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3rd International Conference on Big Data and Artificial Intelligence and software engineering, ICBASE 2022

作者： Jiang, Guangjun Mao, Xinjun College of Computer National University of Defense Technology Changsha China Key Laboratory of Software Engineering for Complex Systems National University of Defense Technology Changsha China

Robot Operating System (ROS) is a widely used software architecture for robotic systems, which provides hardware abstractions and common functions in robotics. Launch files, as one of the configuration files in the actual program development of ROS, provide a method to start multiple nodes and set global parameters on the local or multiple machines at the same time. For launch files with complex and complex content, we need to get the questions we encounter in writing and using launch files from the Q&A community. We crawled 854 Q&A posts about Launch files from SO and ROS Answers respectively, and conducted in-depth empirical research. Based on the content of the Q&A posts, we roughly divided them into five topics, including syntax specification, function implementation, running error, dependency related and environment configuration. We used topic modelling LDA to categorize Q&A posts by topics, and segmented them by combining the five topics already obtained. Through the results, we found that parameter configuration and syntax errors were the main problems, and then we made a rough analysis of the answer content of the post, and found that the quality of the ROS-related Q&A posts was not high. The above findings inspired us to develop a template tool to simplify the writing of Launch flies and use the node information in the Launch files to recommend ROS nodes to lay a good foundation. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0)

关键词： Robot Operating System

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Study on the Performance of Amorphous IGZO TFT Thin-Film Transistor with Ti/Au Stacking as the Source/Drain 8

Study on the Performance of Amorphous IGZO TFT Thin-Film Tra...

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8th International Conference on Signal and Image Processing, ICSIP 2023

作者： Lu, Qingru Li, Bing Chen, Rui Huang, Xiaodong Southeast University Cheng Xian College School of Electron and Computer Nanjing China Southeast University School of Cyber Science and Engineering Nanjing China Nanjing Vocational University of Industry Technology School of Computer and Software Engineering Nanjing China Southeast University Key Laboratory of MEMS of the Ministry of Education Nanjing China

ISBN: (纸本)9798350397932

Through magnetron sputtering, a Ti/Au stack was adopted as the source/drain of the transistor, and Amorphous indium-gallium-zinc-oxide (α-IGZO or IGZO) was utilized as the active layer (150μm×150μm) to fabricate a bottom-gate contact thin-film transistor (TFT) with a structure of Ti/Au/IGZO (150μm×150μm)/SiO2/P. The electrical characteristics of this device were studied. The experiment showed that by depositing Ti/Au electrodes as the source/drain, and generating a TiOx oxide layer at the interface between the IGZO layer and the Ti/Au electrodes after annealing, the threshold voltage of the device was 4.7V, and the sub-threshold swing was relatively small. The gate bias voltage had excellent regulation on the leakage current of the device, and the device exhibited good cut-off characteristics. © 2023 IEEE.

关键词： Thin film transistors

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Lightweight Anonymous Authentication Frameworks for Wireless Sensor Networks

Lightweight Anonymous Authentication Frameworks for Wireless...

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computer Technologies (ICCTech), International Conference on

作者： Qin Fu Ling Xiong Mingxing He Xingchun Yang School of Computer and Software Engineering Xihua University Chengdu China Intelligent Policing Key Laboratory of Sichuan Province Chengdu China

ISBN: (数字)9798350395419

ISBN: (纸本)9798350395426

Due to the limited resources, authentication schemes for low-cost wireless sensors networks (WSNs) environment prefer to use lightweight cryptographic primitives, like symmetric key encryption/decryption or hash functions. It makes the designing of a lightweight anonymous authentication scheme with forward secrecy to be a challenge. In the past decade, researchers have proposed a large number of lightweight authentication schemes to achieve user anonymity and forward secrecy. However, to the best of our knowledge, most of these schemes suffers from a variety of security issues or design flaws, such as the problem of asynchronization, impractical gateway node search operation, etc. In this work, two general frameworks are presented for both security features from existing authentication schemes in WSNs. Then, we compare the security features and performances of representative schemes, and an assessment result is provided for lightweight anonymous authentication with forward secrecy.

关键词： Wireless communication Hash functions Wireless sensor networks Authentication Sensor phenomena and characterization Logic gates Search problems

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PredRacer: Predictively Detecting Data Races in Android Applications

PredRacer: Predictively Detecting Data Races in Android Appl...

