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"http:\/\/eprints.org\/relation\/isVersionOf", "uri": "\/id\/document\/769735" }, { "type": "http:\/\/eprints.org\/relation\/isVolatileVersionOf", "uri": "\/id\/document\/769735" }, { "type": "http:\/\/eprints.org\/relation\/isCoversheetVersionOf", "uri": "\/id\/document\/769735" } ], "security": "public", "pos": 8, "formatdesc": "Coversheet version" } ], "divisions_browse": [ "5_a2014a1p", "3_fc22d959" ], "rioxx2_title": "Monitoring multimode nonlinear dynamic processes: an efficient sparse dynamic approach with continual learning ability", "rioxx2_description": "Industrial processes generally operate under multiple modes and a global monitoring approach, built upon combining local models which are aimed at each mode, requires complete data from all potential modes to be available.\r\nHowever, practical data are generated and collected in\r\na steady stream, which makes it difficult if not impossible to process. This paper proposes an efficient sparse dynamic\r\ninner principal component analysis algorithm for multimode\r\nnonlinear dynamic process monitoring, which aims to\r\nbuild a single monitoring model with continual learning\r\nability for successive modes. To reduce the storage and\r\ncomputational costs, only a few representative data from\r\neach mode are selected based on cosine similarity and\r\nreplayed for retraining when a new mode arrives, which are\r\nsufficient to reflect the operating condition of each mode.\r\nInspired by replay continual learning, data from all existing modes are preprocessed by its own statistics and then regarded as a whole data set, followed by building a single multimode monitoring model. The multimode dynamic\r\nlatent variables are extracted from data in raw format, via\r\na vector autoregressive model. Therefore, the proposed\r\nmethod is not constrained by the mode similarity, which\r\nmakes it appropriate for diverse modes and convenient for\r\nlong-term monitoring tasks. Besides, the proposed method\r\ncan deal with nonlinearity and a regularization term is\r\nadded to avoid the potential overfitting issue. Compared\r\nwith state-of-the-art multimode monitoring methods, the\r\neffectiveness of the proposed approach is demonstrated\r\nby continuous stirred tank heater and a practical industrial\r\nsystem.", "hoa_exclude": "FALSE", "hoa_date_foa": "2023-07-03", "date_type": "published", "notify_on_approval": "yes", "rioxx2_identifier": "https:\/\/centaur.reading.ac.uk\/107580\/1\/TII-22-2512.pdf", "hoa_compliant": 318, "datestamp": "2022-10-10 17:16:32", "uri": "https:\/\/centaur.reading.ac.uk\/id\/eprint\/107580", "altmetric": { "last_updated": null, "score": null, "datestamp": "2022-10-01 02:01:11" }, "rioxx2_author": [ { "author": "Zhang, Jinxin" }, { "author": "Chen, Maoyin" }, { "author": "Hong, Xia" } ], "divs_irstats": [ "5_a2014a1p", "3_fc22d959", "1_76083589" ], "title": "Monitoring multimode nonlinear dynamic processes: an efficient sparse dynamic approach with continual learning ability", "citation_count": { "num": 0, "datestamp": "2022-11-06 04:24:53" }, "number": 7, "rev_number": 55, "metadata_checked": "yes", "dir": "disk0\/00\/10\/75\/80", "rioxx2_format": "application\/pdf", "has_ug_creators": "FALSE", "ispublished": "pub", "metadata_visibility": "show", "eprint_status": "archive", "rioxx2_language": "en", "hoa_date_pub": "2022-10-20", "coversheets_dirty": "FALSE", "full_text_status": "public", "rioxx2_type": "Journal Article\/Review", "item_issues2": [ { "timestamp": "2022-11-16 22:18:52", "status": "discovered", "type": "duplicate_title", "id": "duplicate_title_108917", "description": "Duplicate Title to \n\nMonitoring Multimode Nonlinear Dynamic Processes: An Efficient Sparse Dynamic Approach With Continual Learning Ability<\/a>\n\n\n
User Workarea, \n\nUnnamed user with username JiscRouter<\/span><\/a>\n\n- \n[ Manage<\/a> ] [ Compare & Merge<\/a> ] [ Acknowledge<\/a> ]" } ], "refereed": "TRUE", "divisions": [ "5_a2014a1p" ], "hoa_date_acc": "2022-09-27", "reading_wip": "FALSE", "sjr": { "num": 0, "datestamp": "2023-09-03 01:34:56", "year": 0 }, "dates": [ { "date_type": "published", "date": "2023-07" }, { "date_type": "published_online", "date": "2022-10-20" }, { "date_type": "accepted", "date": "2022-09-27" } ], "hoa_gold": "FALSE", "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\/TII.2022.3215971", "publisher": "IEEE", "pagerange": "8029-8038", "snip": { "num": 0, "datestamp": "2023-09-03 01:34:56", "year": 0 }, "ros_action": "auto", "public_doc_count": 1, "creators_browse_email": [ "x.hong@reading.ac.uk" ], "creators_sort": [ { "name": { "lineage": null, "given": "Jinxin", "honourific": null, "family": "Zhang" }, "id": null }, { "name": { "lineage": null, "given": "Maoyin", "honourific": null, "family": "Chen" }, "id": null }, { "name": { "lineage": null, "given": "Xia", "honourific": null, "family": "Hong" }, "id": 90000432 } ], "rioxx2_dateAccepted": "2022-09-27", "has_pgr_creators": "FALSE", "rioxx2_publication_date": "2023-07", "creators_browse_id": [ 90000432 ], "type": "article", "abstract": "Industrial processes generally operate under multiple modes and a global monitoring approach, built upon combining local models which are aimed at each mode, requires complete data from all potential modes to be available.\r\nHowever, practical data are generated and collected in\r\na steady stream, which makes it difficult if not impossible to process. This paper proposes an efficient sparse dynamic\r\ninner principal component analysis algorithm for multimode\r\nnonlinear dynamic process monitoring, which aims to\r\nbuild a single monitoring model with continual learning\r\nability for successive modes. To reduce the storage and\r\ncomputational costs, only a few representative data from\r\neach mode are selected based on cosine similarity and\r\nreplayed for retraining when a new mode arrives, which are\r\nsufficient to reflect the operating condition of each mode.\r\nInspired by replay continual learning, data from all existing modes are preprocessed by its own statistics and then regarded as a whole data set, followed by building a single multimode monitoring model. The multimode dynamic\r\nlatent variables are extracted from data in raw format, via\r\na vector autoregressive model. Therefore, the proposed\r\nmethod is not constrained by the mode similarity, which\r\nmakes it appropriate for diverse modes and convenient for\r\nlong-term monitoring tasks. Besides, the proposed method\r\ncan deal with nonlinearity and a regularization term is\r\nadded to avoid the potential overfitting issue. Compared\r\nwith state-of-the-art multimode monitoring methods, the\r\neffectiveness of the proposed approach is demonstrated\r\nby continuous stirred tank heater and a practical industrial\r\nsystem.", "publication": "IEEE Transactions on Industrial Informatics", "rioxx2_version": "AM", "hoa_ref_pan": "AB", "hoa_date_fcd": "2022-09-28", "userid": 311, "issn": "1941-0050", "creators": [ { "name": { "lineage": null, "given": "Jinxin", "honourific": null, "family": "Zhang" }, "id": null }, { "name": { "lineage": null, "given": "Maoyin", "honourific": null, "family": "Chen" }, "id": null }, { "name": { "lineage": null, "given": "Xia", "honourific": null, "family": "Hong" }, "id": 90000432 } ], "ros_submitted": "FALSE", "lastmod": "2023-09-03 01:34:56", "creators_browse_name": "Zhang, J., Chen, M. and Hong, X. 