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The rule of law and compliance : legal quadrant and conceptual clustering

- Casanovas, Pompeu, Hashmi, Mustafa, de Koker, Louis

Time-expanded method improving throughput in dynamic renewable networks

- Zhang, Jianhui, Guan, Siqi, Wang, Jiacheng, Liu, Liming, Wang, HanXiang, Xia, Feng

  • Authors: Zhang, Jianhui , Guan, Siqi , Wang, Jiacheng , Liu, Liming , Wang, HanXiang , Xia, Feng
  • Date: 2021
  • Type: Text , Conference paper
  • Relation: 29th IEEE/ACM International Symposium on Quality of Service, IWQOS 2021
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  • Description: In the Dynamic Rechargeable Networks (DRNs), the existing studies usually consider the spatio-temporal dynamics of the harvested energy so as to maximize the throughput by efficient energy allocation. However, the network dynamics have seldom been considered simultaneously including the time variable link quality, communication power and battery charge efficiency. Furthermore, the wireless interference brings extra challenge. To take these dynamics into account together, this paper studies the quite challenging problem, the network throughput maximization in the DRNs, by proper energy allocation while considering the additional affection of wireless interference. We introduce the Time-Expanded Graph (TEG) to describe the above dynamics in a feasible easy way, and then look into the scenario where there is only one pair of source-target firstly. To maximize the throughput, this paper designs the Single Pair Throughput maximization (SPT) algorithm based on TEG while considering the wireless interference. In the case of multiple pairs of source-targets, it's quite complex to solve the network throughput maximization problem directly. This paper introduces the Garg and Könemanns framework and then designs the Multiple Pairs Throughput (MPT) algorithm to maximize the overall throughput of all pairs. MPT is a fast approximation solution with the ratio of 1-3ϵ, where 0 < ϵ < 1 is a small positive constant. This paper also conducts the extensive numerical evaluation based on the simulated data and the data collected by our real system. The numerical simulation results demonstrate the throughput improvement of our algorithms. © 2021 IEEE.

Towards a formal framework for partial compliance of business processes

- Lam, Ho-Pun, Hashmi, Mustafa, Kumar, Akhil

Understanding the gap between academics and game developers : an analysis of gamasutra blogs

- Greenwood, Jordan, Achterbosch, Leigh, Stranieri, Andrew, Meredith, Grang

Web of students : class-level friendship network discovery from educational big data

- Guo, Teng, Tang, Tang, Zhang, Dongyu, Li, Jianxin, Xia, Feng

  • Authors: Guo, Teng , Tang, Tang , Zhang, Dongyu , Li, Jianxin , Xia, Feng
  • Date: 2021
  • Type: Text , Conference paper
  • Relation: 22nd International Conference on Web Information Systems Engineering, WISE 2021 p. 497-511
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  • Description: Classmate friendships are a major aspect of university social experience. Taking classes together is one of the main ways for students to build friendships. Consequently, class-level friendship networks have attracted tremendous attention from researchers. They are also very useful in student support and early intervention. However, these networks are normally invisible for educators. Discovering such an important web of students effectively is a pressing problem. Against this background, we propose a data-driven framework called CANDY which automatically discovers the class-level friendship networks based on educational big data. We first represent features through representation learning methods. Secondly, the data is augmented with the randomly shuffling method. Thirdly, a conditional generative adversarial network model is used to mine the class-level friendship networks. A deep adversarial optimization strategy is proposed here for problems caused by network sparsity. To evaluate the performance of the proposed approach, we build a real-world dataset that contains rich student information. Extensive experiments have been conducted and the results demonstrate the effectiveness of our framework. © 2021, Springer Nature Switzerland AG.

