2022 13th International Conference on Information, Intelligence, Systems & Applications (IISA)最新文献_第6页

The ASU-DCU International Research and Workforce Development Program on Sensors and Machine Learning ASU-DCU传感器和机器学习国际研究和劳动力发展计划

2022 13th International Conference on Information, Intelligence, Systems & Applications (IISA) Pub Date : 2022-07-18 DOI: 10.1109/IISA56318.2022.9904409

A. Spanias, V. Narayanaswamy, E. Forzani, Greg Raupp, N. Kellam, M. O'Donnell, W. Barnard, Jean S. Larson, N. O’Connor, Nicholas Dunne, Stephen Daniels, S. Little

{"title":"The ASU-DCU International Research and Workforce Development Program on Sensors and Machine Learning","authors":"A. Spanias, V. Narayanaswamy, E. Forzani, Greg Raupp, N. Kellam, M. O'Donnell, W. Barnard, Jean S. Larson, N. O’Connor, Nicholas Dunne, Stephen Daniels, S. Little","doi":"10.1109/IISA56318.2022.9904409","DOIUrl":"https://doi.org/10.1109/IISA56318.2022.9904409","url":null,"abstract":"The Arizona State University (ASU) - Dublin City University (DCU) International Research Experiences for Students (IRES) project is a summer workforce development program that embeds students in machine learning and sensor research. This collaborative IRES program, funded by the National Science Foundation (NSF), engages faculty mentors from the Sensor, Signal, Information Processing (SenSIP) Center at ASU, and the Insight SFI Research Centre for Data Analytics and Biodesign Europe at DCU to train students in sensor design, analytics, and machine learning algorithm development. Sensor and machine learning research addresses engineering and computing problems in health care and other related applications. IRES participants are tasked with studying hardware, algorithms and software for various tasks including activity detection, gait modeling, imaging, hemochromatosis prediction, and health care analytics. Crosscutting efforts include training in international research presentations, research documentation and building awareness of international policies and standards. The program includes weekly research updates by the students, participation in workshops and continued engagement after the summer experience. This paper describes the various components of IRES sensor and machine learning research through ongoing center projects at ASU and DCU.","PeriodicalId":217519,"journal":{"name":"2022 13th International Conference on Information, Intelligence, Systems & Applications (IISA)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122543448","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 Reasoning Engine Architecture for Building Energy Metadata Management 一种用于建筑能源元数据管理的推理引擎架构

2022 13th International Conference on Information, Intelligence, Systems & Applications (IISA) Pub Date : 2022-07-18 DOI: 10.1109/IISA56318.2022.9904419

Panagiotis Kapsalis, Georgios Kormpakis, Konstantinos A. Psilopanagiotis, Evangelos Karakolis, S. Mouzakitis, D. Askounis

引用次数: 0

Undeclared Work Prediction Using Machine Learning: Dealing with the Class Imbalance and Class Overlap Problems 使用机器学习的未声明工作预测:处理类不平衡和类重叠问题

2022 13th International Conference on Information, Intelligence, Systems & Applications (IISA) Pub Date : 2022-07-18 DOI: 10.1109/IISA56318.2022.9904366

Eleni Alogogianni, M. Virvou

{"title":"Undeclared Work Prediction Using Machine Learning: Dealing with the Class Imbalance and Class Overlap Problems","authors":"Eleni Alogogianni, M. Virvou","doi":"10.1109/IISA56318.2022.9904366","DOIUrl":"https://doi.org/10.1109/IISA56318.2022.9904366","url":null,"abstract":"Undeclared work is a complex and ever-changing problem severely impacting society and the economy. It is one of the structural parts of the informal sector and undermines the well-being of workers and businesses and the foundations of the welfare state. Labour inspectorates are among the leading public institutions dealing with undeclared work, but they face difficulties lacking human and financial resources and the appropriate tools. Yet, they own large volumes of data produced by the increasing use of e-Government services and ICT tools, which, if properly processed and analysed employing advanced machine learning techniques, are able to provide significant assistance in undeclared work prediction and understanding its features. Notably, classification algorithms may learn from datasets containing past labour inspection findings and produce classifiers that effectively predict labour law violations and provide understandable explanations for these predictions. Still, undeclared work is usually underrepresented in such datasets since it is not often detected in onsite inspections due to its hidden and multifaceted nature. In addition, several onsite inspection cases with similar characteristics may usually reveal different findings. These facts introduce the issues of class imbalance and class overlap in datasets of this application domain, which impede the machine learning process. The current research work focuses on data engineering techniques to address them. It uses data from real-life inspections and presents the effects of these techniques by creating several different classifiers and assessing their performance in predicting undeclared work, concluding with identifying the best approach.","PeriodicalId":217519,"journal":{"name":"2022 13th International Conference on Information, Intelligence, Systems & Applications (IISA)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122068990","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

