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Department of Meteorology – University of Reading

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Extensive comparative experimental studies are employed to demonstrate the effectiveness of the proposed method. It is shown that the probabilistic neural network (PNN) based on Bhattacharyya distance measure is superior to other measures and using the subset of uniform LBP features is generally better than using full LBP features.", "hoa_exclude": "FALSE", "event_title": "The 21st UK Workshop on Computational Intelligence", "date_type": "accepted", "pres_type": "paper", "ros_action": "auto", "snip": { "num": null, "datestamp": "2022-07-31 01:02:49", "year": null }, "creators_sort": [ { "name": { "lineage": null, "given": "Shadi Al", "honourific": null, "family": "Amoudi" }, "id": null }, { "name": { "lineage": null, "given": "Xia", "honourific": null, "family": "Hong" }, "id": 90000432 }, { "name": { "lineage": null, "given": "Hong", "honourific": null, "family": "Wei" }, "id": 90000399 } ], "creators_browse_email": [ "x.hong@reading.ac.uk", "h.wei@reading.ac.uk" ], "notify_on_approval": "yes", "public_doc_count": 0, "rioxx2_dateAccepted": "2022-07-19", "event_type": "workshop", "event_dates": "7-9, Sept,2022", "hoa_compliant": 304, "datestamp": "2022-07-28 08:24:43", "uri": "https:\/\/centaur.reading.ac.uk\/id\/eprint\/106359", "altmetric": { "last_updated": null, "score": null, "datestamp": "2022-07-22 02:01:42" }, "event_location": "Sheffield", "rioxx2_author": [ { "author": "Amoudi, Shadi Al" }, { "author": "Hong, Xia" }, { "author": "Wei, Hong" } ], "has_pgr_creators": "TRUE", "creators_browse_id": [ 90000432, 90000399 ], "divs_irstats": [ "5_a2014a1p", "3_fc22d959", "1_76083589" ], "abstract": "Modified probabilistic neural networks are introduced for image classification based on various distance measures in probability space, in which the input to the model is local binary pattern histogram of images. Conventional probabilistic neural networks have input layer, which computes Euclidean distance of pairwise input features. The proposed modified probabilistic neural networks considered various probability distance measures for computing distances of LBP histograms between images. Extensive comparative experimental studies are employed to demonstrate the effectiveness of the proposed method. It is shown that the probabilistic neural network (PNN) based on Bhattacharyya distance measure is superior to other measures and using the subset of uniform LBP features is generally better than using full LBP features.", "type": "conference_item", "title": "Modified Probabilistic Neural Networks LBP Classification Based on Distance Measures in Probability Space", "rioxx2_version": "NA", "hoa_ref_pan": "AB", "userid": 311, "rev_number": 15, "metadata_checked": "yes", "ros_submitted": "FALSE", "dir": "disk0\/00\/10\/63\/59", "creators": [ { "name": { "lineage": null, "given": "Shadi Al", "honourific": null, "family": "Amoudi" }, "id": null, "role": "pgr" }, { "name": { "lineage": null, "given": "Xia", "honourific": null, "family": "Hong" }, "id": 90000432, "role": null }, { "name": { "lineage": null, "given": "Hong", "honourific": null, "family": "Wei" }, "id": 90000399, "role": null } ], "lastmod": "2022-07-31 01:02:49", "creators_browse_name": "Amoudi, S. A., Hong, X. and Wei, H. ", "has_ug_creators": "FALSE", "ispublished": "inpress", "rioxx2_source": "The 21st UK Workshop on Computational Intelligence", "metadata_visibility": "show", "eprint_status": "archive", "status_changed": "2022-07-28 08:24:43", "rioxx2_language": "en", "suggestions": "Out of scope, but metadata record created I-2207-2782 CB 27\/07\/2022", "nofunding": "TRUE", "full_text_status": "none", "rioxx2_type": "Conference Paper\/Proceeding\/Abstract", "further_checking": "no", "refereed": "TRUE", "divisions": [ "5_a2014a1p" ], "hoa_date_acc": "2022-07-19" }, { "eprintid": 106167, "date": "2022-07-11", "documents": [ { "placement": 1, "eprintid": 106167, "files": [ { "hash_type": "MD5", "mtime": "2022-07-11 12:41:54", "datasetid": "document", "fileid": 12238271, "objectid": 762041, "uri": "https:\/\/centaur.reading.ac.uk\/id\/file\/12238271", "mime_type": "application\/pdf", "hash": "a74fbf8dd207aa1cc7675ded7ce0092e", "filesize": 1281666, "filename": "0SAofEA_main.pdf" } ], "content": 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"application\/pdf", "docid": 763225, "format": "other", "relation": [ { "type": "http:\/\/eprints.org\/relation\/isVersionOf", "uri": "\/id\/document\/762041" }, { "type": "http:\/\/eprints.org\/relation\/isVolatileVersionOf", "uri": "\/id\/document\/762041" }, { "type": "http:\/\/eprints.org\/relation\/isCoversheetVersionOf", "uri": "\/id\/document\/762041" } ], "security": "staffonly", "pos": 8, "formatdesc": "Coversheet version" } ], "divisions_browse": [ "5_a2014a1p", "3_fc22d959", "5_2015a1l" ], "rioxx2_title": "Assessing ranking and effectiveness of evolutionary algorithm hyperparameters using global sensitivity analysis methodologies", "rioxx2_description": "We present a comprehensive global sensitivity analysis of two single-objective and two multi-objective state-of-the-art global optimization evolutionary algorithms as an algorithm configuration problem. That is, we investigate the quality of influence hyperparameters have on the performance of algorithms in terms of their direct effect and interaction effect with other hyperparameters. Using three sensitivity analysis methods, Morris LHS, Morris, and Sobol, to systematically analyze tunable hyperparameters of covariance matrix adaptation evolutionary strategy, differential evolution, non-dominated sorting genetic algorithm III, and multi-objective evolutionary algorithm based on decomposition, the framework reveals the behaviors of hyperparameters to sampling methods and performance metrics. That is, it answers questions like what hyperparameters influence patterns, how they interact, how much they interact, and how much their direct influence is. Consequently, the ranking of hyperparameters suggests their order of tuning, and the pattern of influence reveals the stability of the algorithms.", "hoa_exclude": "FALSE", "date_type": "accepted", "notify_on_approval": "yes", "rioxx2_identifier": "https:\/\/centaur.reading.ac.uk\/106167\/1\/0SAofEA_main.pdf", "hoa_compliant": 318, "datestamp": "2022-07-22 10:36:32", "uri": "https:\/\/centaur.reading.ac.uk\/id\/eprint\/106167", "altmetric": { "last_updated": null, "score": null, "datestamp": "2022-07-13 02:01:13" }, "rioxx2_author": [ { "id": "https:\/\/orcid.org\/0000-0002-9256-1192", "author": "Ojha, Varun" }, { "author": "Timmis, Jon" }, { "author": "Nicosia, Giuseppe" } ], "divs_irstats": [ "5_2015a1h", "5_a2014a1p", "3_fc22d959", "5_2015a1l", "1_76083589" ], "title": "Assessing ranking and effectiveness of evolutionary algorithm hyperparameters using global sensitivity analysis methodologies", "rev_number": 22, "metadata_checked": "yes", "dir": "disk0\/00\/10\/61\/67", "rioxx2_format": "application\/pdf", "has_ug_creators": "FALSE", "ispublished": "inpress", "metadata_visibility": "show", "eprint_status": "archive", "rioxx2_language": "en", "rioxx2_license_ref": { "license_ref": "https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0", "start_date": null }, "coversheets_dirty": "FALSE", "full_text_status": "restricted", "rioxx2_type": "Journal Article\/Review", "item_issues2": [ { "timestamp": "2022-07-26 22:17:41", "status": "discovered", "type": "duplicate_title", "id": "duplicate_title_106454", "description": "Duplicate Title to \n\nAssessing Ranking and Effectiveness of Evolutionary Algorithm Hyperparameters Using Global Sensitivity Analysis Methodologies<\/a>\n\n\n
User Workarea, \n\n