A blockchain based privacy-preserving system for electric vehicles through local communication

- Yahaya, Adamu, Javaid, Nadeem, Khalid, Rabiya, Imran, Muhammad, Naseer, Nidal

A blockchain-based decentralized energy management in a P2P trading system

- Khalid, Rabiya, Javaid, Nadeem, Javaid, Sakeena, Imran, Muhammad, Naseer, Nidal

  • Authors: Khalid, Rabiya , Javaid, Nadeem , Javaid, Sakeena , Imran, Muhammad , Naseer, Nidal
  • Date: 2020
  • Type: Text , Conference paper
  • Relation: 2020 IEEE International Conference on Communications, ICC 2020, Dublin, Ireland, 7 to 11 June, IEEE International Conference on Communications Vol. 2020-June
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  • Description: Local energy generation and peer to peer (P2P) energy trading in the local market can reduce energy consumption cost, emission of harmful gases (as renewable energy sources (RESs) are used to generate energy at user's premises) and increase smart grid resilience. In this paper, to implement a hybrid P2P energy trading market, a blockchain-based solution is proposed. A blockchain-based system is fully decentralized and it allows the market members to interact with each other and trade energy without involving any third party. Smart contracts play a very important role in the blockchain-based energy trading market. They contain all the necessary rules for energy trading. We have proposed three smart contracts to implement the hybrid electricity trading market. The market members interact with main smart contract which requests P2P smart contract and prosumer to grid (P2G) smart contract for further processing. The main objectives of this paper are to propose a model to implement an efficient hybrid energy trading market while reducing cost and peak to average ratio (PAR) of electricity. © 2020 IEEE.

A blockchain-based privacy-preserving mechanism with aggregator as common communication point

- Yahaya, Adamu, Javaid, Nadeem, Khalid, Rabiya, Imran, Muhammad, Guizani, Mohsen

  • Authors: Yahaya, Adamu , Javaid, Nadeem , Khalid, Rabiya , Imran, Muhammad , Guizani, Mohsen
  • Date: 2020
  • Type: Text , Conference paper
  • Relation: 2020 IEEE International Conference on Communications, ICC 2020, Dublin, Ireland, 7 to 11 June, IEEE International Conference on Communications Vol. 2020-June
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  • Description: The high penetration of renewable energy resources into the distributed system and their intermittent behavior of the non-dispatchable generation causes issues of demand supply mismatch and serious security and privacy concerned in the system. It is believed that incorporating blockchain will reduce costs, enhance data security, and improve the system efficiency. However, privacy issues are not completely eliminated and can hinder the wide applications of blockchain. In the study, we present a Reputation Based Starvation Free Energy Allocation Policy (Reputation-SFEAP) in a decentralized and distributed blockchain-based energy trading; while keeping Aggregator as Common Communication Point. In addition, Identity-Based encryption (ID-Based encryption) technique is added that improves transactional information privacy. According to the research analysis, it is observed that the proposed system model has optimal and fair energy allocation algorithms, which prevent all the energy users from energy starvation and share the available energy accordingly. Moreover, the incorporated encryption system has greater security-privacy level, which protects passive attacker and disguises attacker from penetration. © 2020 IEEE.

A coarse representation of frames oriented video coding by leveraging cuboidal partitioning of image data

- Ahmmed, Ashe, Paul, Manoranjan, Murshed, Manzur, Taubman, David

  • Authors: Ahmmed, Ashe , Paul, Manoranjan , Murshed, Manzur , Taubman, David
  • Date: 2020
  • Type: Text , Conference paper
  • Relation: 22nd IEEE International Workshop on Multimedia Signal Processing, MMSP 2020, Virtual Tampere, Finland 21-24 September 2020
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  • Description: Video coding algorithms attempt to minimize the significant commonality that exists within a video sequence. Each new video coding standard contains tools that can perform this task more efficiently compared to its predecessors. In this work, we form a coarse representation of the current frame by minimizing commonality within that frame while preserving important structural properties of the frame. The building blocks of this coarse representation are rectangular regions called cuboids, which are computationally simple and has a compact description. Then we propose to employ the coarse frame as an additional source for predictive coding of the current frame. Experimental results show an improvement in bit rate savings over a reference codec for HEVC, with minor increase in the codec computational complexity. © 2020 IEEE.