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Internet access is crucial to the smooth operation of many critical services, including medical care, banking, retail, information sharing, and transportation. Since most applications are hosted in the cloud, it makes sense for data owners to be very concerned about data integrity. Malicious actors attempting to access the cloud environment must be stopped using strong security measures. Several types of attackers target the network at the same time, using different methods. The purpose of this project is to protect the database against attacks that originate from the client side. Examples of such attacks include application-layer distributed denial-of-service attacks and SQL injection attacks. Distributed denial-of-service attacks, often known as DDoS attacks, occur at the application layer when an attacker sends a flood of requests to a target service. SQL injection attacks, on the other hand, are a kind of attack that bypasses normal safeguards by launching malicious scripts directly into the database. In order to prevent application-layer DDoS attacks and SQL injection attacks, a new method has been proposed. This strategy involves ensuring that the login data (a legitimate username and password) matches both the usernames and passwords stored in the database on the client side. Additionally, it involves being able to handle this data in the form of hashing, making use of datamining, and employing the Python programming language for the implementation of cryptographic algorithms using the SHA-256 hash function. Both of these types of attacks can be prevented by implementing this strategy. Since only a few changes to the source code of the programming language are needed, this strategy can be quickly added to any web application that has already been built. This is true no matter what programming language or database was used to build the application.", "hoa_exclude": "FALSE", "event_title": "2023 IEEE International Conference on Advanced Systems and Emergent Technologies", "date_type": "published", "pres_type": "paper", "notify_on_approval": "yes", "event_dates": "29 Apr 2023 - 01 May 2023", "hoa_compliant": 304, "datestamp": "2023-07-03 12:02:04", "uri": "https:\/\/centaur.reading.ac.uk\/id\/eprint\/112421", "altmetric": { "last_updated": null, "score": null, "datestamp": "2023-07-04 02:01:16" }, "rioxx2_author": [ { "author": "Ashlam, Ahmed Abadulla" }, { "author": "Badii, Atta" }, { "author": "Stahl, Frederic" } ], "divs_irstats": [ "5_a2014a1p", "3_fc22d959", "1_76083589" ], "title": "Data-mining and hashing to prevent application-layer DDoS and SQL injection attacks", "pages": 0, "citation_count": { "num": 0, "datestamp": "2023-07-23 04:25:14" }, "metadata_checked": "yes", "rev_number": 12, "dir": "disk0\/00\/11\/24\/21", "has_ug_creators": "FALSE", "ispublished": "pub", "metadata_visibility": "show", "eprint_status": "archive", "rioxx2_language": "en", "sword_depositor": 16597, "hoa_date_pub": "2023-04-29", "full_text_status": "none", "rioxx2_type": "Conference Paper\/Proceeding\/Abstract", "refereed": "TRUE", "divisions": [ "5_a2014a1p" ], "reading_wip": "FALSE", "sjr": { "num": null, "datestamp": "2023-07-09 01:02:07", "year": null }, "dates": [ { "date_type": "published", "date": "2023-04-29" } ], "hoa_gold": "FALSE", "has_pgt_creators": "FALSE", "restricted_doc_count": 0, "rioxx2_version_of_record": "https:\/\/dx.doi.org\/10.1109\/ic_aset58101.2023.10150694", "publisher": "IEEE", "snip": { "num": null, "datestamp": "2023-07-09 01:02:07", "year": null }, "ros_action": "auto", "public_doc_count": 0, "creators_browse_email": [ "atta.badii@reading.ac.uk" ], "creators_sort": [ { "name": { "lineage": null, "given": "Ahmed Abadulla", "honourific": null, "family": "Ashlam" }, "id": null }, { "name": { "lineage": null, "given": "Atta", "honourific": null, "family": "Badii" }, "id": 90000900 }, { "name": { "lineage": null, "given": "Frederic", "honourific": null, "family": "Stahl" }, "id": null } ], "event_type": "conference", "event_location": "Hammamet, Tunisia", "has_pgr_creators": "FALSE", "creators_browse_id": [ 90000900 ], "rioxx2_publication_date": "2023-04-29", "type": "conference_item", "abstract": "Applications built specifically for the web are rapidly growing in significance. Internet access is crucial to the smooth operation of many critical services, including medical care, banking, retail, information sharing, and transportation. Since most applications are hosted in the cloud, it makes sense for data owners to be very concerned about data integrity. Malicious actors attempting to access the cloud environment must be stopped using strong security measures. Several types of attackers target the network at the same time, using different methods. The purpose of this project is to protect the database against attacks that originate from the client side. Examples of such attacks include application-layer distributed denial-of-service attacks and SQL injection attacks. Distributed denial-of-service attacks, often known as DDoS attacks, occur at the application layer when an attacker sends a flood of requests to a target service. SQL injection attacks, on the other hand, are a kind of attack that bypasses normal safeguards by launching malicious scripts directly into the database. In order to prevent application-layer DDoS attacks and SQL injection attacks, a new method has been proposed. This strategy involves ensuring that the login data (a legitimate username and password) matches both the usernames and passwords stored in the database on the client side. Additionally, it involves being able to handle this data in the form of hashing, making use of datamining, and employing the Python programming language for the implementation of cryptographic algorithms using the SHA-256 hash function. Both of these types of attacks can be prevented by implementing this strategy. Since only a few changes to the source code of the programming language are needed, this strategy can be quickly added to any web application that has already been built. 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"rioxx2_description": "This paper introduces a novel sparse dynamic inner\r\nprincipal component analysis (SDiPCA) based monitoring for\r\nmultimode dynamic processes. 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Different from traditional multimode monitoring algorithms, a model is updated for sequential modes by memorizing the significant features of existing modes. By adopting the concept of intelligent synapses in continual learning, a loss of quadratic term is introduced to penalize the changes of mode–relevant parameters, where modified synaptic intelligence (MSI) is proposed to estimate the parameter importance. Thus, the proposed algorithm is referred to as SDiPCA–MSI. When a new mode arrives, a set of normal samples should be collected. The previous significant features are consolidated without explicitly storing training samples, while extracting new\r\ninformation from the current mode. Consequently, SDiPCA–\r\nMSI can provide outstanding performance for successive modes.\r\nCharacteristics of the proposed approach are discussed, including the computational complexity, advantages and potential limitations. 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SQLI attacks provide opportunities by malicious actors to exploit the data, particularly client personal data. To counter these attacks security measures need to be deployed at all layers, namely application layer, network layer, and database layer; otherwise, the database remains vulnerable to attacks at all levels. Research studies have demonstrated that lack of input validation, incorrect use of dynamic SQL, and inconsistent error handling have continued to expose databased to SQLI attacks. The security measures commonly deployed presently, being mostly focused on the network layer only, still leave the program code and the database at risk despite well-established approaches such as web server requests filtering, network firewalls and database access control. To overcome this deficiency, a Multi-Phase algorithmic framework is proposed with improved parameterised machine learning and deep learning to enhance database security in realtime at the database layer. The proposed method has been tested within a university and also in one of the branches of a commercial bank. The results show that the proposed method is able to i) prevent SQLi; ii) classify the type of attack during the detection process, and therefore iii) secure the database.", "hoa_exclude": "FALSE", "hoa_date_foa": "2023-01-30", "event_title": "15th International Conference on Security of Information and Networks (SIN)", "date_type": "published", "pres_type": "paper", "notify_on_approval": "yes", "rioxx2_identifier": "https:\/\/centaur.reading.ac.uk\/108629\/1\/paper%2010.pdf", "event_dates": "11 - 13 November 2022", "hoa_compliant": 318, "datestamp": "2022-12-22 16:51:00", "uri": "https:\/\/centaur.reading.ac.uk\/id\/eprint\/108629", "altmetric": { "last_updated": null, "score": null, "datestamp": "2022-11-04 02:01:27" }, "rioxx2_author": [ { "author": "Ashlam, Ahmed Abadulla" }, { "author": "Badii, Atta" }, { "id": "https:\/\/orcid.org\/0000-0002-4860-0203", "author": "Stahl, Frederic" } ], "divs_irstats": [ "5_a2014a1p", "3_fc22d959", "1_76083589" ], "title": "Multi-phase algorithmic framework to prevent SQL injection attacks using improved machine learning and deep learning to enhance database security in real-time", "citation_count": { "num": 0, "datestamp": "2023-01-29 04:22:24" }, "rev_number": 34, "metadata_checked": "yes", "dir": "disk0\/00\/10\/86\/29", "rioxx2_format": "application\/pdf", "has_ug_creators": "FALSE", "ispublished": "pub", "metadata_visibility": "show", "eprint_status": "archive", "rioxx2_language": "en", "citation_extra": "IEEE Xplore", "hoa_date_pub": "2022-12-16", "coversheets_dirty": "FALSE", "full_text_status": "public", "rioxx2_type": "Conference Paper\/Proceeding\/Abstract", "item_issues2": [ { "timestamp": "2022-12-28 22:19:07", "status": "discovered", "type": "duplicate_title", "id": "duplicate_title_109547", "description": "Duplicate Title to \n\nMulti-Phase Algorithmic Framework to Prevent SQL Injection Attacks using Improved Machine learning and Deep learning to Enhance Database security in Real-time<\/a>\n\n\n
