2022 IEEE 22nd International Conference on Software Quality, Reliability, and Security Companion (QRS-C)最新文献_第5页

A Natural Language-enabled Virtual Assistant for Human-Robot Interaction in Industrial Environments 一个支持自然语言的虚拟助手，用于工业环境中的人机交互

2022 IEEE 22nd International Conference on Software Quality, Reliability, and Security Companion (QRS-C) Pub Date : 2022-12-01 DOI: 10.1109/QRS-C57518.2022.00107

Chen Li, D. Chrysostomou, Hongji Yang

{"title":"A Natural Language-enabled Virtual Assistant for Human-Robot Interaction in Industrial Environments","authors":"Chen Li, D. Chrysostomou, Hongji Yang","doi":"10.1109/QRS-C57518.2022.00107","DOIUrl":"https://doi.org/10.1109/QRS-C57518.2022.00107","url":null,"abstract":"This paper introduces a natural language-enabled virtual assistant (VA), called Max, developed to enhance human-robot interaction (HRI) with industrial robots. Regardless of the numerous natural language interfaces already available for commercial use and social robots, most VAs remain tightly bound to a specific robotic system. Besides, they lack a natural and efficient human-robot communication protocol to advance the user experience and the required robustness for use on the industrial floor. Therefore, the proposed framework is designed based on three key elements. A Client-Server style architecture that provides a centralised solution for managing and controlling various types of robots deployed on the shop floor. A communication protocol inspired by human-human conversation strategies, i.e., lexical-semantic strategy and general diversion strategy, is used to guide Max's response generation. These conversation strategies are embedded in Max's architecture to improve the engagement of the operators during the execution of industrial tasks. Finally, the state-of-the-art pre-trained model, Bidirectional Encoder Representations from Transformers (BERT), is fine-tuned to support a highly accurate prediction of requested intents from the operator and robot services. Multiple experiments were conducted for validating Max's performance in a real industrial environment.","PeriodicalId":183728,"journal":{"name":"2022 IEEE 22nd International Conference on Software Quality, Reliability, and Security Companion (QRS-C)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126431728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Real-Time Control Algorithm of Intelligent Energy-Saving Lights based on IoT 基于物联网的智能节能灯实时控制算法

2022 IEEE 22nd International Conference on Software Quality, Reliability, and Security Companion (QRS-C) Pub Date : 2022-12-01 DOI: 10.1109/QRS-C57518.2022.00025

Bo Su, Zeyuan Zhang, Yuansheng Zhang, Qingyue Yang, Jiong Jiang

引用次数: 0

TSDTest: A Efficient Coverage Guided Two-Stage Testing for Deep Learning Systems TSDTest:深度学习系统的有效覆盖引导两阶段测试

2022 IEEE 22nd International Conference on Software Quality, Reliability, and Security Companion (QRS-C) Pub Date : 2022-12-01 DOI: 10.1109/QRS-C57518.2022.00033

Hao Li, Shihai Wang, Tengfei Shi, Xinyue Fang, Jian Chen

引用次数: 0

Multi Pair Swap-Based Weather Derivative DeFi 基于多对互换的天气衍生工具定义

2022 IEEE 22nd International Conference on Software Quality, Reliability, and Security Companion (QRS-C) Pub Date : 2022-12-01 DOI: 10.1109/QRS-C57518.2022.00018

Shinya Haga, Taisei Takahashi, Kazumasa Omote

引用次数: 0

Intelligent Guidance Method for Elevator Emergency Treatment based on Automatic Recommendation and Fault Prediction 基于自动推荐和故障预测的电梯应急处理智能引导方法

2022 IEEE 22nd International Conference on Software Quality, Reliability, and Security Companion (QRS-C) Pub Date : 2022-12-01 DOI: 10.1109/QRS-C57518.2022.00075

Guangwei Qing, Qianfei Zhou, Huifang Wang

{"title":"Intelligent Guidance Method for Elevator Emergency Treatment based on Automatic Recommendation and Fault Prediction","authors":"Guangwei Qing, Qianfei Zhou, Huifang Wang","doi":"10.1109/QRS-C57518.2022.00075","DOIUrl":"https://doi.org/10.1109/QRS-C57518.2022.00075","url":null,"abstract":"In order to reduce the handling time of elevator failure and speed up the rescue of trapped personnel, an intelligent guidance method for elevator emergency treatment based on automatic recommendation of rescue units and prediction of fault causes is studied on the basis of elevator emergency treatment platform. The automatic recommendation module builds a multi-dimensional rescue unit capability evaluation index system, which establishes result recommendation methods such as recall, single-index recommendation, and comprehensive recommendation to achieve the optimal rescue unit recommendation for faulty elevators. The fault cause prediction module uses a variety of pre-trained word embedding models to vectorize fault text data on historical fault data sets, uses elevator fault text clustering algorithm based on attention mechanism and BI-LSTM model to obtain elevator fault labels, and uses the Boosting ensemble learning algorithm to construct an elevator fault prediction classification model for the marked elevator historical fault data set. The experimental results show that when the elevator fails, the automatic recommendation module can recommend the optimal rescue unit, and the fault prediction module can predict the cause of the elevator failure in real time, which quickly and accurately locates the fault area. For rescuers, it is convenient to deal with elevator failure in a targeted manner and greatly reduces the repair time. Therefore, this research is of great significance for speeding up rescue, improving emergency response capabilities, and ensuring the safe operation of elevators.","PeriodicalId":183728,"journal":{"name":"2022 IEEE 22nd International Conference on Software Quality, Reliability, and Security Companion (QRS-C)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132268815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Simulation of Sensor Spoofing Attacks on Unmanned Aerial Vehicles using the Gazebo Simulator 基于Gazebo模拟器的无人机传感器欺骗攻击仿真

