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14th IEEE International Conference on software engineering and Service Science, ICSESS 2023

作者： Ma, Penghui Shi, Peichang Yi, Guodong College of Computer Science National University of Defense Technology National Key Laboratory of Parallel and Distributed Processing Changsha410073 China College of Computer Science National University of Defense Technology Key Laboratory of Software Engineering for Complex Systems Changsha410073 China Xiangjiang Lab Changsha410073 China School of Advanced Interdisciplinary Studies Hunan University of Technology and Business Changsha410073 China

ISBN: (纸本)9798350336269

With serverless computing offering more efficient and cost-effective application deployment, the diversity of serverless platforms presents challenges to users, including platform lock-in and costly migration. Moreover, due to the black box nature of function computing, traditional performance benchmarking methods are not applicable, necessitating new studies. This article presents a detailed comparison of six major public cloud function computing platforms and introduces a benchmarking framework for function computing performance. This framework aims to help users make comprehensive comparisons and select the most suitable platform for their specific needs. © 2023 IEEE.

关键词： Benchmarking

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A Video Surveillance System Based on Single-Mode Fiber for Energy and Signal Co-Transmission

A Video Surveillance System Based on Single-Mode Fiber for E...

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Chinese Automation Congress (CAC)

作者： Yuanle Su Xueliang Gu Zhiguo Zhang Xu Wang Haoyu Wu Jialin Wei Xiangning Song State Key Lab of Information Photonics and Optical Communications Beijing University of Posts and Telecommunications Beijing China School of Electronic Engineering Beijing University of Posts and Telecommunications Beijing China School of Computer Science (National Pilot Software Engineering School) Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

This paper introduces an innovative video surveillance system that utilizes single-mode fiber for the simultaneous transmission of energy and information. Traditional surveillance systems relying on electrical cables pose safety risks and operational challenges, particularly in hazardous and remote environments. The proposed system leverages power-over-fiber (PoF) technology, using a single-mode fiber to co-transmit high-power laser energy and video signals. This approach overcomes the limitations of multimode fibers, such as signal loss and modal dispersion, and enables long-distance transmission up to 10 kilometers. At the remote end, the energy is converted into electrical power by a photovoltaic converter, ensuring the operation of surveillance equipment. The system also incorporates low-power micro-controller (MCU) and a lossless compression algorithm to enhance video transmission efficiency, achieving a frame rate of 16–17 frames per second at a resolution of 320×240. Experimental results demonstrate the system's effectiveness in delivering stable energy supply and high-quality video transmission, making it suitable for use in special environments with stringent safety and operational requirements.

关键词： Protocols Energy resolution Optical fiber cables Video surveillance Propagation losses Optical fiber communication Optical fiber devices Safety Signal resolution Optical fiber dispersion

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Generalizing 6-DoF Grasp Detection via Domain Prior Knowledge

Generalizing 6-DoF Grasp Detection via Domain Prior Knowledg...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Haoxiang Ma Modi Shi Boyang Gao Di Huang State Key Laboratory of Software Development Environment Beihang University Beijing China School of Computer Science and Engineering Beihang University Beijing China School of Computer Science and Technology Harbin Institute of Technology Harbin China Geometry Robotics

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

We focus on the generalization ability of the 6-DoF grasp detection method in this paper. While learning-based grasp detection methods can predict grasp poses for unseen ob-jects using the grasp distribution learned from the training set, they often exhibit a significant performance drop when encountering objects with diverse shapes and struc-tures. To enhance the grasp detection methods' general-ization ability, we incorporate domain prior knowledge of robotic grasping, enabling better adaptation to objects with significant shape and structure differences. More specifi-cally, we employ the physical constraint regularization during the training phase to guide the model towards predicting grasps that comply with the physical rule on grasping. For the unstable grasp poses predicted on novel objects, we design a contact-score joint optimization using the pro-jection contact map to refine these poses in cluttered sce-narios. Extensive experiments conducted on the GraspNet-1 billion benchmark demonstrate a substantial performance gain on the novel object set and the real-world grasping experiments also demonstrate the effectiveness of our gen-eralizing 6-DoF grasp detection method. Code is available at https://***/mahaoxiang822/Generalizing-Grasp.

关键词： Training computer vision Shape Grasping Benchmark testing Predictive models Performance gain

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A study on the impact of pre-trained model on Just-In-Time defect prediction

A study on the impact of pre-trained model on Just-In-Time d...

