2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC)最新文献_第2页

An Approach to Real-Time Fall Detection based on OpenPose and LSTM 基于OpenPose和LSTM的实时跌倒检测方法

2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC) Pub Date : 2022-06-01 DOI: 10.1109/COMPSAC54236.2022.00250

Po-Chih Chen, Chih-Hung Chang, Yu-Wei Chan, Yin-Te Tsai, W. Chu

引用次数: 2

A Structure-Focused Deep Learning Approach for Table Recognition from Document Images 基于结构的深度学习文档图像表识别方法

2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC) Pub Date : 2022-06-01 DOI: 10.1109/COMPSAC54236.2022.00105

Mengxi Zhou, R. Ramnath

引用次数: 0

Predicting Mortality Rate based on Comprehensive Features of Intensive Care Unit Patients 基于重症监护病房患者综合特征的死亡率预测

2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC) Pub Date : 2022-06-01 DOI: 10.1109/COMPSAC54236.2022.00222

Jagan Moahan Reddy Danda, Kumar Priyansh, H. Shahriar, Hisham M. Haddad, A. Cuzzocrea, Nazmus Sakib

引用次数: 1

Towards a Distributed Inference Detection System in a Multi-Database Context 多数据库环境下的分布式推理检测系统

2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC) Pub Date : 2022-06-01 DOI: 10.1109/COMPSAC54236.2022.00246

Sad Rafik, P. Lachat, N. Bennani, V. Rehn-Sonigo

引用次数: 1

Grasp Position Estimation from Depth Image Using Stacked Hourglass Network Structure 基于堆叠沙漏网络结构的深度图像抓取位置估计

2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC) Pub Date : 2022-06-01 DOI: 10.1109/COMPSAC54236.2022.00187

Keisuke Hamamoto, Huimin Lu, Yujie Li, Tohru Kamiya, Y. Nakatoh, S. Serikawa

{"title":"Grasp Position Estimation from Depth Image Using Stacked Hourglass Network Structure","authors":"Keisuke Hamamoto, Huimin Lu, Yujie Li, Tohru Kamiya, Y. Nakatoh, S. Serikawa","doi":"10.1109/COMPSAC54236.2022.00187","DOIUrl":"https://doi.org/10.1109/COMPSAC54236.2022.00187","url":null,"abstract":"In recent years, robots have been used not only in factories. However, most robots currently used in such places can only perform the actions programmed to perform in a predefined space. For robots to become widespread in the future, not only in factories, distribution warehouses, and other places but also in homes and other environments where robots receive complex commands and their surroundings are constantly being updated, it is necessary to make robots intelligent. Therefore, this study proposed a deep learning grasp position estimation model using depth images to achieve intelligence in pick-and-place. This study used only depth images as the training data to build the deep learning model. Some previous studies have used RGB images and depth images. However, in this study, we used only depth images as training data because we expect the inference to be based on the object's shape, independent of the color information of the object. By performing inference based on the target object's shape, the deep learning model is expected to minimize the need for re-training when the target object package changes in the production line since it is not dependent on the RGB image. In this study, we propose a deep learning model that focuses on the stacked encoder-decoder structure of the Stacked Hourglass Network. We compared the proposed method with the baseline method in the same evaluation metrics and a real robot, which shows higher accuracy than other methods in previous studies.","PeriodicalId":330838,"journal":{"name":"2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"120 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121523912","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

Formal Analysis and Verification of DPSTM v2 Architecture Using CSP 基于CSP的DPSTM v2体系结构形式化分析与验证

2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC) Pub Date : 2022-06-01 DOI: 10.1109/COMPSAC54236.2022.00138