A compact design for a switchable wireless charger

- Wang, H., Cheng, K., Hu, Jiefeng

  • Authors: Wang, H. , Cheng, K. , Hu, Jiefeng
  • Date: 2020
  • Type: Text , Conference paper
  • Relation: 8th International Conference on Power Electronics Systems and Applications, PESA 2020, Hong Kong 7 to 10 December 2020, 2020 8th International Conference on Power Electronics Systems and Applications: Future Mobility and Future Power Transfer, PESA 2020
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  • Description: With the continuous miniaturization of electronic products, the compact structure of wireless power transfer (WPT) is extraordinarily necessary for consumer electronics. In addition, to meet the need in charging, load independent output current (CC) and constant output voltage (CV) are supposed to be taken into consideration since it is regarded as one of the most popular methods for charging batteries. This paper studies a switchable and compact design for a wireless charger. The compensation network can be changed between double LCL topology and LCL-S topology. Configurable CC and CV outputs can be achieved by adopting two switches without utilizing sophisticated control loops or any communication between the transmitter and the receiver. Compared with LCC compensation networks, the proposed special structure can save components under both double LCL and LCL-S conditions. The ferrite cores naturally take the responsibility as magnetic shielding for unwanted couplings in a such compact structure. This special design not only greatly alleviates the cross-coupling phenomenon but surely makes the compensation networks design straightforward as well. Ultimately, the fundamental analysis, the related mathematical derivation, detailed circuit topologies, switchable compensation designs and the experimental platform are all discussed and investigated. The proposed design is also analyzed and validated by experimental tests under different charging conditions. © 2020 IEEE.

A dynamic surface controller based on adaptive neural network for dual arm robots

- Le, Hai, Nguyen, Linh, Thiyagarajan, Karthick

  • Authors: Le, Hai , Nguyen, Linh , Thiyagarajan, Karthick
  • Date: 2020
  • Type: Text , Conference paper
  • Relation: 15th IEEE Conference on Industrial Electronics and Applications, ICIEA 2020 p. 555-560
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  • Description: The paper introduces an adaptive controller to efficiently manipulate the dual arms of a robot (DAR) under uncertainties including actuator nonlinearities, system parameter variations and external disturbances. It is proposed that by the use of the dynamic surface control (DSC) method, the control strategy is first established, which enables the robot arms to robustly operate on the desired trajectories. Nevertheless, the dynamic model parameters of the DAR system are unknown and impractically estimated due to its uncertain nonlinearities and unexpected external factors. Hence, it is then proposed to employ the radial basis function network (RBFN) to adaptively estimate the uncertain system parameters. The Lyapunov theory is theoretically utilized to derive the adaptation mechanism so that the stability of the closed-loop control system is guaranteed. The proposed RBFN-DSC approach was validated in a synthetic environment with the promising results. © 2020 IEEE.

A joint SLM and precoding based PAPR reduction scheme for 5G UFMC cellular networks

- Baig, Imran, Farooq, Umer, Hasan, Najam, Zghaibeh, Manaf, Arshad, Muhammad, Imran, Muhammad

A new current limiting and overload protection strategy for droop-controlled voltage-source converters in islanded AC microgrids under grid faulted conditions

- Li, Zi-Lin, Hu, Jiefeng, Chan, Ka Wing

  • Authors: Li, Zi-Lin , Hu, Jiefeng , Chan, Ka Wing
  • Date: 2020
  • Type: Text , Conference paper
  • Relation: 12th Annual IEEE Energy Conversion Congress and Exposition, ECCE 2020,Detroit, MI, USA, USA, 11-15 Oct. 2020 p. 3888-3893
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  • Description: Grid-forming voltage source converter (VSC) plays a vital role in the future renewable energy-based utility grid. Limited by the thermal capability of semiconductor switches, grid-forming VSC-based distributed generation units (DGs) cannot stand excess overcurrent like synchronous generators (SG) during large transient disturbances. In order to protect the VSC from overcurrent and ride through the transient disturbances, a new current limiting and overload protection strategy is proposed in this paper. By properly selecting the maximum current thresholds in the synchronous rotating frame, overcurrent and overload protection are achieved simultaneously. The synchronization among DGs is enhanced by feeding back the output voltage in the q-axis to the active power droop control. A comparison study between the proposed strategy and two existing methods are conducted using a networked microgrid in PSCAD/EMTDC, examining the effectiveness of the proposed strategy. © 2020 IEEE.