Unnamed user with username JiscRouter<\/span><\/a>\n\n- \n[ Manage<\/a> ] [ Compare & Merge<\/a> ] [ Acknowledge<\/a> ]" }, { "timestamp": "2022-08-06 22:17:57", "status": "discovered", "type": "duplicate_title", "id": "duplicate_title_106632", "description": "Duplicate Title to \n\nAssessing ranking and effectiveness of evolutionary algorithm hyperparameters using global sensitivity analysis methodologies<\/a>\n\n\n
User Workarea, \n\n
Unnamed user with username JiscRouter<\/span><\/a>\n\n- \n[ Manage<\/a> ] [ Compare & Merge<\/a> ] [ Acknowledge<\/a> ]" } ], "refereed": "TRUE", "divisions": [ "5_2015a1l", "5_a2014a1p" ], "hoa_date_acc": "2022-07-11", "reading_wip": "FALSE", "sjr": { "num": null, "datestamp": "2022-07-17 01:01:45", "year": null }, "dates": [ { "date_type": "accepted", "date": "2022-07-11" } ], "hoa_gold": "FALSE", "has_pgt_creators": "FALSE", "restricted_doc_count": 1, "publisher": "Elsevier", "snip": { "num": null, "datestamp": "2022-07-17 01:01:45", "year": null }, "ros_action": "auto", "public_doc_count": 0, "creators_browse_email": [ "v.k.ojha@reading.ac.uk", "g.nicosia@reading.ac.uk" ], "creators_sort": [ { "name": { "lineage": null, "given": "Varun", "honourific": null, "family": "Ojha" }, "id": 90009423 }, { "name": { "lineage": null, "given": "Jon", "honourific": null, "family": "Timmis" }, "id": null }, { "name": { "lineage": null, "given": "Giuseppe", "honourific": null, "family": "Nicosia" }, "id": 90008704 } ], "rioxx2_dateAccepted": "2022-07-11", "has_pgr_creators": "FALSE", "creators_browse_id": [ 90009423, 90008704 ], "type": "article", "abstract": "We present a comprehensive global sensitivity analysis of two single-objective and two multi-objective state-of-the-art global optimization evolutionary algorithms as an algorithm configuration problem. That is, we investigate the quality of influence hyperparameters have on the performance of algorithms in terms of their direct effect and interaction effect with other hyperparameters. Using three sensitivity analysis methods, Morris LHS, Morris, and Sobol, to systematically analyze tunable hyperparameters of covariance matrix adaptation evolutionary strategy, differential evolution, non-dominated sorting genetic algorithm III, and multi-objective evolutionary algorithm based on decomposition, the framework reveals the behaviors of hyperparameters to sampling methods and performance metrics. That is, it answers questions like what hyperparameters influence patterns, how they interact, how much they interact, and how much their direct influence is. Consequently, the ranking of hyperparameters suggests their order of tuning, and the pattern of influence reveals the stability of the algorithms.", "publication": "Swarm and Evolutionary Computation", "rioxx2_version": "AM", "hoa_ref_pan": "AB", "hoa_date_fcd": "2022-07-11", "userid": 18504, "issn": "2210-6502", "creators": [ { "orcid": "0000-0002-9256-1192", "name": { "lineage": null, "given": "Varun", "honourific": null, "family": "Ojha" }, "id": 90009423 }, { "orcid": null, "name": { "lineage": null, "given": "Jon", "honourific": null, "family": "Timmis" }, "id": null }, { "orcid": null, "name": { "lineage": null, "given": "Giuseppe", "honourific": null, "family": "Nicosia" }, "id": 90008704 } ], "ros_submitted": "FALSE", "lastmod": "2022-07-25 09:14:13", "creators_browse_name": "Ojha, V. , Timmis, J. and Nicosia, G. ", "rioxx2_source": "2210-6502", "status_changed": "2022-07-22 10:36:32", "hoa_version_fcd": "AM", "suggestions": "Leave for CC - SD 21\/7\/22. FTEMB12 Accepted version requires 12 month embargo once item fully published - SD 21\/7\/22", "rioxx2_publisher": "Elsevier", "nofunding": "TRUE", "further_checking": "no" }, { "eprintid": 106224, "date": "2022-06-13", "divisions_browse": [ "5_a2014a1p", "3_fc22d959" ], "rioxx2_title": "ML-Quadrat & DriotData: a model-driven engineering tool and a low-code platform for smart IoT services", "rioxx2_description": "In this paper, we present ML-Quadrat, an open-source research prototype that is based on the Eclipse Modeling Framework (EMF) and the state of the art in the literature of Model-Driven Software Engineering (MDSE) for smart Cyber-Physical Systems (CPS) and the Internet of Things (IoT). Its envisioned users are mostly software developers who might not have deep knowledge and skills in the heterogeneous IoT platforms and the diverse Artificial Intelligence (AI) technologies, specifically regarding Machine Learning (ML). ML-Quadrat is released under the terms of the Apache 2.0 license on Github1. Additionally, we demonstrate an early tool prototype of DriotData, a web-based Low-Code platform targeting citizen data scientists and citizen\/end-user software developers. DriotData exploits and adopts ML-Quadrat in the industry by offering an ex-tended version of it as a subscription-based service to companies, mainly Small- and Medium-Sized Enterprises (SME). The current preliminary version of DriotData has three web-based model editors: text-based, tree-\/form-based and diagram-based. The latter is designed for domain experts in the problem or use case domains (namely the IoT vertical domains) who might not have knowledge and skills in the field of IT. Finally, a short video demonstrating the tools is available on YouTube: https:\/\/youtu.be\/VAuz25w0a5k.", "hoa_exclude": "FALSE", "event_title": "2022 IEEE\/ACM 44th International Conference on Software Engineering", "date_type": "published_online", "pres_type": "paper", "notify_on_approval": "yes", "event_dates": "22-24 May 2022", "hoa_compliant": 304, "datestamp": "2022-07-14 13:46:30", "uri": "https:\/\/centaur.reading.ac.uk\/id\/eprint\/106224", "altmetric": { "last_updated": null, "score": null, "datestamp": "2022-07-16 02:01:18" }, "rioxx2_author": [ { "author": "Moin, Armin" }, { "author": "Mituca, Andrei" }, { "author": "Challenger, Moharram" }, { "author": "Badii, Atta" }, { "author": "Günnemann, Stephan" } ], "divs_irstats": [ "5_a2014a1p", "3_fc22d959", "1_76083589" ], "title": "ML-Quadrat & DriotData: a model-driven engineering tool and a low-code platform for smart IoT services", "citation_count": { "num": 0, "datestamp": "2022-07-24 04:24:36" }, "rev_number": 11, "metadata_checked": "yes", "dir": "disk0\/00\/10\/62\/24", "has_ug_creators": "FALSE", "ispublished": "pub", "metadata_visibility": "show", "eprint_status": "archive", "rioxx2_language": "en", "hoa_date_pub": "2022-06-13", "full_text_status": "none", "rioxx2_type": "Conference Paper\/Proceeding\/Abstract", "refereed": "TRUE", "divisions": [ "5_a2014a1p" ], "reading_wip": "FALSE", "sjr": { "num": null, "datestamp": "2022-07-17 01:01:22", "year": null }, "dates": [ { "date_type": "published_online", "date": "2022-06-13" } ], "hoa_gold": "FALSE", "has_pgt_creators": "FALSE", "restricted_doc_count": 0, "rioxx2_version_of_record": "https:\/\/dx.doi.org\/10.1109\/ICSE-Companion55297.2022.9793752", "pagerange": "144-148", "snip": { "num": null, "datestamp": "2022-07-17 01:01:22", "year": null }, "ros_action": "auto", "public_doc_count": 0, "creators_browse_email": [ "atta.badii@reading.ac.uk" ], "creators_sort": [ { "name": { "lineage": null, "given": "Armin", "honourific": null, "family": "Moin" }, "id": null }, { "name": { "lineage": null, "given": "Andrei", "honourific": null, "family": "Mituca" }, "id": null }, { "name": { "lineage": null, "given": "Moharram", "honourific": null, "family": "Challenger" }, "id": null }, { "name": { "lineage": null, "given": "Atta", "honourific": null, "family": "Badii" }, "id": 90000900 }, { "name": { "lineage": null, "given": "Stephan", "honourific": null, "family": "Günnemann" }, "id": null } ], "event_type": "conference", "book_title": "2022 IEEE\/ACM 44th International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)", "event_location": "Pittsburgh, PA, USA", "has_pgr_creators": "FALSE", "creators_browse_id": [ 90000900 ], "rioxx2_publication_date": "2022-06-13", "type": "conference_item", "abstract": "In this paper, we present ML-Quadrat, an open-source research prototype that is based on the Eclipse Modeling Framework (EMF) and the state of the art in the literature of Model-Driven Software Engineering (MDSE) for smart Cyber-Physical Systems (CPS) and the Internet of Things (IoT). 