2022 7th International Conference on Computer and Communication Systems (ICCCS)最新文献

Generating Robot Framework Test Script for Android Application Using Kotlin 使用Kotlin为Android应用生成机器人框架测试脚本

2022 7th International Conference on Computer and Communication Systems (ICCCS) Pub Date : 2022-04-22 DOI: 10.1109/icccs55155.2022.9846504

Chor. Gitpit Petchpaisit, T. Suwannasart

{"title":"Generating Robot Framework Test Script for Android Application Using Kotlin","authors":"Chor. Gitpit Petchpaisit, T. Suwannasart","doi":"10.1109/icccs55155.2022.9846504","DOIUrl":"https://doi.org/10.1109/icccs55155.2022.9846504","url":null,"abstract":"As known that a wide variety of hardware and operating system versions are circulating in the Android market. In addition, for the past decade more and more organizations have been adopting iterative and incremental software development approaches to handle rapid requirement changes in software requirements and to accelerate their products to the market. These factors have pushed tons of pressure on software testing, especially in the regression testing step. Regression testing is a pivotal step to ensure that code alterations do not impact the existing functionalities of the software. As software grows larger, regression testing could become impractical, if done manually, since human resource consumption would exponentially increase. To alleviate this issue, many organizations have embraced automation testing in their regression testing steps. Keyword-driven testing is one of the automation testing techniques that has been frequently used on Android projects. However, constructing a test case for automation testing (also known as a test script) is an arduous task which tends to easily cause errors [1]. Therefore, in this research a tool to generate Robot Framework test scripts for Android applications written in Kotlin is proposed. With this tool, an Android project's source code is analyzed for its flow. Next, test cases are generated using AppiumLibrary keywords and the source code. Test data sets embedded in the test cases are generated using Boundary Value Testing and randomization techniques. Finally, the generated test cases are ready to be run on Robot Framework to test the software.","PeriodicalId":121713,"journal":{"name":"2022 7th International Conference on Computer and Communication Systems (ICCCS)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114982336","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

Design of a Wideband Power Synthesizer for Vector Network Analyzer 矢量网络分析仪宽带功率合成器的设计

2022 7th International Conference on Computer and Communication Systems (ICCCS) Pub Date : 2022-04-22 DOI: 10.1109/icccs55155.2022.9846377

E. Chen, Zhiying Cao, Yubin Yang, Qinglong Zhang, Xincheng Jiang, Shengkai Sun, Chunqing Xu

引用次数: 0

Design of Integrated Satellite-ground Lasercom and Ranging System Based on Pulse Position Modulation 基于脉冲位置调制的星地一体化激光通信测距系统设计

2022 7th International Conference on Computer and Communication Systems (ICCCS) Pub Date : 2022-04-22 DOI: 10.1109/icccs55155.2022.9846334

Chongyang Li, Jinhui Fang, C. Hu

引用次数: 0

Research on Power Line Carrier Adaptation for Internet of Things 面向物联网的电力线载波自适应研究

2022 7th International Conference on Computer and Communication Systems (ICCCS) Pub Date : 2022-04-22 DOI: 10.1109/icccs55155.2022.9846004

Zhan Shi, Bo Li, Jiangang Lu

引用次数: 0

Target Detection Method of Infrared Images with Strong Wave Based on Morphological Characteristics 基于形态学特征的红外强波图像目标检测方法

2022 7th International Conference on Computer and Communication Systems (ICCCS) Pub Date : 2022-04-22 DOI: 10.1109/icccs55155.2022.9845854

Meng Zhang, Lili Dong, Chang Tian

引用次数: 0

Improving CycleGAN for Image-to-Image Style Transfer by DenseNet 基于DenseNet改进CycleGAN的图像到图像样式转换

2022 7th International Conference on Computer and Communication Systems (ICCCS) Pub Date : 2022-04-22 DOI: 10.1109/icccs55155.2022.9846381

Haoyang Wang, Xiangqun Lu, Fang Deng

{"title":"Improving CycleGAN for Image-to-Image Style Transfer by DenseNet","authors":"Haoyang Wang, Xiangqun Lu, Fang Deng","doi":"10.1109/icccs55155.2022.9846381","DOIUrl":"https://doi.org/10.1109/icccs55155.2022.9846381","url":null,"abstract":"With the continuous progress and development of machine learning, deep learning has become a hot research field today. Among them, image-to-image style transfer is one of the research hotspots. Generally, the main goal of image style transfer is to extract the features of the original image and convert them into images with the target style through a deep neural network. Applying the cycle-consistent adversarial network known as CycleGAN to transfer the style of images is one of the existing image style transfer techniques, but CycleGAN has problems such as insufficient brightness enhancement, color distortion, and low authenticity of the generated images. To address this problem, in this paper, we construct an improved CycleGAN, use DenseNet to replace the original ResNet, and compare the image effects generated by the improved network using DenseNet and the original CycleGAN network based on ResNet. After calculating through the image evaluation indicators PSNR and SSIM, the results show that the images generated by the improved recurrent consistency network based on DenseNet have better visual effects and realism.","PeriodicalId":121713,"journal":{"name":"2022 7th International Conference on Computer and Communication Systems (ICCCS)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129865836","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: 1