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IEEE International Conference on software Quality, Reliability and Security (QRS)

作者： Yuxiang Guo Xiaopeng Gao Zhenyu Zhang W.K. Chan Bo Jiang State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University Beijing China State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China Department of Computer Science City University of Hong Kong Hong Kong China

Previous researchers conducting Just-In-Time (JIT) defect prediction tasks have primarily focused on the performance of individual pre-trained models, without exploring the relationship between different pre-trained models as backbones. In this study, we build six models: RoBERTaJIT, CodeBERTJIT, BARTJIT, PLBARTJIT, GPT2JIT, and CodeGPTJIT, each with a distinct pre-trained model as its backbone. We systematically explore the differences and connections between these models. Specifically, we investigate the performance of the models when using Commit code and Commit message as inputs, as well as the relationship between training efficiency and model distribution among these six models. Additionally, we conduct an ablation experiment to explore the sensitivity of each model to inputs. Furthermore, we investigate how the models perform in zero-shot and few-shot scenarios. Our findings indicate that each model based on different backbones shows improvements, and when the backbone’s pre-training model is similar, the training resources that need to be consumed are closer. We also observe that Commit code plays a significant role in defect detection, and different pre-trained models demonstrate better defect detection ability with a balanced dataset under few-shot scenarios. These results provide new insights for optimizing JIT defect prediction tasks using pre-trained models and highlight the factors that require more attention when constructing such models. Additionally, CodeGPTJIT and GPT2JIT achieved better performance than DeepJIT and CC2Vec on the two datasets respectively under 2000 training samples. These findings emphasize the effectiveness of transformer-based pre-trained models in JIT defect prediction tasks, especially in scenarios with limited training data.

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Learning Transferable Negative Prompts for Out-of-Distribution Detection

arXiv

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arXiv 2024年

作者： Li, Tianqi Pang, Guansong Bai, Xiao Miao, Wenjun Zheng, Jin School of Computer Science and Engineering State Key Laboratory of Complex & Critical Software Environment Jiangxi Research Institute Beihang University China School of Computing and Information Systems Singapore Management University Singapore

Existing prompt learning methods have shown certain capabilities in Out-of-Distribution (OOD) detection, but the lack of OOD images in the target dataset in their training can lead to mismatches between OOD images and In-Distribution (ID) categories, resulting in a high false positive rate. To address this issue, we introduce a novel OOD detection method, named ‘NegPrompt’, to learn a set of negative prompts, each representing a negative connotation of a given class label, for delineating the boundaries between ID and OOD images. It learns such negative prompts with ID data only, without any reliance on external outlier data. Further, current methods assume the availability of samples of all ID classes, rendering them ineffective in open-vocabulary learning scenarios where the inference stage can contain novel ID classes not present during training. In contrast, our learned negative prompts are transferable to novel class labels. Experiments on various ImageNet benchmarks show that NegPrompt surpasses state-of-the-art prompt-learning-based OOD detection methods and maintains a consistent lead in hard OOD detection in closed- and open-vocabulary classification scenarios. Code is available at https://***/mala-lab/negprompt. © 2024, CC BY.

关键词： Chemical detection

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User-level failure detection and auto-recovery of parallel programs in HPC systems

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Frontiers of computer Science 2021年第6期15卷 31-42页

作者： Guozhen ZHANG Yi LIU Hailong YANG Jun XU Depei QIAN State Key Laboratory of Software Development Environment Beijing 100191China Sino-German Joint Software Institute Beihang UniversityBeijing 100191China School of Computer Science and Engineering Beihang UniversityBeijing 100191China Science and Technology on Space System Simulation Laboratory Beijing Simulation Center Beijing 100854China

As the mean-time-between-failures(MTBF)continues to decline with the increasing number of components on large-scale high performance computing(HPC)systems,program failures might occur during the execution period with high *** successful execution of the HPC programs has become an issue that the unprivileged users should be *** the user perspective,if the program failure cannot be detected and handled in time,it would waste resources and delay the progress of program ***,the unprivileged users are unable to perform program state checking due to execution control by the job management system as well as the limited ***,automated tools for supporting user-level failure detection and autorecovery of parallel programs in HPC systems are *** paper proposes an innovative method for the unprivileged user to achieve failure detection of job execution and automatic resubmission of failed *** state checker in our method is encapsulated as an independent job to reduce interference with the user *** addition,we propose a dual-checker mechanism to improve the robustness of our *** implement the proposed method as a tool named automatic re-launcher(ARL)and evaluate it on the Tianhe-2 *** results show that ARL can detect the execution failures effectively on Tianhe-2 *** addition,the communication and performance overhead caused by ARL is *** good scalability of ARL makes it applicable for large-scale HPC systems.

关键词： high performance computing parallel program failure detection failure auto-recovery

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Zone Based Writing Optimization in User Space 23

Zone Based Writing Optimization in User Space

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23rd IEEE International Conference on High Performance Computing and Communications, 7th IEEE International Conference on Data Science and Systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Shen, Runnan Liu, Yuting Zhang, Rui Zhu, Jinbin Liu, Zhonglin Xiao, Limin School of Computer Science and Engineering Beihang University Beijing100191 China North China Institute of Computing Technology Beijing China Beihang University State Key Laboratory of Software Development Environment Beijing100191 China

ISBN: (纸本)9781665494571

Write operation of the latest storage media such as NAND SSDs has certain restrictions. Data can only be written in an additional write mode. When erasing, some valid data still needs to be written again, which will cause write amplification. Researches about write amplification focused on conventional NVMe protocol and did not consider some forward features. In response to this problem, this paper designs a user-mode write operation optimization algorithm based on the dynamic adjustment of zone space. When the block is erased, the impact caused by write amplification can be significantly resolved, and operations of multiple tenants can be effectively isolated. The Zone Based Algorithm will implement a mapping algorithm based on zone space, and select a suitable GC to maximize the performance. We also designed and implemented a variety of mapping algorithms and GC algorithms to improve the processing efficiency of IO requests. Experiments based on FEMU show at least 25% improvement when increasing data of different life span. © 2021 IEEE.