Increasing the public space ratio (PSR) using G.I.S. 利用gis提高公共空间比例(PSR)

2022 13th International Conference on Information, Intelligence, Systems & Applications (IISA) Pub Date : 2022-07-18 DOI: 10.1109/IISA56318.2022.9904359

Elizabeth Paraskevi Crawford, S. Lycourghiotis

引用次数: 0

In Search of Deep Learning Architectures for Load Forecasting: A Comparative Analysis and the Impact of the Covid-19 Pandemic on Model Performance 寻找用于负荷预测的深度学习架构:Covid-19大流行对模型性能的比较分析和影响

2022 13th International Conference on Information, Intelligence, Systems & Applications (IISA) Pub Date : 2022-07-18 DOI: 10.1109/IISA56318.2022.9904363

Sotiris Pelekis, Evangelos Karakolis, Francisco Silva, Vasileios Schoinas, S. Mouzakitis, Georgios Kormpakis, N. Amaro, J. Psarras

{"title":"In Search of Deep Learning Architectures for Load Forecasting: A Comparative Analysis and the Impact of the Covid-19 Pandemic on Model Performance","authors":"Sotiris Pelekis, Evangelos Karakolis, Francisco Silva, Vasileios Schoinas, S. Mouzakitis, Georgios Kormpakis, N. Amaro, J. Psarras","doi":"10.1109/IISA56318.2022.9904363","DOIUrl":"https://doi.org/10.1109/IISA56318.2022.9904363","url":null,"abstract":"In power grids, short-term load forecasting (STLF) is crucial as it contributes to the optimization of their reliability, emissions, and costs, while it enables the participation of energy companies in the energy market. STLF is a challenging task, due to the complex demand of active and reactive power from multiple types of electrical loads and their dependence on numerous exogenous variables. Amongst them, special circumstances-such as the COVID-19 pandemic-can often be the reason behind distribution shifts of load series. This work conducts a comparative study of Deep Learning (DL) architectures-namely Neural Basis Expansion Analysis Time Series Forecasting (N-BEATS), Long Short-Term Memory (LSTM), and Temporal Convolutional Networks (TCN)-with respect to forecasting accuracy and training sustainability, meanwhile examining their out-of-distribution generalization capabilities during the COVID-19 pandemic era. A Pattern Sequence Forecasting (PSF) model is used as baseline. The case study focuses on day-ahead forecasts for the Portuguese nationa115-minute resolution net load time series. The results can be leveraged by energy companies and network operators (i) to reinforce their forecasting toolkit with state-of-the-art DL models; (ii) to become aware of the serious consequences of crisis events on model performance; (iii) as a high-level model evaluation, deployment, and sustainability guide within a smart grid context.","PeriodicalId":217519,"journal":{"name":"2022 13th International Conference on Information, Intelligence, Systems & Applications (IISA)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128870888","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

ARCHAEORAMA: Proposing a multimodal application for the documentation of archaeological excavations in situ ARCHAEORAMA:提出了一个多模式的应用程序，用于记录考古发掘的原位

2022 13th International Conference on Information, Intelligence, Systems & Applications (IISA) Pub Date : 2022-07-18 DOI: 10.1109/IISA56318.2022.9904356

Doukas Psarros, O. Eleftheriou, Emmanouil Babatsikos, Nikoletta-Anna Kapogianni, M. Konioti, Athina Chroni, Dimitrios Athanasoulis, D. Giannoulis, G. Kalamaras, Christos-Nikolaos Anagnostopoulos

