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Prof. Victor Chang
Computational Systems Biology and Data Analytics (CBD) Research Group, School of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough, Tees Valley, TS1 3BX, UK

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0 Information Systems
0 Internet of Things - IoT
0 Cybersecuirty
0 Data Science
0 artificial intelligence

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Internet of Things - IoT
Information Systems
Data Science
artificial intelligence

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Chapter
Published: 30 July 2021 in Information Security Technologies for Controlling Pandemics
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The increase and advancements of network technology play a significant role in the lives of us. This is through homes, businesses and people in a professional and social capacity. The way we use technology in everyday life aids the friendships, achievements and entertainment parts of our day to day life. This makes the fundamental device in the network important to every one of us for conducting our day to day life–that device is the router. Much like a fridge or cooker would have in the 1950 s, the router is considered as a critical device in the home and business setting. This paper aims to demonstrate a penetration test documentation of a standard home or small business Router–TP-Link WR940N by using the operating system Kali Linux 2019/2020. The main aim of the paper is to show the extent of what someone can do and the lengths someone can go from essentially sitting outside your house in a car with a raspberry pi, a Wi-Fi adapter, a seven-inch screen, and a travel battery for power.

ACS Style

Lewis Golightly; Victor Chang; Qianwen Ariel Xu. Towards Ethical Hacking—The Performance of Hacking a Router. Information Security Technologies for Controlling Pandemics 2021, 435 -461.

AMA Style

Lewis Golightly, Victor Chang, Qianwen Ariel Xu. Towards Ethical Hacking—The Performance of Hacking a Router. Information Security Technologies for Controlling Pandemics. 2021; ():435-461.

Chicago/Turabian Style

Lewis Golightly; Victor Chang; Qianwen Ariel Xu. 2021. "Towards Ethical Hacking—The Performance of Hacking a Router." Information Security Technologies for Controlling Pandemics , no. : 435-461.

Original research
Published: 08 July 2021 in Annals of Operations Research
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The widespread Internet of Things (IoT) technologies in day life indoor environments result in an enormous amount of daily generated data, which require reliable data analysis techniques to enable efficient exploitation of this data. The recent developments in deep learning (DL) have facilitated the processing and learning from the massive IoT data and learn essential features swiftly and professionally for a variety of IoT applications on smart indoor environments. This study surveys the recent literature on exploiting DL for different indoor IoT applications. We aim to give insights into how the DL approaches can be employed from various viewpoints to develop improved Indoor IoT applications in two distinct domains: indoor positioning/tracking and activity recognition. A primary target is to effortlessly amalgamate the two disciplines of IoT and DL, resultant in a broad range of innovative strategies in indoor IoT applications, such as health monitoring, smart home control, robotics, etc. Further, we have derived a thematic taxonomy from the comparative analysis of technical studies of the three beforementioned domains. Eventually, we proposed and discussed a set of matters, challenges, and some new directions in incorporating DL to improve the efficiency of indoor IoT applications, encouraging and stimulating additional advances in this auspicious research area.

ACS Style

Mohamed Abdel-Basset; Victor Chang; Hossam Hawash; Ripon K. Chakrabortty; Michael Ryan. Deep learning approaches for human-centered IoT applications in smart indoor environments: a contemporary survey. Annals of Operations Research 2021, 1 -49.

AMA Style

Mohamed Abdel-Basset, Victor Chang, Hossam Hawash, Ripon K. Chakrabortty, Michael Ryan. Deep learning approaches for human-centered IoT applications in smart indoor environments: a contemporary survey. Annals of Operations Research. 2021; ():1-49.

Chicago/Turabian Style

Mohamed Abdel-Basset; Victor Chang; Hossam Hawash; Ripon K. Chakrabortty; Michael Ryan. 2021. "Deep learning approaches for human-centered IoT applications in smart indoor environments: a contemporary survey." Annals of Operations Research , no. : 1-49.

Article
Published: 01 July 2021 in International Journal of Distributed Systems and Technologies
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Agriculture occupation has been the prime occupation in India since the primeval era. Nowadays, the country is ranked second in the prime occupations threatening global warming. Apart from this, diseases in plants are challenging to this prime source of livelihood. The present research can help in recognition of different diseases among plants and help to find out the solution or remedy that can be a defense mechanism in counter to the diseases. Finding diseases among plant DL is considered to the most perfect and exact paradigms. Four labels are classified as “bacterial spot,” “yellow leaf curl virus,” “late blight,” and “healthy leaf.” An exemplar model of the drone is also designed for the purpose. The said model will be utilized for a live report for extended large crop fields. In this exemplar drone model, a high-resolution camera is attached. The captured images of plants will act as software input. On this basis, the software will immediately tell which plants are healthy and which are diseased.

ACS Style

Anshul Tripathi; Uday Chourasia; Priyanka Dixit; Victor Chang. A Survey. International Journal of Distributed Systems and Technologies 2021, 12, 1 -26.

