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Cyberbullying detection on social networks using machine learning approaches

- Islam, Md Manowarul, Uddin, Md Ashraf, Islam, Linta, Akter, Arnisha, Sharmin, Selina, Acharjee, Uzzal

  • Authors: Islam, Md Manowarul , Uddin, Md Ashraf , Islam, Linta , Akter, Arnisha , Sharmin, Selina , Acharjee, Uzzal
  • Date: 2020
  • Type: Text , Conference paper
  • Relation: 2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2020
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  • Description: The use of social media has grown exponentially over time with the growth of the Internet and has become the most influential networking platform in the 21st century. However, the enhancement of social connectivity often creates negative impacts on society that contribute to a couple of bad phenomena such as online abuse, harassment cyberbullying, cybercrime and online trolling. Cyberbullying frequently leads to serious mental and physical distress, particularly for women and children, and even sometimes force them to attempt suicide. Online harassment attracts attention due to its strong negative social impact. Many incidents have recently occurred worldwide due to online harassment, such as sharing private chats, rumours, and sexual remarks. Therefore, the identification of bullying text or message on social media has gained a growing amount of attention among researchers. The purpose of this research is to design and develop an effective technique to detect online abusive and bullying messages by merging natural language processing and machine learning. Two distinct freatures, namely Bag-of Words (BoW) and term frequency-inverse text frequency (TFIDF), are used to analyse the accuracy level of four distinct machine learning algorithms. © 2020 IEEE.

DE-RUSBoost : an efficient electricity theft detection scheme with additive communication layer

- Mujeeb, Sana, Javaid, Nadeem, Khalid, Rabiya, Imran, Muhammad, Naseer, Nidal

  • Authors: Mujeeb, Sana , Javaid, Nadeem , Khalid, Rabiya , 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: Modern power grids depend on the Advanced Metering Infrastructure (AMI) for consumption monitoring, energy management and billing. However, AMIs are vulnerable to electricity theft cyber attacks due to addition of communication layer. Electricity theft is one of the major Non-Technical Losses (NTLs) in the electricity distribution systems that has become a global concern, recently. Although the machine learning techniques are widely used for Electricity Theft Detection (ETD) in literature, some significant challenges need to be address. (i) The consumption data is usually unlabeled, there should be proper method to label the data. (ii) The fair consumers significantly outnumber the fraudulent consumers, which negatively impacts the performance of classification algorithm. (iii) The performance of classifier must be validated using proper performance evaluation measures. In this paper, an enhanced ETD model is proposed that is an optimized classifier Differential Evaluation Random Under Sampling Boosting (DE-RUSBoost) is used for classification. Proposed classifier DE-RUSBoost is optimized using a metaheuristic optimization algorithm named Differential Evaluation (DE). The proposed method is evaluated on a real-world dataset, i.e., State Grid Corporation of China (SGCC) datasets. DE-RUSBoost achieves the highest accuracy of 96% and low false detection rate of 0.004. The proposed method outperforms its counterparts in terms of accuracy and false detection rate. © 2020 IEEE.

DEFINE: friendship detection based on node enhancement

- Pan, Hanxiao, Guo, Teng, Bedru, Hayat, Qing, Qing, Zhang, Dongyu, Xia, Feng

  • Authors: Pan, Hanxiao , Guo, Teng , Bedru, Hayat , Qing, Qing , Zhang, Dongyu , Xia, Feng
  • Date: 2020
  • Type: Text , Conference paper
  • Relation: 31st Australasian Database Conference, ADC 2019 Vol. 12008 LNCS, p. 81-92
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  • Description: Network representation learning (NRL) is a matter of importance to a variety of tasks such as link prediction. Learning low-dimensional vector representations for node enhancement based on nodes attributes and network structures can improve link prediction performance. Node attributes are important factors in forming networks, like psychological factors and appearance features affecting friendship networks. However, little to no work has detected friendship using the NRL technique, which combines students’ psychological features and perceived traits based on facial appearance. In this paper, we propose a framework named DEFINE (No enhancement based r e dship D tection) to detect students’ friend relationships, which combines with students’ psychological factors and facial perception information. To detect friend relationships accurately, DEFINE uses the NRL technique, which considers network structure and the additional attributes information for nodes. DEFINE transforms them into low-dimensional vector spaces while preserving the inherent properties of the friendship network. Experimental results on real-world friendship network datasets illustrate that DEFINE outperforms other state-of-art methods. © 2020, Springer Nature Switzerland AG.
  • Description: E1