User Workarea, \n\nUnnamed user with username JiscRouter<\/span><\/a>\n\n- \n[ Manage<\/a> ] [ Compare & Merge<\/a> ] [ Acknowledge<\/a> ]" } ], "refereed": "TRUE", "divisions": [ "5_a2014a1p" ], "hoa_date_acc": "2022-10-18", "reading_wip": "FALSE", "sjr": { "num": null, "datestamp": "2022-11-06 01:03:26", "year": null }, "dates": [ { "date_type": "published", "date": "2022-12" }, { "date_type": "published_online", "date": "2022-12-16" }, { "date_type": "accepted", "date": "2022-10-18" } ], "hoa_gold": "FALSE", "has_pgt_creators": "FALSE", "rioxx2_relation": [ "https:\/\/www.sinconf.org\/sin2022\/" ], "restricted_doc_count": 0, "rioxx2_free_to_read": { "free_to_read": "Yes" }, "rioxx2_version_of_record": "https:\/\/dx.doi.org\/10.1109\/SIN56466.2022.9970504", "snip": { "num": null, "datestamp": "2022-11-06 01:03:26", "year": null }, "ros_action": "auto", "public_doc_count": 1, "creators_browse_email": [ "atta.badii@reading.ac.uk" ], "creators_sort": [ { "name": { "lineage": null, "given": "Ahmed Abadulla", "honourific": null, "family": "Ashlam" }, "id": null }, { "name": { "lineage": null, "given": "Atta", "honourific": null, "family": "Badii" }, "id": 90000900 }, { "name": { "lineage": null, "given": "Frederic", "honourific": null, "family": "Stahl" }, "id": null } ], "rioxx2_dateAccepted": "2022-10-18", "event_type": "conference", "event_location": "Sousse, Tunisia", "has_pgr_creators": "TRUE", "rioxx2_publication_date": "2022-12", "creators_browse_id": [ 90000900 ], "type": "conference_item", "abstract": "Structured Query Language (SQL) Injection constitutes a most challenging type of cyber-attack on the security of databases. SQLI attacks provide opportunities by malicious actors to exploit the data, particularly client personal data. To counter these attacks security measures need to be deployed at all layers, namely application layer, network layer, and database layer; otherwise, the database remains vulnerable to attacks at all levels. Research studies have demonstrated that lack of input validation, incorrect use of dynamic SQL, and inconsistent error handling have continued to expose databased to SQLI attacks. The security measures commonly deployed presently, being mostly focused on the network layer only, still leave the program code and the database at risk despite well-established approaches such as web server requests filtering, network firewalls and database access control. To overcome this deficiency, a Multi-Phase algorithmic framework is proposed with improved parameterised machine learning and deep learning to enhance database security in realtime at the database layer. 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Multimode slow features are extracted and an elastic weight consolidation (EWC) is adopted for sequential modes. EWC was originally introduced in the setting of machine learning of sequential multi-tasks with the aim of avoiding catastrophic forgetting issue, which equally poses as a major challenge in multimode nonstationary process monitoring. When a new mode arrives, a small set of data are collected for continual learning by the proposed algorithm. A regularization term is introduced to prevent new data from significantly interfering with the learned knowledge, where the parameter importance measures are estimated. The proposed method is referred to as PSFA–EWC, which is updated continually and is capable of achieving excellent performance. PSFA–EWC furnishes backward and forward transfer ability by a single model. The significant features of previous modes are retained while consolidating new information, which may contribute to learning new relevant modes. The effectiveness of the proposed method is demonstrated via a continuous stirred tank heater and a practical coal pulverizing system. Note to Practitioners —Since industrial systems operate in varying modes and data are nonstationary within each mode, multimode nonstationary process monitoring is increasingly important. Traditional multimode monitoring methods generally need complete data from all possible modes and may need to be retrained from scratch when a new mode arrives, which require expensive computation and storage resources. Besides, it is difficult to distinguish real faults from normal variations in multimode nonstationary processes. This paper proposes a novel continual learning-based probabilistic slow feature analysis, where elastic weight consolidation is employed to consolidate the previously learned knowledge while extracting multimode slow features. The monitoring model is updated sequentially and provides backward as well as forward transfer learning ability for successive modes. It is able to separate real faults from normal dynamics, which is beneficial to identifying a new mode for multimode nonstationary processes. In addition, the proposed approach delivers excellent model interpretability and deals with missing data as well as uncertainty. In industrial applications, such as power plants and intelligent manufacturing processes, the proposed method can provide excellent monitoring performance.", "hoa_exclude": "FALSE", "hoa_date_foa": "2023-07-03", "date_type": "published_online", "notify_on_approval": "yes", "rioxx2_identifier": "https:\/\/centaur.reading.ac.uk\/108593\/1\/T-ASE-2022-389.pdf", "hoa_compliant": 308, "datestamp": "2023-02-03 14:46:13", "uri": "https:\/\/centaur.reading.ac.uk\/id\/eprint\/108593", "altmetric": { "last_updated": null, "score": null, "datestamp": "2022-11-04 02:01:14" }, "rioxx2_author": [ { "author": "Zhang, Jingxin" }, { "author": "Zhou, Donghua" }, { "author": "Chen, Maoyin" }, { "author": "Hong, Xia" } ], "divs_irstats": [ "5_a2014a1p", "3_fc22d959", "1_76083589" ], "title": "Continual learning-based probabilistic slow feature\r\nanalysis for monitoring multimode nonstationary\r\nprocesses", "citation_count": { "num": 0, "datestamp": "2023-02-12 04:22:31" }, "rev_number": 45, "metadata_checked": "yes", "dir": "disk0\/00\/10\/85\/93", "rioxx2_format": "application\/pdf", "has_ug_creators": "FALSE", "ispublished": "pub", "item_issues_count": 0, "metadata_visibility": "show", "eprint_status": "archive", "rioxx2_language": "en", "hoa_date_pub": "2022-11-10", "coversheets_dirty": "FALSE", "full_text_status": "restricted", "rioxx2_type": "Journal Article\/Review", "refereed": "TRUE", "divisions": [ "5_a2014a1p" ], "hoa_date_acc": "2022-10-30", "reading_wip": "FALSE", "sjr": { "num": 0, "datestamp": "2023-09-17 01:19:30", "year": 0 }, "dates": [ { "date_type": "published_online", "date": "2022-11-10" }, { "date_type": "accepted", "date": "2022-10-30" } ], "hoa_gold": "FALSE", "has_pgt_creators": "FALSE", "restricted_doc_count": 1, "rioxx2_version_of_record": "https:\/\/dx.doi.org\/10.1109\/TASE.2022.3219125", "publisher": "IEEE", "snip": { "num": 0, "datestamp": "2023-09-17 01:19:30", "year": 0 }, "ros_action": "auto", "public_doc_count": 0, "creators_browse_email": [ "x.hong@reading.ac.uk" ], "creators_sort": [ { "name": { "lineage": null, "given": "Jingxin", "honourific": null, "family": "Zhang" }, "id": null }, { "name": { "lineage": null, "given": "Donghua", "honourific": null, "family": "Zhou" }, "id": null }, { "name": { "lineage": null, "given": "Maoyin", "honourific": null, "family": "Chen" }, "id": null }, { "name": { "lineage": null, "given": "Xia", "honourific": null, "family": "Hong" }, "id": 90000432 } ], "rioxx2_dateAccepted": "2022-10-30", "has_pgr_creators": "FALSE", "item_issues": [ { "timestamp": "2022-11-01 05:17:29", "status": "autoresolved", "type": "old_but_not_published", "id": "old_but_not_published", "description": "Date is 30 October 0030<\/strong> but item is still marked as In Press<\/strong>." } ], "rioxx2_publication_date": "2022-11-10", "creators_browse_id": [ 90000432 ], "type": "article", "abstract": "A novel continual learning-based probabilistic slow feature analysis algorithm is introduced for monitoring multimode nonstationary processes. Multimode slow features are extracted and