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In fact, by studying the frequently silenced genes, we found that when the GPH1 gene is knocked out in S. cerevisiae, the isocitrate lyase enzyme is activated, which converts the isocitrate into succinate. Our goals are to simplify and facilitate the in vitro processes. Hence, we present strains with the least possible number of knockout genes and solutions in which the genes are turned off on the same chromosome. Therefore, we present results where the constraints mentioned above are met, like the strains where only two genes are switched off and other strains where half of the knockout genes are on the same chromosome. 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Unnamed user with username JiscRouter<\/span><\/a>\n\n- \n[ Manage<\/a> ] [ Compare & Merge<\/a> ] [ Acknowledge<\/a> ]" } ], "refereed": "TRUE", "divisions": [ "5_2015a1l", "5_a2014a1p" ], "hoa_date_acc": "2022-04-07", "reading_wip": "FALSE", "sjr": { "num": 0.883, "datestamp": "2022-06-12 05:32:16", "year": 2021 }, "dates": [ { "date_type": "accepted", "date": "2022-04-07" } ], "hoa_gold": "FALSE", "has_pgt_creators": "FALSE", "restricted_doc_count": 1, "rioxx2_version_of_record": "https:\/\/dx.doi.org\/10.1002\/bit.28103", "publisher": "Wiley", "snip": { "num": 1.072, "datestamp": "2022-06-12 05:32:16", "year": 2021 }, "ros_action": "auto", "public_doc_count": 0, "creators_browse_email": [ "v.k.ojha@reading.ac.uk", "g.nicosia@reading.ac.uk" ], "creators_sort": [ { "name": { "lineage": null, "given": "Matteo N.", "honourific": null, "family": "Amaradio" }, "id": null }, { "name": { "lineage": null, "given": "Varun", "honourific": null, "family": "Ojha" }, "id": 90009423 }, { "name": { "lineage": null, "given": "Giorgio", "honourific": null, "family": "Jansen" }, "id": null }, { "name": { "lineage": null, "given": "Massimo", "honourific": null, "family": "Gulisano" }, "id": null }, { "name": { "lineage": null, "given": "Jole", "honourific": null, "family": "Costanza" }, "id": null }, { "name": { "lineage": null, "given": "Giuseppe", "honourific": null, "family": "Nicosia" }, "id": 90008704 } ], "rioxx2_dateAccepted": "2022-04-07", "has_pgr_creators": "FALSE", "creators_browse_id": [ 90009423, 90008704 ], "type": "article", "abstract": "Our research aims to help industrial biotechnology develop a sustainable economy using green technology based on microorganisms and synthetic biology through two case studies that improve metabolic capacity in yeast models Yarrowia lipolytica (Y. lipolytica) and Saccharomyces cerevisiae (S. cerevisiae). We aim to increase the production capacity of beta-carotene (β-carotene) and succinic acid, which are among the highest market demands due to their versatile use in numerous consumer products. We performed simulations to identify in silico ranking of strains based on multiple objectives: the growth rate of yeast microorganisms, the number of used chromosomes, and the production capability of β-carotene (for Y. lipolytica) and succinate (for S. cerevisiae). Our multiobjective optimization methodology identified notable gene deletions by searching a vast solution-space to highlight near-optimal strains on Pareto Fronts, balancing the above-cited three objectives. Moreover, preserving the metabolic constraints and the essential genes, this work produced robust results: 7 significant strains of Y. lipolytica and 7 strains for S. cerevisiae. We examined gene knockout to study the function of genes and pathways. In fact, by studying the frequently silenced genes, we found that when the GPH1 gene is knocked out in S. cerevisiae, the isocitrate lyase enzyme is activated, which converts the isocitrate into succinate. Our goals are to simplify and facilitate the in vitro processes. Hence, we present strains with the least possible number of knockout genes and solutions in which the genes are turned off on the same chromosome. Therefore, we present results where the constraints mentioned above are met, like the strains where only two genes are switched off and other strains where half of the knockout genes are on the same chromosome. 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Unnamed user with username JiscRouter<\/span><\/a>\n\n- \n[ Manage<\/a> ] [ Compare & Merge<\/a> ] [ Acknowledge<\/a> ]" } ], "refereed": "TRUE", "divisions": [ "3_fcf4136e", "5_a2014a1p" ], "hoa_date_acc": "2022-04-05", "reading_wip": "FALSE", "sjr": { "num": 0.538, "datestamp": "2022-06-12 01:11:44", "year": 2021 }, "dates": [ { "date_type": "published", "date": "2022-04" }, { "date_type": "published_online", "date": "2022-04-09" }, { "date_type": "accepted", "date": "2022-04-05" } ], "official_url": "http:\/\/dx.doi.org\/10.3390\/math10081234", "hoa_gold": "TRUE", "has_pgt_creators": "FALSE", "restricted_doc_count": 0, "rioxx2_free_to_read": { "free_to_read": "Yes" }, "rioxx2_version_of_record": "https:\/\/dx.doi.org\/10.3390\/math10081234", "publisher": "MDPI", "snip": { "num": 1.162, "datestamp": "2022-06-12 01:11:44", "year": 2021 }, "ros_action": "auto", "public_doc_count": 1, "creators_browse_email": [ "j.m.kunkel@reading.ac.uk", "a.dufour@icmacentre.ac.uk", "atta.badii@reading.ac.uk" ], "creators_sort": [ { "name": { "lineage": null, "given": "Ilia", "honourific": null, "family": "Zaznov" }, "id": null }, { "name": { "lineage": null, "given": "Julian", "honourific": null, "family": "Kunkel" }, "id": 90009151 }, { "name": { "lineage": null, "given": "Alfonso", "honourific": null, "family": "Dufour" }, "id": 90001524 }, { "name": { "lineage": null, "given": "Atta", "honourific": null, "family": "Badii" }, "id": 90000900 } ], "rioxx2_dateAccepted": "2022-04-05", "has_pgr_creators": "TRUE", "rioxx2_publication_date": "2022-04", "creators_browse_id": [ 90009151, 90001524, 90000900 ], "type": "article", "abstract": "This survey starts with a general overview of the strategies for stock price change predictions based on market data and in particular Limit Order Book (LOB) data. The main discussion is devoted to the systematic analysis, comparison, and critical evaluation of the state-of-the-art studies in the research area of stock price movement predictions based on LOB data. LOB and Order Flow data are two of the most valuable information sources available to traders on the stock markets. Academic researchers are actively exploring the application of different quantitative methods and algorithms for this type of data to predict stock price movements. With the advancements in machine learning and subsequently in deep learning, the complexity and computational intensity of these models was growing, as well as the claimed predictive power. Some researchers claim accuracy of stock price movement prediction well in excess of 80%. These models are now commonly employed by automated market-making programs to set bids and ask quotes. 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Using an adversarial targeting algorithm, we correlate these neurons with the distribution of adversarial attacks on the network. Adversarial robustness of neural networks has gained significant attention in recent times and highlights intrinsic weaknesses of deep learning networks against carefully constructed distortion applied to input images. In this paper, we evaluate the robustness of state-of-the-art image classification models trained on the MNIST and CIFAR10 datasets against the fast gradient sign method attack, a simple yet effective method of deceiving neural networks. Our method identifies the specific neurons of a network that are most affected by the adversarial attack being applied. 