Discovery of small group interactions and performance from project emails

- Ivkovic, Sasha, Oseni, Taiwo, Chadhar, Mehmood, Firmin, Sally

  • Authors: Ivkovic, Sasha , Oseni, Taiwo , Chadhar, Mehmood , Firmin, Sally
  • Date: 2020
  • Type: Text , Conference paper
  • Relation: 24th Pacific Asia Conference on Information Systems: Information Systems (IS) for the Future, PACIS 2020
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  • Description: Despite latest advances in small group research, discovery of group interactions and performance from analysis of small group communication, such as project emails, is still minimally represented. This paper presents a novel approach of studying small groups through analysis of the participants' emails sent to the project manager. We examined 1,105 email messages from managers' email in-boxes across five distinct ICT projects from the personal, social, collaborative, and engaging perspective of the email senders and link the findings to group performance. The study provides theoretical evidence that analysis of incoming communication from project managers' email in-box can be used to measure a group's success. For project managers the approach has the potential to be highly beneficial for monitoring of indicators for the state of project health. © Proceedings of the 24th Pacific Asia Conference on Information Systems: Information Systems (IS) for the Future, PACIS 2020. All rights reserved.

Dynamic derivative-droop control for supercapacitor synthetic inertial support

- Akram, Umer, Mithulananthan, N., Shah, Rakibuzzaman, Islam, Rabiul

  • Authors: Akram, Umer , Mithulananthan, N. , Shah, Rakibuzzaman , Islam, Rabiul
  • Date: 2020
  • Type: Text , Conference proceedings , Conference paper
  • Relation: 2020 IEEE Industry Applications Society Annual Meeting, IAS 2020 Vol. 2020-January
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  • Description: Energy storage is recognized as a potential solution to alleviate the impacts of inertia reduction and intermittency due to the integration of inverter based renewable energy sources (RES) in power systems. Out of various rapid responsive energy storage technologies, supercapacitor energy storage (SCES) is the most promising technology for synthetic inertia support. Because the SCES has high power density, very small response time, and large cycle life. In this paper, a dynamic derivative-droop control strategy is developed for SCES to provide the synthetic inertia in low inertia power system. The proposed strategy overcomes the limitations of separately applied derivative and droop controls. In addition, the use of time varying gains (referred as dynamic) instead of fixed gains improves the performance compared to derivative-droop coordinated control. Different types of events are created at different penetration levels of RES to test the robustness of the proposed control. A comparison, based on RoCoF and frequency nadir, between the derivative, droop, derivative-droop coordinated and the proposed controls is presented to show the effectiveness of the proposed control approach. © 2020 IEEE.

Efficient data trading and storage in internet of vehicles using consortium blockchain

- Sadiq, Ayesha, Javaid, Nadeem, Samuel, Omaji, Khalid, Adia, Haider, Noman, Imran, Muhammad

Efficient low bit-rate intra-frame coding using common information for 360-degree video