A Driver Distraction Detection Method Based on Convolutional Neural Network 基于卷积神经网络的驾驶员分心检测方法

2022 7th International Conference on Computer and Communication Systems (ICCCS) Pub Date : 2022-04-22 DOI: 10.1109/icccs55155.2022.9846062

Chuheng Wei, Chuanshi Liu, Shaocui Chi

引用次数: 1

Transformer-based Model for fMRI Data: ABIDE Results 基于变压器的fMRI数据模型:遵守结果

2022 7th International Conference on Computer and Communication Systems (ICCCS) Pub Date : 2022-04-22 DOI: 10.1109/icccs55155.2022.9845999

Wenhui Li, Shiyuan Wang, Guangyuan Liu

{"title":"Transformer-based Model for fMRI Data: ABIDE Results","authors":"Wenhui Li, Shiyuan Wang, Guangyuan Liu","doi":"10.1109/icccs55155.2022.9845999","DOIUrl":"https://doi.org/10.1109/icccs55155.2022.9845999","url":null,"abstract":"Functional magnetic resonance imaging (fMRI), which is a non-invasive technique for measuring brain signals, is widely used in the diagnosis of Alzheimer’s disease (AD), Autistic spectrum disorder (ASD), Depression, and other brain neurological diseases. In the current research, fMRI signals are usually analyzed using convolutional neural network (CNN) and recurrent neural network (RNN). However, these models are ineffective when considering the relationship between nonadjacent brain regions and analyzing long-time span fMRI signals. Transformer, which completely abandons the architecture of the traditional neural network, can overcome the above limitations through positional decoding and self-attention mechanism. In this study, a transformer-based model is proposed, which is the first time to apply transformer to fMRI data analysis. In addition, positional decoding is an essential part of transformer. Functional connection matrix is creatively used as the positional decoding of transformer-based model for fMRI data. The autism brain imaging data exchange (ABIDE) dataset is used to evaluate this model. The transformer-based model achieved a classification accuracy of 74.18% using subject-wise 10-fold cross-validation. It cannot exceed the classification accuracy (79.50%) of the state-of-the-art model based on hand-engineered features but exceed the highest classification accuracy (74.00%) of the model based on original fMRI image data in ABIDE dataset. This study provides a transformer-based model for original fMRI data, which is helpful to realize the early diagnosis of ASD, AD, Depression, and other neurological diseases.","PeriodicalId":121713,"journal":{"name":"2022 7th International Conference on Computer and Communication Systems (ICCCS)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125705461","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: 1

Throughput Maximization for Wireless-Powered IoT Networks with Hybrid Non-orthognal Multiple Access 混合非正交多址无线供电物联网网络的吞吐量最大化

2022 7th International Conference on Computer and Communication Systems (ICCCS) Pub Date : 2022-04-22 DOI: 10.1109/icccs55155.2022.9846219

Huiwen Qi, Yong Liu, Yingsheng Peng, Han Zhang

{"title":"Throughput Maximization for Wireless-Powered IoT Networks with Hybrid Non-orthognal Multiple Access","authors":"Huiwen Qi, Yong Liu, Yingsheng Peng, Han Zhang","doi":"10.1109/icccs55155.2022.9846219","DOIUrl":"https://doi.org/10.1109/icccs55155.2022.9846219","url":null,"abstract":"In this paper, we study a wireless-powered IoT network, where a hybrid access point (HAP) can charge internet of things (IoT) devices by wireless energy transfer technology (WET), and collect their information. To maximize the network throughput, we propose a hybrid non-orthogonal multiple access (NOMA) based transmission scheme, in which both the concurrent WET and wireless information transmission (WIT), and the non-orthogonal WIT are jointly exploited. Specifically, all devices are divided into the interference and non-interference groups, namely device grouping. During the transmission of devices in the interference group, the WET is simultaneously conducted, such that other devices harvest more energy and achieve large rates with a rate loss of devices in the interference group. Meanwhile, how to pair devices to realize the non-orthogonal WIT is also a critical issue, e.g., device pairing. Concerning the joint device grouping and pairing policy, we consider two specific hybrid NOMA based scheme, namely the first-pairing-then-grouping (FPTG) and first-grouping-then-pairing (FGTP) schemes. In the former, devices are firstly paired based on their channel qualities, and then grouped. The latter scheme is opposite. Then, we apply the order statistics to theoretically analyze the achieved performance of two transmission scheme, and the corresponding optimal pairing and grouping is determined. Finally, simulation results validate the significant improvement of the proposed hybrid NOMA based scheme in terms of network throughput.","PeriodicalId":121713,"journal":{"name":"2022 7th International Conference on Computer and Communication Systems (ICCCS)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121747408","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

A Hybrid Reasoning Method of Knowledge Graph for On-line Arts Education based on Reinforcement Learning 基于强化学习的在线艺术教育知识图混合推理方法

2022 7th International Conference on Computer and Communication Systems (ICCCS) Pub Date : 2022-04-22 DOI: 10.1109/icccs55155.2022.9846799

Gang Li, Ruixin Han

引用次数: 0