Pub Date : 2018-09-01DOI: 10.1109/AI4I.2018.8665687
D. D’Auria, Fabio Persia
In this paper, we propose a framework allowing researchers to optimize their academic evaluation. More specifically, we design a specific module for skill management and integrate it with other components of a framework managing a University knowledge base; the main goals of this module are to allow researchers to easily link competences to their papers, to automatically extract the competences acquired by means of a new paper added to the knowledge base, and to automatically detect missing publications and citations in Scopus, and signal them to Elsevier.
{"title":"Design of a Framework Allowing Researchers to Optimize Their Academic Evaluation","authors":"D. D’Auria, Fabio Persia","doi":"10.1109/AI4I.2018.8665687","DOIUrl":"https://doi.org/10.1109/AI4I.2018.8665687","url":null,"abstract":"In this paper, we propose a framework allowing researchers to optimize their academic evaluation. More specifically, we design a specific module for skill management and integrate it with other components of a framework managing a University knowledge base; the main goals of this module are to allow researchers to easily link competences to their papers, to automatically extract the competences acquired by means of a new paper added to the knowledge base, and to automatically detect missing publications and citations in Scopus, and signal them to Elsevier.","PeriodicalId":133657,"journal":{"name":"2018 First International Conference on Artificial Intelligence for Industries (AI4I)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130422314","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}
Pub Date : 2018-09-01DOI: 10.1109/AI4I.2018.8665678
D. Ostrowski
Big Data has become a new source of opportunity among applications in Artificial Intelligence. Many design considerations exist in this relatively new field where parallel processing frameworks can be employed in a more economical fashion. Unlike traditional data sources, Big Data applications present their own unique challenges in order to appropriately harness the utility of open source frameworks including Apache Spark and design patterns predicated on the Directed Acyclic Graph. By embracing this new paradigm, parallel processing can be effectively leveraged to support development at a level of scale and performance that was not possible earlier.
{"title":"Artificial Intelligence with Big Data","authors":"D. Ostrowski","doi":"10.1109/AI4I.2018.8665678","DOIUrl":"https://doi.org/10.1109/AI4I.2018.8665678","url":null,"abstract":"Big Data has become a new source of opportunity among applications in Artificial Intelligence. Many design considerations exist in this relatively new field where parallel processing frameworks can be employed in a more economical fashion. Unlike traditional data sources, Big Data applications present their own unique challenges in order to appropriately harness the utility of open source frameworks including Apache Spark and design patterns predicated on the Directed Acyclic Graph. By embracing this new paradigm, parallel processing can be effectively leveraged to support development at a level of scale and performance that was not possible earlier.","PeriodicalId":133657,"journal":{"name":"2018 First International Conference on Artificial Intelligence for Industries (AI4I)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134269057","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}
Pub Date : 2018-09-01DOI: 10.1109/AI4I.2018.8665712
Florian Jaensch, A. Csiszar, Annika Kienzlen, A. Verl
In this paper the concept of reinforcement learning agent is presented, which can deduce the correct control policy of a plant by acting in its digital twin (the HiL simulation). This way the agent substitutes a real control system. By using reinforcement learning methods, a proof of concept application is presented for a simplistic material flow system, with the same type of access to the digital twin which a PLC controller-hardware would have. With the presented approach the agent is able to find the correct control policy.
{"title":"Reinforcement Learning of Material Flow Control Logic Using Hardware-in-the-Loop Simulation","authors":"Florian Jaensch, A. Csiszar, Annika Kienzlen, A. Verl","doi":"10.1109/AI4I.2018.8665712","DOIUrl":"https://doi.org/10.1109/AI4I.2018.8665712","url":null,"abstract":"In this paper the concept of reinforcement learning agent is presented, which can deduce the correct control policy of a plant by acting in its digital twin (the HiL simulation). This way the agent substitutes a real control system. By using reinforcement learning methods, a proof of concept application is presented for a simplistic material flow system, with the same type of access to the digital twin which a PLC controller-hardware would have. With the presented approach the agent is able to find the correct control policy.","PeriodicalId":133657,"journal":{"name":"2018 First International Conference on Artificial Intelligence for Industries (AI4I)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129689371","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}
Pub Date : 2018-09-01DOI: 10.1109/AI4I.2018.8665720
Peter Shaw
Although quite old, the classic data clustering problem strives to segment the data into homogeneous groupings where homogeneity is measured by, for example, Gini Index. Classical techniques strive to group the data, by what one would argue as “smart” trial-and-error procedure. I will show how data could be clustered using entirely combinatorial techniques where Gini Index or Mean Squared Error receive no mention whatsoever. The Cluster-Editing algorithm aka “Edit-Distance” shows a great promise to help solve those intractable high-dimensional problems because it's totally indifferent to the dimensionality of the data.