- Afsana, Fariha, Paul, Manoranjan, Murshed, Manzur, Taubman, David

  • Authors: Afsana, Fariha , Paul, Manoranjan , Murshed, Manzur , Taubman, David
  • Date: 2020
  • Type: Text , Conference paper
  • Relation: 22nd IEEE International Workshop on Multimedia Signal Processing, MMSP 2020
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  • Description: With the growth of video technologies, super-resolution videos, including 360-degree immersive video has become a reality due to exciting applications such as augmented/virtual/mixed reality for better interaction and a wide-angle user-view experience of a scene compared to traditional video with narrow-focused viewing angle. The new generation video contents are bandwidth-intensive in nature due to high resolution and demand high bit rate as well as low latency delivery requirements that pose challenges in solving the bottleneck of transmission and storage burdens. There is limited optimisation space in traditional video coding schemes for improving video coding efficiency in intra-frame due to the fixed size of processing block. This paper presents a new approach for improving intra-frame coding especially at low bit rate video transmission for 360-degree video for lossy mode of HEVC. Prior to using traditional HEVC intra-prediction, this approach exploits the global redundancy of entire frame by extracting common important information using multi-level discrete wavelet transformation. This paper demonstrates that the proposed method considering only low frequency information of a frame and encoding this can outperform the HEVC standard at low bit rates. The experimental results indicate that the proposed intra-frame coding strategy achieves an average of 54.07% BD-rate reduction and 2.84 dB BD-PSNR gain for low bit rate scenario compared to the HEVC. It also achieves a significant improvement in encoding time reduction of about 66.84% on an average. Moreover, this finding also demonstrates that the existing HEVC block partitioning can be applied in the transform domain for better exploitation of information concentration as we applied HEVC on wavelet frequency domain. © 2020 IEEE.

Electric Load Forecasting using EEMD and machine learning techniques

- Naz, Aqdas, Javaid, Nadeem, Khalid, Adia, Shoaib, Muhammad, Imran, Muhammad

Electricity theft detection using pipeline in machine learning

- Anwar, Mubbashra, Javaid, Nadeem, Khalid, Adia, Imran, Muhammad, Shoaib, Muhammad

  • Authors: Anwar, Mubbashra , Javaid, Nadeem , Khalid, Adia , Imran, Muhammad , Shoaib, Muhammad
  • Date: 2020
  • Type: Text , Conference paper
  • Relation: 16th IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2020, Limassol, Cyprus, 15 to 19 June 2020, 2020 International Wireless Communications and Mobile Computing, IWCMC 2020 p. 2138-2142
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  • Description: Electricity theft is the primary cause of electrical power loss that significantly affects the revenue loss and the quality of electrical power. Nevertheless, the existing methods for the detection of this criminal behavior of theft are diversified and complicated since the imbalanced nature of the dataset, and high dimensionality of time-series data make it challenging to extract meaningful information. This paper addresses these problems by developing a novel electricity theft detection model, integrating three algorithms in a pipeline. The proposed method first applies the synthetic minority oversampling technique (SMOTE) for balancing the dataset, secondly integration of kernel function and principal component analysis (KPCA) for the feature extraction from high dimensional time-series data, and support vector machine (SVM) for the classification. Besides, the performance of the proposed pipeline is measured using a comprehensive list of performance metrics. Extensive experiments are performed by using real electricity consumption data, and results show that the proposed method outperforms other methods in terms of theft detection. © 2020 IEEE.

Energy storage for short-term frequency stability enhancement in low-inertia power systems

- Akram, Umer, Mithulananthan, N., Shah, Rakibuzzaman, Basit, Saad

  • Authors: Akram, Umer , Mithulananthan, N. , Shah, Rakibuzzaman , Basit, Saad
  • Date: 2020
  • Type: Text , Conference paper
  • Relation: 2020 Australasian Universities Power Engineering Conference, AUPEC 2020
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  • Description: Power systems with large scale integration of inverter interfaced generation plants become vulnerable to short term frequency instability. The short term frequency stability is related to limiting the RoCoF and frequency nadir within acceptable standards following a contingency. Failure to limit frequency nadir and RoCoF can result in the activation of RoCoF relays and under frequency load shedding relays, leading to generation disconnection and unintentional load shedding. This can result in blackout. In this work energy storage system (ESS) is deployed in the two-area power system for improvement of short term frequency stability. A capacity optimization algorithm and dispatch scheme are developed for the ESS. The capacity estimation algorithm determines the required capacity of ESS. The proposed algorithm estimates the required capacity of energy storage while considering the complete dynamics of conventional generators, wind turbine, and power conversion systems. The dispatch scheme operates the ESS to inject the real power in the system to improve the short term frequency stability. The proposed methodology is verified in PSCAD. Simulation results depict the effectiveness of the proposed approach. © 2020 University of Tasmania.