{"title":"ARCHAEORAMA: Proposing a multimodal application for the documentation of archaeological excavations in situ","authors":"Doukas Psarros, O. Eleftheriou, Emmanouil Babatsikos, Nikoletta-Anna Kapogianni, M. Konioti, Athina Chroni, Dimitrios Athanasoulis, D. Giannoulis, G. Kalamaras, Christos-Nikolaos Anagnostopoulos","doi":"10.1109/IISA56318.2022.9904356","DOIUrl":"https://doi.org/10.1109/IISA56318.2022.9904356","url":null,"abstract":"The age we live in is the age of the technological revolution: the social networks, virtual communities, 3D worlds, digital applications, immersive and collaborative games, are able to change our perception of the world as well as the way information is shared and transmitted. The shift of our society towards new technologies has greatly facilitated the integration of these technologies (Virtual Reality, Augmented Reality, Photogrammetry, etc.) in places such as archaeology, which in the past seemed like a very difficult idea. In recent years, archaeologists have begun to incorporate new technologies that can assist them in archaeological excavations. Such technologies include 3D Imaging Surveying methods (LiDAR, Mobile and Terrestrial 3D Scanners), Unmanned Aerial Systems (UAS), Photogrammetry as well as 3D Visualization Methods (Virtual and Augmented Reality) for the three-dimensional or two-dimensional display of sites where archaeological excavations are carried out. A big advantage of new technologies is the highly increasing capabilities and user friendliness over cost ratio, which encourages archaeologists to enter the emerging realm of Digital Archaeology. In this article we aim to present you our proposal on an application that will be a powerful tool in the hands of archaeologists during the excavations. This paper aims to present to you the structure and the methodology that we have followed for the creation of the ARCHAEORAMA.","PeriodicalId":217519,"journal":{"name":"2022 13th International Conference on Information, Intelligence, Systems & Applications (IISA)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131536789","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

DLMetaChain: An IoT Data Lake Architecture Based on the Blockchain DLMetaChain:基于区块链的物联网数据湖架构

2022 13th International Conference on Information, Intelligence, Systems & Applications (IISA) Pub Date : 2022-07-18 DOI: 10.1109/IISA56318.2022.9904404

Michalis Pingos, Panayiotis Christodoulou, Andreas Andreou

{"title":"DLMetaChain: An IoT Data Lake Architecture Based on the Blockchain","authors":"Michalis Pingos, Panayiotis Christodoulou, Andreas Andreou","doi":"10.1109/IISA56318.2022.9904404","DOIUrl":"https://doi.org/10.1109/IISA56318.2022.9904404","url":null,"abstract":"Nowadays, the IoT ecosystem is evolving rapidly, with multiple heterogeneous sources producing high volumes of data and processes transforming this data into meaningful or “smart” information. These volumes of data, including IoT data, need to be stored in repositories that can host raw, unprocessed, relational and non-relational types of data, such as Data Lakes. Due to the weakness of metadata management, security & access control is one of the main challenges of Big Data storage architectures as Data Lakes can be replaced without oversight of the contents. Recently, the Blockchain technology has been introduced as an effective solution to build trust between different entities, where trust is either nonexistent or unproven, and to address security and privacy concerns. In this paper we introduce DLMetaChain, an extended Data Lake metadata mechanism that consists of data from heterogeneous data sources which interact with IoT data. The extended mechanism mainly focuses on developing an architecture to ensure that the data in the Data Lake is not modified or altered by taking into advantage the capabilities of the Blockchain.","PeriodicalId":217519,"journal":{"name":"2022 13th International Conference on Information, Intelligence, Systems & Applications (IISA)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134089750","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

Fairness-Oriented Edge Allocation for Interactive Group Gaming in Edge Computing 边缘计算中面向公平的交互式群体博弈边缘分配

2022 13th International Conference on Information, Intelligence, Systems & Applications (IISA) Pub Date : 2022-07-18 DOI: 10.1109/IISA56318.2022.9904397