Pei Li, Jiaqi Yin, Huibiao Zhu, Lili Xiao, M. Popovic

{"title":"Formal Analysis and Verification of DPSTM v2 Architecture Using CSP","authors":"Pei Li, Jiaqi Yin, Huibiao Zhu, Lili Xiao, M. Popovic","doi":"10.1109/COMPSAC54236.2022.00138","DOIUrl":"https://doi.org/10.1109/COMPSAC54236.2022.00138","url":null,"abstract":"Transactional memory is designed for developing parallel programs and improving the efficiency of parallel pro-grams. PSTM (python software transactional memory) mainly supports multi-core parallel programs based on the python language. In order to better adapt to the developing requirements of distributed concurrent programs and enhance the safety of the system, DPSTM (distributed python software transactional memory) was developed. Compared with PSTM, DPSTM has the advantages of higher operating efficiency and stronger fault tolerance. In this paper, we apply CSP (Communicating Sequential Processes) to formally analyze the components of DPSTM v2 architecture, the data exchange process between components, and two different transaction processing modes. We use the model checker PAT (Process Analysis Toolkit) to model the DPSTM v2 architecture and verify eight properties, including deadlock freedom, ACI (atomicity, isolation, and consistency), sequential consistency, data server availability, read tolerance, and crash tolerance. The verification results show that the DPSTM v2 archi-tecture can guarantee all of the above properties. In particular, the normal operation of the system can be maintained when some of the data servers are crashed, ensuring the safety of a distributed system.","PeriodicalId":330838,"journal":{"name":"2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121665634","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

Concept drift detection for distributed multi-model machine learning systems 分布式多模型机器学习系统的概念漂移检测

2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC) Pub Date : 2022-06-01 DOI: 10.1109/COMPSAC54236.2022.00168

Beverly Abadines Quon, J. Gaudiot

{"title":"Concept drift detection for distributed multi-model machine learning systems","authors":"Beverly Abadines Quon, J. Gaudiot","doi":"10.1109/COMPSAC54236.2022.00168","DOIUrl":"https://doi.org/10.1109/COMPSAC54236.2022.00168","url":null,"abstract":"Many works focus on optimizing machine learning models during their training phase, but fail to account how these models adapt into their model-serving phase once they are deployed into real world applications. In this phase models must process through streams of data that can evolve over time and distort the relationship between incoming data, causing concept drift. This paper proposes leveraging the advantages of emerging features stores in order to improve concept drift detection on unlabeled, dynamic data streams across multiple models. Firstly, we introduce Drift Detection on Distributed Datasets (QuaD), which combines classical drift detectors to make use of labeled and unlabeled data, and create local context (i.e. per live model) and global context (i.e. across multiple models). Secondly, we propose using feature store entities, SHAP values, and Collaborative Filtering (CF) to augment unlabeled data across multiple models. To the best of our knowledge, QuaD is the first work that examines the collective behavior of concept drift across multiple models and discerns associations between models that may share a susceptibility in a dynamic setting. QuaD uses a combination of performance-based and data distribution-based drift detectors and CF to capture varying types of concept drifts for labeled and unlabeled data streams and is modeled around the data abstraction provided by emerging feature stores.","PeriodicalId":330838,"journal":{"name":"2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124334679","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

Big data architectures for data lakes: A systematic literature review 面向数据湖的大数据架构:系统文献综述

2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC) Pub Date : 2022-06-01 DOI: 10.1109/COMPSAC54236.2022.00179

Sonam Ramchand, Tariq Mahmood

{"title":"Big data architectures for data lakes: A systematic literature review","authors":"Sonam Ramchand, Tariq Mahmood","doi":"10.1109/COMPSAC54236.2022.00179","DOIUrl":"https://doi.org/10.1109/COMPSAC54236.2022.00179","url":null,"abstract":"The rise in big technologies has been demanding different concepts and practices for data exploitation; among them data lake is a recently emerged concept that is meant to deal with the heterogeneous data. Data lakes have been residing in the big data era since 2010, but there has not been any systematic review yet over data lake implementation. In this research survey, we conduct a review and provide a road map to researcher that elaborates what has happened to data lakes till now. We aim to give understanding for basic concept of data lakes and propose a novel data lake definition that could best describe the concept based on the literature review. One of the main problem while implementing data lake is deciding the technologies to use, this study covers technologies that can potentially be used for data lake implementation. Furthermore, data lake architectures and their variants are discussed in detail. Moreover, we analyze current state, challenges, pros and cons of the data lake. This study is all in one place for researchers who try to understand data lake concept, architectures, technologies, approaches, current state and challenges.","PeriodicalId":330838,"journal":{"name":"2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124421949","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: 3

