Improving authorship attribution in twitter through topic-based sampling
- Authors: Pan, Luoxi , Gondal, Iqbal , Layton, Robert
- Date: 2017
- Type: Text , Conference proceedings
- Relation: 30th Australasian Joint Conference on Artificial Intelligence, AI 2017 : Advances in Artificial Intelligence; Melbourne, Australia; 19th-20th August 2017; published in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 10400 LNAI, p. 250-261
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- Description: Aliases are used as a means of anonymity on the Internet in environments such as IRC (internet relay chat), forums and micro-blogging websites such as Twitter. While there are genuine reasons for the use of aliases, such as journalists operating in politically oppressive countries, they are increasingly being used by cybercriminals and extremist organisations. In recent years, we have seen increased research on authorship attribution of Twitter messages, including authorship analysis of aliases. Previous studies have shown that anti-aliasing of randomly generated sub-aliases yields high accuracies when linking the sub-aliases, but become much less accurate when topic-based sub-aliases are used. N-gram methods have previously been demonstrated to perform better than other methods in this situation. This paper investigates the effect of topic-based sampling on authorship attribution accuracy for the popular micro-blogging website Twitter. Features are extracted using character n-grams, which accurately capture differences in authorship style. These features are analysed using support vector machines using a one-versus-all classifier. The predictive performance of the algorithm is then evaluated using two different sampling methodologies - authors that were sampled through a context-sensitive topic-based search and authors that were sampled randomly. Topic-based sampling of authors is found to produce more accurate authorship predictions. This paper presents several theories as to why this might be the case. © Springer International Publishing AG 2017.
A multilevel longitudinal study of experiencing virtual presence in adolescence : The role of anxiety and openness to experience in the classroom
- Authors: Stavropoulos, Vasileios , Wilson, Peter , Kuss, Daria , Griffiths, Mark , Gentile, Douglas
- Date: 2017
- Type: Text , Journal article
- Relation: Behaviour & Information Technology Vol. 36, no. 5 (2017), p. 524-539
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- Description: Presence describes the feeling of reality and immersion that users of virtual/Internet environments have. Importantly, it has been suggested that there are individual and contextual differences regarding susceptibility to presence. These aspects of presence have been linked to both beneficial and disadvantageous uses of the Internet, such as online therapeutic applications and addictive Internet behaviours. In the present study, presence was studied in relation to individual anxiety symptoms and classroom-level openness to experience (OTE) using a normative sample of 648 adolescents aged between 16 and 18 years. Presence was assessed with the Presence II questionnaire, anxiety symptoms with the relevant subscales of the SCL-90-R, and OTE with the Five-Factor Questionnaire. A three-level hierarchical linear model was calculated. Results showed that experiencing presence in virtual environments dropped between the ages of 16 and 18 years. Additionally, although anxiety symptoms were associated with higher presence at 16 years, this association decreased with age. Results also demonstrated that adolescents in classrooms higher on OTE reported reduced level of experiencing presence. The practical and theoretical implications of these findings are discussed.