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719692, "format": "other", "relation": [ { "type": "http:\/\/eprints.org\/relation\/isVersionOf", "uri": "\/id\/document\/688484" }, { "type": "http:\/\/eprints.org\/relation\/isVolatileVersionOf", "uri": "\/id\/document\/688484" }, { "type": "http:\/\/eprints.org\/relation\/isCoversheetVersionOf", "uri": "\/id\/document\/688484" } ], "security": "public", "pos": 7, "formatdesc": "Coversheet version" } ], "divisions_browse": [ "5_a2014a1p", "3_fc22d959" ], "rioxx2_title": "Parameter tracking of time-varying Hammerstein-Wiener Systems", "rioxx2_description": "A two-stage identification algorithm is introduced for tracking the parameters in time-varying Hammerstein-Wiener systems. The Kalman filtering algorithm and parameter separation technique are employed in the proposed algorithm. The convergence analysis of this two-stage algorithm is provided. It is shown that the proposed algorithm can guarantee the boundedness of the parameter estimation error. 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We evaluated structures of 15 different cell designs simulated by varying material types and photodiode doping strategies. At first, non-dominated sorting genetic algorithm II (NSGA-II) produced Pareto-optimal-solutions sets for respective cell designs. Then, on investigating quantum efficiencies of all cell designs produced by NSGA-II, we applied a new multi-objective optimization algorithm II (OptIA-II) to discover the Pareto fronts of select (three) best cell designs. Our designed OptIA-II algorithm improved the quantum efficiencies of all select cell designs and reduced their fabrication costs. We observed that the cell design comprising an optimally doped zinc-oxide-based transparent conductive oxide (TCO) layer and rough silver back reflector (BR) offered a quantum efficiency (Qe) of 0.6031. Overall, this paper provides a full characterization of cell structure designs. 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"other", "relation": [ { "type": "http:\/\/eprints.org\/relation\/isVersionOf", "uri": "\/id\/document\/675513" }, { "type": "http:\/\/eprints.org\/relation\/isVolatileVersionOf", "uri": "\/id\/document\/675513" }, { "type": "http:\/\/eprints.org\/relation\/isCoversheetVersionOf", "uri": "\/id\/document\/675513" } ], "security": "staffonly", "pos": 8, "formatdesc": "Coversheet version" } ], "divisions_browse": [ "5_a2014a1p", "3_fc22d959" ], "keywords": "fraud detection, machine learning, anomaly detection, Fintech, cybercrime", "rioxx2_title": "Follow the trail: machine learning for fraud detection in Fintech applications", "rioxx2_description": "Financial technology, or Fintech, represents an emerging industry on the global market. With online transactions on the rise, the use of IT for automation of financial services is of increasing importance. Fintech enables institutions to deliver services to customers worldwide on a 24\/7 basis. Its services are often easy to access and enable customers to perform transactions in real-time. In fact, advantages such as these make Fintech increasingly popular among clients. However, since Fintech transactions are made up of information, ensuring security becomes a critical issue. Vulnerabilities in such systems leave them exposed to fraudulent acts, which cause severe damage to clients and providers alike. For this reason, techniques from the area of Machine Learning (ML) are applied to identify anomalies in Fintech applications. They target suspicious activity in financial datasets and generate models in order to anticipate future frauds. We contribute to this important issue and provide an evaluation on anomaly detection methods for this matter. Experiments were conducted on several fraudulent datasets from real-world and synthetic databases, respectively. The obtained results confirm that ML methods contribute to fraud detection with varying success. Therefore, we discuss the effectiveness of the individual methods with regard to the detection rate. In addition, we provide an analysis on the influence of selected features on their performance. Finally, we discuss the impact of the observed results for the security of Fintech applications in the future.", "hoa_exclude": "FALSE", "date_type": "published", "notify_on_approval": "yes", "rioxx2_identifier": "https:\/\/centaur.reading.ac.uk\/96583\/1\/sensors-21-01594-v2.pdf", "hoa_compliant": 309, "datestamp": "2021-05-28 14:04:18", "uri": "https:\/\/centaur.reading.ac.uk\/id\/eprint\/96583", "altmetric": { "last_updated": "2021-12-07", "score": 3, "datestamp": "2022-01-04 02:41:24" }, "rioxx2_author": [ { "author": "Stojanović, Branka" }, { "id": "https:\/\/orcid.org\/0000-0001-6086-8846", "author": "Božić, Josip" }, { "id": "https:\/\/orcid.org\/0000-0001-9995-7539", "author": "Hofer-Schmitz, Katharina" }, { "author": "Nahrgang, Kai" }, { "author": "Weber, Andreas" }, { "author": "Badii, Atta" }, { "author": "Sundaram, Maheshkumar" }, { "author": "Jordan, Elliot" }, { "author": "Runevic, Joel" } ], "divs_irstats": [ "5_a2014a1p", "3_fc22d959", "1_76083589" ], "title": "Follow the trail: machine learning for fraud detection in Fintech applications", "pages": 0, "citation_count": { "num": 0, "datestamp": "2021-03-14 04:21:03" }, "number": 5, "metadata_checked": "yes", "rev_number": 27, "dir": "disk0\/00\/09\/65\/83", "rioxx2_format": "application\/pdf", "has_ug_creators": "FALSE", "ispublished": "pub", "metadata_visibility": "show", "eprint_status": "archive", "rioxx2_language": "en", "sword_depositor": 16597, "rioxx2_license_ref": { "license_ref": "https:\/\/creativecommons.org\/licenses\/by\/4.0", "start_date": "2021-02-25" }, "hoa_date_pub": "2021-02-25", "coversheets_dirty": "FALSE", "full_text_status": "restricted", "rioxx2_type": "Journal Article\/Review", "item_issues2": [ { "timestamp": "2021-03-20 22:12:32", "status": "discovered", "type": "duplicate_doi", "id": "duplicate_doi_96982", "description": "Duplicate Identification Number\/DOI to \n\nFollow the Trail: Machine Learning for Fraud Detection in Fintech Applications.<\/a>\n\n\n
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With online transactions on the rise, the use of IT for automation of financial services is of increasing importance. Fintech enables institutions to deliver services to customers worldwide on a 24\/7 basis. Its services are often easy to access and enable customers to perform transactions in real-time. In fact, advantages such as these make Fintech increasingly popular among clients. However, since Fintech transactions are made up of information, ensuring security becomes a critical issue. Vulnerabilities in such systems leave them exposed to fraudulent acts, which cause severe damage to clients and providers alike. For this reason, techniques from the area of Machine Learning (ML) are applied to identify anomalies in Fintech applications. They target suspicious activity in financial datasets and generate models in order to anticipate future frauds. We contribute to this important issue and provide an evaluation on anomaly detection methods for this matter. Experiments were conducted on several fraudulent datasets from real-world and synthetic databases, respectively. The obtained results confirm that ML methods contribute to fraud detection with varying success. Therefore, we discuss the effectiveness of the individual methods with regard to the detection rate. In addition, we provide an analysis on the influence of selected features on their performance. 