| 1. |
- Devagiri, Vishnu Manasa, et al.
(författare)
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A Multi-view Clustering Approach for Analysis of Streaming Data
- 2021
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Ingår i: IFIP Advances in Information and Communication Technology. - Cham : Springer Science and Business Media Deutschland GmbH. - 9783030791490 ; , s. 169-183
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Konferensbidrag (refereegranskat)abstract
- Data available today in smart monitoring applications such as smart buildings, machine health monitoring, smart healthcare, etc., is not centralized and usually supplied by a number of different devices (sensors, mobile devices and edge nodes). Due to which the data has a heterogeneous nature and provides different perspectives (views) about the studied phenomenon. This makes the monitoring task very challenging, requiring machine learning and data mining models that are not only able to continuously integrate and analyze multi-view streaming data, but also are capable of adapting to concept drift scenarios of newly arriving data. This study presents a multi-view clustering approach that can be applied for monitoring and analysis of streaming data scenarios. The approach allows for parallel monitoring of the individual view clustering models and mining view correlations in the integrated (global) clustering models. The global model built at each data chunk is a formal concept lattice generated by a formal context consisting of closed patterns representing the most typical correlations among the views. The proposed approach is evaluated on two different data sets. The obtained results demonstrate that it is suitable for modelling and monitoring multi-view streaming phenomena by providing means for continuous analysis and pattern mining. © 2021, IFIP International Federation for Information Processing.
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| 2. |
- Haas, B., et al.
(författare)
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Neural Network Compression Through Shunt Connections and Knowledge Distillation for Semantic Segmentation Problems
- 2021
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Ingår i: IFIP Advances in Information and Communication Technology. - Cham : Springer Science and Business Media Deutschland GmbH. - 9783030791490 ; , s. 349-361
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Konferensbidrag (refereegranskat)abstract
- Employing convolutional neural network models for large scale datasets represents a big challenge. Especially embedded devices with limited resources cannot run most state-of-the-art model architectures in real-time, necessary for many applications. This paper proves the applicability of shunt connections on large scale datasets and narrows this computational gap. Shunt connections is a proposed method for MobileNet compression. We are the first to provide results of shunt connections for the MobileNetV3 model and for segmentation tasks on the Cityscapes dataset, using the DeeplabV3 architecture, on which we achieve compression by 28%, while observing a 3.52 drop in mIoU. The training of shunt-inserted models are optimized through knowledge distillation. The full code used for this work will be available online. © 2021, IFIP International Federation for Information Processing.
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| 3. |
- Islam, Shareeful, et al.
(författare)
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An Automated Tool to Support an Intelligence Learner Management System Using Learning Analytics and Machine Learning
- 2021
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Ingår i: Artificial Intelligence Applications and Innovations. - Cham : Springer Nature. - 9783030791490 - 9783030791520 - 9783030791506 ; , s. 494-504
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Konferensbidrag (refereegranskat)abstract
- Learner Management Systems (LMSs) are widely deployed across the industry as they provide a cost-saving approach that can support flexible learning opportunities. Despite their benefits, LMSs fail to cater for individual learning behavior and needs and support individualised prediction and progression. Learning Analytics (LAs) support these gaps by correlating existing learner data to provide meaningful predictive and prescriptive analysis. The industry and research community have already recognised the necessity of LAs to support modern learning needs. But a little effort has been directed towards the integration of LA into LMSs. This paper presents a novel automated Intelligence Learner Management System (iLMS) that integrates learner management and learning analytics into a single platform. The presented iLMS considers Machine Learning techniques to support learning analytics including descriptive, predictive and perspective analytics.
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| 4. |
- Tzamaras, Sotirios, et al.
(författare)
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Intelligent Techniques and Hybrid Systems Experiments Using the Acumen Modeling and Simulation Environment
- 2021
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Ingår i: IFIP Advances in Information and Communication Technology. - Cham : Springer. - 9783030791490 - 9783030791506 ; , s. 531-542
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Konferensbidrag (refereegranskat)abstract
- Hybrid systems are dynamical systems of both continuous and discrete nature and constitute an important field of control systems theory and engineering. On the other hand, intelligent data processing has become one of the most critical devices of modern computer based systems as these systems operate in environments featuring increasing uncertainty and unpredictability. While these two approaches set completely different objectives, modern cyber-physical systems, taken as variants of hybrid systems, seem to constitute a field of increasing interest for applying intelligent techniques. Moreover, the examples of, not so recent, intelligent control systems are suggestive for considering a study on getting intelligent techniques close to hybrid systems. In this paper we present the experimental investigation we undertook in this direction. More specifically, we present and discuss the experiments carried out using Acumen a hybrid systems modeling and simulation environment. Without urging towards setting and solving questions of conceptual order we tried to figure out whether it is possible to represent intelligent behavior using a tool for modeling dynamical systems focusing on the study of its ability to permit the representation of both continuous and discrete intelligent techniques, namely, Reinforcement Learning and Hopfield neural networks. The results obtained are indicative of the problems related to the specific computational context and are useful in deriving conclusions concerning the functionality that needs to be provided by such modeling and simulation environments, in order to allow for the coexistence of hybrid systems and intelligent techniques. © 2021, IFIP International Federation for Information Processing
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| 5. |
- Yeboah-Ofori, Abel, et al.
(författare)
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Cyber Supply Chain Threat Analysis and Prediction Using Machine Learning and Ontology
- 2021
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Ingår i: Artificial Intelligence Applications and Innovations. - Cham : Springer Nature. - 9783030791490 - 9783030791520 - 9783030791506 ; , s. 518-530
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Konferensbidrag (refereegranskat)abstract
- Cyber Supply Chain (CSC) security requires a secure integrated network among the sub-systems of the inbound and outbound chains. Adversaries are deploying various penetration and manipulation attacks on an CSC integrated network’s node. The different levels of integrations and inherent system complexities pose potential vulnerabilities and attacks that may cascade to other parts of the supply chain system. Thus, it has become imperative to implement systematic threats analyses and predication within the CSC domain to improve the overall security posture. This paper presents a unique approach that advances the current state of the art on CSC threat analysis and prediction by combining work from three areas: Cyber Threat Intelligence (CTI), Ontologies, and Machine Learning (ML). The outcome of our work shows that the conceptualization of cybersecurity using ontological theory provides clear mechanisms for understanding the correlation between the CSC security domain and enables the mapping of the ML prediction with 80% accuracy of potential cyberattacks and possible countermeasures.
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