Collecting health and exposure data in Australian olympic combat sports : Feasibility study utilizing an electronic system
- Authors: Bromley, Sally , Drew, Michael , Talpey, Scott , McIntosh, Andrew , Finch, Caroline
- Date: 2018
- Type: Text , Journal article
- Relation: Journal of Medical Internet Research Vol. 20, no. 10 (2018), p. 1-11
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- Description: Background: Electronic methods are increasingly being used to manage health-related data among sporting populations. Collection of such data permits the analysis of injury and illness trends, improves early detection of injuries and illnesses, collectively referred to as health problems, and provides evidence to inform prevention strategies. The Athlete Management System (AMS) has been employed across a range of sports to monitor health. Australian combat athletes train across the country without dedicated national medical or sports science teams to monitor and advocate for their health. Employing a Web-based system, such as the AMS, May provide an avenue to increase the visibility of health problems experienced by combat athletes and deliver key information to stakeholders detailing where prevention programs May be targeted. Objective: The objectives of this paper are to (1) report on the feasibility of utilizing the AMS to collect longitudinal injury and illness data of combat sports athletes and (2) describe the type, location, severity, and recurrence of injuries and illnesses that the cohort of athletes experience across a 12-week period. Methods: We invited 26 elite and developing athletes from 4 Olympic combat sports (boxing, judo, taekwondo, and wrestling) to participate in this study. Engagement with the AMS was measured, and collected health problems (injuries or illnesses) were coded using the Orchard Sports Injury Classification System (version 10.1) and International Classification of Primary Care (version 2). Results: Despite >160 contacts, athlete engagement with online tools was poor, with only 13% compliance across the 12-week period. No taekwondo or wrestling athletes were compliant. Despite low overall engagement, a large number of injuries or illness were recorded across 11 athletes who entered data—22 unique injuries, 8 unique illnesses, 30 recurrent injuries, and 2 recurrent illnesses. The most frequent injuries were to the knee in boxing (n=41) and thigh in judo (n=9). In this cohort, judo players experienced more severe, but less frequent, injuries than boxers, yet judo players sustained more illnesses than boxers. In 97.0% (126/130) of cases, athletes in this cohort continued to train irrespective of their health problems. Conclusions: Among athletes who reported injuries, many reported multiple conditions, indicating a need for health monitoring in Australian combat sports. A number of factors May have influenced engagement with the AMS, including access to the internet, the design of the system, coach views on the system, previous experiences with the system, and the existing culture within Australian combat sports. To increase engagement, there May be a requirement for sports staff to provide relevant feedback on data entered into the system. Until the Barriers are addressed, it is not feasible to implement the system in its current form across a larger cohort of combat athletes.
Global optimal solutions to general sensor network localization problem
- Authors: Ruan, Ning , Gao, David
- Date: 2014
- Type: Text , Journal article
- Relation: Performance Evaluation Vol. 75-76, no. (2014), p. 1-16
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- Description: Sensor network localization problem is to determine the position of the sensor nodes in a network given pairwise distance measurements. Such problem can be formulated as a quartic polynomial minimization via the least squares method. This paper presents a canonical duality theory for solving this challenging problem. It is shown that the nonconvex minimization problem can be reformulated as a concave maximization dual problem over a convex set in a symmetrical matrix space, and hence can be solved efficiently by combining a general (linear or quadratic) perturbation technique with existing optimization techniques. Applications are illustrated by solving some relatively large-scale problems. Our results show that the general sensor network localization problem is not NP-hard unless its canonical dual problem has no solution in its positive definite domain. Fundamental ideas for solving general NP-hard problems are discussed. (C) 2014 Elsevier B.V. All rights reserved.
Non-functional regression : A new challenge for neural networks
- Authors: Vamplew, Peter , Dazeley, Richard , Foale, Cameron , Choudhury, Tanveer
- Date: 2018
- Type: Text , Journal article
- Relation: Neurocomputing Vol. 314, no. (2018), p. 326-335
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- Description: This work identifies an important, previously unaddressed issue for regression based on neural networks – learning to accurately approximate problems where the output is not a function of the input (i.e. where the number of outputs required varies across input space). Such non-functional regression problems arise in a number of applications, and can not be adequately handled by existing neural network algorithms. To demonstrate the benefits possible from directly addressing non-functional regression, this paper proposes the first neural algorithm to do so – an extension of the Resource Allocating Network (RAN) which adds additional output neurons to the network structure during training. This new algorithm, called the Resource Allocating Network with Varying Output Cardinality (RANVOC), is demonstrated to be capable of learning to perform non-functional regression, on both artificially constructed data and also on the real-world task of specifying parameter settings for a plasma-spray process. Importantly RANVOC is shown to outperform not just the original RAN algorithm, but also the best possible error rates achievable by any functional form of regression.