Enhancing linear time complexity time series classification with hybrid bag-of-patterns

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

  • Authors: Liang, Shen , Zhang, Yanchun , Ma, Jiangang
  • Date: 2020
  • Type: Text , Conference paper
  • Relation: 25th International Conference on Database Systems for Advanced Applications, DASFAA 2020 Vol. 12112 LNCS, p. 717-735
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  • Description: In time series classification, one of the most popular models is Bag-Of-Patterns (BOP). Most BOP methods run in super-linear time. A recent work proposed a linear time BOP model, yet it has limited accuracy. In this work, we present Hybrid Bag-Of-Patterns (HBOP), which can greatly enhance accuracy while maintaining linear complexity. Concretely, we first propose a novel time series discretization method called SLA, which can retain more information than the classic SAX. We use a hybrid of SLA and SAX to expressively and compactly represent subsequences, which is our most important design feature. Moreover, we develop an efficient time series transformation method that is key to achieving linear complexity. We also propose a novel X-means clustering subroutine to handle subclasses. Extensive experiments on over 100 datasets demonstrate the effectiveness and efficiency of our method. © 2020, Springer Nature Switzerland AG.

Evaluating the Performances of the Agoraphilic Navigation Algorithm under Dead-Lock Situations

- Hewawasam, Hasitha, Ibrahim, Yousef, Kahandawa, Gayan, Choudhury, Tanveer

  • Authors: Hewawasam, Hasitha , Ibrahim, Yousef , Kahandawa, Gayan , Choudhury, Tanveer
  • Date: 2020
  • Type: Text , Conference proceedings , Conference paper
  • Relation: 29th IEEE International Symposium on Industrial Electronics, ISIE 2020 Vol. 2020-June, p. 536-542
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  • Description: This paper presents a summary of the research which was conducted in developing a new free-space based (Agoraphilic) navigation algorithm. This new methodology is capable of maneuvering robots in static as well as dynamically cluttered unknown environments. The new algorithm uses only one force to drive the robot. This force is always an attractive force created by the freespace. This force is focused towards the goal by a force shaping module. Consequently, the robot is motivated to follow free-space directing towards the goal. As this method only based on the attractive forces, the robot always moves towards the goal as long as there is free-space . This method has eradicated many drawbacks of the traditional APF method. Several experimental tests were conducted using Turtlebot3 research platform. These tests were focused on testing the behavior of the new algorithm under dead-lock (local minima) situations for APF method. The test results proved that the proposed algorithm has successfully eliminated the local minima problem of APF method. © 2020 IEEE.

Friendly jammer against an adaptive eavesdropper in a relay-aided network

- Giti, J. E., Sakzad, A., Srinivasan, B., Kamruzzaman, Joarder, Gaire, R.

  • Authors: Giti, J. E. , Sakzad, A. , Srinivasan, B. , Kamruzzaman, Joarder , Gaire, R.
  • Date: 2020
  • Type: Text , Conference paper
  • Relation: 16th IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2020; Limassol, Cyprus; 15-19 June 2020, International Wireless Communications and Mobile Computing, IWCMC 2020 p. 1707-1712
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  • Description: In this paper, we consider the problem of information theoretic security for a single-input single-output (SISO) relay-aided network in the presence of an adaptive eavesdropper. We assess the impact of deceptive friendly jammers on the secrecy of communication in this network when countering adaptive eavesdroppers. Specifically, we derive the secrecy capacity and secrecy outage probability of the network and compare the results in the absence and presence of a deceptive friendly jammer. Our results show that the secrecy capacity of the network increases while the achievable secrecy outage probability decreases significantly in the presence of friendly jammer to nullify the effect of the adversary. Numerical results, obtained through computer simulations, under different scenarios of varying jamming power and average main channel gain to average eavesdropper channel gain ratio demonstrate the effectiveness of friendly jammer in providing physical layer security. © 2020 IEEE.