AMA Style

Anshul Tripathi, Uday Chourasia, Priyanka Dixit, Victor Chang. A Survey. International Journal of Distributed Systems and Technologies. 2021; 12 (3):1-26.

Chicago/Turabian Style

Anshul Tripathi; Uday Chourasia; Priyanka Dixit; Victor Chang. 2021. "A Survey." International Journal of Distributed Systems and Technologies 12, no. 3: 1-26.

Journal article
Published: 01 July 2021 in International Journal of Business Intelligence Research
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Near Field Communication (NFC) mobile payment systems allow users to utilize services through smartphones. There is insufficient literature exploring the adoption of NFC with payment scenarios in developing countries. This study aims to explore the influential factors of consumer adoption of NFC, taking payment behaviors through NFC in Indonesia as an example. One hundred forty-seven participants were enrolled in the 5-point Likert scale survey, and 124 valid samples were analyzed with Partial Least Squares Structural Equation Modeling (PLS-SEM). The results show that trust mediates the effect of context on consumers' continuous intention to use NFC mobile payment. Additionally, trust mediates the effect of perceived risk on consumers' continuous intention to use. The perceived ease of use and perceived usefulness have no effects on consumers' continuous intention to use. The mediating effect of religiosity has not been observed in this study. The findings can enbale service providers and local governments to offer better mobile payment services.

ACS Style

Siwei Sun; Fangyu Zhang; Kaicheng Liao; Victor Chang. Determine Factors of NFC Mobile Payment Continuous Adoption in Shopping Malls. International Journal of Business Intelligence Research 2021, 12, 1 -20.

AMA Style

Siwei Sun, Fangyu Zhang, Kaicheng Liao, Victor Chang. Determine Factors of NFC Mobile Payment Continuous Adoption in Shopping Malls. International Journal of Business Intelligence Research. 2021; 12 (2):1-20.

Chicago/Turabian Style

Siwei Sun; Fangyu Zhang; Kaicheng Liao; Victor Chang. 2021. "Determine Factors of NFC Mobile Payment Continuous Adoption in Shopping Malls." International Journal of Business Intelligence Research 12, no. 2: 1-20.

Journal article
Published: 24 June 2021 in Journal of Parallel and Distributed Computing
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Mobile devices (MDs) and applications are receiving extensive popularity and attracting significant attention. Mobile applications, especially for artificial intelligence (AI) applications, require powerful computation-intensive resources. Hence, running all the AI applications on a single MD introduces high energy consumption and application delay, as it has limited battery capacity and computation resources. Fortunately, the emerging edge-cloud computing (ECC) architecture pushes the computation resource to both the network edge and remote cloud to cope with challenging AI applications. Although the advantage of ECC greatly benefits various mobile applications, data security remains an important open issue in this scenario, which has not been well studied. This paper focuses on the profit maximization (PM) problem for security-aware task offloading in an ECC environment, i.e., considering the tasks from MDs with different service demands, edge nodes should decide them to be processed on the edge node or the remote cloud with a security guarantee. Specifically, we first construct the security model to measure the time overhead for each task under various scenarios. We then formulate the PM problem by jointly considering the security demand and deadline constraints of tasks. Finally, we propose a genetic algorithm-based PM (GA-PM) algorithm, the coding strategy of which considers the task execution location and execution order. Moreover, the crossover and mutation operations are implemented based on the coding strategy. Extensive simulation experiments with various parameters varying demonstrate that our GA-PM can achieve better performance than all the comparison algorithms.

ACS Style

Zhongjin Li; Victor Chang; Haiyang Hu; Dongjin Yu; Jidong Ge; Binbin Huang. Profit maximization for security-aware task offloading in edge-cloud environment. Journal of Parallel and Distributed Computing 2021, 157, 43 -55.

AMA Style

Zhongjin Li, Victor Chang, Haiyang Hu, Dongjin Yu, Jidong Ge, Binbin Huang. Profit maximization for security-aware task offloading in edge-cloud environment. Journal of Parallel and Distributed Computing. 2021; 157 ():43-55.

Chicago/Turabian Style

Zhongjin Li; Victor Chang; Haiyang Hu; Dongjin Yu; Jidong Ge; Binbin Huang. 2021. "Profit maximization for security-aware task offloading in edge-cloud environment." Journal of Parallel and Distributed Computing 157, no. : 43-55.