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"divisions_browse": [ "5_a2014a1p", "3_fc22d959" ], "rioxx2_title": "A frequent pattern conjunction Heuristic for rule generation in data streams", "rioxx2_description": "This paper introduces a new and expressive algorithm for inducing descriptive rule-sets from streaming data in real-time in order to describe frequent patterns explicitly encoded in the stream. Data Stream Mining (DSM) is concerned with the automatic analysis of data streams in real-time. Rapid flows of data challenge the state-of-the art processing and communication infrastructure, hence the motivation for research and innovation into real-time algorithms that analyse data streams on-the-fly and can automatically adapt to concept drifts. To date, DSM techniques have largely focused on predictive data mining applications that aim to forecast the value of a particular target feature of unseen data instances, answering questions such as whether a credit card transaction is fraudulent or not. A real-time, expressive and descriptive Data Mining technique for streaming data has not been previously established as part of the DSM toolkit. This has motivated the work reported in this paper, which has resulted in developing and validating a Generalised Rule Induction (GRI) tool, thus producing expressive rules as explanations that can be easily understood by human analysts. The expressiveness of decision models in data streams serves the objectives of transparency, underpinning the vision of ‘explainable AI’ and yet is an area of research that has attracted less attention despite being of high practical importance. The algorithm introduced and described in this paper is termed Fast Generalised Rule Induction (FGRI). FGRI is able to induce descriptive rules incrementally for raw data from both categorical and numerical features. FGRI is able to adapt rule-sets to changes of the pattern encoded in the data stream (concept drift) on the fly as new data arrives and can thus be applied continuously in real-time. 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Data Stream Mining (DSM) is concerned with the automatic analysis of data streams in real-time. Rapid flows of data challenge the state-of-the art processing and communication infrastructure, hence the motivation for research and innovation into real-time algorithms that analyse data streams on-the-fly and can automatically adapt to concept drifts. To date, DSM techniques have largely focused on predictive data mining applications that aim to forecast the value of a particular target feature of unseen data instances, answering questions such as whether a credit card transaction is fraudulent or not. A real-time, expressive and descriptive Data Mining technique for streaming data has not been previously established as part of the DSM toolkit. This has motivated the work reported in this paper, which has resulted in developing and validating a Generalised Rule Induction (GRI) tool, thus producing expressive rules as explanations that can be easily understood by human analysts. The expressiveness of decision models in data streams serves the objectives of transparency, underpinning the vision of ‘explainable AI’ and yet is an area of research that has attracted less attention despite being of high practical importance. The algorithm introduced and described in this paper is termed Fast Generalised Rule Induction (FGRI). FGRI is able to induce descriptive rules incrementally for raw data from both categorical and numerical features. FGRI is able to adapt rule-sets to changes of the pattern encoded in the data stream (concept drift) on the fly as new data arrives and can thus be applied continuously in real-time. 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A few attempts have been made to extend it to address either multi-task or multi-objective optimization problems. This research work presents the Multi-Task Multi-Objective Deep Neuroevolution method, a highly parallelizable algorithm that can be adopted for tackling both multi-task and multi-objective problems. In this method prior knowledge on the tasks is used to explicitly define multiple utility functions, which are optimized simultaneously. Experimental results on some Atari 2600 games, a challenging testbed for deep reinforcement learning algorithms, show that a single neural network with a single set of parameters can outperform previous state of the art techniques. In addition to the standard analysis, all results are also evaluated using the Hypervolume indicator and the Kullback-Leibler divergence to get better insights on the underlying training dynamics. The experimental results show that a neural network trained with the proposed evolution strategy can outperform networks individually trained respectively on each of the tasks.", "hoa_exclude": "FALSE", "hoa_date_foa": "2020-11-03", "event_title": "The 29th International Conference on Artificial Neural Networks (ICANN 2020)", "date_type": "published", "pres_type": "paper", "notify_on_approval": "yes", "rioxx2_identifier": "https:\/\/centaur.reading.ac.uk\/92333\/1\/MTMOES-distr.pdf", "event_dates": "15-18 September 2020", "hoa_compliant": 511, "datestamp": "2020-08-20 08:31:42", "uri": "https:\/\/centaur.reading.ac.uk\/id\/eprint\/92333", "altmetric": { "last_updated": "2020-11-03", "score": 1, "datestamp": "2020-11-04 02:30:58" }, "rioxx2_author": [ { "author": "Salvatore D., Riccio" }, { "author": "Deyan, Dyankov" }, { "author": "Giorgio, Jansen" }, { "author": "Di Fatta, Giuseppe" }, { "author": "Nicosia, Giuseppe" } ], "divs_irstats": [ "5_a2014a1p", "3_fc22d959", "1_76083589" ], "title": "Pareto multi-task deep learning", "rev_number": 35, "metadata_checked": "yes", "dir": "disk0\/00\/09\/23\/33", "rioxx2_format": "application\/pdf", "has_ug_creators": "FALSE", "ispublished": "pub", "metadata_visibility": "show", "eprint_status": "archive", "rioxx2_language": "en", "citation_extra": "Part II", "hoa_date_pub": "2020-11-03", "coversheets_dirty": "FALSE", "full_text_status": "public", "rioxx2_type": "Conference Paper\/Proceeding\/Abstract", "refereed": "TRUE", "contact_email": "g.difatta@reading.ac.uk", "divisions": [ "5_a2014a1p" ], "hoa_date_acc": "2020-07-31", "reading_wip": "FALSE", "sjr": { "num": null, "datestamp": "2020-08-16 01:02:11", "year": null }, "dates": [ { "date_type": "published", "date": 2020 }, { "date_type": "published_online", "date": "2020-11-03" }, { "date_type": "accepted", "date": "2020-07-31" } ], "hoa_gold": "FALSE", "has_pgt_creators": "FALSE", "restricted_doc_count": 0, "hoa_emb_len": 1, "rioxx2_free_to_read": { "free_to_read": "Yes" }, "rioxx2_version_of_record": "https:\/\/dx.doi.org\/10.1007\/978-3-030-61616-8", "pagerange": "132-141", "snip": { "num": null, "datestamp": "2020-08-16 01:02:11", "year": null }, "ros_action": "auto", "public_doc_count": 1, "creators_browse_email": [ "g.difatta@reading.ac.uk", "g.nicosia@reading.ac.uk" ], "creators_sort": [ { "name": { "lineage": null, "given": "Riccio", "honourific": null, "family": "Salvatore D." }, "id": null }, { "name": { "lineage": null, "given": "Dyankov", "honourific": null, "family": "Deyan" }, "id": null }, { "name": { "lineage": null, "given": "Jansen", "honourific": null, "family": "Giorgio" }, "id": null }, { "name": { "lineage": null, "given": "Giuseppe", "honourific": null, "family": "Di Fatta" }, "id": 90000558 }, { "name": { "lineage": null, "given": "Giuseppe", "honourific": null, "family": "Nicosia" }, "id": 90008704 } ], "rioxx2_dateAccepted": "2020-07-31", "event_type": "conference", "has_pgr_creators": "FALSE", "rioxx2_publication_date": 2020, "creators_browse_id": [ 90000558, 90008704 ], "type": "conference_item", "abstract": "Neuroevolution has been used to train Deep Neural Networks on reinforcement learning problems. A few attempts have been made to extend it to address either multi-task or multi-objective optimization problems. This research work presents the Multi-Task Multi-Objective Deep Neuroevolution method, a highly parallelizable algorithm that can be adopted for tackling both multi-task and multi-objective problems. In this method prior knowledge on the tasks is used to explicitly define multiple utility functions, which are optimized simultaneously. Experimental results on some Atari 2600 games, a challenging testbed for deep reinforcement learning algorithms, show that a single neural network with a single set of parameters can outperform previous state of the art techniques. In addition to the standard analysis, all results are also evaluated using the Hypervolume indicator and the Kullback-Leibler divergence to get better insights on the underlying training dynamics. The experimental results show that a neural network trained with the proposed evolution strategy can outperform networks individually trained respectively on each of the tasks.", "rioxx2_version": "AM", "hoa_ref_pan": "AB", "hoa_date_fcd": "2020-08-14", "userid": 240, "creators": [ { "name": { "lineage": null, "given": "Riccio", "honourific": null, "family": "Salvatore D." }, "id": null }, { "name": { "lineage": null, "given": "Dyankov", "honourific": null, "family": "Deyan" }, "id": null }, { "name": { "lineage": null, "given": "Jansen", "honourific": null, "family": "Giorgio" }, "id": null }, { "name": { "lineage": null, "given": "Giuseppe", "honourific": null, "family": "Di Fatta" }, "id": 90000558 }, { "name": { "lineage": null, "given": "Giuseppe", "honourific": null, "family": "Nicosia" }, "id": 90008704 } ], "ros_submitted": "FALSE", "lastmod": "2021-07-09 19:19:47", "creators_browse_name": "Salvatore D., R., Deyan, D., Giorgio, J., Di Fatta, G. and Nicosia, G. 