Blockchain adaptation of remote patient monitoring with internet of medical things
- Authors: Godly, Cinthia , Balasubramanian, Venki , Jinila, Bevesh
- Date: 2022
- Type: Text , Conference proceedings
- Relation: 2022 International Conference on Computing, Communication, Security and Intelligent Systems (IC3SIS); Kochi, India; 23-25 June, 2022 p. 1-7
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Expressing metaphorically, writing creatively: Metaphor identification for creativity assessment
- Authors: Zhang, Dongyu , Zhang, Minghao , Peng, Ciyuan , Xia, Feng
- Date: 2022
- Type: Text , Conference proceedings
- Relation: WWW '22: Companion Proceedings of the Web Conference , Virtual event , April 2022 p. 1198-
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- Description: Metaphor, which can implicitly express profound meanings and emotions, is a unique writing technique frequently used in human language. In writing, meaningful metaphorical expressions can enhance the literariness and creativity of texts. Therefore, the usage of metaphor is a significant impact factor when assessing the creativity and literariness of writing. However, little to no automatic writing assessment system considers metaphorical expressions when giving the score of creativity. For improving the accuracy of automatic writing assessment, this paper proposes a novel creativity assessment model that imports a token-level metaphor identification method to extract metaphors as the indicators for creativity scoring. The experimental results show that our model can accurately assess the creativity of different texts with precise metaphor identification. To the best of our knowledge, we are the first to apply automatic metaphor identification to assess writing creativity. Moreover, identifying features (e.g., metaphors) that influence writing creativity using computational approaches can offer fair and reliable assessment methods for educational settings.
GraphLearning’22: 1st International Workshop on Graph Learning
- Authors: Xia, Feng , Lambiotte, Renaud , Aggarwal, Charu
- Date: 2022
- Type: Text , Conference proceedings
- Relation: WWW '22: Companion Proceedings of the Web Conference 2022, Virtual Event, Lyon France April 25 - 29, 2022 p. 1004-1005
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- Description: The First Workshop on Graph Learning aims to bring together researchers and practitioners from academia and industry to discuss recent advances and core challenges of graph learning. This workshop will be established as a platform for multiple disciplines such as computer science, applied mathematics, physics, social sciences, data science, complex networks, and systems engineering. Core challenges in regard to theory, methodology, and applications of graph learning will be the main center of discussions at the workshop.
Incorporating price information in Blockchain-based energy trading
- Authors: Islam, Ezazul , Chetty, Madhu , Lim, Suryani , Chadhar, Mehmood , Islam, Syed
- Date: 2022
- Type: Text , Conference proceedings
- Relation: SIG SAND -Systems Analysis and Design, 2022; Minneapolis; August 10th-14th, 2022 in AMCIS 2022 Proceedings. 6.
- Full Text: false
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- Description: Blockchain-based peer-to-peer (P2P) ecosystem is well suited for distributed energy trading as it is inherently decentralised. In a distributed energy trading, an auctioneer passes unspent reservations to the next auctioneer, as dictated by the passing mechanism. However, traditional P2P energy trading systems used passing mechanisms that only partially consider the auction capability of the next auctioneer. We propose iPass, which incorporates price information when passing unspent auction reservations in P2P energy trading environment. The three performance metrics applied to measure the trading efficiency are (a) auction convergence time, (b) the number of auction settlements, and (c) the economic surplus of buyers and sellers. We simulated the proposed mechanism in Hyperledger Fabric, a permissioned blockchain framework. Hyperledger Fabric manages the data storage and smart contracts. Experiments show iPass is more efficient compared to existing passing mechanisms.
What is the nature of stem learning in junior school makerspaces? A cross-case analysis
- Authors: Falloon, Garry , Forbes, Anne
- Date: 2022
- Type: Text , Conference proceedings
- Relation: STEM 2022, 7th International STEM in Education Conference, Sydney, 23-26th November, 2022 in Proceedings of the 7th International STEM in Education Conference (STEM 2022)
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- Description: THEME: Engaging students in STEM education BACKGROUND AND AIMS In recent years, many schools have developed Makerspaces as a means of fostering students’ STEM-related knowledge and skills. Typically, activities in these spaces involve students in practical work designing and creating artefacts, models and mock ups responding to problems, needs and opportunities, often working collaboratively using a range of materials, tools and equipment. Much literature associates these activities with developing STEM discipline conceptual knowledge and a variety of so-called ‘21St Century’ competencies such as problem solving, critical and creative thinking, teamwork, communication and other social skills.