Athanasios Tsipis, Vasileios Komianos, Konstantinos Oikonomou

{"title":"Fairness-Oriented Edge Allocation for Interactive Group Gaming in Edge Computing","authors":"Athanasios Tsipis, Vasileios Komianos, Konstantinos Oikonomou","doi":"10.1109/IISA56318.2022.9904397","DOIUrl":"https://doi.org/10.1109/IISA56318.2022.9904397","url":null,"abstract":"In recent years, edge computing has emerged as a highly promising paradigm for the extension of cloud multimedia systems, enabling network latency reduction by pushing services and data from remote clouds to edge servers. For immersive distributed interactive applications, such as virtual reality and multiplayer online games, this shift has revolutionized the way nearby mobile users interact with one another to jointly participate in collaborative virtual events. However, because the virtual world is shared among many users, one fundamental decision problem for the game providers relates to: (i) how the servers will be deployed, given the limited budget in edge infrastructure investment; and, subsequently, (ii) in what manner the users will be assigned to maintain group fairness across all participating parties, given the lag variance in game view consistency. In this paper, we call this the “Fairness-oriented Edge Allocation” (FEA) problem, and formally formulate its properties and discuss its complexity. To provide solutions efficiently, we present a heuristic algorithm, namely the h-FEA, and theoretically analyze its time complexity. We, then, evaluate h-FEA against several benchmark alternatives using comprehensive simulations on a real-world topological trace. The results clearly demonstrate, under different scenarios, the superiority of our proposed algorithm in minimizing the fairness loss for users engaged with the same games, while achieving high edge user admission rates.","PeriodicalId":217519,"journal":{"name":"2022 13th International Conference on Information, Intelligence, Systems & Applications (IISA)","volume":"147 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123085210","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

Hybrid team members appraisal criteria in feedback provided through collaborative work environment-An educational perspective. 通过协作工作环境提供反馈的混合团队成员评估标准-教育视角。

2022 13th International Conference on Information, Intelligence, Systems & Applications (IISA) Pub Date : 2022-07-18 DOI: 10.1109/IISA56318.2022.9904421

Ioannis Katsenos, C. Pierrakeas

{"title":"Hybrid team members appraisal criteria in feedback provided through collaborative work environment-An educational perspective.","authors":"Ioannis Katsenos, C. Pierrakeas","doi":"10.1109/IISA56318.2022.9904421","DOIUrl":"https://doi.org/10.1109/IISA56318.2022.9904421","url":null,"abstract":"Objective appraisal of team members’ contribution is a difficult and currently unresolved issue. When teamwork is performed trough a Digital Collaborative Environment (DCE) both the digital traces left into it by the collaborating team members, as well as peer appraisal by the team members themselves could assist to draw objective conclusions on the individuals’ performance within the team. In this work we investigated, what would be the criteria of individuals to evaluate their fellow team members at a peer review in an educational environment. We created a collaborative digital environment combining open-source tools and let teams collaborate through it. We then asked the team-members to appraise others’ work and rank their own personal criteria. We found that innovation would be the main criterion when individuals appraising their peers, followed by the degree of their knowledge related to the tasks performed. On the other hand, personal relation is the least preferred criterion. More research work is to follow, investigating possible objective measures based on the traces left in the DCE.","PeriodicalId":217519,"journal":{"name":"2022 13th International Conference on Information, Intelligence, Systems & Applications (IISA)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125078684","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

ENERGY TRANSITIONS, INTELLIGENCE AND BIG DATA: Towards a prosumer concept with energy autonomy 能源转型、智能和大数据:走向能源自主的产消概念

2022 13th International Conference on Information, Intelligence, Systems & Applications (IISA) Pub Date : 2022-07-18 DOI: 10.1109/IISA56318.2022.9904415

H. Doukas

{"title":"ENERGY TRANSITIONS, INTELLIGENCE AND BIG DATA: Towards a prosumer concept with energy autonomy","authors":"H. Doukas","doi":"10.1109/IISA56318.2022.9904415","DOIUrl":"https://doi.org/10.1109/IISA56318.2022.9904415","url":null,"abstract":"Energy transition refers to the transition of the global energy sector towards renewable energy sources and energy efficiency, replacing fossil-based energy production and consumption systems (oil, gas and coal). The four main pillars towards this energy transition, also known as 4 Ds, will be presented as follows: Decarbonization, Decentralization, Digitization and Democratization. These dimensions are combined to change the nature of the way energy is produced, purchased and sold. In this context, consumer-centric big data application services and applications are being developed and will be discussed, based on the large amount of information available and the combination of the evolution of artificial intelligence and “big” data management technologies. Emphasis will be given to the optimal processing and use of data with the help of innovative techniques towards intelligent energy services. Such services can enable consumers to adopt energy-efficient practices, their transition to prosumers and the promotion of energy autonomy concepts, through the creation of resilient and energy-efficient communities, bringing multiple benefits to households and industry.","PeriodicalId":217519,"journal":{"name":"2022 13th International Conference on Information, Intelligence, Systems & Applications (IISA)","volume":"56 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127195502","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