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Full-Text Articles in Engineering
Towards A More Effective Bidirectional Lstm-Based Learning Model For Human-Bacterium Protein-Protein Interactions, Huaming Chen, Jun Shen, Lei Wang, Yaochu Jin
Towards A More Effective Bidirectional Lstm-Based Learning Model For Human-Bacterium Protein-Protein Interactions, Huaming Chen, Jun Shen, Lei Wang, Yaochu Jin
Faculty of Engineering and Information Sciences - Papers: Part B
The identification of protein-protein interaction (PPI) is one of the most important tasks to understand the biological functions and disease mechanisms. Although numerous databases of biological interactions have been published in debt to advanced high-throughput technology, the study of inter-species protein-protein interactions, especially between human and bacterium pathogens, remains an active yet challenging topic to harness computational models tackling the complex analysis and prediction tasks. In this paper, we comprehensively revisit the prediction task of human-bacterium protein-protein interactions (HB-PPI), which is a first ever endeavour to report an empirical evaluation in learning and predicting HB-PPI based on machine learning models. …
Utilizing Qr Codes To Verify The Visual Fidelity Of Image Datasets For Machine Learning, Yang-Wai Chow, Willy Susilo, Jianfang Wang, Richard Buckland, Joon Sang Baek, Jongkil Kim, Nan Li
Utilizing Qr Codes To Verify The Visual Fidelity Of Image Datasets For Machine Learning, Yang-Wai Chow, Willy Susilo, Jianfang Wang, Richard Buckland, Joon Sang Baek, Jongkil Kim, Nan Li
Faculty of Engineering and Information Sciences - Papers: Part B
Machine learning is becoming increasingly popular in modern technology and has been adopted in various application areas. However, researchers have demonstrated that machine learning models are vulnerable to adversarial examples in their inputs, which has given rise to a field of research known as adversarial machine learning. Potential adversarial attacks include methods of poisoning datasets by perturbing input samples to mislead machine learning models into producing undesirable results. While such perturbations are often subtle and imperceptible from the perspective of a human, they can greatly affect the performance of machine learning models. This paper presents two methods of verifying the …
From Ideal To Reality: Segmentation, Annotation, And Recommendation, The Vital Trajectory Of Intelligent Micro Learning, Jiayin Lin, Geng Sun, Tingru Cui, Jun Shen, Dongming Xu, Ghassan Beydoun, Ping Yu, David Pritchard, Li Li, Shiping Chen
From Ideal To Reality: Segmentation, Annotation, And Recommendation, The Vital Trajectory Of Intelligent Micro Learning, Jiayin Lin, Geng Sun, Tingru Cui, Jun Shen, Dongming Xu, Ghassan Beydoun, Ping Yu, David Pritchard, Li Li, Shiping Chen
Faculty of Engineering and Information Sciences - Papers: Part B
The soaring development of Web technologies and mobile devices has blurred time-space boundaries of people’s daily activities. Such development together with the life-long learning requirement give birth to a new learning style, micro learning. Micro learning aims to effectively utilize learners’ fragmented time to carry out personalized learning activities through online education resources. The whole workflow of a micro learning system can be separated into three processing stages: micro learning material generation, learning materials annotation and personalized learning materials delivery. Our micro learning framework is firstly introduced in this paper from a higher perspective. Then we will review representative segmentation …
Apex2s: A Two-Layer Machine Learning Model For Discovery Of Host-Pathogen Protein-Protein Interactions On Cloud-Based Multiomics Data, Huaming Chen, Jun Shen, Lei Wang, Chi-Hung Chi
Apex2s: A Two-Layer Machine Learning Model For Discovery Of Host-Pathogen Protein-Protein Interactions On Cloud-Based Multiomics Data, Huaming Chen, Jun Shen, Lei Wang, Chi-Hung Chi
Faculty of Engineering and Information Sciences - Papers: Part B
No abstract provided.