Journal article
Published: 24 June 2021 in Future Generation Computer Systems
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Deep neural network (DNN) has been widely applied in many fields of artificial intelligence (AI), gaining great popularity both in industry and academia. Increasing the size of DNN models does dramatically improve the learning accuracy. However, training large-scale DNN models on a single GPU takes unacceptable waiting time. In order to speed up the training process, many distributed deep learning (DL) systems and frameworks have been published and designed for parallel DNN training with multiple GPUs. However, most of the existing studies concentrate only on improving the training speed of a single DNN model under centralized or decentralized systems with synchronous or asynchronous approaches. Few works consider the issue of multi-DNN training on the GPU cluster, which is the joint optimization problem of job scheduling and resource allocation. This paper proposes an optimizing makespan and resource utilization (OMRU) approach to minimize job completion time and improve resource utilization for multi-DNN training in a GPU cluster. Specifically, we first collect the training speed/time data of all DNN models by running a job for one epoch on a different number of GPUs. The OMRU algorithm, integrating job scheduling, resource allocation, and GPU reuse strategies, is then devised to minimize the total job completion time (also called makespan) and improve GPU cluster resource utilization. The linear scaling rule (LSR) is adopted for adjusting the learning rate when a DNN model is trained on multiple GPUs with large minibatch size, which can guarantee model accuracy without the other hyper-parameters tune-up. We implement the OMRU algorithm on the Pytorch with Ring-Allreduce communication architecture and a GPU cluster with 8 nodes, each of which has 4 NVIDIA V100 GPUs. Experimental results on image classification and action recognition show that OMRU achieves a makespan reduction of up to 30% compared to the baseline scheduling algorithms and reach an average of 98.4% and 99.2% resource utilization on image classification and action recognition, respectively, with the state-of-the-art model accuracy.

ACS Style

Zhongjin Li; Victor Chang; Haiyang Hu; Maozhong Fu; Jidong Ge; Francesco Piccialli. Optimizing makespan and resource utilization for multi-DNN training in GPU cluster. Future Generation Computer Systems 2021, 125, 206 -220.

AMA Style

Zhongjin Li, Victor Chang, Haiyang Hu, Maozhong Fu, Jidong Ge, Francesco Piccialli. Optimizing makespan and resource utilization for multi-DNN training in GPU cluster. Future Generation Computer Systems. 2021; 125 ():206-220.

Chicago/Turabian Style

Zhongjin Li; Victor Chang; Haiyang Hu; Maozhong Fu; Jidong Ge; Francesco Piccialli. 2021. "Optimizing makespan and resource utilization for multi-DNN training in GPU cluster." Future Generation Computer Systems 125, no. : 206-220.

Journal article
Published: 25 May 2021 in Applied Sciences
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This paper studies the generalized normal distribution algorithm (GNDO) performance for tackling the permutation flow shop scheduling problem (PFSSP). Because PFSSP is a discrete problem and GNDO generates continuous values, the largest ranked value rule is used to convert those continuous values into discrete ones to make GNDO applicable for solving this discrete problem. Additionally, the discrete GNDO is effectively integrated with a local search strategy to improve the quality of the best-so-far solution in an abbreviated version of HGNDO. More than that, a new improvement using the swap mutation operator applied on the best-so-far solution to avoid being stuck into local optima by accelerating the convergence speed is effectively applied to HGNDO to propose a new version, namely a hybrid-improved GNDO (HIGNDO). Last but not least, the local search strategy is improved using the scramble mutation operator to utilize each trial as ideally as possible for reaching better outcomes. This improved local search strategy is integrated with IGNDO to produce a new strong algorithm abbreviated as IHGNDO. Those proposed algorithms are extensively compared with a number of well-established optimization algorithms using various statistical analyses to estimate the optimal makespan for 41 well-known instances in a reasonable time. The findings show the benefits and speedup of both IHGNDO and HIGNDO over all the compared algorithms, in addition to HGNDO.

ACS Style

Mohamed Abdel-Basset; Reda Mohamed; Mohamed Abouhawwash; Victor Chang; S. Askar. A Local Search-Based Generalized Normal Distribution Algorithm for Permutation Flow Shop Scheduling. Applied Sciences 2021, 11, 4837 .

AMA Style

Mohamed Abdel-Basset, Reda Mohamed, Mohamed Abouhawwash, Victor Chang, S. Askar. A Local Search-Based Generalized Normal Distribution Algorithm for Permutation Flow Shop Scheduling. Applied Sciences. 2021; 11 (11):4837.

Chicago/Turabian Style

Mohamed Abdel-Basset; Reda Mohamed; Mohamed Abouhawwash; Victor Chang; S. Askar. 2021. "A Local Search-Based Generalized Normal Distribution Algorithm for Permutation Flow Shop Scheduling." Applied Sciences 11, no. 11: 4837.

Journal article
Published: 24 May 2021 in Computer Networks
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In device-to-device (D2D) networks, multiple resource-limited mobile devices cooperate with one another to execute computation tasks. As the battery capacity of mobile devices is limited, the computation tasks running on the mobile devices will terminate once the battery is dead. In order to achieve sustainable computation, energy-harvesting technology has been introduced into D2D networks. At present, how to make multiple energy harvesting mobile devices work collaboratively to minimize the long-term system cost for task execution under limited computing, network and battery capacity constraint is a challenging issue. To deal with such a challenge, in this paper, we design a multi-agent deep deterministic policy gradient (MADDPG) based cost-aware collaborative task-execution (CACTE) scheme in energy harvesting D2D (EH-D2D) networks. To validate the CACTE scheme's performance, we conducted extensive experiments to compare the CACTE scheme with four baseline algorithms, including Local, Random, ECLB (Energy Capacity Load Balance) and CCLB (Computing Capacity Load Balance). Experiments were accompanied by various system parameters, such as the mobile device's battery capacity, task workload, the bandwidth and so on. The experimental results show that the CACTE scheme can make multiple mobile devices cooperate effectively with one another to execute many more tasks and achieve a higher long-term reward, including lower task latency and fewer dropped tasks.