Diagnosing transformer winding deformation faults based on the analysis of binary image obtained from FRA signature
- Authors: Zhao, Zhongyong , Yao, Chenguo , Tang, Chao , Li, Chengxiang , Yan, Fayou , Islam, Syed
- Date: 2019
- Type: Text , Journal article
- Relation: IEEE Access Vol. 7, no. (2019), p. 40463-40474
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- Description: Frequency response analysis (FRA) has been widely accepted as a diagnostic tool for power transformer winding deformation faults. Typically, both amplitude-frequency and phase-frequency signatures are obtained by an FRA analyzer. However, most existing FRA analyzers use only the information on amplitude-frequency signature, while phase-frequency information is neglected. It is also found that in some cases, the diagnostic results obtained by FRA amplitude-frequency signatures do not comply with some hard evidence. This paper introduces a winding deformation diagnostic method based on the analysis of binary images obtained from FRA signatures to improve FRA outcomes. The digital image processing technique is used to process the binary image and obtain a diagnostic indicator, to arrive at an outcome for interpreting winding faults with improved accuracy.
Redesigning the assessment of an entrepreneurship course in an information technology degree program : Embedding assessment for learning practices
- Authors: Pardede, Eric , Lyons, Judith
- Date: 2012
- Type: Text , Journal article
- Relation: IEEE Transactions on Education Vol. 55, no. 4 (2012), p. 566 - 572
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- Description: Entrepreneurship is a novel course in the curriculum for students in the Information Technology (IT) degree program at La Trobe University, Bundoora, Australia. In comparison to other IT-related courses, the Entrepreneurship course seeks to develop business management knowledge and skills; its learning design is thus different to that of other courses in the IT program. The concept of constructive alignment for curriculum renewal suggests that there are several components of good course design. In this paper, we use the principles of constructive alignment to analyze and redesign several components of the Entrepreneurship course. The focus is on reviewing and aligning the assessment tasks to ensure an effective evaluation and the achievement of student learning outcomes. Since assessment drives student learning, we describe the innovative assessment tasks that were implemented to enhance student learning, provide the rationale for the design of these tasks as supported by the current literature, and reflect on possible future improvements. The course redesign process and the constructive alignment and innovative assessment can be applied to other courses in the field, and more broadly to curriculum, teaching, and learning in higher education.
Data-Driven System Reliability and Failure Behavior Modeling Using FMECA
- Authors: Khorshidi, Hadi , Gunawan, Indra , Ibrahim, Yousef
- Date: 2016
- Type: Text , Journal article
- Relation: IEEE Transactions on Industrial Informatics Vol. 12, no. 3 (2016), p. 1253-1260
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- Description: System reliability modeling needs a large amount of data to estimate the parameters. In addition, reliability estimation is associated with uncertainty. This paper aims to propose a new method to evaluate the failure behavior and reliability of a large system using failure modes, effects, and criticality analysis (FMECA). Therefore, qualitative data based on the judgment of experts are used when data are not sufficient. The subjective data of failure modes and causes have been aggregated through the system to develop an overall failure index (OFI). This index not only represents the system reliability behavior, but also prioritizes corrective actions based on improvements in system failure. In addition, two optimization models are presented to select optimal actions subject to budget constraint. The associated costs of each corrective action are considered in risk evaluation. Finally, a case study of a manufacturing line is introduced to verify the applicability of the proposed method in industrial environments. The proposed method is compared with conventional FMECA approach. It is shown that the proposed method has a better performance in risk assessment. A sensitivity analysis is provided on the budget amount and the results are discussed. © 2015 IEEE.