AFES: An advanced forensic evidence system
- Authors: Black, Paul , Gondal, Iqbal , Brooks, Richard , Yu, Lu
- Date: 2021
- Type: Text , Conference proceedings
- Relation: 2021 IEEE 25th International Enterprise Distributed Object Computing Workshop (EDOCW), Gold Coast, Australia, 25-29th October, 2021 p. 67-74
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- Description: News media often contain reports that raise doubt related to policing operations. We examine the question of how to improve policing integrity during the execution of search warrants and provide an outline for law enforcement search warrants and digital forensic analysis procedures. Existing techniques for improving the integrity of search warrants are reviewed, limitations are noted, and we propose an Advanced Forensic Evidence System (AFES) to address these limitations.AFES provides an immutable record and biometric authentication of the officers present during the execution of a search warrant, time and location, video recording, seizure record, contemporaneous notes, and photographs. AFES records digital evidence items, imaging details, evidence hashes, provides an access control system, and an immutable record of access to all stored items. AFES uses a permissioned distributed ledger prototype, called Scrybe, developed under NSF aegis, to ensure evidence seizure integrity. Scrybe is run as multiple blockchain instances at law enforcement, prosecution, judicial, and defence organisations to ensure that an immutable record is maintained.
Multi-classifier predictive maintenance strategy for a manufacturing plant
- Authors: Singh, Prashant , Agrawal, Sunil , Chakraborty, Ayon
- Date: 2021
- Type: Text , Conference proceedings
- Relation: 2021 International Conference on Maintenance and Intelligent Asset Management (ICMIAM), Ballarat, Australia, 12-15 December 2021 p. 1-4
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- Description: Predictive Maintenance Management in an industry can play a pivotal role in asset management and revenue generation. This work proposes a data-driven-based multi classifier model for implementing predictive maintenance to simultaneously reduce the downtime and idle time of the machines in a manufacturing plant. A case study of the plant comprising of 100 machines has been done to identify the early prediction of failure, its nature, and the attributing cause. Gradient Boosting Tree Classifier and Random Forest Classifier machine learning algorithms have been used to develop the models for fault prediction. A comparative analysis of results obtained using these methods has also been done. Random Forest Classifier outperforms Gradient Boost tree classifier in all evaluation parameters - accuracy, precision and recall.
Supplemental Conference Proceedings for the Short Papers (Non-peer reviewed) of IEEE CIBCB 2021
- Date: 2021
- Type: Text , Conference proceedings
- Relation: IEEE CIBCB 2021, Melbourne ; 13th to 15th October 2021
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A Realistic and efficient real-time plant environment simulator
- Authors: Seo, Jeongwon , Gong, Mingwei , Naha, Ranesh Kumar , Mahanti, Aniket
- Date: 2020
- Type: Text , Conference proceedings
- Relation: 2020 International Symposium on Networks, Computers and Communications (ISNCC); Montreal, Canada; 20-22 October, 2020 p. 1-6
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- Description: This paper aims to develop a real-time Plant Environment Simulator (PES), which simulates a corrugated plant effectively and realistically. The resultant solution of this work can be used to provide factory workers or new developers with a responsive, simulated learning environment on teaching how to use existing software correctly. The work is carried out for a large cardbox maker that can be used to test new prototypes without using the actual plant facilities, so it will economically and efficiently contribute to the creation of new robust software products for the corrugated plant.
A weighted overlook graph representation of eeg data for absence epilepsy detection
- 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
- Full Text: false
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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
- 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
- Full Text: false
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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
Dynamic derivative-droop control for supercapacitor synthetic inertial support
- 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.