ACS Style

Binbin Huang; Xiao Liu; Shangguang Wang; Linxuan Pan; Victor Chang. Multi-agent reinforcement learning for cost-aware collaborative task execution in energy-harvesting D2D networks. Computer Networks 2021, 195, 108176 .

AMA Style

Binbin Huang, Xiao Liu, Shangguang Wang, Linxuan Pan, Victor Chang. Multi-agent reinforcement learning for cost-aware collaborative task execution in energy-harvesting D2D networks. Computer Networks. 2021; 195 ():108176.

Chicago/Turabian Style

Binbin Huang; Xiao Liu; Shangguang Wang; Linxuan Pan; Victor Chang. 2021. "Multi-agent reinforcement learning for cost-aware collaborative task execution in energy-harvesting D2D networks." Computer Networks 195, no. : 108176.

Journal article
Published: 07 May 2021 in Computers & Electrical Engineering
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Recent studies have shown that cyberbullying is a rising youth epidemic. In this paper, we develop a novel automated classification model that identifies the cyberbullying texts without fitting them into large dimensional space. On the other hand, a classifier .cannot provide a limited convergent solution due to its overfitting problem. Considering such limitations, we developed a text classification engine that initially pre-processes the tweets, eliminates noise and other background information, extracts the selected features and classifies without data overfitting. The study develops a novel Deep Decision Tree classifier that utilizes the hidden layers of Deep Neural Network (DNN) as its tree node to process the input elements. The validation confirms the accuracy of classification using the novel Deep classifier with its improved text classification accuracy.

ACS Style

Natarajan Yuvaraj; Victor Chang; Balasubramanian Gobinathan; Arulprakash Pinagapani; Srihari Kannan; Gaurav Dhiman; Arsath Raja Rajan. Automatic detection of cyberbullying using multi-feature based artificial intelligence with deep decision tree classification. Computers & Electrical Engineering 2021, 92, 107186 .

AMA Style

Natarajan Yuvaraj, Victor Chang, Balasubramanian Gobinathan, Arulprakash Pinagapani, Srihari Kannan, Gaurav Dhiman, Arsath Raja Rajan. Automatic detection of cyberbullying using multi-feature based artificial intelligence with deep decision tree classification. Computers & Electrical Engineering. 2021; 92 ():107186.

Chicago/Turabian Style

Natarajan Yuvaraj; Victor Chang; Balasubramanian Gobinathan; Arulprakash Pinagapani; Srihari Kannan; Gaurav Dhiman; Arsath Raja Rajan. 2021. "Automatic detection of cyberbullying using multi-feature based artificial intelligence with deep decision tree classification." Computers & Electrical Engineering 92, no. : 107186.

Original article
Published: 06 May 2021 in Expert Systems
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A smart and scalable system is required to schedule various machine learning applications to control pandemics like COVID‐19 using computing infrastructure provided by cloud and fog computing. This paper proposes a framework that considers the use case of smart office surveillance to monitor workplaces for detecting possible violations of COVID effectively. The proposed framework uses deep neural networks, fog computing and cloud computing to develop a scalable and time‐sensitive infrastructure that can detect two major violations: wearing a mask and maintaining a minimum distance of 6 feet between employees in the office environment. The proposed framework is developed with the vision to integrate multiple machine learning applications and handle the computing infrastructures for pandemic applications. The proposed framework can be used by application developers for the rapid development of new applications based on the requirements and do not worry about scheduling. The proposed framework is tested for two independent applications and performed better than the traditional cloud environment in terms of latency and response time. The work done in this paper tries to bridge the gap between machine learning applications and their computing infrastructure for COVID‐19.

ACS Style

Ajay Singh; Vaibhav Jindal; Rajinder Sandhu; Victor Chang. A scalable framework for smart COVID surveillance in the workplace using Deep Neural Networks and cloud computing. Expert Systems 2021, e12704 .

AMA Style

Ajay Singh, Vaibhav Jindal, Rajinder Sandhu, Victor Chang. A scalable framework for smart COVID surveillance in the workplace using Deep Neural Networks and cloud computing. Expert Systems. 2021; ():e12704.

Chicago/Turabian Style

Ajay Singh; Vaibhav Jindal; Rajinder Sandhu; Victor Chang. 2021. "A scalable framework for smart COVID surveillance in the workplace using Deep Neural Networks and cloud computing." Expert Systems , no. : e12704.