an elastic weight consolidation (EWC) is adopted for sequential modes. EWC was originally introduced in the setting of machine learning of sequential multi-tasks with the aim of avoiding catastrophic forgetting issue, which equally poses as a major challenge in multimode nonstationary process monitoring. When a new mode arrives, a small set of data are collected for continual learning by the proposed algorithm. A regularization term is introduced to prevent new data from significantly interfering with the learned knowledge, where the parameter importance measures are estimated. The proposed method is referred to as PSFA–EWC, which is updated continually and is capable of achieving excellent performance. PSFA–EWC furnishes backward and forward transfer ability by a single model. The significant features of previous modes are retained while consolidating new information, which may contribute to learning new relevant modes. The effectiveness of the proposed method is demonstrated via a continuous stirred tank heater and a practical coal pulverizing system. Note to Practitioners —Since industrial systems operate in varying modes and data are nonstationary within each mode, multimode nonstationary process monitoring is increasingly important. Traditional multimode monitoring methods generally need complete data from all possible modes and may need to be retrained from scratch when a new mode arrives, which require expensive computation and storage resources. Besides, it is difficult to distinguish real faults from normal variations in multimode nonstationary processes. This paper proposes a novel continual learning-based probabilistic slow feature analysis, where elastic weight consolidation is employed to consolidate the previously learned knowledge while extracting multimode slow features. The monitoring model is updated sequentially and provides backward as well as forward transfer learning ability for successive modes. It is able to separate real faults from normal dynamics, which is beneficial to identifying a new mode for multimode nonstationary processes. In addition, the proposed approach delivers excellent model interpretability and deals with missing data as well as uncertainty. In industrial applications, such as power plants and intelligent manufacturing processes, the proposed method can provide excellent monitoring performance.", "publication": "IEEE Transactions on Automation Science and Engineering", "rioxx2_version": "AM", "hoa_ref_pan": "AB", "hoa_date_fcd": "2023-03-16", "userid": 311, "issn": "1558-3783", "creators": [ { "name": { "lineage": null, "given": "Jingxin", "honourific": null, "family": "Zhang" }, "id": null }, { "name": { "lineage": null, "given": "Donghua", "honourific": null, "family": "Zhou" }, "id": null }, { "name": { "lineage": null, "given": "Maoyin", "honourific": null, "family": "Chen" }, "id": null }, { "name": { "lineage": null, "given": "Xia", "honourific": null, "family": "Hong" }, "id": 90000432 } ], "ros_submitted": "FALSE", "lastmod": "2023-09-17 01:19:30", "creators_browse_name": "Zhang, J., Zhou, D., Chen, M. and Hong, X. 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We argue that the state of practice suffers from two key issues. First, data scientists often follow a trial-and-error process and use certain heuristics to practice machine learning engineering. Therefore, their results are typically far from optimized as we show through an example in this study. Second, software engineers without deep knowledge of machine learning are often required to collaborate with data scientists, integrate and maintain their code, or even take over their tasks due to a general shortage of data scientists worldwide. Hence, there is an urgent need for tools that can support these novice machine learning practitioners. To address the mentioned issues, we deploy the model-driven engineering paradigm and enable automated machine learning in an existing software development methodology and tool that supports this paradigm.", "hoa_exclude": "FALSE", "hoa_date_foa": "2022-11-02", "date_type": "accepted", "notify_on_approval": "yes", "rioxx2_identifier": "https:\/\/centaur.reading.ac.uk\/108613\/1\/paper%206.pdf", "hoa_compliant": 318, "datestamp": "2022-11-02 16:41:45", "uri": "https:\/\/centaur.reading.ac.uk\/id\/eprint\/108613", "altmetric": { "last_updated": null, "score": null, "datestamp": "2022-11-04 02:01:22" }, "rioxx2_author": [ { "author": "Moin, Armin" }, { "author": "Wattanavaekin, Ukrit" }, { "author": "Lungu, Alexandra" }, { "author": "Badii, Atta" }, { "author": "Gunnemann, Stephan" }, { "author": "Challenger, Moharram" } ], "divs_irstats": [ "5_a2014a1p", "3_fc22d959", "1_76083589" ], "title": "Enabling automated machine learning for model-driven AI engineering", "rev_number": 20, "metadata_checked": "yes", "dir": "disk0\/00\/10\/86\/13", "rioxx2_format": "application\/pdf", "has_ug_creators": "FALSE", "ispublished": "inpress", "metadata_visibility": "show", "eprint_status": "archive", "rioxx2_language": "en", "coversheets_dirty": "FALSE", "full_text_status": "public", "rioxx2_type": "Journal Article\/Review", "refereed": "FALSE", "divisions": [ "5_a2014a1p" ], "hoa_date_acc": "2022-10-04", "reading_wip": "FALSE", "sjr": { "num": 0, "datestamp": "2023-09-03 01:57:07", "year": 0 }, "dates": [ { "date_type": "accepted", "date": "2022-10-04" } ], "hoa_gold": "FALSE", "has_pgt_creators": "FALSE", "restricted_doc_count": 0, "hoa_emb_len": 1, "rioxx2_free_to_read": { "free_to_read": "Yes" }, "publisher": "Institute of Electrical and Electronics Engineers", "snip": { "num": 0, "datestamp": "2023-09-03 01:57:07", "year": 0 }, "ros_action": "auto", "public_doc_count": 1, "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": "Ukrit", "honourific": null, "family": "Wattanavaekin" }, "id": null }, { "name": { "lineage": null, "given": "Alexandra", "honourific": null, "family": "Lungu" }, "id": null }, { "name": { "lineage": null, "given": "Atta", "honourific": null, "family": "Badii" }, "id": 90000900 }, { "name": { "lineage": null, "given": "Stephan", "honourific": null, "family": "Gunnemann" }, "id": null }, { "name": { "lineage": null, "given": "Moharram", "honourific": null, "family": "Challenger" }, "id": null } ], "rioxx2_dateAccepted": "2022-10-04", "has_pgr_creators": "FALSE", "creators_browse_id": [ 90000900 ], "type": "article", "abstract": "This article presents our work in progress in supporting automated machine learning in the model-driven engineering process of AI-enabled software systems. 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SE models may specify the architecture at different levels of abstraction and for addressing different concerns at various stages of the software development life-cycle, from early conceptualization and design, to verification, implementation, testing and evolution. However, AI models may provide smart capabilities, such as prediction and decision-making support. For instance, in Machine Learning (ML), which is currently the most popular sub-discipline of AI, mathematical models may learn useful patterns in the observed data and can become capable of making predictions. The goal of this work is to create synergy by bringing models in the said communities together and proposing a holistic approach to model-driven software development for intelligent systems that require ML. We illustrate how software models can become capable of creating and dealing with ML models in a seamless manner. 