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- Atif, Yacine, 1967-, et al.
(författare)
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A Cyberphysical Learning Approach for Digital Smart Citizenship Competence Development
- 2017
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Ingår i: WWW '17. - New York, New York, USA : ACM Digital Library. - 9781450349130 ; , s. 397-405
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Konferensbidrag (refereegranskat)abstract
- Smart Cities have emerged as a global concept that argues for the effective exploitation of digital technologies to drive sustainable innovation and well-being for citizens. Despite the large investments being placed on Smart City infrastructure, however, there is still very scarce attention on the new learning approaches that will be needed for cultivating Digital Smart Citizenship competences, namely the competences which will be needed by the citizens and workforce of such cities for exploiting the digital technologies in creative and innovative ways for driving financial and societal sustainability. In this context, this paper introduces cyberphysical learning as an overarching model of cultivating Digital Smart Citizenship competences by exploiting the potential of Internet of Things technologies and social media, in order to create authentic blended and augmented learning experiences.
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| 4. |
- Bae, Juhee, et al.
(författare)
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Short Text Topic Modeling to Identify Trends on Wearable Bio-sensors in Different Media Types
- 2019
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Ingår i: Proceedings - 6th International Symposium on Computational and Business Intelligence, ISCBI 2018. - : IEEE Computer Society. - 9781538694503 - 9781538694510 ; , s. 89-93
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Konferensbidrag (refereegranskat)abstract
- The technology and techniques for bio-sensors are rapidly evolving. Accordingly, there is significant business interest to identify upcoming technologies and new targets for the near future. Text information from internet reflects much of the recent information and public interests that help to understand the trend of a certain field. Thus, we utilize Dirichlet process topic modeling on different media sources containing short text (e.g., blogs, news) which is able to self-adapt the learned topic space to the data. We share the observations from the domain experts on the results derived from topic modeling on wearable biosensors from multiple media sources over more than eight years. We analyze the topics on wearable devices, forecast and market analysis, and bio-sensing techniques found from our method.
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- Bae, Juhee, et al.
(författare)
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Understanding Robust Target Prediction in Basic Oxygen Furnace
- 2021
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Ingår i: IEIM 2021. - New York, NY : Association for Computing Machinery (ACM). - 9781450389143 ; , s. 56-62
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Konferensbidrag (refereegranskat)abstract
- The problem of using machine learning (ML) to predict the process endpoint for a Basic Oxygen Furnace (BOF) process used for steelmaking has been largely studied. However, current research often lacks both the usage of a rich dataset and does not address revealing influential factors that explain the process. The process is complex and difficult to control and has a multi-objective target endpoint with a proper range of heat temperature combined with sufficiently low levels of carbon and phosphorus. Reaching this endpoint requires skilled process operators, who are manually controlling the heat throughout the process by using both implicit and explicit control variables in their decisions. Trained ML models can reach good BOF target prediction results, but it is still a challenge to extract the influential factors that are significant to the ML prediction accuracy. Thus, it becomes a challenge to explain and validate an ML prediction model that claims to capture the process well. This paper makes use of a complex and full production dataset to evaluate and compare different approaches for understanding how the data can determine the process target prediction. One approach is based on the collected process data and the other on the ML approach trained on that data to find the influential factors. These complementary approaches aim to explain the BOF process to reveal actionable information on how to improve process control.
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- Bae, Juhee, et al.
(författare)
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Using Machine Learning for Robust Target Prediction in a Basic Oxygen Furnace System
- 2020
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Ingår i: Metallurgical and materials transactions. B, process metallurgy and materials processing science. - : Springer. - 1073-5615 .- 1543-1916. ; 51:4, s. 1632-1645
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Tidskriftsartikel (refereegranskat)abstract
- The steel-making process in a Basic Oxygen Furnace (BOF) must meet a combination of target values such as the final melt temperature and upper limits of the carbon and phosphorus content of the final melt with minimum material loss. An optimal blow end time (cut-off point), where these targets are met, often relies on the experience and skill of the operators who control the process, using both collected sensor readings and an implicit understanding of how the process develops. If the precision of hitting the optimal cut-off point can be improved, this immediately increases productivity as well as material and energy efficiency, thus decreasing environmental impact and cost. We examine the usage of standard machine learning models to predict the end-point targets using a full production dataset. Various causes of prediction uncertainty are explored and isolated using a combination of raw data and engineered features. In this study, we reach robust temperature, carbon, and phosphorus prediction hit rates of 88, 92, and 89 pct, respectively, using a large production dataset. © 2020, The Author(s).
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- Ding, Jianguo, et al.