Gestalt based evaluation of health information diagrams

- Sharma, Vishakha, Stranieri, Andrew, Burstein, Frada, Warren, Jim, Firmin, Sally

  • Authors: Sharma, Vishakha , Stranieri, Andrew , Burstein, Frada , Warren, Jim , Firmin, Sally
  • Date: 2020
  • Type: Text , Conference paper
  • Relation: 24th International Conference Information Visualisation, IV 2020 Vol. 2020-September, p. 195-201
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  • Description: Diagrams for four different health care settings have been proposed: Snapshot Diagram, Diagnosis Diagram, Strength of Evidence Diagram and Patient Pathway Diagram The availability of large amount of digital health care data and potential to utilize its benefits led to the development of these diagrams. This paper presents an analysis of the diagrams based on the selection of a subset of Gestalt principles deemed relevant for each diagram. Although Gestalt and human-computer interaction principles are advanced to apply to all diagrams or user interfaces, in practice a sub-set of principles must be selected to evaluate a diagram or interface The selection of a subset of principles to use on a diagram has not been widely studied. This paper presents an approach for identifying a subset of relevant Gestalt principles tailored for each of the four diagrams advanced for health care settings. © 2020 IEEE.

Graduate employment prediction with bias

- Guo, Teng, Xia, Feng, Zhen, Shihao, Bai, Xiaomei, Zhang, Dongyu

  • Authors: Guo, Teng , Xia, Feng , Zhen, Shihao , Bai, Xiaomei , Zhang, Dongyu
  • Date: 2020
  • Type: Text , Conference paper
  • Relation: AAAI 2020 - 34th AAAI Conference on Artificial Intelligence p. 670-677
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  • Description: The failure of landing a job for college students could cause serious social consequences such as drunkenness and suicide. In addition to academic performance, unconscious biases can become one key obstacle for hunting jobs for graduating students. Thus, it is necessary to understand these unconscious biases so that we can help these students at an early stage with more personalized intervention. In this paper, we develop a framework, i.e., MAYA (Multi-mAjor emploYment stAtus) to predict students’ employment status while considering biases. The framework consists of four major components. Firstly, we solve the heterogeneity of student courses by embedding academic performance into a unified space. Then, we apply a generative adversarial network (GAN) to overcome the class imbalance problem. Thirdly, we adopt Long Short-Term Memory (LSTM) with a novel dropout mechanism to comprehensively capture sequential information among semesters. Finally, we design a bias-based regularization to capture the job market biases. We conduct extensive experiments on a large-scale educational dataset and the results demonstrate the effectiveness of our prediction framework. Copyright © 2020, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Feng Xia” is provided in this record**

Graph Force Learning

- Sun, Ke, Liu, Jiaying, Yu, Shuo, Xu, Bo, Xia, Feng

  • Authors: Sun, Ke , Liu, Jiaying , Yu, Shuo , Xu, Bo , Xia, Feng
  • Date: 2020
  • Type: Text , Conference paper
  • Relation: 8th IEEE International Conference on Big Data, Big Data 2020 p. 2987-2994
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  • Description: Features representation leverages the great power in network analysis tasks. However, most features are discrete which poses tremendous challenges to effective use. Recently, increasing attention has been paid on network feature learning, which could map discrete features to continued space. Unfortunately, current studies fail to fully preserve the structural information in the feature space due to random negative sampling strategy during training. To tackle this problem, we study the problem of feature learning and novelty propose a force-based graph learning model named GForce inspired by the spring-electrical model. GForce assumes that nodes are in attractive forces and repulsive forces, thus leading to the same representation with the original structural information in feature learning. Comprehensive experiments on three benchmark datasets demonstrate the effectiveness of the proposed framework. Furthermore, GForce opens up opportunities to use physics models to model node interaction for graph learning. © 2020 IEEE.