关键词： Protocols Smart cities Heuristic algorithms Clustering algorithms Writing Media Jitter

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Zero-Shot Scene Graph Generation via Triplet Calibration and Reduction

arXiv

引用

arXiv 2023年

作者： Li, Jiankai Wang, Yunhong Li, Weixin State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University Shanghai Artificial Intelligence Laboratory 100191 China

Scene Graph Generation (SGG) plays a pivotal role in downstream vision-language tasks. Existing SGG methods typically suffer from poor compositional generalizations on unseen triplets. They are generally trained on incompletely annotated scene graphs that contain dominant triplets and tend to bias toward these seen triplets during inference. To address this issue, we propose a Triplet Calibration and Reduction (T-CAR) framework in this paper. In our framework, a triplet calibration loss is first presented to regularize the representations of diverse triplets and to simultaneously excavate the unseen triplets in incompletely annotated training scene graphs. Moreover, the unseen space of scene graphs is usually several times larger than the seen space since it contains a huge number of unrealistic compositions. Thus, we propose an unseen space reduction loss to shift the attention of excavation to reasonable unseen compositions to facilitate the model training. Finally, we propose a contextual encoder to improve the compositional generalizations of unseen triplets by explicitly modeling the relative spatial relations between subjects and objects. Extensive experiments show that our approach achieves consistent improvements for zero-shot SGG over state-of-the-art methods. The code is available at https://***/jkli1998/T-CAR. Copyright © 2023, The Authors. All rights reserved.

关键词： Zero-shot learning

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A Theoretical Explanation of Activation Sparsity through Flat Minima and Adversarial Robustness

arXiv

引用

arXiv 2023年

作者： Peng, Ze Qi, Lei Shi, Yinghuan Gao, Yang State Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Computer Science and Engineering Southeast University Nanjing China

A recent empirical observation (Li et al., 2022b) of activation sparsity in MLP blocks offers an opportunity to drastically reduce computation costs for free. Although having attributed it to training dynamics, existing theoretical explanations of activation sparsity are restricted to shallow networks, small training steps and special training, despite its emergence in deep models standardly trained for a large number of steps. To fill these gaps, we propose the notion of gradient sparsity as one source of activation sparsity and a theoretical explanation based on it that sees sparsity as a necessary step to adversarial robustness w.r.t. hidden features and parameters, which is approximately the flatness of minima for well-learned models. The theory applies to standardly trained LayerNorm-ed MLPs, and further to Transformers or other architectures trained with weight noises. Eliminating other sources of flatness except for sparsity, we discover the phenomenon that the ratio between the largest and smallest non-zero singular values of weight matrices is small. When discussing the emergence of this spectral concentration, we use random matrix theory (RMT) as a powerful tool to analyze stochastic gradient noises. Experiments for validation are conducted to verify our gradient-sparsity-based explanation. We propose two plug-and-play modules for both training and finetuning for sparsity. Experiments on ImageNet-1K and C4 demonstrate their 50% sparsity improvements, indicating further potential cost reduction in both training and inference. Copyright © 2023, The Authors. All rights reserved.

关键词： Chemical activation

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Compositional Verification of Interacting Systems Using Event Monads 13

Compositional Verification of Interacting Systems Using Even...

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13th International Conference on Interactive Theorem Proving, ITP 2022

作者： Zhan, Bohua Lv, Yi Wang, Shuling Zhao, Gehang Hao, Jifeng Ye, Hong Xia, Bican State Key Lab. of Computer Science Institute of Software Chinese Academy of Sciences Beijing China School of Mathematical Sciences Peking University Beijing China Aeronautics Computing Technique Research Institute Xi'an China

ISBN: (纸本)9783959772525

Large software systems are usually divided into multiple components that interact with each other. How to verify interacting components in a modular way is one of the major problems in formal verification. In many cases, interaction between components can be modeled asynchronously, where events are sent without requiring a response in order to continue with execution of the component. In this paper, we propose a lightweight, event-based framework for verification of components with asynchronous interaction. We define event monads and event systems, and a Hoare logic-style calculus for reasoning about them. The framework is implemented in Isabelle and applied to several case studies, including models for distributed computing, cache-coherence protocols, and verification of partition scheduling in a real-time operating system. © Bohua Zhan, Yi Lv, Shuling Wang, Gehang Zhao, Jifeng Hao, Hong Ye, and Bican Xia.

关键词： Formal verification

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