A new cascaded multilevel inverter topology with galvanic isolation
- Authors: Hasan, Mubashwar , Abu-Siada, Ahmed , Islam, Syed , Dahidah, Mohamed
- Date: 2018
- Type: Text , Journal article
- Relation: IEEE Transactions on Industry Applications Vol. 54, no. 4 (2018), p. 3463-3472
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- Description: IEEE This paper presents a new compact three-phase cascaded multilevel inverter (CMLI) topology with reduced device count and high frequency magnetic link. The proposed topology overcomes the predominant limitation of separate DC power supplies, which CMLI always require. The high frequency magnetic link also provides a galvanic isolation between the input and output sides of the inverter, which is essential for various grid-connected applications. The proposed topology utilizes an asymmetric inverter configuration that consists of cascaded H-bridge cells and a conventional three-phase two-level inverter. A toroidal core is employed for the high frequency magnetic link to ensure compact size and high-power density. Compared with counterpart CMLI topologies available in the literatures, the proposed inverter has the advantage of utilizing the least number of power electronic components without compromising the overall performance, particularly when a high number of output voltage levels is required. The feasibility of the proposed inverter is confirmed through extensive simulation and experimentally validated studies.
Time-delay analysis of wide area voltage control considering smart grid contingences in real-time environment
- Authors: Musleh, Ahmed , Muyeen, S. , Al-Durra, Ahmed , Kamwa, Innocent , Masoum, Mohammad , Islam, Syed
- Date: 2018
- Type: Text , Journal article
- Relation: IEEE Transactions on Industrial Informatics Vol. 14, no. 3 (2018), p. 1242-1252
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- Description: IEEE This paper addresses the time delay effects of the wide area monitoring and control systems (WAMCS) in smart power grids which may critically impact system stability. The main purpose is to conduct a detailed delay analysis of the WAMCS in case of grid contingences. This analysis is performed via an advanced WAMCS testbed where a wide area controller (WAC) for a flexible AC transmission system (FACTS) device is implemented. The real-time measurements for the WAC are collected using phasor measurements units (PMU). The testbed is resulted from an interface of four main segments known as the WAC, the actual FACTS device, the local area controller, and the power grid system along with the PMUs are simulated via real time digital simulator (RTDS). To mimic the real case scenario both hardware-in-the-loop (HIL) and software-in-the-loop (SIL) schemes are adopted in the experimental testbed, considering time delay effects. The results obtained clarify the effect of delay in WAMCS in case of smart grid contingences.
Continuous patient monitoring with a patient centric agent : A block architecture
- Authors: Uddin, Ashraf , Stranieri, Andrew , Gondal, Iqbal , Balasubramanian, Venki
- Date: 2018
- Type: Text , Journal article
- Relation: IEEE Access Vol. 6, no. (2018), p. 32700-32726
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- Description: The Internet of Things (IoT) has facilitated services without human intervention for a wide range of applications, including continuous remote patient monitoring (RPM). However, the complexity of RPM architectures, the size of data sets generated and limited power capacity of devices make RPM challenging. In this paper, we propose a tier-based End to End architecture for continuous patient monitoring that has a patient centric agent (PCA) as its center piece. The PCA manages a blockchain component to preserve privacy when data streaming from body area sensors needs to be stored securely. The PCA based architecture includes a lightweight communication protocol to enforce security of data through different segments of a continuous, real time patient monitoring architecture. The architecture includes the insertion of data into a personal blockchain to facilitate data sharing amongst healthcare professionals and integration into electronic health records while ensuring privacy is maintained. The blockchain is customized for RPM with modifications that include having the PCA select a Miner to reduce computational effort, enabling the PCA to manage multiple blockchains for the same patient, and the modification of each block with a prefix tree to minimize energy consumption and incorporate secure transaction payments. Simulation results demonstrate that security and privacy can be enhanced in RPM with the PCA based End to End architecture.
Adaptive weighted non-parametric background model for efficient video coding
- Authors: Chakraborty, Subrata , Paul, Manoranjan , Murshed, Manzur , Ali, Mortuza
- Date: 2017
- Type: Text , Journal article
- Relation: Neurocomputing Vol. 226, no. (2017), p. 35-45
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- Description: Dynamic background frame based video coding using mixture of Gaussian (MoG) based background modelling has achieved better rate distortion performance compared to the H.264 standard. However, they suffer from high computation time, low coding efficiency for dynamic videos, and prior knowledge requirement of video content. In this paper, we introduce the application of the non-parametric (NP) background modelling approach for video coding domain. We present a novel background modelling technique, called weighted non-parametric (WNP) which balances the historical trend and the recent value of the pixel intensities adaptively based on the content and characteristics of any particular video. WNP is successfully embedded into the latest HEVC video coding standard for better rate-distortion performance. Moreover, a novel scene adaptive non-parametric (SANP) technique is also developed to handle video sequences with high dynamic background. Being non-parametric, the proposed techniques naturally exhibit superior performance in dynamic background modelling without a priori knowledge of video data distribution.
