2020 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)最新文献_第7页

Central versus Distributed Statistical Computing Algorithms-A Comparison 中央与分布式统计计算算法的比较

2020 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON) Pub Date : 2020-10-28 DOI: 10.1109/UEMCON51285.2020.9298174

N. Madathil, S. Harous

引用次数: 1

ADCR: An Adaptive TOOL to select ”Appropriate Developer for Code Review” based on Code Context ADCR:基于代码上下文选择“合适的代码审查开发人员”的自适应工具

2020 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON) Pub Date : 2020-10-28 DOI: 10.1109/UEMCON51285.2020.9298102

Nafiz Sadman, M. Ahsan, M. Mahmud

引用次数: 1

Latent Walking Techniques for Conditioning GAN-Generated Music 调节gan生成音乐的潜在行走技术

2020 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON) Pub Date : 2020-10-28 DOI: 10.1109/UEMCON51285.2020.9298149

Logan Eisenbeiser

{"title":"Latent Walking Techniques for Conditioning GAN-Generated Music","authors":"Logan Eisenbeiser","doi":"10.1109/UEMCON51285.2020.9298149","DOIUrl":"https://doi.org/10.1109/UEMCON51285.2020.9298149","url":null,"abstract":"Artificial music generation is a rapidly developing field focused on the complex task of creating neural networks that can produce realistic-sounding music. As computer-generated music improves in quality, it has potential to revolutionize the multi-billion dollar music industry by providing additional tools to musicians as well as creating new music for consumers. Beyond simply generating music lies the challenge of controlling or conditioning that generation. Conditional generation can be used to specify a tempo for the generated song, increase the density of notes, or even change the genre. Latent walking is one of the most popular techniques for conditional image generation, but its effectiveness on music-domain generation is largely unexplored, especially for generative adversarial networks (GANs). In this paper, latent walking is implemented with the MuseGAN generator to successfully control two semantic values: note count and polyphonicity (when more than one note is played at a time). This shows that latent walking is a viable technique for GANs in the music domain and can be used to improve the quality, among other features, of the generated music.","PeriodicalId":433609,"journal":{"name":"2020 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127050238","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

Automatic Numerical Question Answering on Table using BERT-GNN 基于BERT-GNN的表格自动数值答题

2020 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON) Pub Date : 2020-10-28 DOI: 10.1109/UEMCON51285.2020.9298028

Ruchi Bagwe, K. George

{"title":"Automatic Numerical Question Answering on Table using BERT-GNN","authors":"Ruchi Bagwe, K. George","doi":"10.1109/UEMCON51285.2020.9298028","DOIUrl":"https://doi.org/10.1109/UEMCON51285.2020.9298028","url":null,"abstract":"The table base numerical question-answering task requires a mechanism to understand the relation between table content and numbers (present in table and question). It also needs an efficient method to address complex reasoning on table context. Most of the existing approaches in the natural language processing technology address the context-based questions on the table but fail to address the numerical reasoning part. They are also built on a large search database, which makes it challenging to use them in multiple domains. These approaches use pre-trained models like BERT to perform context encoding of a complete table. Hence these models fail when a large table is provided as input, as full table encoding is a very resource and time-consuming task. In this paper, a framework is proposed to answer questions on the table with numerical reasoning. This framework uses a context-snapshot mechanism to filter irrelevant table rows before tokenizing the table content. The filtered context and tokenized question are converted into vector representation using a pre-trained BERT model. This proposed model finds the correlation between the tokenized context-snapshot and numbers in question using graph neural networks. Further, it uses a feed-forward neural network to perform the numerical operation to compute the answer. The model is trained and evaluated on WikiTableQuestions datasets, shows a promising result.","PeriodicalId":433609,"journal":{"name":"2020 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132803272","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

EMG-based Hand Gesture Recognition by Deep Time-frequency Learning for Assisted Living & Rehabilitation 基于肌电图的深度时频学习手势识别在辅助生活与康复中的应用

2020 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON) Pub Date : 2020-10-28 DOI: 10.1109/UEMCON51285.2020.9298181

Qi Wang, Xianping Wang

引用次数: 3

Facial Expression Recognition and Recommendations Using Deep Neural Network with Transfer Learning 基于迁移学习的深度神经网络面部表情识别与推荐

2020 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON) Pub Date : 2020-10-28 DOI: 10.1109/UEMCON51285.2020.9298082