A new effective and efficient measure for outlying aspect mining

- Samariya, Durgesh, Aryal, Sunil, Ting, Kai, Ma, Jiangang

  • Authors: Samariya, Durgesh , Aryal, Sunil , Ting, Kai , Ma, Jiangang
  • Date: 2020
  • Type: Text , Conference paper
  • Relation: 21st International Conference on Web Information Systems Engineering, WISE 2020, Amsterdam. 20-24 October 2020, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 12343 LNCS, p. 463-474
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  • Description: Outlying Aspect Mining (OAM) aims to find the subspaces (a.k.a. aspects) in which a given query is an outlier with respect to a given data set. Existing OAM algorithms use traditional distance/density-based outlier scores to rank subspaces. Because these distance/density-based scores depend on the dimensionality of subspaces, they cannot be compared directly between subspaces of different dimensionality. Z-score normalisation has been used to make them comparable. It requires to compute outlier scores of all instances in each subspace. This adds significant computational overhead on top of already expensive density estimation—making OAM algorithms infeasible to run in large and/or high-dimensional datasets. We also discover that Z-score normalisation is inappropriate for OAM in some cases. In this paper, we introduce a new score called Simple Isolation score using Nearest Neighbor Ensemble (SiNNE), which is independent of the dimensionality of subspaces. This enables the scores in subspaces with different dimensionalities to be compared directly without any additional normalisation. Our experimental results revealed that SiNNE produces better or at least the same results as existing scores; and it significantly improves the runtime of an existing OAM algorithm based on beam search. © 2020, Springer Nature Switzerland AG.

A novel cooperative link selection mechanism for enhancing the robustness in scale-free IoT networks

- Khan, Muhammad, Javaid, Nadeem, Javaid, Sakeena, Khalid, Adia, Nasser, Nidal, Imran, Muhammad

  • Authors: Khan, Muhammad , Javaid, Nadeem , Javaid, Sakeena , Khalid, Adia , Nasser, Nidal , Imran, Muhammad
  • Date: 2020
  • Type: Text , Conference paper
  • Relation: 16th IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2020, Limassol, Cyrprus, 15 to 19 June 2020, 2020 International Wireless Communications and Mobile Computing, IWCMC 2020 p. 2222-2227
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  • Description: In today's world, Internet of Things (IoT) helps people in many fields by enabling smart city projects in health monitoring, smart parking, industrial optimization, home energy management, etc. Daily life objects are connected with the Internet to allow access to their owners to keep an eye on their surroundings. The IoT network is comprised of nodes that are smart enough to perform any function and provide benefits to the people. However, any fault in the network opens up the risk of leaking personal information. The aim is to develop a scale-free network, which controls the effects of malicious attacks and consequently improves the network robustness. In this paper, our prime focus is to mitigate the effect of malicious nodes by providing a robust strategy to maintain the network stability. In this regard, we propose a topology named as a Cooperation based Edge Swap (CES) for improving the network robustness in the scale-free network. The CES uses the edge/link selection mechanism by involving the cooperation using a Rayleigh fading to swap the network topology for improving the network robustness. The simulations' outcome depicts the performance of the CES in terms of improving the network robustness. © 2020 IEEE.

