Quantifying success in science : an overview
- Bai, Xiaomei, Pan, Habxiao, Hou, Jie, Guo, Teng, Lee, Ivan, Xia, Feng
- Authors: Bai, Xiaomei , Pan, Habxiao , Hou, Jie , Guo, Teng , Lee, Ivan , Xia, Feng
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Access Vol. 8, no. (2020), p. 123200-123214
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- Description: Quantifying success in science plays a key role in guiding funding allocations, recruitment decisions, and rewards. Recently, a significant amount of progresses have been made towards quantifying success in science. This lack of detailed analysis and summary continues a practical issue. The literature reports the factors influencing scholarly impact and evaluation methods and indices aimed at overcoming this crucial weakness. We focus on categorizing and reviewing the current development on evaluation indices of scholarly impact, including paper impact, scholar impact, and journal impact. Besides, we summarize the issues of existing evaluation methods and indices, investigate the open issues and challenges, and provide possible solutions, including the pattern of collaboration impact, unified evaluation standards, implicit success factor mining, dynamic academic network embedding, and scholarly impact inflation. This paper should help the researchers obtaining a broader understanding of quantifying success in science, and identifying some potential research directions. © 2013 IEEE.
- Description: This work was supported in part by the Liaoning Provincial Key Research and Development Guidance Project under Grant 2018104021, and in part by the Liaoning Provincial Natural Fund Guidance Plan under Grant 20180550011.
- Authors: Bai, Xiaomei , Pan, Habxiao , Hou, Jie , Guo, Teng , Lee, Ivan , Xia, Feng
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Access Vol. 8, no. (2020), p. 123200-123214
- Full Text:
- Reviewed:
- Description: Quantifying success in science plays a key role in guiding funding allocations, recruitment decisions, and rewards. Recently, a significant amount of progresses have been made towards quantifying success in science. This lack of detailed analysis and summary continues a practical issue. The literature reports the factors influencing scholarly impact and evaluation methods and indices aimed at overcoming this crucial weakness. We focus on categorizing and reviewing the current development on evaluation indices of scholarly impact, including paper impact, scholar impact, and journal impact. Besides, we summarize the issues of existing evaluation methods and indices, investigate the open issues and challenges, and provide possible solutions, including the pattern of collaboration impact, unified evaluation standards, implicit success factor mining, dynamic academic network embedding, and scholarly impact inflation. This paper should help the researchers obtaining a broader understanding of quantifying success in science, and identifying some potential research directions. © 2013 IEEE.
- Description: This work was supported in part by the Liaoning Provincial Key Research and Development Guidance Project under Grant 2018104021, and in part by the Liaoning Provincial Natural Fund Guidance Plan under Grant 20180550011.
Canonical duality theory and algorithm for solving bilevel knapsack problems with applications
- Authors: Gao, David
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Transactions on Systems, Man, and Cybernetics: Systems Vol. 51, no. 2 (2021), p. 893-904
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- Description: A novel canonical duality theory (CDT) is presented for solving general bilevel mixed integer nonlinear optimization governed by linear and quadratic knapsack problems. It shows that the challenging knapsack problems can be solved analytically in term of their canonical dual solutions. The existence and uniqueness of these analytical solutions are proved. NP-hardness of the knapsack problems is discussed. A powerful CDT algorithm combined with an alternative iteration and a volume reduction method is proposed for solving the NP-hard bilevel knapsack problems. Application is illustrated by benchmark problems in optimal topology design. The performance and novelty of the proposed method are compared with the popular commercial codes. © 2013 IEEE.