Madelyn: Multi-Domain Multi-Agent Reinforcement Learning for Data-center Networks Madelyn:数据中心网络的多域多智能体强化学习

2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC) Pub Date : 2022-06-01 DOI: 10.1109/COMPSAC54236.2022.00109

A. Kattepur, S. David

{"title":"Madelyn: Multi-Domain Multi-Agent Reinforcement Learning for Data-center Networks","authors":"A. Kattepur, S. David","doi":"10.1109/COMPSAC54236.2022.00109","DOIUrl":"https://doi.org/10.1109/COMPSAC54236.2022.00109","url":null,"abstract":"Data-center network configurations are crucial in ensuring end-to-end differentiated service performance within 5G. Data-center networks encom-pass two domains: (i) the fat-tree networking fabric with leaf, spine and super-spine layers (ii) data-center server nodes with container and workload placement policies. These have traditionally been managed within silos with context and configurations driven within each domain. In this work, we examine the effect of configuration changes in one domain and its effect on the other. We develop Madelyn, a multi-domain multi-agent rein-forcement learning framework for data-center networks that can propose network-aware, virtual network function placement. This framework takes into account the data-center fabric wights, drop rates, capacities, load balancing and traffic shaping. It also considers the network function pod placements based on affinity / anti-affinity rules, node capacities and taints/tolerations. Using this multi-agent framework, we provide network aware scheduling policies for differentiated network function virtualization services running on Kubernetes pods within data-center networks. The results are demonstrated over a real traffic dataset collected over Ericsson's testbed networks.","PeriodicalId":330838,"journal":{"name":"2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124098264","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 Correlation-based Real-time Segmentation Scheme for Multi-user Collaborative Activities 一种基于关联的多用户协同活动实时分割方案

2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC) Pub Date : 2022-06-01 DOI: 10.1109/COMPSAC54236.2022.00150

Kisoo Kim, Hyunju Kim, Dongman Lee

{"title":"A Correlation-based Real-time Segmentation Scheme for Multi-user Collaborative Activities","authors":"Kisoo Kim, Hyunju Kim, Dongman Lee","doi":"10.1109/COMPSAC54236.2022.00150","DOIUrl":"https://doi.org/10.1109/COMPSAC54236.2022.00150","url":null,"abstract":"Activity Segmentation, dividing a continuous sensor stream into a set of activity segments, is a crucial pre-process in Human Activity Recognition (HAR) and it is required to be done in real-time for real-world smart services. Existing single-user activity segmentation schemes fail to correctly detect transition points due to concurrent and overlapping events from multiple users in case of Multi-user Collaborative Activity Recognition (MCAR). In this paper, we propose a novel scheme for activity segmentation for MCAR that expresses complex events and the correlations between them. For this, the proposed scheme first creates an event stream from a sensor stream and defines event sets in terms of time windows. For each time window, two types of correlations for every event pair are calculated: duration correlation and history correlation. After calculating event correlation, the change score of a time window is measured by comparing the calculated correlation values with those of the preceding windows. Then, the proposed scheme elects as an activity transition point a time window whose change score exceeds the transition threshold. We evaluate the proposed method on two multi-user collaborative activity datasets and experiment results show that the proposed scheme achieves better segmentation performance than existing approaches.","PeriodicalId":330838,"journal":{"name":"2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126549218","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