A patient centric agent assisted private blockchain on hyperledger fabric for managing remote patient monitoring

- Wadud, Md Anwar, Amir-Ul-Haque Bhuiyan, T., Uddin, Md Ashraf, Rahman, Md Motiur

  • Authors: Wadud, Md Anwar , Amir-Ul-Haque Bhuiyan, T. , Uddin, Md Ashraf , Rahman, Md Motiur
  • Date: 2020
  • Type: Text , Conference paper
  • Relation: 11th International Conference on Electrical and Computer Engineering, ICECE 2020 p. 194-197
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  • Description: Recently, during the COVID-19 situation, the requirement and importance of tracking patients from a remote location have increased significantly. Most patients now prefer to obtain their doctor's care and check their health status through their mobile phone call, Skype, Facebook Messenger, or other online resources. There is, however, a major concern about the privacy of patients when using online resources. Patients usually choose to keep their information confidential, which should be only accessible to authorized individuals. The most current remote patient monitoring system is organization-centric and patient's privacy and security rely on healthcare providers' mercy. Blockchain technologies have attracted the attention of researchers for designing eHealth applications to provide patients with secure and privacy-preserving health services. Blockchain researchers have recently proposed some models for remote patient monitoring systems. However, most of those researchers have applied public blockchains where health data is available to all participants with the property of data tamper-proof. In this paper, we propose a novel remote patient monitoring model using a decentralized private blockchain to protect patient's privacy and increase the system's efficiency. The private blockchain will be implemented on Hyperledger Fabric where a Patient-centric Agents (PCA) manage patient's data and coordinate authorization to form a secure channel to transmit data to the private blockchain. A hybrid consensus by combining Proof of Integrity (PoI) and Proof of Validity (PoV) is used to protect data privacy and integrity when retrieving data from a blockchain-based cloud database. Finally, the Merkle Tree algorithm was used for data processing and authentication when collecting data and uploading it to a cloud database. © 2020 IEEE.

A trust management system for multi-agent system in smart grids using blockchain technology

- Samuel, Omaji, Javaid, Nadeem, Khalid, Adia, Imrarn, Muhammad, Nasser, Nidal

  • Authors: Samuel, Omaji , Javaid, Nadeem , Khalid, Adia , Imrarn, Muhammad , Nasser, Nidal
  • Date: 2020
  • Type: Text , Conference paper
  • Relation: 2020 IEEE Global Communications Conference, GLOBECOM 2020, Virtual, Taipei China, 7 to 11 December 2020, 2020 IEEE Global Communications Conference, GLOBECOM 2020 - Proceedings Vol. 2020-January
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  • Description: In a multi-agent system (MAS), the trust of each agent has become hot research issues in the smart grids. The traditional trust systems that use access control and cryptography are not sufficient to handle the dynamic behavior of agents. Also, they are inefficient to solve the computational overhead of the cryptographic primitives. Based on these limitations, this paper proposes a blockchain-based trust management system for MAS. The proposed system consists of two layers: a lower layer that enables an agent to perform direct and indirect trust evaluations of other agents during interactions. Multi-source feedback from the interactions among different aggregators is feed to the blockchain. The upper layer is used to perform trust credibility of agents based on trust distortion, consistency and reliability. The credibility evaluation is used to determine the dynamic behavior of agents and also detect dishonest agents in the system. Trust model and security analysis of the proposed system are provided. Moreover, simulation results evaluate the effectiveness of the proposed trust system while the system is secure against bad-mouthing and on-off attacks. © 2020 IEEE.

A weighted overlook graph representation of eeg data for absence epilepsy detection