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In general, detecting the gait events that mark the transition from one gait sub-phase to\r\nanother as well as the sequence of sub-phases is essential to evaluate gait abnormalities. However, finding a\r\nreliable segmentation for pathological gait has been a challenging task. This manuscript entails a generic\r\napproach for the gait segmentation into sub-phases as developed within the CORBYS1\r\nsystem. Accordingly\r\n,a number of distinctive features are extracted from the Hip joints motion data which are able to partition and\r\nsegment the gait cycles in an efficient way. The degree of deviation (i.e. anomaly) in each sub-phase is then\r\ncalculated with respect to an optimal gait reference which is used for robot-assisted gait rehabilitation. The\r\nproposed gait segmentation method is applicable to gait with many types of pathology since training on the\r\npathology specific templates is not required. 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In general, detecting the gait events that mark the transition from one gait sub-phase to\r\nanother as well as the sequence of sub-phases is essential to evaluate gait abnormalities. However, finding a\r\nreliable segmentation for pathological gait has been a challenging task. This manuscript entails a generic\r\napproach for the gait segmentation into sub-phases as developed within the CORBYS1\r\nsystem. Accordingly\r\n,a number of distinctive features are extracted from the Hip joints motion data which are able to partition and\r\nsegment the gait cycles in an efficient way. The degree of deviation (i.e. anomaly) in each sub-phase is then\r\ncalculated with respect to an optimal gait reference which is used for robot-assisted gait rehabilitation. The\r\nproposed gait segmentation method is applicable to gait with many types of pathology since training on the\r\npathology specific templates is not required. 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Our method is based on the synergy of different computational techniques and it is especially designed for the thin-film cell technology. In particular, we aim to efficiently simulate light trapping and plasmonic effects to enhance the light harvesting of the cell. The methodology is based on the sequential application of a hierarchy of approaches: (a) full Maxwell simulations are applied to derive the photon’s scattering probability in systems presenting textured interfaces; (b) calibrated Photonic Monte Carlo is used in junction with the scattering matrices method to evaluate coherent and scattered photon absorption in the full cell architectures; (c) the results of these advanced optical simulations are used as the pair generation terms in model implemented in an effective Technology Computer Aided Design tool for the derivation of the cell performance; (d) the models are investigated by qualitative and quantitative sensitivity analysis algorithms, to evaluate the importance of the design parameters considered on the models output and to get a first order descriptions of the objective space; (e) sensitivity analysis results are used to guide and simplify the optimization of the model achieved through both Single Objective Optimization (in order to fully maximize devices efficiency) and Multi Objective Optimization (in order to balance efficiency and cost); (f) Local, Global and “Glocal” robustness of optimal solutions found by the optimization algorithms are statistically evaluated; (g) data-based Identifiability Analysis is used to study the relationship between parameters. 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Our method is based on the synergy of different computational techniques and it is especially designed for the thin-film cell technology. In particular, we aim to efficiently simulate light trapping and plasmonic effects to enhance the light harvesting of the cell. The methodology is based on the sequential application of a hierarchy of approaches: (a) full Maxwell simulations are applied to derive the photon’s scattering probability in systems presenting textured interfaces; (b) calibrated Photonic Monte Carlo is used in junction with the scattering matrices method to evaluate coherent and scattered photon absorption in the full cell architectures; (c) the results of these advanced optical simulations are used as the pair generation terms in model implemented in an effective Technology Computer Aided Design tool for the derivation of the cell performance; (d) the models are investigated by qualitative and quantitative sensitivity analysis algorithms, to evaluate the importance of the design parameters considered on the models output and to get a first order descriptions of the objective space; (e) sensitivity analysis results are used to guide and simplify the optimization of the model achieved through both Single Objective Optimization (in order to fully maximize devices efficiency) and Multi Objective Optimization (in order to balance efficiency and cost); (f) Local, Global and “Glocal” robustness of optimal solutions found by the optimization algorithms are statistically evaluated; (g) data-based Identifiability Analysis is used to study the relationship between parameters. 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The resulting model reproduces well real-world systems as diverse as airplane, train and bus networks, thus suggesting that such systems are indeed compatible with the proposed local optimization\r\nmechanisms. In the specific case of airline transportation systems, we show that the networks of routes operated by each company are placed very close to the theoretical Pareto front in the efficiency-competition plane, and that most of the largest carriers of a continent belong to the corresponding Pareto front. Our results shed light on the fundamental role played by multi-objective\r\noptimization principles in shaping the structure of large-scale multilayer transportation systems, and provide novel insights to service providers on the strategies for the smart selection of novel routes.", "hoa_exclude": "FALSE", "hoa_ex_fur": "a", "hoa_date_foa": "2018-11-05", "date_type": "published", "notify_on_approval": "yes", "rioxx2_identifier": "https:\/\/centaur.reading.ac.uk\/78736\/1\/Nicosia-Physical-Review-Letters.pdf", "hoa_compliant": 12789, "datestamp": "2018-08-28 11:47:02", "uri": "https:\/\/centaur.reading.ac.uk\/id\/eprint\/78736", "altmetric": { "last_updated": "2018-09-23", "score": 23, "datestamp": "2021-09-01 02:39:43" }, "rioxx2_author": [ { "author": "Santoro, Andrea" }, { "author": "Latora, Vito" }, { "author": "Nicosia, Giuseppe" }, { "author": "Nicosia, Vincenzo" } ], "divs_irstats": [ "5_a2014a1p", "3_fc22d959", "1_76083589" ], "title": "Pareto optimality in multilayer network growth", "citation_count": { "num": 3, "datestamp": "2020-08-30 08:22:39" }, "number": 12, "rev_number": 135, "metadata_checked": "yes", "dir": "disk0\/00\/07\/87\/36", "rioxx2_format": "application\/pdf", "has_ug_creators": "FALSE", "ispublished": "pub", "metadata_visibility": "show", "eprint_status": "archive", "rioxx2_language": "en", "hoa_date_pub": "2018-09-20", "coversheets_dirty": "FALSE", "full_text_status": "public", "rioxx2_type": "Journal Article\/Review", "item_issues2": [ { "timestamp": "2018-11-05 22:06:53", "status": "discovered", "type": "duplicate_doi", "id": "duplicate_doi_80102", "description": "Duplicate Identification Number\/DOI to \n\nPareto Optimality in Multilayer Network Growth.<\/a>\n\n\n