Article
Published: 20 April 2021 in Information Systems Frontiers
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With the rise in cases of COVID-19, a bizarre situation of pressure was mounted on each country to make arrangements to control the population and utilize the available resources appropriately. The swiftly rising of positive cases globally created panic, anxiety and depression among people. The effect of this deadly disease was found to be directly proportional to the physical and mental health of the population. As of 28 October 2020, more than 40 million people are tested positive and more than 1 million deaths have been recorded. The most dominant tool that disturbed human life during this time is social media. The tweets regarding COVID-19, whether it was a number of positive cases or deaths, induced a wave of fear and anxiety among people living in different parts of the world. Nobody can deny the truth that social media is everywhere and everybody is connected with it directly or indirectly. This offers an opportunity for researchers and data scientists to access the data for academic and research use. The social media data contains many data that relate to real-life events like COVID-19. In this paper, an analysis of Twitter data has been done through the R programming language. We have collected the Twitter data based on hashtag keywords, including COVID-19, coronavirus, deaths, new case, recovered. In this study, we have designed an algorithm called Hybrid Heterogeneous Support Vector Machine (H-SVM) and performed the sentiment classification and classified them positive, negative and neutral sentiment scores. We have also compared the performance of the proposed algorithm on certain parameters like precision, recall, F1 score and accuracy with Recurrent Neural Network (RNN) and Support Vector Machine (SVM).

ACS Style

Harleen Kaur; Shafqat Ul Ahsaan; Bhavya Alankar; Victor Chang. A Proposed Sentiment Analysis Deep Learning Algorithm for Analyzing COVID-19 Tweets. Information Systems Frontiers 2021, 1 -13.

AMA Style

Harleen Kaur, Shafqat Ul Ahsaan, Bhavya Alankar, Victor Chang. A Proposed Sentiment Analysis Deep Learning Algorithm for Analyzing COVID-19 Tweets. Information Systems Frontiers. 2021; ():1-13.

Chicago/Turabian Style

Harleen Kaur; Shafqat Ul Ahsaan; Bhavya Alankar; Victor Chang. 2021. "A Proposed Sentiment Analysis Deep Learning Algorithm for Analyzing COVID-19 Tweets." Information Systems Frontiers , no. : 1-13.

Article
Published: 09 April 2021 in Cluster Computing
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Network function virtualization (NFV) has gained prominence in next-generation cloud computing, such as the fog-based radio access network, due to their ability to support better QoS in network service provision. However, most of the current service function chain (SFC) deployment researches do not consider the Security-Service-Level-Agreement (SSLA) in the deployment solution. Therefore, in this work, we introduce the SSLA into SFC deployment to defend attacks. Firstly, we formulate the SSLA guaranteed SFC deployment problem by using linear programming. Then, we propose the Maximal-security SFC deployment algorithm (MS) to maximize the security of the SFC deployment. However, the MS algorithm results in a high deployment cost. To reduce the deployment cost, we propose the Minimal-cost and SSLA-guaranteed SFC deployment algorithm (MCSG) to minimize the deployment while satisfying the SSLA. In order to reduce the blocking ratio caused by MCSG, the Minimal-cost and SSLA-guaranteed SFC deployment algorithm with feedback adjustment (MCSG-FA) is proposed. Finally, we evaluate our proposed algorithms through simulations. The simulation results show that the blocking ratio and the deployment cost of our algorithms are better than that of the existing algorithm when meeting the SSLAs.

ACS Style

Dongcheng Zhao; Long Luo; Hongfang Yu; Victor Chang; Rajkumar Buyya; Gang Sun. Security-SLA-guaranteed service function chain deployment in cloud-fog computing networks. Cluster Computing 2021, 1 -16.

AMA Style

Dongcheng Zhao, Long Luo, Hongfang Yu, Victor Chang, Rajkumar Buyya, Gang Sun. Security-SLA-guaranteed service function chain deployment in cloud-fog computing networks. Cluster Computing. 2021; ():1-16.

Chicago/Turabian Style

Dongcheng Zhao; Long Luo; Hongfang Yu; Victor Chang; Rajkumar Buyya; Gang Sun. 2021. "Security-SLA-guaranteed service function chain deployment in cloud-fog computing networks." Cluster Computing , no. : 1-16.

Journal article
Published: 08 April 2021 in Electronics
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Pedestrian detection has attracted great research attention in video surveillance, traffic statistics, and especially in autonomous driving. To date, almost all pedestrian detection solutions are derived from conventional framed-based image sensors with limited reaction speed and high data redundancy. Dynamic vision sensor (DVS), which is inspired by biological retinas, efficiently captures the visual information with sparse, asynchronous events rather than dense, synchronous frames. It can eliminate redundant data transmission and avoid motion blur or data leakage in high-speed imaging applications. However, it is usually impractical to directly apply the event streams to conventional object detection algorithms. For this issue, we first propose a novel event-to-frame conversion method by integrating the inherent characteristics of events more efficiently. Moreover, we design an improved feature extraction network that can reuse intermediate features to further reduce the computational effort. We evaluate the performance of our proposed method on a custom dataset containing multiple real-world pedestrian scenes. The results indicate that our proposed method raised its pedestrian detection accuracy by about 5.6–10.8%, and its detection speed is nearly 20% faster than previously reported methods. Furthermore, it can achieve a processing speed of about 26 FPS and an AP of 87.43% when implanted on a single CPU so that it fully meets the requirement of real-time detection.