Evolutionary Learner Profile Optimization Using Rare And Negative Association Rules For Micro Open Learning, Geng Sun, Jiayin Lin, Jun Shen, Tingru Cui, Dongming Xu, Huaming Chen
Evolutionary Learner Profile Optimization Using Rare And Negative Association Rules For Micro Open Learning, Geng Sun, Jiayin Lin, Jun Shen, Tingru Cui, Dongming Xu, Huaming Chen
Faculty of Engineering and Information Sciences - Papers: Part B
The actual data availability, readiness and publicity has slowed down the research of making use of computational intelligence to improve the knowledge delivery in an emerging learning mode, namely adaptive micro open learning, which naturally has high demand in quality and quantity of data to be fed. In this study, we contribute a novel approach to tackle the current scarcity of both data and rules in micro open learning, by adopting evolutionary algorithm to produce association rules with both rare and negative associations taken into account. These rules further drive the generation and optimization of learner profiles through refinement and …
Deep Sequence Labelling Model For Information Extraction In Micro Learning Service, Jiayin Lin, Zhexuan Zhou, Geng Sun, Jun Shen, David Pritchard, Tingru Cui, Dongming Xu, Li Li, Ghassan Beydoun
Deep Sequence Labelling Model For Information Extraction In Micro Learning Service, Jiayin Lin, Zhexuan Zhou, Geng Sun, Jun Shen, David Pritchard, Tingru Cui, Dongming Xu, Li Li, Ghassan Beydoun
Faculty of Engineering and Information Sciences - Papers: Part B
Micro learning aims to assist users in making good use of smaller chunks of spare time and provides an effective online learning service. However, to provide such personalized online services on the Web, a number of information overload challenges persist. Effectively and precisely mining and extracting valuable information from massive and redundant information is a significant preprocessing procedure for personalizing online services. In this study, we propose a deep sequence labelling model for locating, extracting, and classifying key information for micro learning services. The proposed model is general and combines the advantages of different types of classical neural network. Early …
Refinement And Augmentation For Data In Micro Learning Activity With An Evolutionary Rule Generators, Geng Sun, Jiayin Lin, Tingru Cui, Jun Shen, Dongming Xu, Mahesh Kayastha
Refinement And Augmentation For Data In Micro Learning Activity With An Evolutionary Rule Generators, Geng Sun, Jiayin Lin, Tingru Cui, Jun Shen, Dongming Xu, Mahesh Kayastha
Faculty of Engineering and Information Sciences - Papers: Part B
Improving both the quantity and quality of existing data are placed at the center of research for adaptive micro open learning. To cover this research gap, our work targets on the current scarcity of both data and rules that represent open learning activities. An evolutionary rule generator is constructed, which consists of an outer loop and an inner loop. The outer loop runs a genetic algorithm (GA) to produce association rules that can be effective in the micro open learning scenario from a small amount of available data sources; while the inner loop optimizes generated candidates by taking into account …
Ensemble Machine Learning Approaches For Webshell Detection In Internet Of Things Environments, Binbin Yong, Wei Wei, Kuan-Ching Li, Jun Shen, Qingguo Zhou, Marcin Wozniak, Dawid Polap, Robertas Damasevicius
Ensemble Machine Learning Approaches For Webshell Detection In Internet Of Things Environments, Binbin Yong, Wei Wei, Kuan-Ching Li, Jun Shen, Qingguo Zhou, Marcin Wozniak, Dawid Polap, Robertas Damasevicius
Faculty of Engineering and Information Sciences - Papers: Part B
The Internet of things (IoT), made up of a massive number of sensor devices interconnected, can be used for data exchange, intelligent identification, and management of interconnected “things.” IoT devices are proliferating and playing a crucial role in improving the living quality and living standard of the people. However, the real IoT is more vulnerable to attack by countless cyberattacks from the Internet, which may cause privacy data leakage, data tampering and also cause significant harm to society and individuals. Network security is essential in the IoT system, and Web injection is one of the most severe security problems, especially …
Cooperative Secondary Voltage Control Of Static Converters In A Microgrid Using Model-Free Reinforcement Learning, Edward Smith, Duane A. Robinson, Ashish P. Agalgaonkar
Cooperative Secondary Voltage Control Of Static Converters In A Microgrid Using Model-Free Reinforcement Learning, Edward Smith, Duane A. Robinson, Ashish P. Agalgaonkar
Faculty of Engineering and Information Sciences - Papers: Part B
Agent-based secondary voltage regulation in an islanded MicroGrid is complicated by non-linear system dynamics, state couplings and uncertain communication network topology information. This paper proposes an off-policy learning algorithm for cooperative secondary voltage control which can synthesize an optimal feedback controller in real-time without knowledge of the system model. A simulation model has been developed using MATLAB/Simulink, which demonstrates a working controller. Results from the simulations are included, and practical considerations regarding implementation on a real system discussed.