Dynamic voltage signature of large scale PV enriched streesed power system
- Authors: Alzahrani, Saeed , Shah, Rakibuzzaman , Mithulananthan, Nadarajah , Sode-Yome, Arthit
- Date: 2020
- Type: Text , Conference proceedings
- Relation: 2nd International Conference on Smart Power and Internet Energy Systems, SPIES 2020; Bangkok, Thailand; 15th-18th September 2020 p. 275-280
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- Description: Renewable power generations including flexible demand and energy storage systems leverage significant changes in network operation. Thereby, power systems with high renewable penetration manifest deteriorated resilience to disturbances. Hence, the stable operation of the system could be affected. With a paradigm shift, dynamic voltage stability becomes one of the major concerns for the transmission system operators (TSOs). Predicting the dynamic voltage signature for the transmission system with high penetration of renewables is essential to assist in selecting appropriate corrective control. This paper utilized a comprehensive assessment framework to identify the dynamic voltage signature of the power system with PV and various loads. The voltage recovery index has been chosen as the quantifiable index to extricate the dynamic voltage signature. The applicability of the proposed framework is discussed using simulation studies on the IEEE-39 bus test system. © 2020 IEEE.
Dynamically recommending repositories for health data : a machine learning model
- Authors: Uddin, Md Ashraf , Stranieri, Andrew , Gondal, Iqbal , Balasubramanian, Venki
- Date: 2020
- Type: Text , Conference proceedings
- Relation: 2020 Australasian Computer Science Week Multiconference, ACSW 2020
- Full Text: false
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- Description: Recently, a wide range of digital health record repositories has emerged. These include Electronic Health record managed by the government, Electronic Medical Record (EMR) managed by healthcare providers, Personal Health Record (PHR) managed directly by the patient and new Blockchain-based systems mainly managed by technologies. Health record repositories differ from one another on the level of security, privacy, and quality of services (QoS) they provide. Health data stored in these repositories also varies from patient to patient in sensitivity, and significance depending on medical, personal preference, and other factors. Decisions regarding which digital record repository is most appropriate for the storage of each data item at every point in time are complex and nuanced. The challenges are exacerbated with health data continuously streamed from wearable sensors. In this paper, we propose a recommendation model for health data storage that can accommodate patient preferences and make storage decisions rapidly, in real-time, even with streamed data. The model maps health data to be stored in the repositories. The mapping between health data features and characteristics of each repository is learned using a machine learning-based classifier mediated through clinical rules. Evaluation results demonstrate the model's feasibility. © 2020 ACM.
- Description: E1
Energy efficient elliptical concave visibility graph algorithm for unmanned aerial vehicle in an obstacle-rich environment
- Authors: Debnath, Sanjoy , Omar, Rosli , Bagchi, Susama , Nafea, Marwan , Naha, Ranesh , Nadira Sabudin, Elia
- Date: 2020
- Type: Text , Conference proceedings
- Relation: 2020 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS); Shah Alam, Malaysia;20 June 2020 p. 129-134
- Full Text: false
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- Description: This paper proposes a path planning algorithm for unmanned aerial vehicle (UAV) called Elliptical Concave Visibility Graph (ECoVG). The algorithm, which is based on visibility graph (VG), overcomes the limitations of VG computation time and hence, it can be applied in real-time and in obstacle-rich environments. An experimental investigation has been done to compare the performance between ECoVG and another VG based method namely Equilateral-Space Oriented VG (ESOVG) in terms of computational time and path length. The investigation was done in identical scenarios through simulation to show that the ECoVG has a better computation time than that of ESOVG for its efficient selection of a region in calculating the path. It is also found that the proposed algorithm is energy efficient and complete since it can find a path if one exists.
Evaluating the Performances of the Agoraphilic Navigation Algorithm under Dead-Lock Situations
- 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.
Impact of PV plant and load models on system strength and voltage recovery of power systems
- Authors: Alshareef, Abdulrhman , Shah, Rakibuzzaman , Mithulananthan, Nadarajah
- Date: 2020
- Type: Text , Conference proceedings
- Relation: 2nd International Conference on Smart Power and Internet Energy Systems, SPIES 2020; Bangkok, Thailand; 15th-18th September 2020 p. 263-268
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- Description: In recent years, non-conventional inverter-based sources, namely, wind, PV, and others have emerged as excellent alternatives to the traditional synchronous machine for power generation. It has also been reported that the so-called system strength may be reduced with high penetration of non-conventional generations (NCGs). A number of methods have been used to assess system strength which may not reflect the interdependency or reciprocal influence of various factors affecting it. This paper presents a thorough assessment to quantify the implications of and the interaction of various factors affecting system strength, with the voltage recovery index being used as a quantification tool. © 2020 IEEE.