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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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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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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. If these results were also applicable to arbitrage trading strategies, then those algorithms could make a fortune for their developers. Thus, the open question is whether these results could be used to generate buy and sell signals that could be exploited with active trading. Therefore, this survey paper is intended to answer this question by reviewing these results and scrutinising their reliability. The ultimate conclusion from this analysis is that although considerable progress was achieved in this direction, even the state-of-art models can not guarantee a consistent profit in active trading. Taking this into account several suggestions for future research in this area were formulated along the three dimensions: input data, model’s architecture, and experimental setup. In particular, from the input data perspective, it is critical that the dataset is properly processed, up-to-date, and its size is sufficient for the particular model training. From the model architecture perspective, even though deep learning models are demonstrating a stronger performance than classical models, they are also more prone to over-fitting. To avoid over-fitting it is suggested to optimize the feature space, as well as a number of layers and neurons, and apply dropout functionality. The over-fitting problem can be also addressed by optimising the experimental setup in several ways: Introducing the early stopping mechanism; Saving the best weights of the model achieved during the training; Testing the model on the out-of-sample data, which should be separated from the validation and training samples. Finally, it is suggested to always conduct the trading simulation under realistic market conditions considering transactions costs, bid–ask spreads, and market impact.", "hoa_exclude": "FALSE", "hoa_date_foa": "2022-04-25", "date_type": "published", "notify_on_approval": "yes", "rioxx2_identifier": "https:\/\/centaur.reading.ac.uk\/104707\/1\/mathematics-10-01234-v2.pdf", "hoa_compliant": 511, "datestamp": "2022-04-22 13:30:55", "uri": "https:\/\/centaur.reading.ac.uk\/id\/eprint\/104707", "altmetric": { "last_updated": "2023-02-05", "score": 8, "datestamp": "2023-09-25 03:15:19" }, "rioxx2_author": [ { "id": "https:\/\/orcid.org\/0000-0003-1229-5515", "author": "Zaznov, Ilia" }, { "author": "Kunkel, Julian" }, { "id": "https:\/\/orcid.org\/0000-0003-0519-648X", "author": "Dufour, Alfonso" }, { "author": "Badii, Atta" } ], "divs_irstats": [ "1_3d300ab3", "5_a2014a1p", "3_fc22d959", "1_76083589", "3_fcf4136e" ], "title": "Predicting stock price changes based on the limit order book: a survey", "citation_count": { "num": 0, "datestamp": "2022-05-15 04:22:52" }, "number": 8, "rev_number": 38, "metadata_checked": "yes", "dir": "disk0\/00\/10\/47\/07", "rioxx2_format": "application\/pdf", "has_ug_creators": "FALSE", "ispublished": "pub", "metadata_visibility": "show", "eprint_status": "archive", "rioxx2_language": "en", "rioxx2_license_ref": { "license_ref": "https:\/\/creativecommons.org\/licenses\/by\/4.0", "start_date": "2022-04" }, "hoa_date_pub": "2022-04-09", "coversheets_dirty": "FALSE", "full_text_status": "public", "rioxx2_type": "Journal Article\/Review", "item_issues2": [ { "timestamp": "2022-04-14 22:17:12", "status": "discovered", "type": "duplicate_doi", "id": "duplicate_doi_104634", "description": "Duplicate Identification Number\/DOI to \n\nPredicting Stock Price Changes Based on the Limit Order Book: A Survey<\/a>\n\n\n
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User Workarea, \n\nUnnamed 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": 0.446, "datestamp": "2023-06-25 01:03:08", "year": 2022 }, "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": 1.008, "datestamp": "2023-06-25 01:03:08", "year": 2022 }, "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. If these results were also applicable to arbitrage trading strategies, then those algorithms could make a fortune for their developers. Thus, the open question is whether these results could be used to generate buy and sell signals that could be exploited with active trading. Therefore, this survey paper is intended to answer this question by reviewing these results and scrutinising their reliability. The ultimate conclusion from this analysis is that although considerable progress was achieved in this direction, even the state-of-art models can not guarantee a consistent profit in active trading. Taking this into account several suggestions for future research in this area were formulated along the three dimensions: input data, model’s architecture, and experimental setup. In particular, from the input data perspective, it is critical that the dataset is properly processed, up-to-date, and its size is sufficient for the particular model training. From the model architecture perspective, even though deep learning models are demonstrating a stronger performance than classical models, they are also more prone to over-fitting. To avoid over-fitting it is suggested to optimize the feature space, as well as a number of layers and neurons, and apply dropout functionality. The over-fitting problem can be also addressed by optimising the experimental setup in several ways: Introducing the early stopping mechanism; Saving the best weights of the model achieved during the training; Testing the model on the out-of-sample data, which should be separated from the validation and training samples. Finally, it is suggested to always conduct the trading simulation under realistic market conditions considering transactions costs, bid–ask spreads, and market impact.", "publication": "Mathematics", "rioxx2_version": "VoR", "hoa_ref_pan": "AB", "hoa_date_fcd": "2022-04-14", "userid": 10359, "issn": "2227-7390", "creators": [ { "orcid": "0000-0003-1229-5515", "name": { "lineage": null, "given": "Ilia", "honourific": null, "family": "Zaznov" }, "id": null, "role": "pgr" }, { "orcid": null, "name": { "lineage": null, "given": "Julian", "honourific": null, "family": "Kunkel" }, "id": 90009151, "role": null }, { "orcid": "0000-0003-0519-648X", "name": { "lineage": null, "given": "Alfonso", "honourific": null, "family": "Dufour" }, "id": 90001524, "role": null }, { "orcid": null, "name": { "lineage": null, "given": "Atta", "honourific": null, "family": "Badii" }, "id": 90000900, "role": null } ], "ros_submitted": "FALSE", "document_number": 1234, "lastmod": "2023-09-25 03:15:19", "creators_browse_name": "Zaznov, I., Kunkel, J. , Dufour, A. and Badii, A. 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They affect network security and user data, which leads to a considerable loss of money and data every year. This paper presents the use of classification algorithms in machine learning using a method to classify the login data filtering inputs into \"SQLi\" or \"Non-SQLi,” thus increasing the reliability and accuracy of results in terms of deciding whether an operation is an attack or a valid operation. A method as a Web-App is developed for auto-generated data replication to provide a twin of the targeted data structure. Shielding against SQLi attacks (WebAppShield) that verifies all users and prevents attackers (SQLi attacks) from entering and or accessing the database, which the machine learning module predicts as \"Non-SQLi\", has been developed. A special login form has been developed with a special instance of the data validation; this verification process secures the web application from its early stages. 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It is shown that if the Gaussian kernel centres and kernel width are known, then the maximum likelihood parameter estimator can be formulated as a Riemannian optimisation problem on sphere manifold. The first order Riemannian geometry of the sphere manifold and vector transport are initially explored, then the well-known Riemannian conjugate gradient algorithm is used to estimate the model parameters. For completeness, the k-means clustering algorithm and a grid search are employed to determine the centres and kernel width, respectively. 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The field of research in Data Stream Mining (DSM) has emerged to respond to the challenges and opportunities of developing the required analytics to unlock valuable knowledge. Thus DSM is focused on building Data Mining models, workflows and algorithms enabling the efficient and effective analysis of such streaming data at a large scale- the so-called “Big Data”. Examples of application areas of Data Stream Mining techniques include real-time telecommunication data, telemetric data from large industrial plants, credit card transactions, cyber security threat modelling, social media data, etc. For some applications it is acceptable to provide data processing, modelling and analysis in batch mode using the traditional Data Mining approaches. 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We propose a multi-output neural tree (MONT) algorithm, which is an evolutionary learning algorithm trained by the non-dominated sorting genetic algorithm (NSGA)-III. Since evolutionary learning is stochastic, a hypothesis found in the form of MONT is unique for each run of evolutionary learning, i.e., each hypothesis (tree) generated bears distinct properties compared to any other hypothesis both in topological space and parameter-space. This leads to a challenging optimisation problem where the aim is to minimise the tree-size and maximise the classification accuracy. Therefore, the Pareto-optimality concerns were met by hypervolume indicator analysis. We used nine benchmark classification learning problems to evaluate the performance of the MONT. As a result of our experiments, we obtained MONTs which are able to tackle the classification problems with high accuracy. 