ACS Style

Jixiang Wan; Ming Xia; Zunkai Huang; Li Tian; Xiaoying Zheng; Victor Chang; Yongxin Zhu; Hui Wang. Event-Based Pedestrian Detection Using Dynamic Vision Sensors. Electronics 2021, 10, 888 .

AMA Style

Jixiang Wan, Ming Xia, Zunkai Huang, Li Tian, Xiaoying Zheng, Victor Chang, Yongxin Zhu, Hui Wang. Event-Based Pedestrian Detection Using Dynamic Vision Sensors. Electronics. 2021; 10 (8):888.

Chicago/Turabian Style

Jixiang Wan; Ming Xia; Zunkai Huang; Li Tian; Xiaoying Zheng; Victor Chang; Yongxin Zhu; Hui Wang. 2021. "Event-Based Pedestrian Detection Using Dynamic Vision Sensors." Electronics 10, no. 8: 888.

Journal article
Published: 22 March 2021 in IEEE Transactions on Industrial Informatics
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The recent diagnosis of COVID-19 is based on real-time reverse-transcriptase polymerase chain reaction (RT-PCR) and is regarded as the gold standard for confirmation of infection. It has already been widely recognized that deep learning techniques can potentially have a substantial role in streamlining and accelerating the diagnosis of COVID-19 patients. Numerous open dataset enterprises have been set up over the past weeks to help the researchers develop and check methods that could contribute to countering the Corona pandemic. In order to report the above unique problems in the diagnosis of COVID-19, pioneering techniques should be developed. This special issue focuses on novel deep learning imaging analysis techniques related to COVID-19.

ACS Style

Victor Chang; Mohamed Abdel-Basset; Rahat Iqbal; Gary Wills. Guest Editorial:Advanced Deep Learning Techniques for COVID-19. IEEE Transactions on Industrial Informatics 2021, 17, 6476 -6479.

AMA Style

Victor Chang, Mohamed Abdel-Basset, Rahat Iqbal, Gary Wills. Guest Editorial:Advanced Deep Learning Techniques for COVID-19. IEEE Transactions on Industrial Informatics. 2021; 17 (9):6476-6479.

Chicago/Turabian Style

Victor Chang; Mohamed Abdel-Basset; Rahat Iqbal; Gary Wills. 2021. "Guest Editorial:Advanced Deep Learning Techniques for COVID-19." IEEE Transactions on Industrial Informatics 17, no. 9: 6476-6479.

Journal article
Published: 17 March 2021 in EURASIP Journal on Wireless Communications and Networking
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With the development of the wireless network, increasing mobile applications are emerging and receiving great popularity. These applications cover a wide area, such as traffic monitoring, smart homes, real-time vision processing, objective tracking, and so on, and typically require computation-intensive resources to achieve a high quality of experience. Although the performance of mobile devices (MDs) has been continuously enhanced, running all the applications on a single MD still causes high energy consumption and latency. Fortunately, mobile edge computing (MEC) allows MDs to offload their computation-intensive tasks to proximal eNodeBs (eNBs) to augment computational capabilities. However, the current task offloading schemes mainly concentrate on average-based performance metrics, failing to meet the deadline constraint of the tasks. Based on the deep reinforcement learning (DRL) approach, this paper proposes an Energy-aware Task Offloading with Deadline constraint (DRL-E2D) algorithm for a multi-eNB MEC environment, which is to maximize the reward under the deadline constraint of the tasks. In terms of the actor-critic framework, we integrate the action representation into DRL-E2D to handle the large discrete action space problem, i.e., using the low-complexity k-nearest neighbor as an approximate approach to extract optimal discrete actions from the continuous action space. The extensive experimental results show that DRL-E2D achieves better performance than the comparison algorithms on all parameter settings, indicating that DRL-E2D is robust to the state changes in the MEC environment.

ACS Style

Zhongjin Li; Victor Chang; Jidong Ge; Linxuan Pan; Haiyang Hu; Binbin Huang. Energy-aware task offloading with deadline constraint in mobile edge computing. EURASIP Journal on Wireless Communications and Networking 2021, 2021, 1 -24.

AMA Style

Zhongjin Li, Victor Chang, Jidong Ge, Linxuan Pan, Haiyang Hu, Binbin Huang. Energy-aware task offloading with deadline constraint in mobile edge computing. EURASIP Journal on Wireless Communications and Networking. 2021; 2021 (1):1-24.

Chicago/Turabian Style

Zhongjin Li; Victor Chang; Jidong Ge; Linxuan Pan; Haiyang Hu; Binbin Huang. 2021. "Energy-aware task offloading with deadline constraint in mobile edge computing." EURASIP Journal on Wireless Communications and Networking 2021, no. 1: 1-24.