{"title":"Combinatorial Algorithms in Machine Learning","authors":"Peter Shaw","doi":"10.1109/AI4I.2018.8665720","DOIUrl":"https://doi.org/10.1109/AI4I.2018.8665720","url":null,"abstract":"Although quite old, the classic data clustering problem strives to segment the data into homogeneous groupings where homogeneity is measured by, for example, Gini Index. Classical techniques strive to group the data, by what one would argue as “smart” trial-and-error procedure. I will show how data could be clustered using entirely combinatorial techniques where Gini Index or Mean Squared Error receive no mention whatsoever. The Cluster-Editing algorithm aka “Edit-Distance” shows a great promise to help solve those intractable high-dimensional problems because it's totally indifferent to the dimensionality of the data.","PeriodicalId":133657,"journal":{"name":"2018 First International Conference on Artificial Intelligence for Industries (AI4I)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115681540","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}
Pub Date : 2018-09-01DOI: 10.1109/AI4I.2018.8665705
B. Beaton
This work chronicles a strategy that became popular among early data scientists for explaining undesirable research outcomes and research process slowdowns to both themselves and clients.
{"title":"The Moat Effects of Data Swamps","authors":"B. Beaton","doi":"10.1109/AI4I.2018.8665705","DOIUrl":"https://doi.org/10.1109/AI4I.2018.8665705","url":null,"abstract":"This work chronicles a strategy that became popular among early data scientists for explaining undesirable research outcomes and research process slowdowns to both themselves and clients.","PeriodicalId":133657,"journal":{"name":"2018 First International Conference on Artificial Intelligence for Industries (AI4I)","volume":"299 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132616548","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}
Pub Date : 2018-09-01DOI: 10.1109/AI4I.2018.8665681
D. Cogliati, M. Falchetto, D. Pau, M. Roveri, Gabriele Viscardi
Cyber-Physical Systems (CPSs) represent the technological asset of Industry 4.0. This paper introduces a novel generation of CPSs, called Intelligent CPSs, able to integrate intelligent functionalities such as fault prediction, autonomous behavior and self-adaptation directly at the CPS units. Such functionalities will increase the autonomy, reduce the required bandwidth and increase the energy-efficiency of CPSs making them able to fully address the challenging needs and increasing performance in Industry 4.0 (as well as other relevant technological scenarios, e.g., smart Internet-of-Things). The effectiveness and efficiency of the proposed intelligent CPSs have been tested in two real-world application scenarios.
{"title":"Intelligent Cyber-Physical Systems for Industry 4.0","authors":"D. Cogliati, M. Falchetto, D. Pau, M. Roveri, Gabriele Viscardi","doi":"10.1109/AI4I.2018.8665681","DOIUrl":"https://doi.org/10.1109/AI4I.2018.8665681","url":null,"abstract":"Cyber-Physical Systems (CPSs) represent the technological asset of Industry 4.0. This paper introduces a novel generation of CPSs, called Intelligent CPSs, able to integrate intelligent functionalities such as fault prediction, autonomous behavior and self-adaptation directly at the CPS units. Such functionalities will increase the autonomy, reduce the required bandwidth and increase the energy-efficiency of CPSs making them able to fully address the challenging needs and increasing performance in Industry 4.0 (as well as other relevant technological scenarios, e.g., smart Internet-of-Things). The effectiveness and efficiency of the proposed intelligent CPSs have been tested in two real-world application scenarios.","PeriodicalId":133657,"journal":{"name":"2018 First International Conference on Artificial Intelligence for Industries (AI4I)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124233473","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}
Pub Date : 2018-09-01DOI: 10.1109/AI4I.2018.8665713
James Obert, T. Mannos
To counter manufacturing irregularities and ensure ASIC design integrity, it is essential that robust design verification methods are employed. It is possible to ensure such integrity using ASIC static timing analysis (STA) and machine learning. In this research, uniquely devised machine and statistical learning methods which quantify anomalous variations in Register Transfer Level (RTL) or Graphic Design System II (GDSII) formats are discussed. To measure the variations in ASIC analysis data, the timing delays in relation to path electrical characteristics are explored. It is shown that semi-supervised learning techniques are powerful tools in characterizing variations within STA path data and has much potential for identifying anomalies in ASIC RTL and GDSII design data.