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IEEE International Conference on software Analysis, Evolution and Reengineering (SANER)

作者： Xin Guo Xiaofang Qi Yanhui Li Chao Wu School of Computer Science and Engineering Southeast University China State Key Laboratory for Novel Software Technology Nanjing University China

ISBN: (数字)9798350330663

ISBN: (纸本)9798350330670

Android platform offers a hybrid concurrency model encompassing multi-threading and asynchronous messaging for concurrent programming. The model is powerful but complex, making it difficult for developers to analyze concurrent behaviors. Data race, a prevalent concurrency defect in real-world Android applications, often results in abnormal executions of mobile applications, even crashes. Despite quite a few studies on detecting data races, it still suffers from high false positives with static analysis techniques and high false negatives with dynamic analysis techniques. To address this issue, this paper presents a predictive approach, PredRacer, for detecting data races in Android applications. It first captures an execution trace of an Android application, and then reorders the events within the trace based on partial orders. Finally, it checks the feasibility of the generated event sequence, which contains a potential data race. PredRacer increases the search scope and reduces false negatives while mitigating false positives by incorporating the happen-before relations specific to the Android concurrency model. The effectiveness of PredRacer is evaluated using the BenchERoid data set. Experimental results demonstrate that PredRacer achieves high precision, recall, and F1 score, outperforming the state-of-the-art techniques. A collection of 20 open-source Android applications is further utilized to assess the effectiveness of PredRacer, and an evaluation of 300 wild apps is conducted to assess its efficiency and scalability.

关键词： Concurrent computing Java Operating systems Scalability Static analysis Predictive models Programming

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SWin: A Set-Window Division Mechanism to Alleviate I/O Jitters of NVMe SSD Drives 24

SWin: A Set-Window Division Mechanism to Alleviate I/O Jitte...

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24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on Data Science and Systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Liu, Yuting Wang, Liang Zhu, Jinbin Shen, Runnan Jiang, Shixuan Xiao, Limin Beihang University State Key Laboratory of Software Development Environment Beijing100191 China School of Computer Science and Engineering Beihang University Beijing100191 China

ISBN: (纸本)9798350319934

NVMe SSDs increase the performance of I/O system significantly, but they still suffer from the problem of performance jitters. Although existing studies adopt optimized GC or schedulers to make up for the vulnerabilities, they cannot provide a near 'GC-free' performance under various scenarios. In this paper, we propose SWin, a Set-Window division mechanism to alleviate I/O jitters of NVMe SSDs, in which we build an optimized scheduler with cache management on sets. First, we first divide NVMe SSD into set level and determine time windows based on sets. Then, an efficient I/O scheduler is designed to process requests of applications. Finally, we develop a host cache management strategy to ensure a stable response time for I/O requests. The experimental results indicate that the OCSSD system integrated with SWin can gain performance improvement by 32%, 34% and 14% in completion time, IOPS and latency performance, respectively. © 2022 IEEE.

关键词： Jitter

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Confidence-aware contrastive learning for selective classification 24

Confidence-aware contrastive learning for selective classifi...

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Proceedings of the 41st International Conference on Machine Learning

作者： Yu-Chang Wu Shen-Huan Lyu Haopu Shang Xiangyu Wang Chao Qian National Key Laboratory for Novel Software Technology and School of Artificial Intelligence Nanjing University China Key Laboratory of Water Big Data Technology of Ministry of Water Resources and College of Computer Science and Software Engineering Hohai University China and National Key Laboratory for Novel Software Technology Nanjing University China

Selective classification enables models to make predictions only when they are sufficiently confident, aiming to enhance safety and reliability, which is important in high-stakes scenarios. Previous methods mainly use deep neural networks and focus on modifying the architecture of classification layers to enable the model to estimate the confidence of its prediction. This work provides a generalization bound for selective classification, disclosing that optimizing feature layers helps improve the performance of selective classification. Inspired by this theory, we propose to explicitly improve the selective classification model at the feature level for the first time, leading to a novel Confidence-aware Contrastive Learning method for Selective Classification, CCL-SC, which similarizes the features of homogeneous instances and differentiates the features of heterogeneous instances, with the strength controlled by the model's confidence. The experimental results on typical datasets, i.e., CIFAR-10, CIFAR-100, CelebA, and ImageNet, show that CCL-SC achieves significantly lower selective risk than state-of-the-art methods, across almost all coverage degrees. Moreover, it can be combined with existing methods to bring further improvement.

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To cool or not to cool? temperature network meets large foundation models via DRO 24

To cool or not to cool? temperature network meets large foun...

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Proceedings of the 41st International Conference on Machine Learning

作者： Zi-Hao Qiu Siqi Guo Mao Xu Tuo Zhao Lijun Zhang Tianbao Yang National Key Laboratory for Novel Software Technology Nanjing University Nanjing China Computer Science and Engineering Texas A&M University College Station Computer Science and Engineering Georgia Institute of Technology Atlanta National Key Laboratory for Novel Software Technology Nanjing University Nanjing China and Pazhou Laboratory (Huangpu) Guangzhou China

The temperature parameter plays a profound role during training and/or inference with large foundation models (LFMs) such as large language models (LLMs) and CLIP models. Particularly, it adjusts the logits in the softmax function in LLMs, which is crucial for next token generation, and it scales the similarities in the contrastive loss for training CLIP models. A significant question remains: "Is it viable to learn a neural network to predict a personalized temperature of any input data for enhancing LFMs?" In this paper, we present a principled framework for learning a small yet generalizable temperature prediction network (TempNet) to improve LFMs. Our solution is composed of a novel learning framework with a robust loss underpinned by constrained distributionally robust optimization (DRO), and a properly designed TempNet with theoretical inspiration. TempNet can be trained together with a large foundation model from scratch or learned separately given a pretrained foundation model. It is not only useful for predicting personalized temperature to promote the training of LFMs but also generalizable and transferable to new tasks. Our experiments on LLMs and CLIP models demonstrate that TempNet greatly improves the performance of existing solutions or models, e.g. Table 1. The code to reproduce the experimental results in this paper can be found at https://***/zhqiu/TempNet.