A modified immune network optimization algorithm
- Authors: Hong, Lu , Kamruzzaman, Joarder
- Date: 2014
- Type: Text , Journal article
- Relation: IAENG International Journal of Computer Science Vol. 41, no. 4 (2014), p. 231-236
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- Description: This study proposes a modified artificial immune network algorithm for function optimization problems based on idiotypic immune network theory. A hyper-cubic mutation operator was introduced to reduce the heavy computational cost of the traditional opt-AINet algorithm. Moreover, the new symmetrical mutation can effectively improve local search. To maintain population diversity, we also devised an immune selection mechanism based on density and fitness. The global convergence of the algorithm was deduced through the method of pure probability and iterative formula. Simulation results of benchmark function optimization show that the modified algorithm converges more effectively than other immune network algorithms.
Data analytics identify glycated haemoglobin co-markers for type 2 diabetes mellitus diagnosis
- Authors: Jelinek, Herbert , Stranieri, Andrew , Yatsko, Andrew , Venkatraman, Sitalakshmi
- Date: 2016
- Type: Text , Journal article
- Relation: Computers in Biology and Medicine Vol. 75, no. (2016), p. 90-97
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- Description: Glycated haemoglobin (HbA1c) is being more commonly used as an alternative test for the identification of type 2 diabetes mellitus (T2DM) or to add to fasting blood glucose level and oral glucose tolerance test results, because it is easily obtained using point-of-care technology and represents long-term blood sugar levels. HbA1c cut-off values of 6.5% or above have been recommended for clinical use based on the presence of diabetic comorbidities from population studies. However, outcomes of large trials with a HbA1c of 6.5% as a cut-off have been inconsistent for a diagnosis of T2DM. This suggests that a HbA1c cut-off of 6.5% as a single marker may not be sensitive enough or be too simple and miss individuals at risk or with already overt, undiagnosed diabetes. In this study, data mining algorithms have been applied on a large clinical dataset to identify an optimal cut-off value for HbA1c and to identify whether additional biomarkers can be used together with HbA1c to enhance diagnostic accuracy of T2DM. T2DM classification accuracy increased if 8-hydroxy-2-deoxyguanosine (8-OhdG), an oxidative stress marker, was included in the algorithm from 78.71% for HbA1c at 6.5% to 86.64%. A similar result was obtained when interleukin-6 (IL-6) was included (accuracy=85.63%) but with a lower optimal HbA1c range between 5.73 and 6.22%. The application of data analytics to medical records from the Diabetes Screening programme demonstrates that data analytics, combined with large clinical datasets can be used to identify clinically appropriate cut-off values and identify novel biomarkers that when included improve the accuracy of T2DM diagnosis even when HbA1c levels are below or equal to the current cut-off of 6.5%. © 2016 Elsevier Ltd.
Flow on the Internet : A longitudinal study of Internet addiction symptoms during adolescence
- Authors: Stavropoulos, Vasileios , Griffiths, Mark , Burleigh, Tyrone , Kuss, Daria , Doh, Young , Gomez, Rapson
- Date: 2018
- Type: Text , Journal article
- Relation: Behaviour and Information Technology Vol. 37, no. 2 (2018), p. 159-172
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- Description: Internet Addiction (IA) constitutes an excessive Internet use behavior with a significant impact on the user’s well-being. Online flow describes the users’ level of being absorbed by their online activity. The present study investigated age-related, gender, and flow effects on IA in adolescence. The sample comprised 648 adolescents who were assessed twice at age 16 and 18 years. IA was assessed using the Internet Addiction Test and online flow was assessed using the Online Flow Questionnaire. A three-level hierarchical model estimated age-related, gender, and online flow effects on IA symptoms and controlled for clustered random effects. IA symptoms decreased over time (for both genders) with a slower rate in males. Online flow was associated with IA symptoms and this remained consistent over time. Findings expand upon the available literature suggesting that IA symptoms could function as a development-related manifestation at the age of 16 years, while IA-related gender differences gradually increase between 16 and 18 years. Finally, the association between online flow and IA symptoms remained stable independent of age-related effects. The study highlights individual differences and provides directions for more targeted prevention and intervention initiatives for IA. © 2018 Informa UK Limited, trading as Taylor & Francis Group.