Narayana Darapaneni, Rahul Choubey, Pratik Salvi, Ankur Pathak, Sajal Suryavanshi, A. Paduri

引用次数: 4

Deep Attractor with Convolutional Network for Monaural Speech Separation 基于卷积网络的单耳语音分离深度吸引子

2020 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON) Pub Date : 2020-10-28 DOI: 10.1109/UEMCON51285.2020.9298070

Tian Lan, Yuxin Qian, Wenxin Tai, Boce Chu, Qiao Liu

引用次数: 0

Enhanced Security Architecture for Visual Cryptography Based on Image Secret Sharing 基于图像秘密共享的增强视觉密码安全体系结构

2020 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON) Pub Date : 2020-10-28 DOI: 10.1109/UEMCON51285.2020.9298166

Manas Abhilash Gundapuneni, Anzum Bano, Navjot Singh

引用次数: 1

Anomaly Detection and Identification Using Visual Techniques in Streaming Video 基于视觉技术的流媒体视频异常检测与识别

2020 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON) Pub Date : 2020-10-28 DOI: 10.1109/UEMCON51285.2020.9298178

T. A. Wanigaaratchi, V. T. N. Vidanagama

{"title":"Anomaly Detection and Identification Using Visual Techniques in Streaming Video","authors":"T. A. Wanigaaratchi, V. T. N. Vidanagama","doi":"10.1109/UEMCON51285.2020.9298178","DOIUrl":"https://doi.org/10.1109/UEMCON51285.2020.9298178","url":null,"abstract":"there are many intelligent systems and tools which uses highly efficient processing models to identify different anomalies with high accuracy. The anomaly detection is of high importance and mostly will come as an absolute requirement at high risk environments and situations. The amount of processing involved in quick decision taking systems bare high deployment costs which restricts the anomaly detection only to a selected few who are capable of building such resource centered systems. Modern world uses drones and other video feeds in order to find and keep track of any anomalous events around a specific area. But most such detection requires absolute manual attention as well as processing power to keep up with real time detection and recognition. The proposed research solution aims to automate this process and includes a two-step anomaly detection system which gives a quicker anomaly detection in an average processing unit time with an advanced recognition model with up to 90% accuracy. The deep learning model (VGG 16) together with alert system and comparison techniques on videos leads into unsupervised anomaly detection of a landscape. The system generates alerts and recognizes anomalies on the alerted video frames. The proposed solution can also be used by any source and does not require high capacity of capability system to get the optimal output. Moreover, the solution brings a simple yet sophisticated technique to address modern anomaly detection and quick alerting system.","PeriodicalId":433609,"journal":{"name":"2020 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133119594","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

Efficiency of Different Machine Learning Algorithms on the Multivariate Classification of IoT Botnet Attacks 不同机器学习算法对物联网僵尸网络攻击多元分类的效率

2020 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON) Pub Date : 2020-10-28 DOI: 10.1109/UEMCON51285.2020.9298095

Shreehar Joshi, Eman Abdelfattah

{"title":"Efficiency of Different Machine Learning Algorithms on the Multivariate Classification of IoT Botnet Attacks","authors":"Shreehar Joshi, Eman Abdelfattah","doi":"10.1109/UEMCON51285.2020.9298095","DOIUrl":"https://doi.org/10.1109/UEMCON51285.2020.9298095","url":null,"abstract":"The Internet of Things, with its enormous growth in the recent decades, has not just brought convenience to the different aspects of our lives. It has also increased the risks of various forms of cybercriminal attacks, ranging from personal information theft to the disruption of the entire network of a service provider. As the demands of such devices increase rapidly on a global scale, it has become increasingly difficult for different corporations to focus on security efficiently. As such, the demand for methodologies that can aptly respond to prevent intrusion within a network has soared disturbingly. Various utilization of anomaly traffic detection techniques has been conducted in the past, all with the similar aim to prevent disruption in networks. This research aims to find an efficient classifier that detects anomaly traffic from N_BaIoT dataset with the highest overall precision and recall by experimenting with four machine learning techniques. Four binary classifiers: Decision Trees, Extra Trees Classifiers, Random Forests, and Support Vector Machines are tested and validated to produce the result. The outcome demonstrates that all the classifiers perform exceptionally well when used to train and test the anomaly within a single device. Moreover, Random Forests classifier outperforms all others when training is done on a particular device to test the anomaly on completely unrelated devices.","PeriodicalId":433609,"journal":{"name":"2020 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)","volume":"148 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125869362","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: 5