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We propose a multi-output neural tree (MONT) algorithm, which is an evolutionary learning algorithm trained by the non-dominated sorting genetic algorithm (NSGA)-III. Since evolutionary learning is stochastic, a hypothesis found in the form of MONT is unique for each run of evolutionary learning, i.e., each hypothesis (tree) generated bears distinct properties compared to any other hypothesis both in topological space and parameter-space. This leads to a challenging optimisation problem where the aim is to minimise the tree-size and maximise the classification accuracy. Therefore, the Pareto-optimality concerns were met by hypervolume indicator analysis. We used nine benchmark classification learning problems to evaluate the performance of the MONT. As a result of our experiments, we obtained MONTs which are able to tackle the classification problems with high accuracy. The performance of MONT emerged better over a set of problems tackled in this study compared with a set of well-known classifiers: multilayer perceptron, reduced-error pruning tree, naive Bayes classifier, decision tree, and support vector machine. Moreover, the performances of three versions of MONT’s training using genetic programming, NSGA-II, and NSGA-III suggests that the NSGA-III gives the best Pareto-optimal solution.", "rioxx2_version": "AM", "hoa_ref_pan": "AB", "hoa_date_fcd": "2020-04-14", "userid": 18504, "creators": [ { "name": { "lineage": null, "given": "Varun", "honourific": null, "family": "Ojha" }, "id": 90009423 }, { "name": { "lineage": null, "given": "Giuseppe", "honourific": null, "family": "Nicosia" }, "id": 90008704 } ], "ros_submitted": "FALSE", "lastmod": "2022-07-25 09:15:47", "creators_browse_name": "Ojha, V. and Nicosia, G. 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Both dataset-based\r\nand end user evaluations of system functionalities are reported in order to determine the\r\neffectiveness and efficiency of the components directly involved and the platform as a\r\nwhole.", "hoa_exclude": "FALSE", "hoa_date_foa": "2020-04-29", "date_type": "published", "notify_on_approval": "yes", "rioxx2_identifier": "https:\/\/centaur.reading.ac.uk\/85107\/1\/SAM_MTA_paper-final-pre-print-September%202019.pdf", "hoa_compliant": 501, "datestamp": "2019-07-18 10:58:20", "uri": "https:\/\/centaur.reading.ac.uk\/id\/eprint\/85107", "altmetric": { "last_updated": null, "score": null, "datestamp": "2019-07-19 02:01:38" }, "rioxx2_author": [ { "author": "Tomás, David" }, { "author": "Gutiérrez, Yoan" }, { "author": "Badii, Atta" }, { "author": "Tiemann, Marco" }, { "author": "Aisopos, Fotis" } ], "divs_irstats": [ "5_a2014a1p", "3_fc22d959", "1_76083589" ], "title": "Socialising around media. 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This article describes three key functionalities used in the SAM platform in order to create an advanced interactive and social second screen experience for\r\nusers: semantic analysis, context awareness and dynamic communities. Both dataset-based\r\nand end user evaluations of system functionalities are reported in order to determine the\r\neffectiveness and efficiency of the components directly involved and the platform as a\r\nwhole.", "publication": "Multimedia Tools and Applications", "rioxx2_version": "AM", "hoa_ref_pan": "AB", "hoa_date_fcd": "2020-02-18", "userid": 19032, "issn": "1573-772", "creators": [ { "name": { "lineage": null, "given": "David", "honourific": null, "family": "Tomás" }, "id": null }, { "name": { "lineage": null, "given": "Yoan", "honourific": null, "family": "Gutiérrez" }, "id": null }, { "name": { "lineage": null, "given": "Atta", "honourific": null, "family": "Badii" }, "id": 90000900 }, { "name": { "lineage": null, "given": "Marco", "honourific": null, "family": "Tiemann" }, "id": 90003301 }, { "name": { "lineage": null, "given": "Fotis", "honourific": null, "family": "Aisopos" }, "id": null } ], "ros_submitted": "FALSE", "lastmod": "2023-06-25 04:12:50", "creators_browse_name": "Tomás, D., Gutiérrez, Y., Badii, A. , Tiemann, M. and Aisopos, F.", "rioxx2_source": "1573-772", "id_number": "10.1007\/s11042-019-7706-1", "status_changed": "2019-07-18 10:58:20", "hoa_version_fcd": "AM", "volume": 78, "suggestions": "Sourced from Scopus. 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The analyses on E. coli highlighted a single knockout strategy producing 16.49mmolgDW−1h−1 (+679.29% ) ethanol, with biomass equals to 0.23h−1 (−77.45% ). We also discuss results obtained by applying MOME to metabolic models of: (i) S. aureus; (ii) S. enterica; (iii) Y. pestis; (iv) S. cerevisiae; (v) C. reinhardtii; (vi) Y. lipolytica. We finally present a set of simulations in which constrains over essential genes and minimum allowable biomass were included. A bound over the maximum allowable biomass was also added, along with other settings representing rich media compositions. 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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. Performance of the proposed algorithm is evaluated by\r\nstatistical analysis of results which produced 100% gait segmentation accuracy for healthy subjects and over\r\n99% for pathological subjects.", "hoa_exclude": "FALSE", "date_type": "published", "notify_on_approval": "yes", "rioxx2_identifier": "https:\/\/centaur.reading.ac.uk\/88158\/1\/Khan-Badii-Gait-Rehabilitation-2019.pdf", "hoa_compliant": 309, "datestamp": "2020-01-09 10:49:18", "uri": "https:\/\/centaur.reading.ac.uk\/id\/eprint\/88158", "altmetric": { "last_updated": null, "score": null, "datestamp": "2020-01-04 02:01:19" }, "rioxx2_author": [ { "author": "Badii, Atta" }, { "author": "Khan, Wasiq" } ], "divs_irstats": [ "5_a2014a1p", "3_fc22d959", "1_76083589" ], "title": "Pathological gait abnormality detection and segmentation by processing the hip joints motion data to support mobile gait rehabilitation", "number": 3, "rev_number": 41, "metadata_checked": "yes", "dir": "disk0\/00\/08\/81\/58", "rioxx2_format": "application\/pdf", "has_ug_creators": "FALSE", "ispublished": "pub", "metadata_visibility": "show", "eprint_status": "archive", "rioxx2_language": "en", "hoa_date_pub": "2019-01-09", "coversheets_dirty": "FALSE", "full_text_status": "restricted", "rioxx2_type": "Journal Article\/Review", "refereed": "TRUE", "divisions": [ "5_a2014a1p" ], "hoa_date_acc": "2019-07-31", "reading_wip": "FALSE", "sjr": { "num": 0, "datestamp": "2023-06-25 04:24:11", "year": 0 }, "dates": [ { "date_type": "published", "date": 2019 }, { "date_type": "published_online", "date": "2019-01-09" }, { "date_type": "accepted", "date": "2019-07-31" } ], "official_url": "https:\/\/crimsonpublishers.com\/rmes\/pdf\/RMES.000662.pdf", "hoa_gold": "FALSE", "has_pgt_creators": "FALSE", "restricted_doc_count": 1, "publisher": "Crimson Publishers", "pagerange": "754-762", "snip": { "num": 0, "datestamp": "2023-06-25 04:24:11", "year": 0 }, "ros_action": "auto", "public_doc_count": 0, "creators_browse_email": [ "atta.badii@reading.ac.uk" ], "creators_sort": [ { "name": { "lineage": null, "given": "Atta", "honourific": null, "family": "Badii" }, "id": 90000900 }, { "name": { "lineage": null, "given": "Wasiq", "honourific": null, "family": "Khan" }, "id": null } ], "rioxx2_dateAccepted": "2019-07-31", "has_pgr_creators": "FALSE", "rioxx2_publication_date": 2019, "creators_browse_id": [ 90000900 ], "type": "article", "abstract": "An accurate detection of the gait sub-phases is fundamental in clinical gait analysis to interpret kinetic and\r\nkinematic data. 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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ambient noise\r\nwithin the acoustic stream. The above three types of acoustic signals were recorded from subjects, without\r\nany clinical symptoms of dysphagia, with a microphone attached to the neck at a pre-studied position midway\r\nbetween the Laryngeal Prominence and the Jugular Notch. Frequency-based analysis detection algorithms\r\nwere developed to distinguish the above three types of acoustic signals with an accuracy of 86.09%.