Mlaas: A Cloud-Based System For Delivering Adapative Micro Learning In Mobile Mooc Learning, Geng Sun, Tingru Cui, Jianming Yong, Jun Shen, Shiping Chen
Mlaas: A Cloud-Based System For Delivering Adapative Micro Learning In Mobile Mooc Learning, Geng Sun, Tingru Cui, Jianming Yong, Jun Shen, Shiping Chen
Faculty of Engineering and Information Sciences - Papers: Part B
No abstract provided.
Ontological Learner Profile Identification For Cold Start Problem In Micro Learning Resources Delivery, Geng Sun, Tingru Cui, Jun Shen, Dongming Xu, Ghassan Beydoun, Shiping Chen
Ontological Learner Profile Identification For Cold Start Problem In Micro Learning Resources Delivery, Geng Sun, Tingru Cui, Jun Shen, Dongming Xu, Ghassan Beydoun, Shiping Chen
Faculty of Engineering and Information Sciences - Papers: Part B
No abstract provided.
Vdes J2325-5229 A Z = 2.7 Gravitationally Lensed Quasar Discovered Using Morphology-Independent Supervised Machine Learning, Fernanda Ostrovski, Richard G. Mcmahon, Andrew J. Connolly, Cameron A. Lemon, Matthew W. Auger, Manda Banerji, Johnathan M. Hung, Sergey E. Koposov, Christopher E. Lidman
Vdes J2325-5229 A Z = 2.7 Gravitationally Lensed Quasar Discovered Using Morphology-Independent Supervised Machine Learning, Fernanda Ostrovski, Richard G. Mcmahon, Andrew J. Connolly, Cameron A. Lemon, Matthew W. Auger, Manda Banerji, Johnathan M. Hung, Sergey E. Koposov, Christopher E. Lidman
Faculty of Engineering and Information Sciences - Papers: Part B
We present the discovery and preliminary characterization of a gravitationally lensed quasar with a source redshift zs = 2.74 and image separation of 2.9 arcsec lensed by a foreground zl = 0.40 elliptical galaxy. Since optical observations of gravitationally lensed quasars show the lens system as a superposition of multiple point sources and a foreground lensing galaxy, we have developed a morphology-independent multi-wavelength approach to the photometric selection of lensed quasar candidates based on Gaussian Mixture Models (GMM) supervised machine learning. Using this technique and gi multicolour photometric observations from the Dark Energy Survey (DES), near-IR JK photometry …
A Framework Of Mlaas For Facilitating Adaptive Micro Learning Through Open Education Resources In Mobile Environment, Geng Sun, Tingru Cui, Wanwu Guo, Shiping Chen, Jun Shen
A Framework Of Mlaas For Facilitating Adaptive Micro Learning Through Open Education Resources In Mobile Environment, Geng Sun, Tingru Cui, Wanwu Guo, Shiping Chen, Jun Shen
Faculty of Engineering and Information Sciences - Papers: Part B
No abstract provided.
Sbar: A Conceptual Framework To Support Learning Path Adaptation In Mobile Learning, Alva Hendi Muhammad, Jun Shen, Ghassan Beydoun, Dongming Xu
Sbar: A Conceptual Framework To Support Learning Path Adaptation In Mobile Learning, Alva Hendi Muhammad, Jun Shen, Ghassan Beydoun, Dongming Xu
Faculty of Engineering and Information Sciences - Papers: Part B
No abstract provided.
A Comparison Study For Supervised Machine Learning Models In Cancer Classification, Huaming Chen, Hong Zhao, Lei Wang, Jiangning Song, Jun Shen
A Comparison Study For Supervised Machine Learning Models In Cancer Classification, Huaming Chen, Hong Zhao, Lei Wang, Jiangning Song, Jun Shen
Faculty of Engineering and Information Sciences - Papers: Part B
No abstract provided.