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Attribute-Based Data Sharing Scheme with Flexible Search Functionality for Cloud-Assisted Autonomous Transportation System

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IEEE Transactions on Industrial Informatics 2023年第11期19卷 10977-10986页

作者： Xiong, Hu Wang, Hanxiao Meng, Weizhi Yeh, Kuo-Hui University of Electronic Science and Technology of China School of Information and Software Engineering Network and Data Security Key Laboratory of Sichuan Province Chengdu610054 China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu610025 China Technical University of Denmark Department of Applied Mathematics and Computer Science Kongens Lyngby2800 Denmark National Dong Hwa University Department of Information Management Hualien97401 Taiwan National Sun Yat-sen University Department of Computer Science and Engineering Kaohsiung804201 Taiwan

The existing group public key encryption with equality test schemes could only support one-to-one data sharing and are not suitable for cloud-assisted autonomous transportation systems, which demand one-to-many data sharing. To tackle this problem efficiently, in this article, we put forward the group-attribute-based encryption with equality test (G-ABEET) scheme. The presented G-ABEET allows sensors equipped in vehicles to encrypt traffic data with an expressive access policy before sharing it. Only users with attributes required by the access policy ought to access the shared ciphertexts, thus achieving selective one-to-many data sharing. Meanwhile, the authorized cloud server could provide group users with equality tests over the ciphertexts, realizing ciphertext search ability. Furthermore, with the group mechanism, the G-ABEET scheme could resist offline message recovery attacks. Besides, in the standard model, we give rigorous security proof of the G-ABEET construction. The feasibility and efficiency of G-ABEET are demonstrated by experimental simulations. © 2005-2012 IEEE.

关键词： Cloud computing

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Long-tail learning with foundation model: heavy fine-tuning hurts 24

Long-tail learning with foundation model: heavy fine-tuning ...

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Proceedings of the 41st International Conference on Machine Learning

作者： Jiang-Xin Shi Tong Wei Zhi Zhou Jie-Jing Shao Xin-Yan Han Yu-Feng Li National Key Laboratory for Novel Software Technology and School of Artificial Intelligence Nanjing University China School of Computer Science and Engineering Southeast University China and Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education China National Key Laboratory for Novel Software Technology Nanjing University China

The fine-tuning paradigm in addressing longtail learning tasks has sparked significant interest since the emergence of foundation models. Nonetheless, how fine-tuning impacts performance in long-tail learning was not explicitly quantified. In this paper, we disclose that heavy fine-tuning may even lead to nonnegligible performance deterioration on tail classes, and lightweight fine-tuning is more effective. The reason is attributed to inconsistent class conditions caused by heavy fine-tuning. With the observation above, we develop a low-complexity and accurate long-tail learning algorithms LIFT with the goal of facilitating fast prediction and compact models by adaptive lightweight fine-tuning. Experiments clearly verify that both the training time and the learned parameters are significantly reduced with more accurate predictive performance compared with state-of-the-art approaches. The implementation code is available at https://***/shijxcs/LIFT.

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HiMo: End-to-End Congestion Control for High Speed Rail Data Networking

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IEEE Transactions on Intelligent Transportation Systems 2025年第6期26卷 7715-7725页

作者： Chenren Xu Yuhan Zhou Jing Wang Ruihan Li Lingyang Song Guangyu Zhu Key Laboratory of High Confidence Software Technologies Ministry of Education School of Computer Science Peking University Beijing China Zhongguancun Laboratory Beijing China School of Computer Science Peking University Beijing China Department of Electronics Peking University Beijing China School of Automation and Software Engineering Shanxi University Taiyuan Shanxi China

The highly variable nature of cellular networks challenges end-to-end network transmissions in achieving low-latency and high-throughput performance. In high-speed rail (HSR) networks, the intermittent connectivity and capacity dynamics imposed by high client mobility further add complexity and difficulty in providing seamless service. While congestion control algorithms (CCAs) play an essential role in ensuring optimal network performance, prior works on congestion control have predominantly concentrated on enhancing network performance within stationary or low-mobility mobile networks without considering frequent disconnections and highly dynamic network capacities imposed by HSR networks, resulting in severe RTT inflation and slow loss recovery. In this paper, we argue that a dedicated transport layer protocol is necessary for high-mobility scenarios. We propose an end-to-end low-latency congestion control algorithm HiMo for HSR networks that reacts to abrupt bandwidth changes quickly, handles frequent handovers, and is immediately deployable. Our trace-driven emulation on real-world datasets demonstrates that HiMo can reduce 51.3% 95th-percentile latency with comparable throughput on high-speed rail networks, compared to state-of-the-art CCAs.

关键词： Rails Protocols Heuristic algorithms Noise Bandwidth Throughput Market research Rail transportation Low latency communication Intelligent transportation systems

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