\r\nIntegrated automatic detection algorithms with classification based on Gaussian Mixture Model (GMM) using\r\nthe Expectation Maximisation algorithm (EM), achieved an overall validated recognition rate of 87.60% which\r\nincreased to 88.87 recognition accuracy if the validated false alarm classifications were also to be included.\r\nThe proposed approach thus enables the recovery from ambient signals, detection and time-stamping of the\r\nacoustic footprints of the swallowing process chain and thus further analytics to characterise the swallowing\r\nprocess in terms of consistency, normality and possibly risk-assessing and localising the level of any\r\nswallowing abnormality i.e. the dysphagia. As such this helps reduce the need for invasive techniques for\r\nexamination and evaluation of patient’s swallowing process and enables diagnostic clinical evaluation based\r\nonly on acoustic data analytics and non-invasive clinical observations.", "isbn": 9789897583537, "hoa_exclude": "FALSE", "rioxx2_contributor": [ "Moucek, Roman", "Fred, Ana", "Gamboa, Hugo" ], "date_type": "published", "notify_on_approval": "yes", "rioxx2_identifier": "https:\/\/centaur.reading.ac.uk\/88155\/1\/Automatic_detection_and_recognition_of_swallowing_sounds-Khlaifi-Badii%20et%20al.pdf", "datestamp": "2020-01-09 10:46:19", "uri": "https:\/\/centaur.reading.ac.uk\/id\/eprint\/88155", "altmetric": { "last_updated": null, "score": null, "datestamp": "2020-01-04 02:01:18" }, "rioxx2_author": [ { "author": "Khlaifi, Hajer" }, { "author": "Badii, Atta" }, { "author": "Istrate, Dan" }, { "author": "Demongeot, Jacques" } ], "divs_irstats": [ "5_a2014a1p", "3_fc22d959", "1_76083589" ], "editors": [ { "name": { "lineage": null, "given": "Roman", "honourific": null, "family": "Moucek" } }, { "name": { "lineage": null, "given": "Ana", "honourific": null, "family": "Fred" } }, { "name": { "lineage": null, "given": "Hugo", "honourific": null, "family": "Gamboa" } } ], "title": "Automatic detection and recognition of swallowing sounds", "citation_count": { "num": 0, "datestamp": "2020-08-02 04:37:25" }, "metadata_checked": "yes", "rev_number": 33, "dir": "disk0\/00\/08\/81\/55", "rioxx2_format": "application\/pdf", "has_ug_creators": "FALSE", "ispublished": "pub", "metadata_visibility": "show", "eprint_status": "archive", "rioxx2_language": "en", "hoa_date_pub": 2019, "coversheets_dirty": "FALSE", "full_text_status": "restricted", "rioxx2_type": "Book chapter", "item_issues2": [ { "timestamp": "2020-01-02 22:09:36", "status": "discovered", "type": "duplicate_doi", "id": "duplicate_doi_82936", "description": "Duplicate Identification Number\/DOI to \n\nAutomatic Detection and Recognition of Swallowing Sounds<\/a>\n\n\n
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Retired, \n\nUnnamed user with username JiscRouter<\/span><\/a>\n\n- \n[ Manage<\/a> ] [ Acknowledge<\/a> ]" } ], "refereed": "TRUE", "divisions": [ "5_a2014a1p" ], "hoa_date_acc": "2018-11-31", "reading_wip": "FALSE", "sjr": { "num": null, "datestamp": "2020-01-05 01:04:22", "year": null }, "dates": [ { "date_type": "published", "date": 2019 }, { "date_type": "accepted", "date": "2018-11-31" } ], "has_pgt_creators": "FALSE", "restricted_doc_count": 1, "rioxx2_version_of_record": "https:\/\/dx.doi.org\/10.5220\/0007310802210229", "publisher": "SciTePress", "pagerange": "221-229", "snip": { "num": null, "datestamp": "2020-01-05 01:04:22", "year": null }, "ros_action": "auto", "public_doc_count": 0, "creators_browse_email": [ "atta.badii@reading.ac.uk" ], "creators_sort": [ { "name": { "lineage": null, "given": "Hajer", "honourific": null, "family": "Khlaifi" }, "id": null }, { "name": { "lineage": null, "given": "Atta", "honourific": null, "family": "Badii" }, "id": 90000900 }, { "name": { "lineage": null, "given": "Dan", "honourific": null, "family": "Istrate" }, "id": null }, { "name": { "lineage": null, "given": "Jacques", "honourific": null, "family": "Demongeot" }, "id": null } ], "rioxx2_dateAccepted": "2018-11-31", "book_title": "Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies", "has_pgr_creators": "FALSE", "rioxx2_publication_date": 2019, "creators_browse_id": [ 90000900 ], "type": "book_section", "abstract": "This paper proposes a non-invasive, acoustic-based method to i) automatically detect sounds through a neckworn microphone providing a stream of acoustic input comprising of a) swallowing-related, b) speech and c)\r\nother ambient sounds (noise); ii) classify and detect swallowing-related sounds, speech or ambient noise\r\nwithin the acoustic stream. The above three types of acoustic signals were recorded from subjects, without\r\nany clinical symptoms of dysphagia, with a microphone attached to the neck at a pre-studied position midway\r\nbetween the Laryngeal Prominence and the Jugular Notch. Frequency-based analysis detection algorithms\r\nwere developed to distinguish the above three types of acoustic signals with an accuracy of 86.09%.\r\nIntegrated automatic detection algorithms with classification based on Gaussian Mixture Model (GMM) using\r\nthe Expectation Maximisation algorithm (EM), achieved an overall validated recognition rate of 87.60% which\r\nincreased to 88.87 recognition accuracy if the validated false alarm classifications were also to be included.\r\nThe proposed approach thus enables the recovery from ambient signals, detection and time-stamping of the\r\nacoustic footprints of the swallowing process chain and thus further analytics to characterise the swallowing\r\nprocess in terms of consistency, normality and possibly risk-assessing and localising the level of any\r\nswallowing abnormality i.e. the dysphagia. As such this helps reduce the need for invasive techniques for\r\nexamination and evaluation of patient’s swallowing process and enables diagnostic clinical evaluation based\r\nonly on acoustic data analytics and non-invasive clinical observations.", "publication": "Proceedings of the", "rioxx2_version": "AM", "place_of_pub": "Prague, Czech Republic", "hoa_date_fcd": "2020-01-02", "userid": 19401, "creators": [ { "name": { "lineage": null, "given": "Hajer", "honourific": null, "family": "Khlaifi" }, "id": null }, { "name": { "lineage": null, "given": "Atta", "honourific": null, "family": "Badii" }, "id": 90000900 }, { "name": { "lineage": null, "given": "Dan", "honourific": null, "family": "Istrate" }, "id": null }, { "name": { "lineage": null, "given": "Jacques", "honourific": null, "family": "Demongeot" }, "id": null } ], "ros_submitted": "FALSE", "lastmod": "2021-07-09 22:33:22", "creators_browse_name": "Khlaifi, H., Badii, A. , Istrate, D. and Demongeot, J.", "rioxx2_source": 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This longitudinal empirical research is focused on the study of the impact on the management of cardio-vascular disease if supported by sustained health monitoring using wearable connected devices. One of the key objectives is stress monitoring and its classification during the daily routine of life thus enabling psycho-physiological monitoring to study the correlation between emotional states including variable stress levels and the evolution and prognosis of cardiovascular disease. In this paper, the calibration phase will be studied in order to distinguish between two emotional states: i) meditation and ii) stress condition. For this, the Heart Rate Variability (HRV) features are used as extracted from the RR interval and a support vectors machine (SVM) classifier deployed which resulted in 74% and 87% recognition accuracy based on HRV data for the recognition of the two emotional states, namely meditative, and, stressed, respectively.