- Wang, Jialin, Liang, Shen, Wang, Ye, Zhang, Yanchun, Ma, Jiangang

  • Authors: Wang, Jialin , Liang, Shen , Wang, Ye , Zhang, Yanchun , Ma, Jiangang
  • Date: 2020
  • Type: Text , Conference proceedings , Conference paper
  • Relation: 20th IEEE International Conference on Data Mining, ICDM 2020 Vol. 2020-November, p. 581-590
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  • Description: Absence epilepsy is one of the most common types of epilepsy. The diagnosis of absence epilepsy is among the greatest challenges faced by clinical neurologists due to a lack of easily observable symptoms that are present in conventional epilepsy (e.g. spasm and convulsion), and highly relies on the detection of Spike and Slow Waves (SSWs) in Electroencephalogram (EEG) signals. Recently, graph representations called complex networks have been increasingly applied to characterizing 1D EEG signals. However, existing methods often fail to effectively represent SSWs, struggling to capture the differences between SSW waveforms and their non-SSW counterparts, such as minute differences and distinct shapes. Addressing this issue, in this work, we propose two simple yet effective complex networks, Overlook Graph (OG) and Weighted Overlook Graph (WOG), which have been customized to expressively represent SSWs. Built upon OG and WOG, we then develop a 2D Convolutional Neural Network (2D-CNN) to further learn latent features from the graph representations and accomplish the detection task. Extensive experiments on a real-world absence epilepsy EEG dataset show that the proposed OG/WOG-2D-CNN method can accurately detect SSWs. Additional experiments on the well-known Bonn dataset further show that our method can generalize to the conventional epilepsy seizure detection task with highly competitive performances. © 2020 IEEE. *Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate "Jiangang Ma“ is provided in this record**

Active model selection for positive unlabeled time series classification

- Liang, Shen, Zhang, Yanchun, Ma, Jiangang

  • Authors: Liang, Shen , Zhang, Yanchun , Ma, Jiangang
  • Date: 2020
  • Type: Text , Conference proceedings , Conference paper
  • Relation: 36th IEEE International Conference on Data Engineering, ICDE 2020 Vol. 2020-April, p. 361-372
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  • Description: Positive unlabeled time series classification (PUTSC) refers to classifying time series with a set PL of positive labeled examples and a set U of unlabeled ones. Model selection for PUTSC is a largely untouched topic. In this paper, we look into PUTSC model selection, which as far as we know is the first systematic study in this topic. Focusing on the widely adopted self-training one-nearest-neighbor (ST-1NN) paradigm, we propose a model selection framework based on active learning (AL). We present the novel concepts of self-training label propagation, pseudo label calibration principles and ultimately influence to fully exploit the mechanism of ST-1NN. Based on them, we develop an effective model performance evaluation strategy and three AL sampling strategies. Experiments on over 120 datasets and a case study in arrhythmia detection show that our methods can yield top performance in interactive environments, and can achieve near optimal results by querying very limited numbers of labels from the AL oracle. © 2020 IEEE.
  • Description: E1

Adversarial learning-based bias mitigation for fatigue driving detection in fair-intelligent IoV

- Han, Mingzhe, Wu, Jun, Bashir, Ali, Yang, Wu, Imran, Muhammad, Nasser, Nidal

  • Authors: Han, Mingzhe , Wu, Jun , Bashir, Ali , Yang, Wu , Imran, Muhammad , Nasser, Nidal
  • Date: 2020
  • Type: Text , Conference paper
  • Relation: 2020 IEEE Global Communications Conference, GLOBECOM 2020, Virtual, Taipei, China, 7 to 11 December 2020, 2020 IEEE Global Communications Conference, GLOBECOM 2020 - Proceedings
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  • Description: Fatigue driving is one of main causes of traffic accidents. To avoid such traffic accidents, divers' fatigue detection has been used in Intelligent Internet of Vehicles (IIoV). IIoV usually dynamically allocate computing resources according to drivers' fatigue degree to improve the real-time of fatigue detection model. However, the traditional fatigue detection model may have bias on certain groups, which would further cause unfair resource allocation. To solve the problem, this paper proposes an improved IIoV framework, named Fair-Intelligent Internet of Vehicles (FIIoV). Compared with IIoV, we improve two layers in FIIoV, i.e., the detection layer and the normalization layer. The detection layer uses Convolutional Neural Network (CNN) to detect drivers' fatigue degree, and then uses adversarial network to achieve fairness of detection models. The normalization layer achieves the distribution of different sensitive feature values from historical detection results generated in the detection layer, and then uses the distribution to normalize the output of the detection layer to improve the fairness and accuracy of fatigue detection models. Simulation results show that both accuracy and fairness of FIIoV is improved compared with the original IIoV. © 2020 IEEE.