(författare)
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Towards Threat Modeling for CPS-based Critical Infrastructure Protection
- 2015
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Ingår i: Proceedings of the International Emergency Management Society (TIEMS), 22nd TIEMS Annual Conference. - Brussels : TIEMS, The International Emergency Management Society. - 9789490297138
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Konferensbidrag (refereegranskat)abstract
- With the evolution of modern Critical Infrastructures (CI), more Cyber-Physical systems are integrated into the traditional CIs. This makes the CIs a multidimensional complex system, which is characterized by integrating cyber-physical systems into CI sectors (e.g., transportation, energy or food & agriculture). This integration creates complex interdependencies and dynamics among the system and its components. We suggest using a model with a multi-dimensional operational specification to allow detection of operational threats. Embedded (and distributed) information systems are critical parts of the CI where disruption can lead to serious consequences. Embedded information system protection is therefore crucial. As there are many different stakeholders of a CI, comprehensive protection must be viewed as a cross-sector activity to identify and monitor the critical elements, evaluate and determine the threat, and eliminate potential vulnerabilities in the CI. A systematic approach to threat modeling is necessary to support the CI threat and vulnerability assessment. We suggest a Threat Graph Model (TGM) to systematically model the complex CIs. Such modeling is expected to help the understanding of the nature of a threat and its impact on throughout the system. In order to handle threat cascading, the model must capture local vulnerabilities as well as how a threat might propagate to other components. The model can be used for improving the resilience of the CI by encouraging a design that enhances the system's ability to predict threats and mitigate their damages. This paper surveys and investigates the various threats and current approaches to threat modeling of CI. We suggest integrating both a vulnerability model and an attack model, and we incorporate the interdependencies within CI cross CI sectors. Finally, we present a multi-dimensional threat modeling approach for critical infrastructure protection.
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- Helldin, Tove, et al.
(författare)
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Situation Awareness in Telecommunication Networks Using Topic Modeling
- 2018
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Ingår i: 2018 21st International Conference on Information Fusion, FUSION 2018. - : IEEE. - 9780996452762 - 9780996452779 - 9781538643303 ; , s. 549-556
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Konferensbidrag (refereegranskat)abstract
- For an operator of wireless telecommunication networks to make timely interventions in the network before minor faults escalate into issues that can lead to substandard system performance, good situation awareness is of high importance. Due to the increasing complexity of such networks, as well as the explosion of traffic load, it has become necessary to aid human operators to reach a good level of situation awareness through the use of exploratory data analysis and information fusion techniques. However, to understand the results of such techniques is often cognitively challenging and time consuming. In this paper, we present how telecommunication operators can be aided in their data analysis and sense-making processes through the usage and visualization of topic modeling results. We present how topic modeling can be used to extract knowledge from base station counter readings and make design suggestions for how to visualize the analysis results to a telecommunication operator.
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- Karlsson, Alexander, et al.
(författare)
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Evaluation of the dirichlet process multinomial mixture model for short-text topic modeling
- 2018
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Ingår i: Proceedings - 6th International Symposium on Computational and Business Intelligence, ISCBI 2018. - USA : Institute of Electrical and Electronics Engineers (IEEE). - 9781538694503 - 9781538694510 ; , s. 79-83
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Konferensbidrag (refereegranskat)abstract
- Fast-moving trends, both in society and in highly competitive business areas, call for effective methods for automatic analysis. The availability of fast-moving sources in the form of short texts, such as social media and blogs, allows aggregation from a vast number of text sources, for an up to date view of trends and business insights. Topic modeling is established as an approach for analysis of large amounts of texts, but the scarcity of statistical information in short texts is considered to be a major problem for obtaining reliable topics from traditional models such as LDA. A range of different specialized topic models have been proposed, but a majority of these approaches rely on rather strong parametric assumptions, such as setting a fixed number of topics. In contrast, recent advances in the field of Bayesian non-parametrics suggest the Dirichlet process as a method that, given certain hyper-parameters, can self-adapt to the number of topics of the data at hand. We perform an empirical evaluation of the Dirichlet process multinomial (unigram) mixture model against several parametric topic models, initialized with different number of topics. The resulting models are evaluated, using both direct and indirect measures that have been found to correlate well with human topic rankings. We show that the Dirichlet Process Multinomial Mixture model is a viable option for short text topic modeling since it on average performs better, or nearly as good, compared to the parametric alternatives, while reducing parameter setting requirements and thereby eliminates the need of expensive preprocessing.
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| 10. |
- Mathiason, Gunnar
(författare)
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A Simulation Approach for Evaluating Scalability of a Virtually Fully Replicated Real-time Database
- 2006
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- We use a simulation approach to evaluate large scale resource usage in a distributed real-time database. Scalability is often limited by that resource usage is higher than what is added to the system when a system is scaled up. Our approach of Virtual Full Replication (VFR) makes resource usage scalable, which allows large scale real-time databases. In this paper we simulate a large scale distributed real-time database with VFR, and we compare it to a fully replicated database (FR) for a selected set of system parameters used as independent variables. Both VFR and FR support local timeliness of transactions by ensuring local availability for data objects accessed by transactions. The difference is that VFR has a scalable resource usage due to lower bandwidth usage for data update replication. The simulation shows that a simulator has several advantages for studying large scale distributed real-time databases and for studying scalability in resource usage in such systems.
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