\r\nThe main objective is to prevent Cardio-Vascular Disease (CVD) in healthy people and to treat those who already suffer from it. Creating a reference database was our first step in this research project. The sensor choice was made based on doctors’ recommendations. The work methodology was as follows: first validate the « objective data » issuing from the calibration state. Second, set up the automatic algorithm and detect automatically the patient’s emotional states during the experimentation period (subjective data). Third analyse the physical activities correlated to the blood pressure and emotions. This study has involved the challenge of distinguishing the influence of stress versus relaxation on the Cardio-Vascular function and in particular on the risk of exacerbation of pre-existing Cardio-Vascular Disease.", "hoa_exclude": "FALSE", "date_type": "published", "notify_on_approval": "yes", "rioxx2_identifier": "https:\/\/centaur.reading.ac.uk\/88159\/1\/Cardiovascular%20Stress-2019.pdf", "hoa_compliant": 309, "datestamp": "2020-01-09 10:40:28", "uri": "https:\/\/centaur.reading.ac.uk\/id\/eprint\/88159", "altmetric": { "last_updated": null, "score": null, "datestamp": "2020-01-04 02:01:20" }, "rioxx2_author": [ { "author": "Tlija, Amira" }, { "author": "Istrate, Dan" }, { "author": "Badii, Atta" }, { "author": "Gattoufi, Said" }, { "author": "Bennani, Az-eddine" }, { "author": "Wegrzyn-Wolska, Katarzyna" } ], "divs_irstats": [ "5_a2014a1p", "3_fc22d959", "1_76083589" ], "title": "Stress level classification using heart rate variability", "citation_count": { "num": 0, "datestamp": "2020-08-09 04:36:57" }, "number": 3, "rev_number": 44, "metadata_checked": "yes", "dir": "disk0\/00\/08\/81\/59", "rioxx2_format": "application\/pdf", "has_ug_creators": "FALSE", "ispublished": "pub", "metadata_visibility": "show", "eprint_status": "archive", "rioxx2_language": "en", "hoa_date_pub": "2019-05-09", "coversheets_dirty": "FALSE", "full_text_status": "restricted", "rioxx2_type": "Journal Article\/Review", "refereed": "TRUE", "divisions": [ "5_a2014a1p" ], "hoa_date_acc": "2019-04-08", "reading_wip": "FALSE", "sjr": { "num": 0.188, "datestamp": "2023-06-11 04:52:40", "year": 2021 }, "dates": [ { "date_type": "published", "date": 2019 }, { "date_type": "published_online", "date": "2019-05-09" }, { "date_type": "accepted", "date": "2019-04-08" } ], "hoa_gold": "FALSE", "has_pgt_creators": "FALSE", "restricted_doc_count": 1, "rioxx2_version_of_record": "https:\/\/dx.doi.org\/10.25046\/aj040306", "publisher": "Society of Polish Mechanical Engineers and Technicians", "pagerange": "38-46", "snip": { "num": 0.449, "datestamp": "2023-06-11 04:52:40", "year": 2022 }, "ros_action": "auto", "public_doc_count": 0, "creators_browse_email": [ "atta.badii@reading.ac.uk" ], "creators_sort": [ { "name": { "lineage": null, "given": "Amira", "honourific": null, "family": "Tlija" }, "id": null }, { "name": { "lineage": null, "given": "Dan", "honourific": null, "family": "Istrate" }, "id": null }, { "name": { "lineage": null, "given": "Atta", "honourific": null, "family": "Badii" }, "id": 90000900 }, { "name": { "lineage": null, "given": "Said", "honourific": null, "family": "Gattoufi" }, "id": null }, { "name": { "lineage": null, "given": "Az-eddine", "honourific": null, "family": "Bennani" }, "id": null }, { "name": { "lineage": null, "given": "Katarzyna", "honourific": null, "family": "Wegrzyn-Wolska" }, "id": null } ], "rioxx2_dateAccepted": "2019-04-08", "has_pgr_creators": "FALSE", "rioxx2_publication_date": 2019, "creators_browse_id": [ 90000900 ], "type": "article", "abstract": "The research programme reported in this paper is set within the framework of our research under the theme of ICT support for Active Healthy Ageing (AHA). This longitudinal empirical research is focused on the study of the impact on the management of cardio-vascular disease if supported by sustained health monitoring using wearable connected devices. One of the key objectives is stress monitoring and its classification during the daily routine of life thus enabling psycho-physiological monitoring to study the correlation between emotional states including variable stress levels and the evolution and prognosis of cardiovascular disease. In this paper, the calibration phase will be studied in order to distinguish between two emotional states: i) meditation and ii) stress condition. For this, the Heart Rate Variability (HRV) features are used as extracted from the RR interval and a support vectors machine (SVM) classifier deployed which resulted in 74% and 87% recognition accuracy based on HRV data for the recognition of the two emotional states, namely meditative, and, stressed, respectively.\r\nThe main objective is to prevent Cardio-Vascular Disease (CVD) in healthy people and to treat those who already suffer from it. Creating a reference database was our first step in this research project. The sensor choice was made based on doctors’ recommendations. The work methodology was as follows: first validate the « objective data » issuing from the calibration state. Second, set up the automatic algorithm and detect automatically the patient’s emotional states during the experimentation period (subjective data). Third analyse the physical activities correlated to the blood pressure and emotions. This study has involved the challenge of distinguishing the influence of stress versus relaxation on the Cardio-Vascular function and in particular on the risk of exacerbation of pre-existing Cardio-Vascular Disease.", "publication": "Advances in Science, Technology and Engineering Systems Journal", "rioxx2_version": "AM", "hoa_ref_pan": "AB", "hoa_date_fcd": "2020-01-02", "userid": 19401, "issn": "2415-6698", "creators": [ { "name": { "lineage": null, "given": "Amira", "honourific": null, "family": "Tlija" }, "id": null }, { "name": { "lineage": null, "given": "Dan", "honourific": null, "family": "Istrate" }, "id": null }, { "name": { "lineage": null, "given": "Atta", "honourific": null, "family": "Badii" }, "id": 90000900 }, { "name": { "lineage": null, "given": "Said", "honourific": null, "family": "Gattoufi" }, "id": null }, { "name": { "lineage": null, "given": "Az-eddine", "honourific": null, "family": "Bennani" }, "id": null }, { "name": { "lineage": null, "given": "Katarzyna", "honourific": null, "family": "Wegrzyn-Wolska" }, "id": null } ], "ros_submitted": "FALSE", "lastmod": "2023-06-11 04:52:40", "creators_browse_name": "Tlija, A., Istrate, D., Badii, A. , Gattoufi, S., Bennani, A.-e. and Wegrzyn-Wolska, K.", "rioxx2_source": "2415-6698", "id_number": "10.25046\/aj040306", "status_changed": "2020-01-09 10:40:28", "hoa_version_fcd": "VoR", "volume": 4, "suggestions": "NODOA. 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}, { "type": "http:\/\/eprints.org\/relation\/isVolatileVersionOf", "uri": "\/id\/document\/598927" }, { "type": "http:\/\/eprints.org\/relation\/isCoversheetVersionOf", "uri": "\/id\/document\/598927" } ], "security": "public", "pos": 7, "formatdesc": "Coversheet version" } ], "divisions_browse": [ "5_a2014a1p", "3_fc22d959" ], "rioxx2_title": "Process monitoring based on orthogonal locality preserving projection with maximum likelihood estimation", "rioxx2_description": "By integrating two powerful methods of density reduction and intrinsic dimensionality estimation, a new data-driven method, referred to as OLPP-MLE (orthogonal locality preserving projection-maximum likelihood estimation), is introduced for process monitoring. OLPP is utilized for dimensionality reduction, which provides better locality preserving power than locality preserving projection. Then, the MLE is adopted to estimate intrinsic dimensionality of OLPP. Within the proposed OLPP-MLE, two new static measures for fault detection TOLPP2 and SPEOLPP are defined. In order to reduce algorithm complexity and ignore data distribution, kernel density estimation is employed to compute thresholds for fault diagnosis. The effectiveness of the proposed method is demonstrated by three case studies.", "hoa_exclude": "FALSE", "hoa_date_foa": "2020-03-20", "date_type": "published", "notify_on_approval": "yes", "rioxx2_identifier": "https:\/\/centaur.reading.ac.uk\/80781\/1\/ie-2018-05875r.R3.pdf", "hoa_compliant": 511, "datestamp": "2019-04-09 15:42:39", "uri": "https:\/\/centaur.reading.ac.uk\/id\/eprint\/80781", "altmetric": { "last_updated": "2019-03-29", "score": 1, "datestamp": "2019-06-01 04:51:23" }, "rioxx2_author": [ { "author": "Zhang, Jingxin" }, { "author": "Chen, Maoyin" }, { "author": "Chen, Hao" }, { "author": "Hong, Xia" }, { "author": "Zhou, Donghua" } ], "divs_irstats": [ "5_a2014a1p", "3_fc22d959", "1_76083589" ], "title": "Process monitoring based on orthogonal locality preserving projection with maximum likelihood estimation", "citation_count": { "num": 1, "datestamp": "2020-08-30 08:39:24" }, "number": 14, "rev_number": 65, "metadata_checked": "yes", "dir": "disk0\/00\/08\/07\/81", "rioxx2_format": "application\/pdf", "has_ug_creators": "FALSE", "ispublished": "pub", "metadata_visibility": "show", "eprint_status": "archive", "rioxx2_language": "en", "hoa_date_pub": "2019-03-19", "coversheets_dirty": "FALSE", "full_text_status": "public", "rioxx2_type": "Journal Article\/Review", "item_issues2": [ { "timestamp": "2019-04-18 22:07:44", "status": "discovered", "type": "duplicate_doi", "id": "duplicate_doi_83339", "description": "Duplicate Identification Number\/DOI to \n\nProcess Monitoring Based on Orthogonal Locality Preserving Projection with Maximum Likelihood Estimation<\/a>\n\n\n
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