{"title":"Semi-Supervised Learning and ASIC Path Verification","authors":"James Obert, T. Mannos","doi":"10.1109/AI4I.2018.8665713","DOIUrl":"https://doi.org/10.1109/AI4I.2018.8665713","url":null,"abstract":"To counter manufacturing irregularities and ensure ASIC design integrity, it is essential that robust design verification methods are employed. It is possible to ensure such integrity using ASIC static timing analysis (STA) and machine learning. In this research, uniquely devised machine and statistical learning methods which quantify anomalous variations in Register Transfer Level (RTL) or Graphic Design System II (GDSII) formats are discussed. To measure the variations in ASIC analysis data, the timing delays in relation to path electrical characteristics are explored. It is shown that semi-supervised learning techniques are powerful tools in characterizing variations within STA path data and has much potential for identifying anomalies in ASIC RTL and GDSII design data.","PeriodicalId":133657,"journal":{"name":"2018 First International Conference on Artificial Intelligence for Industries (AI4I)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122163502","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}
Pub Date : 2018-09-01DOI: 10.1109/ai4i.2018.8665704
{"title":"Copyright","authors":"","doi":"10.1109/ai4i.2018.8665704","DOIUrl":"https://doi.org/10.1109/ai4i.2018.8665704","url":null,"abstract":"","PeriodicalId":133657,"journal":{"name":"2018 First International Conference on Artificial Intelligence for Industries (AI4I)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127960462","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}
Pub Date : 2018-09-01DOI: 10.1109/AI4I.2018.8665706
S. Matzka
A method to increase the resource efficiency of screw-fastening processes using machine learning concepts to predict process quality early in the process is proposed. Predictor performance and economic effects of its application are evaluated.
{"title":"Using Process Quality Prediction to Increase Resource Efficiency in Manufacturing Processes","authors":"S. Matzka","doi":"10.1109/AI4I.2018.8665706","DOIUrl":"https://doi.org/10.1109/AI4I.2018.8665706","url":null,"abstract":"A method to increase the resource efficiency of screw-fastening processes using machine learning concepts to predict process quality early in the process is proposed. Predictor performance and economic effects of its application are evaluated.","PeriodicalId":133657,"journal":{"name":"2018 First International Conference on Artificial Intelligence for Industries (AI4I)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130557513","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}
Pub Date : 2018-09-01DOI: 10.1109/AI4I.2018.8665715
Nuha Zamzami, N. Bouguila
Purchasing products, listening to music, visiting locations in physical or virtual environments are examples of applications where users can interact with a large set of items. In this context, making predictions for both previously-consumed and new items for an individual, rather than just recommending new items, is significant in many situations. A recent work has shown that a mixture of Multinomials outperforms the widely-used matrix factorization. We further investigate this problem and propose the use of alternative mixtures based on hierarchical Bayesian frameworks to better balance individual preferences in terms of exploitation and exploration. We evaluate the alternative models accuracy in user consumption predictions using several real-world datasets, and show their efficiency for this problem.
{"title":"Consumption Behavior Prediction Using Hierarchical Bayesian Frameworks","authors":"Nuha Zamzami, N. Bouguila","doi":"10.1109/AI4I.2018.8665715","DOIUrl":"https://doi.org/10.1109/AI4I.2018.8665715","url":null,"abstract":"Purchasing products, listening to music, visiting locations in physical or virtual environments are examples of applications where users can interact with a large set of items. In this context, making predictions for both previously-consumed and new items for an individual, rather than just recommending new items, is significant in many situations. A recent work has shown that a mixture of Multinomials outperforms the widely-used matrix factorization. We further investigate this problem and propose the use of alternative mixtures based on hierarchical Bayesian frameworks to better balance individual preferences in terms of exploitation and exploration. We evaluate the alternative models accuracy in user consumption predictions using several real-world datasets, and show their efficiency for this problem.","PeriodicalId":133657,"journal":{"name":"2018 First International Conference on Artificial Intelligence for Industries (AI4I)","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131697126","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：