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A minimum number of training\r\nsymbols, very close to the number of receiver antenna elements, are used to provide a rough initial least squares estimate of the\r\nbeamformer0s weight vector. A novel cost function combining the constant modulus criterion with decision-directed adaptation is\r\nadopted to adapt the beamformer weight vector. This cost function can be approximated as a quadratic form with a closed-form\r\nsolution, based on which we then derive the recursive least squares (RLS) semi-blind adaptive beamforming algorithm. This semi-blind\r\nadaptive beamforming scheme is capable of converging fast to the minimum mean-square-error beamforming solution, as demonstrated\r\nin our simulation study. Our proposed semi-blind RLS beamforming algorithm therefore provides an e±cient detection scheme for the\r\nfuture generation of MIMO aided mobile communication systems.", "hoa_exclude": "FALSE", "hoa_date_foa": "2018-06-22", "date_type": "published", "notify_on_approval": "yes", "rioxx2_identifier": "https:\/\/centaur.reading.ac.uk\/72077\/1\/IJAC-2016-11-273.pdf", "hoa_compliant": 511, "datestamp": "2017-08-29 14:13:55", "uri": "https:\/\/centaur.reading.ac.uk\/id\/eprint\/72077", "altmetric": { "last_updated": "2018-12-04", "score": 1, "datestamp": "2019-06-01 04:31:42" }, "rioxx2_author": [ { "author": "Hong, Xia" }, { "author": "Chen, Sheng" } ], "divs_irstats": [ "5_a2014a1p", "3_fc22d959", "1_76083589" ], "title": "Recursive least squares semi-blind beamforming for MIMO using decision directed adaptation and constant modulus criterion", "citation_count": { "num": 2, "datestamp": "2021-03-28 05:54:43" }, "number": 4, "rev_number": 124, "metadata_checked": "yes", "dir": "disk0\/00\/07\/20\/77", "rioxx2_format": "application\/pdf", "has_ug_creators": "FALSE", "ispublished": "pub", "metadata_visibility": "show", "eprint_status": "archive", "rioxx2_language": "en", "hoa_date_pub": "2017-06-21", "coversheets_dirty": "FALSE", "full_text_status": "public", "rioxx2_type": "Journal Article\/Review", "refereed": "TRUE", "divisions": [ "5_a2014a1p" ], "hoa_date_acc": "2017-03-07", "reading_wip": "FALSE", "sjr": { "num": 0.799, "datestamp": "2022-06-19 05:49:30", "year": 2021 }, "dates": [ { "date_type": "published", "date": "2017-08" }, { "date_type": "published_online", "date": "2017-06-21" }, { "date_type": "accepted", "date": "2017-03-07" } ], "has_pgt_creators": "FALSE", "restricted_doc_count": 0, "hoa_emb_len": 12, "rioxx2_free_to_read": { "free_to_read": "Yes" }, "rioxx2_version_of_record": "https:\/\/dx.doi.org\/10.1007\/s11633-017-1087-6", "publisher": "Springer", "pagerange": "442-449", "snip": { "num": 1.61, "datestamp": "2022-06-19 05:49:30", "year": 2021 }, "ros_action": "auto", "public_doc_count": 1, "creators_browse_email": [ "x.hong@reading.ac.uk" ], "creators_sort": [ { "name": { "lineage": null, "given": "Xia", "honourific": null, "family": "Hong" }, "id": 90000432 }, { "name": { "lineage": null, "given": "Sheng", "honourific": null, "family": "Chen" }, "id": null } ], "rioxx2_dateAccepted": "2017-03-07", "has_pgr_creators": "FALSE", "rioxx2_publication_date": "2017-08", "creators_browse_id": [ 90000432 ], "type": "article", "abstract": "A new semi-blind adaptive beamforming scheme is proposed for multi-input multi-output (MIMO) induced and space-\r\ndivision multiple-access based wireless systems that employ high order phase shift keying signaling. 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propose a nonlinear hybrid decision feedback equalizer (NHDFE) for single-carrier (SC) block transmission systems with nonlinear transmit high power amplifier (HPA), which significantly outperforms our previous nonlinear SC frequency-domain equalization (NFDE) design. To obtain the coefficients of the channel impulse response (CIR) as well as to estimate the nonlinear mapping and the inverse nonlinear mapping of the HPA, we adopt a complex-valued (CV) B-spline neural network approach. Specifically, we use a CV B-spline neural network to model the nonlinear HPA, and we develop an efficient alternating least squares scheme for estimating the parameters of the Hammerstein channel, including both the CIR coefficients and the parameters of the CV B-spline model. We also adopt another CV B-spline neural network to model the inversion of the nonlinear HPA, and the parameters of this inverting B-spline model can be estimated using the least squares algorithm based on the pseudo training data obtained as a natural byproduct of the Hammerstein channel identification. The effectiveness of our NHDFE design is demonstrated in a simulation study, which shows that the NHDFE achieves a signal-to-noise ratio gain of 4dB over the NFDE at the bit error rate level of 10−4 .", "hoa_exclude": "FALSE", "hoa_date_foa": "2017-05-23", "date_type": "published", "notify_on_approval": "yes", "rioxx2_identifier": "https:\/\/centaur.reading.ac.uk\/70275\/1\/sc-scfd-dfe-T.pdf", "hoa_compliant": 511, "datestamp": "2017-05-16 12:02:18", "uri": "https:\/\/centaur.reading.ac.uk\/id\/eprint\/70275", "altmetric": { "last_updated": null, "score": null, "datestamp": "2019-06-01 04:26:30" }, "rioxx2_author": [ { "author": "Chen, Sheng" }, { "author": "Hong, Xia" }, { "author": "Khalaf, Emad F." }, { "author": "Morfeq, Ali" }, { "author": "Alotaibi, Naif D." }, { "author": "Harris, Chris J." } ], "divs_irstats": [ "5_a2014a1p", "3_fc22d959", "1_76083589" ], "title": "Single-carrier frequency-domain equalization with hybrid decision feedback equalizer for Hammerstein channels containing nonlinear transmit amplifier", "citation_count": { "num": 4, "datestamp": "2020-08-30 07:12:47" }, "number": 5, "rev_number": 162, "metadata_checked": "yes", "dir": "disk0\/00\/07\/02\/75", "rioxx2_format": "application\/pdf", "has_ug_creators": "FALSE", "ispublished": "pub", "metadata_visibility": "show", "eprint_status": "archive", "rioxx2_language": "en", "hoa_date_pub": "2017-03-17", "coversheets_dirty": "FALSE", "full_text_status": "public", "rioxx2_type": "Journal Article\/Review", "refereed": "TRUE", "divisions": [ "5_a2014a1p" ], "hoa_date_acc": "2017-03-08", "reading_wip": "FALSE", "sjr": { "num": 4.436, "datestamp": "2022-06-19 01:30:26", "year": 2021 }, "dates": [ { "date_type": "published", "date": "2017-05" }, { "date_type": "published_online", "date": "2017-03-17" }, { "date_type": "accepted", "date": "2017-03-08" } ], "has_pgt_creators": "FALSE", "restricted_doc_count": 0, "rioxx2_free_to_read": { "free_to_read": "Yes" }, "rioxx2_version_of_record": "https:\/\/dx.doi.org\/10.1109\/TWC.2017.2681083", "publisher": "IEEE Communications Society", "pagerange": "3341-3354", "snip": { "num": 2.321, "datestamp": "2022-06-19 01:30:26", "year": 2021 }, "ros_action": "auto", "public_doc_count": 1, "creators_browse_email": [ "x.hong@reading.ac.uk" ], "creators_sort": [ { "name": { "lineage": null, "given": "Sheng", "honourific": null, "family": "Chen" }, "id": null }, { "name": { "lineage": null, "given": "Xia", "honourific": null, "family": "Hong" }, "id": 90000432 }, { "name": { "lineage": null, "given": "Emad F.", "honourific": null, "family": "Khalaf" }, "id": null }, { "name": { "lineage": null, "given": "Ali", "honourific": null, "family": "Morfeq" }, "id": null }, { "name": { "lineage": null, "given": "Naif D.", "honourific": null, "family": "Alotaibi" }, "id": null }, { "name": { "lineage": null, "given": "Chris J.", "honourific": null, "family": "Harris" }, "id": null } ], "rioxx2_dateAccepted": "2017-03-08", "has_pgr_creators": "FALSE", "rioxx2_publication_date": "2017-05", "creators_browse_id": [ 90000432 ], "type": "article", "abstract": "We propose a nonlinear hybrid decision feedback equalizer (NHDFE) for single-carrier (SC) block transmission systems with nonlinear transmit high power amplifier (HPA), which significantly outperforms our previous nonlinear SC frequency-domain equalization (NFDE) design. To obtain the coefficients of the channel impulse response (CIR) as well as to estimate the nonlinear mapping and the inverse nonlinear mapping of the HPA, we adopt a complex-valued (CV) B-spline neural network approach. Specifically, we use a CV B-spline neural network to model the nonlinear HPA, and we develop an efficient alternating least squares scheme for estimating the parameters of the Hammerstein channel, including both the CIR coefficients and the parameters of the CV B-spline model. We also adopt another CV B-spline neural network to model the inversion of the nonlinear HPA, and the parameters of this inverting B-spline model can be estimated using the least squares algorithm based on the pseudo training data obtained as a natural byproduct of the Hammerstein channel identification. 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Classifying sequential data instances is a very challenging problem in machine learning with applications in network intrusion detection, financial markets and sensor networks. Data stream classification is concerned with the automatic labelling of unseen instances from the stream in real-time. For this the classifier needs to adapt to concept drifts and can only have a single pass through the data if the stream is fast. This research paper presents our work on a real-time pre-processing technique, in particular a feature tracking technique that takes concept drift into consideration. The feature tracking technique is designed to improve Data Stream Mining (DSM) classification algorithms by enabling real-time feature selection. The technique is based on adaptive summaries of the data and class distributions, known as Micro-Clusters. 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Classifying sequential data instances is a very challenging problem in machine learning with applications in network intrusion detection, financial markets and sensor networks. Data stream classification is concerned with the automatic labelling of unseen instances from the stream in real-time. For this the classifier needs to adapt to concept drifts and can only have a single pass through the data if the stream is fast. This research paper presents our work on a real-time pre-processing technique, in particular a feature tracking technique that takes concept drift into consideration. The feature tracking technique is designed to improve Data Stream Mining (DSM) classification algorithms by enabling real-time feature selection. The technique is based on adaptive summaries of the data and class distributions, known as Micro-Clusters. 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by an E-portfolio enhanced with learning analytics", "rioxx2_description": "Electronic portfolios (E-portfolios) are crucial means for workplace-based assessment and feedback. Although E-portfolios provide a useful approach to view each learner’s progress, so far options for personalized feedback and potential data about a learner’s performances at the workplace often remain unexploited. This paper advocates that E-portfolios enhanced with learning analytics, might increase the quality and efficiency of workplace-based feedback and assessment in professional education. Based on a 5-phased iterative design approach, an existing E-portfolio environment was enhanced with learning analytics in professional education. First, information about crucial professional activities for professional domains and suited assessment instruments were collected (phase 1). Thereafter probabilistic student models were defined (phase 2). Next, personalized feedback and visualization of the personal development over time were developed (phase 3). Then the prototype of the E-portfolio—including the student models and feedback and visualization modules—were implemented in professional training-programs (phase 4). Last, evaluation cycles took place and 121 students and 30 supervisors from five institutes for professional education evaluated the perceived usefulness of the design (phase 5). It was concluded that E-portfolios with learning analytics were perceived to assist the development of students’ professional competencies and that the design is only successful when developed and implemented through the eyes of the users. Feedback and assessment methods based upon learning analytics can stimulate learning at the workplace in the long run. 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Although E-portfolios provide a useful approach to view each learner’s progress, so far options for personalized feedback and potential data about a learner’s performances at the workplace often remain unexploited. This paper advocates that E-portfolios enhanced with learning analytics, might increase the quality and efficiency of workplace-based feedback and assessment in professional education. Based on a 5-phased iterative design approach, an existing E-portfolio environment was enhanced with learning analytics in professional education. First, information about crucial professional activities for professional domains and suited assessment instruments were collected (phase 1). Thereafter probabilistic student models were defined (phase 2). Next, personalized feedback and visualization of the personal development over time were developed (phase 3). Then the prototype of the E-portfolio—including the student models and feedback and visualization modules—were implemented in professional training-programs (phase 4). Last, evaluation cycles took place and 121 students and 30 supervisors from five institutes for professional education evaluated the perceived usefulness of the design (phase 5). It was concluded that E-portfolios with learning analytics were perceived to assist the development of students’ professional competencies and that the design is only successful when developed and implemented through the eyes of the users. Feedback and assessment methods based upon learning analytics can stimulate learning at the workplace in the long run. 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In the majority of the existing work on subspace clustering, clusters are built based on feature information, while sample correlations in their original spatial structure are simply ignored. Besides, original high-dimensional feature vector contains noisy\/redundant information, and the time complexity grows exponentially with the number of dimensions. To address these issues, we propose a tensor low-rank representation (TLRR) and sparse coding-based (TLRRSC) subspace clustering method by simultaneously considering feature information and spatial structures. TLRR seeks the lowest rank representation over original spatial structures along all spatial directions. Sparse coding learns a dictionary along feature spaces, so that each sample can be represented by a few atoms of the learned dictionary. The affinity matrix used for spectral clustering is built from the joint similarities in both spatial and feature spaces. 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In the majority of the existing work on subspace clustering, clusters are built based on feature information, while sample correlations in their