Identifying cross-version function similarity using contextual features

- Black, Paul, Gondal, Iqbal, Vamplew, Peter, Lakhotia, Arun

  • Authors: Black, Paul , Gondal, Iqbal , Vamplew, Peter , Lakhotia, Arun
  • Date: 2020
  • Type: Text , Conference paper
  • Relation: 19th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2020 p. 810-818
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  • Description: The identification of similar functions in malware assists analysis by supporting the exclusion of functions that have been previously analysed, allows the identification of new variants, supports authorship attribution, and the analysis of malware phylogeny. A function's context is a set comprising the function itself and all the program functions that may be executed when this function is called. Contextual features consist of data that is extracted from the functions contained in the function context. This paper presents a novel technique called Cross Version Contextual Function Similarity (CVCFS) to identify function pairs in two programs using features based on both individual functions and function context. The CVCFS technique uses Support Vector Machine (SVM) machine learning of function similarity features to pre-filter function pairs and then applies an edit distance technique using function semantics to reduce false positives. A case study is provided where individual and contextual features are extracted from three versions of Zeus malware. The SVM pre-filtering, followed by the use of an edit distance technique to filter false positives, gives a function pair identification accuracy of 85 percent. © 2020 IEEE.

Image encryption based on fractional-order chen hyperchaotic system

- Peng, Jun, Yang, Wu, Jin, Shangzhu, Pang, Shaoning, Tang, Dedong, Bai, Junjie, Zhang, Du

Impact of feature selection on non-technical loss detection

- Ghori, Khawaja, Rabeeh Ayaz, Abbasi, Awais, Muhammad, Imran, Muhammad, Ullah, Atta, Szathmary, Laszlo

  • Authors: Ghori, Khawaja , Rabeeh Ayaz, Abbasi , Awais, Muhammad , Imran, Muhammad , Ullah, Atta , Szathmary, Laszlo
  • Date: 2020
  • Type: Text , Conference paper
  • Relation: 6th Conference on Data Science and Machine Learning Applications, CDMA 2020, Riyadh, Saudi Arabia, 4 to 5 March 2020, Proceedings - 2020 6th Conference on Data Science and Machine Learning Applications, CDMA 2020 p. 19-24
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  • Description: Over the years, many countries have faced huge financial deficits due to Non-Technical Loss (NTL) in power sector. There are many ways of attempting to illegal use of electricity like by-passing and reversing meters. There have been many attempts to bring down NTL using manual and automated techniques. Manual NTL detection is not proving fruitful as it incurs heavy costs and has a low hit ratio. Due to the shortcoming of manual NTL detection, automated detection of NTL using machine learning classifiers is gaining attention in the research community. The datasets containing NTL belong to the class imbalance domain where regular consumers (negative class) out weight the representation of irregular consumers (positive class). To identify the right number of representative records, many techniques are proposed but selecting the right features in deciding NTL is equally an important task where not much has been contributed to the literature. In this paper, we propose the Incremental Feature Selection (IFS) algorithm which first uses feature importance to identify the most relevant features for NTL detection and then these features are used to test three classifiers namely CatBoost, Decision Tree (DT) Classifier and K-Nearest Neighbors (KNN) for NTL detection. This way, we have not only identified the most relevant features for NTL detection in a real dataset but also have brought down the overall computation time of the classifiers. Moreover, our proposed framework is tested on three performance evaluation metrics used in imbalance domain. The results show that using the most relevant features identified by the IFS algorithm, the three classifiers have the same or slightly better efficiency as compared to using all features. © 2020 IEEE.

Impact of MMC-HVDC Control Parameter Selection on the Dynamic Performance of AC System

- Shah, Rakibuzzaman, Islam, Syed, Islam, Md Rabiul

  • Authors: Shah, Rakibuzzaman , Islam, Syed , Islam, Md Rabiul
  • Date: 2020
  • Type: Text , Conference paper
  • Relation: 2020 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices; ASEMD 2020, Virtual Tianjin ;16 to 18 October 2020
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  • Description: High penetration of voltage source converter (VSC) based system is anticipated in future power systems. This study discusses the interaction of modular multilevel converter (MMC) based high voltage dc (HVDC) system with the dynamic of the host ac network in the electromechanical time frame. The respective controls for MMC-HVDC system suitable for the stability studies in the electromechanical time-frame are considered. This paper gives insight into the effects of several important sensitivities such as (i) MMC control implementation, (ii) non linearity associated with the fault ride through implementation, and (iii) phase locked loop parameters on ac system dynamics. The comprehensive analysis has been performed using an equivalent model of Great British transmission system. © 2020 IEEE.