Journal article
Published: 09 March 2021 in Information Sciences
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Cloud computing has become a popular technology for executing scientific workflows. However, with a large number of hosts and virtual machines (VMs) being deployed, the cloud resource failures, such as the permanent failure of hosts (HPF), the transient failure of hosts (HTF), and the transient failure of VMs (VMTF), bring the service reliability problem. Therefore, fault tolerance for time-consuming scientific workflows is highly essential in the cloud. However, existing fault-tolerant (FT) approaches consider only one or two above failure types and easily neglect the others, especially for the HTF. This paper proposes a Real-time and dynamic Fault-tolerant Scheduling (ReadyFS) algorithm for scientific workflow execution in a cloud, which guarantees deadline constraints and improves resource utilization even in the presence of any resource failure. Specifically, we first introduce two FT mechanisms, i.e., the replication with delay execution (RDE) and the checkpointing with delay execution (CDE), to cope with HPF and VMTF, simultaneously. Additionally, the rescheduling (ReSC) is devised to tackle the HTF that affects the resource availability of the entire cloud datacenter. Then, the resource adjustment (RA) strategy, including the resource scaling-up (RS-Up) and the resource scaling-down (RS-Down), is used to adjust resource demands and improve resource utilization dynamically. Finally, the ReadyFS algorithm is presented to schedule real-time scientific workflows by combining all the above FT mechanisms with RA strategy. We conduct the performance evaluation with real-world scientific workflows and compare ReadyFS with five vertical comparison algorithms and three horizontal comparison algorithms. Simulation results confirm that ReadyFS is indeed able to guarantee the fault tolerance of scientific workflow execution and improve cloud resource utilization.

ACS Style

Zhongjin Li; Victor Chang; Haiyang Hu; Hua Hu; Chuanyi Li; Jidong Ge. Real-time and dynamic fault-tolerant scheduling for scientific workflows in clouds. Information Sciences 2021, 568, 13 -39.

AMA Style

Zhongjin Li, Victor Chang, Haiyang Hu, Hua Hu, Chuanyi Li, Jidong Ge. Real-time and dynamic fault-tolerant scheduling for scientific workflows in clouds. Information Sciences. 2021; 568 ():13-39.

Chicago/Turabian Style

Zhongjin Li; Victor Chang; Haiyang Hu; Hua Hu; Chuanyi Li; Jidong Ge. 2021. "Real-time and dynamic fault-tolerant scheduling for scientific workflows in clouds." Information Sciences 568, no. : 13-39.

Journal article
Published: 15 February 2021 in Computer Networks
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While Android smartphones are widely used in 5G networks, third-party application platforms are facing a rapid increase in the screening of applications for market launch. However, on the one hand, due to the receipt of excessive applications for listing, the review requires a lot of time and computing resources. On the other hand, due to the multi-selectivity of Android application features, it is difficult to determine the best feature combination as a criterion for distinguishing benign and malicious software. To address these challenges, this paper proposes an efficient malware detection framework based on deep neural network called DLAMD that can face large-scale samples. An efficient detection framework is designed, which combines the pre-detection phase of rapid detection and the deep detection phase of deep detection. The Android application package (APK) is analyzed in detail, and the permissions and opcodes feature that can distinguish benign from malicious are quickly extracted from the APK. Besides, to obtain the feature subset that can distinguish the attributes most, the random forest with good effect is selected for importance selection and the convolutional neural network (CNN) which automatically extracted the hidden pattern inside features is selected for feature selection. In the experiment, real data from shared malware collection and third-party application download platforms are used to verify the high efficiency of the proposed method. The results show that the comprehensive classification index F1-score of DLAMD can reach 95.69%.

ACS Style

Ning Lu; Dan Li; Wenbo Shi; Pandi Vijayakumar; Francesco Piccialli; Victor Chang. An efficient combined deep neural network based malware detection framework in 5G environment. Computer Networks 2021, 189, 107932 .

AMA Style

Ning Lu, Dan Li, Wenbo Shi, Pandi Vijayakumar, Francesco Piccialli, Victor Chang. An efficient combined deep neural network based malware detection framework in 5G environment. Computer Networks. 2021; 189 ():107932.

Chicago/Turabian Style

Ning Lu; Dan Li; Wenbo Shi; Pandi Vijayakumar; Francesco Piccialli; Victor Chang. 2021. "An efficient combined deep neural network based malware detection framework in 5G environment." Computer Networks 189, no. : 107932.

Journal article
Published: 06 February 2021 in Technological Forecasting and Social Change
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This paper presents the role of global Industry 4.0 technology management in the growth of the wind turbine industry. The article begins with a brief overview of the Industry 4.0 wind turbine industry development, focusing on factors shaping this development. The legal policies are identified as one of the significant factors, especially in PR China and Germany. A detailed secondary data analysis of the country-specific systems is presented, followed by the analysis of patents and companies in both countries to understand better how the development, management and transfer of technology affected the different factors and the global patterns. An effective approach of acquiring technology for local enterprises as well as market development entry mode for the foreign technology holding companies are both identified. Accessing technology through licensing, entering joint ventures, or acquiring knowledge-intensive companies can be identified as common and often successful industry approaches. To develop, obtain, or maintain competitive advantages in the wind turbine industry, we suggest that the governments issue relevant legislation and regulations to support the upgrading of the industry, and the enterprises can access and manage the technology through the approaches mentioned above.