2022 IEEE 22nd International Conference on Software Quality, Reliability, and Security Companion (QRS-C) Pub Date : 2022-12-01 DOI: 10.1109/QRS-C57518.2022.00016

Irdin Pekaric, David Arnold, M. Felderer

{"title":"Simulation of Sensor Spoofing Attacks on Unmanned Aerial Vehicles using the Gazebo Simulator","authors":"Irdin Pekaric, David Arnold, M. Felderer","doi":"10.1109/QRS-C57518.2022.00016","DOIUrl":"https://doi.org/10.1109/QRS-C57518.2022.00016","url":null,"abstract":"Conducting safety simulations in various simulators, such as the Gazebo simulator, became a very popular means of testing vehicles against potential safety risks (i.e. crashes). However, this was not the case with security testing. Performing security testing in a simulator is very difficult because security attacks are performed on a different abstraction level. In addition, the attacks themselves are becoming more sophisticated, which directly contributes to the difficulty of executing them in a simulator. In this paper, we attempt to tackle the aforementioned gap by investigating possible attacks that can be simulated, and then performing their simulations. The presented approach shows that attacks targeting the LiDAR and GPS components of unmanned aerial vehicles can be simulated. This is achieved by exploiting vulnerabilities of the ROS and MAVLink protocol and injecting malicious processes into an application. As a result, messages with arbitrary values can be spoofed to the corresponding topics, which allows attackers to update relevant parameters and cause a potential crash of a vehicle. This was tested in multiple scenarios, thereby proving that it is indeed possible to simulate certain attack types, such as spoofing and jamming.","PeriodicalId":183728,"journal":{"name":"2022 IEEE 22nd International Conference on Software Quality, Reliability, and Security Companion (QRS-C)","volume":"183 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133375468","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Resilience Analysis of Urban Rail Transit Network Under Large Passenger Flow 大客流条件下城市轨道交通网络弹性分析

2022 IEEE 22nd International Conference on Software Quality, Reliability, and Security Companion (QRS-C) Pub Date : 2022-12-01 DOI: 10.1109/QRS-C57518.2022.00072

Ning Wang

引用次数: 0

Face Recognition Fairness Assessment based on Data Augmentation: An Empirical Study 基于数据增强的人脸识别公平性评估实证研究

2022 IEEE 22nd International Conference on Software Quality, Reliability, and Security Companion (QRS-C) Pub Date : 2022-12-01 DOI: 10.1109/QRS-C57518.2022.00053

Fangyuan Tian, Wenhong Liu, Shuang Zhao, Jiawei Liu

引用次数: 0

Similarity Analysis in Data Element Matching based on Word2vec 基于Word2vec的数据元素匹配相似度分析

2022 IEEE 22nd International Conference on Software Quality, Reliability, and Security Companion (QRS-C) Pub Date : 2022-12-01 DOI: 10.1109/QRS-C57518.2022.00054

Wenhong Liu, Zhiyuan Peng, Shuang Zhao, Jiawei Liu

{"title":"Similarity Analysis in Data Element Matching based on Word2vec","authors":"Wenhong Liu, Zhiyuan Peng, Shuang Zhao, Jiawei Liu","doi":"10.1109/QRS-C57518.2022.00054","DOIUrl":"https://doi.org/10.1109/QRS-C57518.2022.00054","url":null,"abstract":"With the increasing demand for computer-aided big data processing, deep learning has gradually become an effective means to help big data processing. There are often many redundant database fields between different departments. These fields are often completely equivalent, but there are certain differences in field names, which brings trouble to data element matching. To this end, we propose a more targeted approach - ‘MetaMatch’ to handle database fields, combining $W$ ord2vec with a high-performance database. To measure the effectiveness of the proposed method, we propose a $W$ ord2vec-based data element matching method. The method performs semantic segmentation on key fields of the database and trains word vectors. Then, we perform tokenization processing on each training case. According to the result of word segmentation, the corresponding word vector is constructed. We use this method to implement data element matching for big data systems in our experiments and design a validation experiment to evaluate the matching accuracy. The matching accuracy rate reached 79.3%.","PeriodicalId":183728,"journal":{"name":"2022 IEEE 22nd International Conference on Software Quality, Reliability, and Security Companion (QRS-C)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129392856","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Software Technology Status Management under the Trend of Ship Informatization 船舶信息化趋势下的软件技术状态管理

2022 IEEE 22nd International Conference on Software Quality, Reliability, and Security Companion (QRS-C) Pub Date : 2022-12-01 DOI: 10.1109/QRS-C57518.2022.00106

Mengao Li, Hanting Zhao, Wenxiu Zhang, Xiqiao Pang, Tao Feng

引用次数: 0