Influence of induction motor in stability of power system with high penetration of large-scale PV
- Authors: Alshareef, Abdulrhman , Nadarajah, Mithulananthan , Shah, Rakibuzzaman
- Date: 2020
- Type: Text , Conference proceedings
- Relation: 2nd International Conference on Smart Power and Internet Energy Systems, SPIES 2020; Bangkok, Thailand; 15th-18th September 2020 p. 269-274
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- Description: Inverter-Based Energy Resources (IBERs) have become an ordinary portion of the generation mix in power systems. Furthermore, converter-based technology has come to dominate modern motor loads on the consumption side. This transition in components towards accommodating power electronic devices alters the dynamic response of the power system. This paper investigates the impact of these elements on the dynamic stability of the power system. Firstly, this study successes to optimize a suitable model for converter-based motor loads. Secondly, indices of transient and voltage stabilities are used to quantify the strength of the power system at different circumstances incorporating the induction motor loads. Finally, this analysis provides an insight into the mutual interactions between transient and voltage stabilities. It is concluded that converter-based motor loads improve the voltage recovery when compared with direct-connected induction motors. However, the system is vulnerable to transient stability with the proliferation of inverter-based motor loads when IBERs dominant in the generation mix. © 2020 IEEE.
Machine learning-based modelling for museum visitations prediction
- Authors: Yap, Norman , Gong, Mingwei , Naha, Ranesh , Mahanti, Aniket
- Date: 2020
- Type: Text , Conference proceedings
- Relation: 2020 International Symposium on Networks, Computers and Communications (ISNCC); Montreal, Canada; 20-22nd October, 2020, p.1-7
- Full Text: false
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- Description: Cultural venues like museums increasingly seek to harness the value of data analytics to make data driven decisions related to exhibitions duration, marketing campaigns, resource planning, and revenue optimization. One key priority is the need to understand the influencing factors behind visitor attendance. Using data collected from a large museum, we investigated whether the weather has a significant impact on visitor attendance or that other factors are more important. We applied the Cross Industry Standard Process for Data Mining (CRISP-DM) methodology to perform the research, developed and built four different types of regression models using R and its machine learning packages to model visitor attendance. The models were trained and evaluated. Predictions of visitor attendance were then generated from each of the four models and forecast accuracy was measured. The extreme gradient boost model was the best model with the highest average forecast accuracy of 93% and lowest forecast variability when benchmarked against the actual visitor attendance from the test data set. The weather was not considered to be as significant in predicting visitor trends and numbers to the museum compared to factors like time of the day, day of the week and school holidays. However, it was still measured to have a slight impact as excluding weather variables resulted in a model with a poorer fit. Weather can potentially have a more marked impact on cultural attractions in more extreme weather environments and outdoor venues.
Motivational factors of Australian mobile gamers
- Authors: Greenwood, Jordan , Achterbosch, Leigh , Meredith, Grant , Vamplew, Peter
- Date: 2020
- Type: Text , Conference proceedings , Conference paper
- Relation: Proceedings of the Australasian Computer Science Week Multiconference (ACSW 2020); Melbourne, Australia; 4th-6th February 2020 p. 6
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- Description: Mobile games are a fast growing industry, overtaking all other video game platforms with year on year increases in revenue. Many studies have been conducted to explore the motivations of why video games players play their selected games. However very little research has focused on mobile gamers. In addition, Australian studies on the topic are sparse. This paper aimed to discover what motivates a mobile gamer from the perspective of the initial motivational factors attracting them to a mobile game, and the motivational factors that provide interest to continue playing and thereby increase game longevity. A survey was conducted online for Australian participants, which attracted 123 respondents. The survey was formulated by focusing on the 12 key subcomponents as motivational factors of the Gamer Motivational Profile v2 model devised by Quantic Foundry. It was discovered that mobile gamers are a completely different breed of gamer in contrast to the general video gamer. Strategy and challenge which are subcomponents of mastery proved popular among all mobile gamers, while destruction and excitement, subcomponents of action, were often the least motivating factors of all. With the newly discovered data, perhaps mobile game developers can pursue the correct avenues of game design when catering to their target audience.