Detection of power transformer winding deformation using improved FRA based on binary morphology and extreme point variation
- Authors: Zhao, Zhongyong , Yao, Chenguo , Li, Chengxiang , Islam, Syed
- Date: 2018
- Type: Text , Journal article
- Relation: IEEE Transactions on Industrial Electronics Vol. 65, no. 4 (2018), p. 3509-3519
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- Description: IEEE Frequency response analysis (FRA) has recently been developed as a widely accepted tool for power transformer winding mechanical deformation diagnosis, and has proven to be effective and powerful in many cases. However, there still exist problems regarding the application of FRA. FRA is a comparative method in which the measured FRA signature should be compared with its fingerprint. Small differences of FRA signatures in certain frequency bands might be produced by external disturbance, which hinders fault diagnosis. Additionally, the existing correlation coefficient indicator recommended by power industry standards cannot reflect key information of signatures, namely the extreme points. This paper proposes an improved FRA based on binary morphology and extreme point variation. Binary morphology is first introduced to extract the certain frequency bands of signatures with significant difference. A composite indicator of extreme point variation is adopted to realize the diagnosis of fault level. A ternary diagram is constructed by the area proportions of the binary image to identify winding faults, which has a potential to realize cluster analysis of fault types.
A mapreduce based technique for mining behavioral patterns from sensor data
- Authors: Rashid, Md. Mamunur , Gondal, Iqbal , Kamruzzaman, Joarder
- Date: 2015
- Type: Text , Conference paper
- Relation: 22nd International Conference on Neural Information Processing, ICONIP 2015; Istanbul, Turkey; 9th-12th November 2015 Vol. 9492, p. 145-153
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- Description: WSNs generate a large amount of data in the form of streams, and temporal regularity in occurrence behavior is considered as an important measure for assessing the importance of patterns in WSN data. A frequent sensor pattern that occurs after regular intervals in WSNs is called regularly frequent sensor patterns (RFSPs). Existing RFSPs techniques assume that the data structure of the mining task is small enough to fit in the main memory of a processor. However, given the emergence of the Internet of Things (IoT), WSNs in future will generate huge volume of data, which means such an assumption does not hold any longer. To overcome this, a distributed solution using MapReduce model has not yet been explored extensively. Since MapReduce is becoming the de-facto model for computation on large data, an efficient RFSPs mining algorithm on this model is likely to provide a highly effective solution. In this work, we propose a regularly frequent sensor patterns mining algorithm called RFSP-H which uses MapReduce based framework. Extensive performance analyses show that our technique is significantly time efficient in finding regularly frequent sensor patterns. © Springer International Publishing Switzerland 2015.
Frequency decomposition based gene clustering
- Authors: Rahman, Md Abdur , Chetty, Madhu , Bulach, Dieter , Wangikar, Pramod
- Date: 2015
- Type: Text , Conference paper
- Relation: 22nd International Conference on Neural Information Processing, ICONIP 2015; Istanbul, Turkey; 9th-12th November 2015 Vol. 9490, p. 170-181
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- Description: Gene expressions have been commonly applied to understand the inherent underlying mechanism of known biological processes. Although the microarray gene expressions usually appear aperiodic, with proper signal processing techniques, its periodic components can be easily obtained. Thus, if expressions of interconnected (regulatory and regulated) genes are decomposed, at least one common frequency component will appear in these genes. Exploiting this novel concept, we propose a frequency decomposition approach for gene clustering to better understand the gene interconnection topology. This method, based on Hilbert Huang Transform (HHT) enables us to segregate every periodic component of the gene expressions. Next, a multilevel clustering is performed based on these frequency components. Unlike existing clustering algorithms, the proposed method assimilates a meaningful knowledge of the gene interactions topology. The information related to underlying gene interactions is vital and can prove useful in many existing evolutionary optimisation algorithms for genetic network reconstruction. We validate the entire approach by its application to a 15-gene synthetic network. © Springer International Publishing Switzerland 2015.
