic Literature Review on Large Language Models for Automated Program Repair

arXiv

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arXiv 2024年

作者： Zhang, Quanjun Fang, Chunrong Xie, Yang Ma, Yuxiang Sun, Weisong Yang, Yun Chen, Zhenyu State Key Laboratory for Novel Software Technology Nanjing University China School of Computer Science and Engineering Nanyang Technological University Singapore Department of Computing Technologies Swinburne University of Technology Australia

Automated Program Repair (APR) attempts to patch software bugs and reduce manual debugging efforts. Very recently, with the advances in Large Language Models (LLMs), a rapidly increasing number of APR techniques have been proposed, significantly facilitating software development and maintenance and demonstrating remarkable performance. However, due to ongoing explorations in the LLM-based APR field, it is challenging for researchers to understand the current achievements, challenges, and potential opportunities. This work provides the first systematic literature review to summarize the applications of LLMs in APR between 2020 and 2024. We analyze 127 relevant papers from LLMs, APR and their integration perspectives. First, we categorize existing popular LLMs that are applied to support APR and outline three types of utilization strategies for their deployment. Besides, we detail some specific repair scenarios that benefit from LLMs, e.g., semantic bugs and security vulnerabilities. Furthermore, we discuss several critical aspects of integrating LLMs into APR research, e.g., input forms and open science. Finally, we highlight a set of challenges remaining to be investigated and the potential guidelines for future research. Overall, our paper provides a systematic overview of the research landscape to the APR community, helping researchers gain a comprehensive understanding of achievements and promote future research. Our artifacts are publicly available at the GitHub repository: https://***/iSEngLab/AwesomeLLM4APR. © 2024, CC BY.

关键词： Repair

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QSroute : A QoS Aware Routing Scheme for software Defined Networking

QSroute : A QoS Aware Routing Scheme for Software Defined Ne...

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International Conference on software engineering and Computer Systems (ICSECS)

作者： Imran Edzereiq Kamarudin Mohamed Ariff Ameedeen Muhamad Idaham Umar Ong Azlee Zabidi Department of Computer Systems and Networking Faculty of Computing Universiti Malaysia Pahang AL-Sultan Abdullah Pekan Malaysia Department of Software Engineering Faculty of Computing Universiti Malaysia Pahang AL-Sultan Abdullah Pekan Malaysia

The increasing demand for bandwidth-intensive network applications, such as video streaming, multimedia, and Internet of Things (IoT) applications, necessitates improved resource management to protect the network without compromising Quality of Service (QoS). Meeting these challenges requires a centralized view of all available network resources. software Defined Networking (SDN), an emerging technology, provides a centralized view and control of network resources. This feature enables administrators to programmatically define and manage network behavior, including routing, making it more flexible and adaptable. This study proposes a QoS-aware routing scheme for SDN that considers available bandwidth, packet delay, and packet loss to determine the optimal routing path. Optimal paths are selected based on meeting predefined threshold criteria. The study concludes by discussing potential directions for future research in this field.

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SUNET: Framework, Security, and Privacy

SUNET: Framework, Security, and Privacy

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Satellite Internet (SAT-NET), International Conference on

作者： Zhan Xie Liangmin Wang Haiqin Wu Xiaoyu Zhang Liang Wang Haotian Yang School of Cyber Science and Engineering Southeast University Nanjing China Engineering Research Center of BASAM Ministry of Education Southeast University Nanjing China Software Engineering Institute (Shanghai Key Laboratory of Trustworthy Computing) East China Normal University Shanghai China

ISBN: (数字)9798350375176

ISBN: (纸本)9798350387469

SUNet (Satellite-assisted Unmanned-system Network) integrates satellite communication technology, unmanned equipment systems, and ground support facilities. Compared to traditional network system, SUNet has the advantages of flexible deployment and wide communication range. SUNet with cloud services (SUNET) breaks the limitation of satellites processing large-scale data, and integrates a large scope of computing and storage resources in a system. However, SUNET also poses more serious security risks than traditional networks for two reasons: i) the unique network framework, such as highly dynamic topology, unfixed network access points and decentralized control, of SUNET increases security risks, and ii) the rising probability of existing neighbor nodes with quantum computing. This paper firstly introduces the term SUNET from definition and compares SUNET with several related network frameworks. Then we anaylze the security risks, such as untrust between nodes, unauthorized access and quantum computing attack, faced by SUNET. Meanwhile, we illustrate the security requirements and the corresponding solutions, such as authentication, fine-grained authorization and quantum security, in SUNET. Finally, we discuss the security countermeasures and point out the potential future research directions.

关键词： Privacy Satellites Quantum computing Network topology Vehicular ad hoc networks Computer architecture Topology Security Satellite communications Network systems

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Performance Verification of State Feedback H∞ State Molecular Controller Via Chemical Reaction Networks 5

Performance Verification of State Feedback H∞ State Molecul...

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5th International Conference on Industrial Artificial Intelligence, IAI 2023

作者： Yao, Jingwang Wang, Xing'An Lv, Hui Li, Yang Zhang, Wujie Dalian University Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian116622 China Dalian University College of Environmental and Chemical Engineering Dalian116622 China Dalian Chivy Biotechnology Co. Ltd Dalian116023 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110004 China

ISBN: (纸本)9798350325294

An important goal in synthetic biology is in the design and implementation of control strategies embedded in biochemical reaction systems to enable control of the process at the molecular level. The problem of set-point tracking and perturbation disturbances in the biochemical reaction process can be solved by a state feedback control strategy and a more stable and better dynamic performance of synthetic controllers. First, it is shown how a set of CRNs can provide state feedback to the biomolecular implementation of the H∞ controller. Then, to facilitate the modularity of biochemical process design, CRNs of trigonometric functions and repetitive sequence step signals are defined for the first time as input signals to the controller. Finally, numerical simulation experiments are designed to verify the effectiveness of the whole scheme from several perspectives, such as setpoint tracking and external perturbations. The results show that the state feedback H∞ molecular controller outperforms the conventional linear controller because it has a faster tracking response and better perturbation rejection performance. © 2023 IEEE.

关键词： Controllers

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Heterogeneous Quasi-Partitioned Scheduling 42

Heterogeneous Quasi-Partitioned Scheduling

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42nd IEEE Real-Time Systems Symposium, RTSS 2021

作者： Massa, Ernesto Lima, George Andersson, Bjorn Petrucci, Vinicius State University of Bahia Brazil Institute of Computing Federal University of Bahia Brazil Software Engineering Institute Carnegie Mellon University United States School of Computing and Information University of Pittsburgh United States

ISBN: (纸本)9781665428026

We consider the problem of scheduling a set of preemptible independent periodic implicit-deadline hard real-time tasks on heterogeneous processors. We divide this problem into two sub-problems: (a) assigning portions of each processor (offline) to each task without jeopardizing schedulability;and (b) generating a schedule satisfying the assigned portions using an online semi-partitioned scheduler, called Heterogeneous Quasi-Partitioned Scheduling (hQPS). The scheduler handles task servers at run-time for ensuring that the processor shares assigned to tasks are timely available to them. Assessments indicate that the proposed solution (i) has good scalability (up to 64 tasks, 64 processors), (ii) is effective in generating schedules with few preemptions and few migrations, and (iii) is effective in managing resources;for task sets where an extra processor speed is required, our solution needs at most 10% extra compared to an optimal scheduler. ©2021 IEEE

关键词： Scheduling

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