ACS Style

Victor Chang; Yian Chen; Zuopeng (Justin) Zhang; Qianwen Ariel Xu; Patricia Baudier; Ben S.C. Liu. The market challenge of wind turbine industry-renewable energy in PR China and Germany. Technological Forecasting and Social Change 2021, 166, 120631 .

AMA Style

Victor Chang, Yian Chen, Zuopeng (Justin) Zhang, Qianwen Ariel Xu, Patricia Baudier, Ben S.C. Liu. The market challenge of wind turbine industry-renewable energy in PR China and Germany. Technological Forecasting and Social Change. 2021; 166 ():120631.

Chicago/Turabian Style

Victor Chang; Yian Chen; Zuopeng (Justin) Zhang; Qianwen Ariel Xu; Patricia Baudier; Ben S.C. Liu. 2021. "The market challenge of wind turbine industry-renewable energy in PR China and Germany." Technological Forecasting and Social Change 166, no. : 120631.

Journal article
Published: 03 February 2021 in Technological Forecasting and Social Change
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As the impacts of the COVID-19 pandemic play out globally, the banking industry has been affected in both positive and negative ways, with the crisis creating both opportunities and threats for the collaborations between FinTech and banks. The aim of this study is to investigate the impact of FinTech products (FTPs) on commercial bank's performance in China. Required data are collected with a quantitative approach and two self-designed questionnaires were distributed to customers and employees of commercial banks in China. The gathered data were examined using the structural equation modeling technique. The results of this study reveal that the perceived usefulness (PU) of FTPs has positive and significant impacts on customer satisfaction, low expectation of bank employee assistance, bank's service quality and employee work efficiency. Additionally, the perceived difficulty of use (PD) of FTPs has negative and significant impacts on customer satisfaction and low expectation of assistance. Interestingly, there is a positive and significant relationship between PD and banks' service quality and work efficiency, meaning that the service quality and work efficiency can reduce some shortcomings of using FTPs. This study recognizes the need to enhance the understanding of FTPs on non-financial firm performance. This is the first study that helps commercial banks in China understand the perception of FTPs from both customer and employee perspectives.

ACS Style

Xihui Chen; Xuyuan You; Victor Chang. FinTech and commercial banks' performance in China: A leap forward or survival of the fittest? Technological Forecasting and Social Change 2021, 166, 120645 .

AMA Style

Xihui Chen, Xuyuan You, Victor Chang. FinTech and commercial banks' performance in China: A leap forward or survival of the fittest? Technological Forecasting and Social Change. 2021; 166 ():120645.

Chicago/Turabian Style

Xihui Chen; Xuyuan You; Victor Chang. 2021. "FinTech and commercial banks' performance in China: A leap forward or survival of the fittest?" Technological Forecasting and Social Change 166, no. : 120645.

Journal article
Published: 23 January 2021 in Future Generation Computer Systems
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Device-to-device (D2D) communication technique is used to establish direct links among mobile devices (MDs) to reduce communication delay and increase network capacity over the underlying wireless networks. Existing D2D schemes for task offloading focus on system throughput, energy consumption, and delay without considering data security. This paper proposes a Security and Energy-aware Collaborative Task Offloading for D2D communication (Sec2D). Specifically, we first build a novel security model, in terms of the number of CPU cores, CPU frequency, and data size, for measuring the security workload on heterogeneous MDs. Then, we formulate the collaborative task offloading problem that minimizes the time-average delay and energy consumption of MDs while ensuring data security. In order to meet this goal, the Lyapunov optimization framework is applied to implement online decision-making. Two solutions, greedy approach and optimal approach, with different time complexities, are proposed to deal with the generated mixed-integer linear programming (MILP) problem. The theoretical proofs demonstrate that Sec2D follows a [O(1∕V),O(V)] energy-delay tradeoff. Simulation results show that Sec2D can guarantee both data security and system stability in the collaborative D2D communication environment.

ACS Style

Zhongjin Li; Haiyang Hu; Hua Hu; Binbin Huang; Jidong Ge; Victor Chang. Security and Energy-aware Collaborative Task Offloading in D2D communication. Future Generation Computer Systems 2021, 118, 358 -373.

AMA Style

Zhongjin Li, Haiyang Hu, Hua Hu, Binbin Huang, Jidong Ge, Victor Chang. Security and Energy-aware Collaborative Task Offloading in D2D communication. Future Generation Computer Systems. 2021; 118 ():358-373.

Chicago/Turabian Style

Zhongjin Li; Haiyang Hu; Hua Hu; Binbin Huang; Jidong Ge; Victor Chang. 2021. "Security and Energy-aware Collaborative Task Offloading in D2D communication." Future Generation Computer Systems 118, no. : 358-373.