On the security of permutation-only image encryption schemes
- Authors: Jolfaei, Alireza , Wu, Xinwen , Muthukkumarasamy, Vallipuram
- Date: 2016
- Type: Text , Journal article
- Relation: IEEE Transactions on Information Forensics and Security Vol. 11, no. 2 (2016), p. 235-246
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- Description: Permutation is a commonly used primitive in multimedia (image/video) encryption schemes, and many permutation-only algorithms have been proposed in recent years for the protection of multimedia data. In permutation-only image ciphers, the entries of the image matrix are scrambled using a permutation mapping matrix which is built by a pseudo-random number generator. The literature on the cryptanalysis of image ciphers indicates that the permutation-only image ciphers are insecure against ciphertext-only attacks and/or known/chosenplaintext attacks. However, the previous studies have not been able to ensure the correct retrieval of the complete plaintext elements. In this paper, we revisited the previous works on cryptanalysis of permutation-only image encryption schemes and made the cryptanalysis work on chosen-plaintext attacks complete and more efficient. We proved that in all permutationonly image ciphers, regardless of the cipher structure, the correct permutation mapping is recovered completely by a chosenplaintext attack. To the best of our knowledge, for the first time, this paper gives a chosen-plaintext attack that completely determines the correct plaintext elements using a deterministic method. When the plain-images are of size M × N and with L different color intensities, the number n of required chosen plain-images to break the permutation-only image encryption algorithm is n = logL(MN). The complexity of the proposed attack is O (n · M N) which indicates its feasibility in a polynomial amount of computation time. To validate the performance of the proposed chosen-plaintext attack, numerous experiments were performed on two recently proposed permutation-only image/video ciphers. Both theoretical and experimental results showed that the proposed attack outperforms the state-of-theart cryptanalytic methods.
A technique for parallel share-frequent sensor pattern mining from wireless sensor networks
- Authors: Rashid, Md. Mamunur , Gondal, Iqbal , Kamruzzaman, Joarder
- Date: 2014
- Type: Text , Conference paper
- Relation: 14th Annual International Conference on Computational Science, ICCS 2014; Cairns, Australia; 10th-12th June 2014; published in Procedia Computer Science p. 124-133
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- Description: WSNs generate huge amount of data in the form of streams and mining useful knowledge from these streams is a challenging task. Existing works generate sensor association rules using occurrence frequency of patterns with binary frequency (either absent or present) or support of a pattern as a criterion. However, considering the binary frequency or support of a pattern may not be a sufficient indicator for finding meaningful patterns from WSN data because it only reflects the number of epochs in the sensor data which contain that pattern. The share measure of sensorsets could discover useful knowledge about numerical values associated with sensor in a sensor database. Therefore, in this paper, we propose a new type of behavioral pattern called share-frequent sensor patterns by considering the non-binary frequency values of sensors in epochs. To discover share-frequent sensor patterns from sensor dataset, we propose a novel parallel technique. In this technique, we develop a novel tree structure, called parallel share-frequent sensor pattern tree (PShrFSP-tree) that is constructed at each local node independently, by capturing the database contents to generate the candidate patterns using a pattern growth technique with a single scan and then merges the locally generated candidate patterns at the final stage to generate global share-frequent sensor patterns. Comprehensive experimental results show that our proposed model is very efficient for mining share-frequent patterns from WSN data in terms of time and scalability.