- Authors: Gao, David
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Transactions on Systems, Man, and Cybernetics: Systems Vol. 51, no. 2 (2021), p. 893-904
- Full Text:
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- Description: A novel canonical duality theory (CDT) is presented for solving general bilevel mixed integer nonlinear optimization governed by linear and quadratic knapsack problems. It shows that the challenging knapsack problems can be solved analytically in term of their canonical dual solutions. The existence and uniqueness of these analytical solutions are proved. NP-hardness of the knapsack problems is discussed. A powerful CDT algorithm combined with an alternative iteration and a volume reduction method is proposed for solving the NP-hard bilevel knapsack problems. Application is illustrated by benchmark problems in optimal topology design. The performance and novelty of the proposed method are compared with the popular commercial codes. © 2013 IEEE.
iPod therefore I can : Enhancing the learning of children with intellectual disabilities through emerging technologies
- Authors: Marks, Genee , Milne, Jay
- Date: 2008
- Type: Text , Conference paper
- Relation: Paper presented at ICICTE 2008: International Conference on Information Communication Technologies in Education, Corfu, Greece : 10th-12th July 2008
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- Description: This paper explores the pedagogical and social potential of emerging technologies, in particular the iPod, in facilitating the learning of young Australians with severe intellectual and social disabilities. The study, which was carried out in a segregated educational setting in Victoria, Australia, sought to establish whether the intrinsic portable, multi-media capabilities of the iPod particularly lent themselves to a practical application for students with severe disabilities. It was concluded that such new technology has considerable power and potential as an emerging pedagogy with students with severe intellectual and physical disabilities.
- Description: 2003006449
- Authors: Marks, Genee , Milne, Jay
- Date: 2008
- Type: Text , Conference paper
- Relation: Paper presented at ICICTE 2008: International Conference on Information Communication Technologies in Education, Corfu, Greece : 10th-12th July 2008
- Full Text:
- Description: This paper explores the pedagogical and social potential of emerging technologies, in particular the iPod, in facilitating the learning of young Australians with severe intellectual and social disabilities. The study, which was carried out in a segregated educational setting in Victoria, Australia, sought to establish whether the intrinsic portable, multi-media capabilities of the iPod particularly lent themselves to a practical application for students with severe disabilities. It was concluded that such new technology has considerable power and potential as an emerging pedagogy with students with severe intellectual and physical disabilities.
- Description: 2003006449
An adaptive and flexible brain energized full body exoskeleton with IoT edge for assisting the paralyzed patients
- Jacob, Sunil, Alagirisamy, Mukil, Menon, Varun, Kumar, B. Manoj, Balasubramanian, Venki
- Authors: Jacob, Sunil , Alagirisamy, Mukil , Menon, Varun , Kumar, B. Manoj , Balasubramanian, Venki
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Access Vol. 8, no. (2020), p. 100721-100731
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- Description: The paralyzed population is increasing worldwide due to stroke, spinal code injury, post-polio, and other related diseases. Different assistive technologies are used to improve the physical and mental health of the affected patients. Exoskeletons have emerged as one of the most promising technology to provide movement and rehabilitation for the paralyzed. But exoskeletons are limited by the constraints of weight, flexibility, and adaptability. To resolve these issues, we propose an adaptive and flexible Brain Energized Full Body Exoskeleton (BFBE) for assisting the paralyzed people. This paper describes the design, control, and testing of BFBE with 15 degrees of freedom (DoF) for assisting the users in their daily activities. The flexibility is incorporated into the system by a modular design approach. The brain signals captured by the Electroencephalogram (EEG) sensors are used for controlling the movements of BFBE. The processing happens at the edge, reducing delay in decision making and the system is further integrated with an IoT module that helps to send an alert message to multiple caregivers in case of an emergency. The potential energy harvesting is used in the system to solve the power issues related to the exoskeleton. The stability in the gait cycle is ensured by using adaptive sensory feedback. The system validation is done by using six natural movements on ten different paralyzed persons. The system recognizes human intensions with an accuracy of 85%. The result shows that BFBE can be an efficient method for providing assistance and rehabilitation for paralyzed patients. © 2013 IEEE. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Venki Balasubramanian” is provided in this record**
- Authors: Jacob, Sunil , Alagirisamy, Mukil , Menon, Varun , Kumar, B. Manoj , Balasubramanian, Venki
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Access Vol. 8, no. (2020), p. 100721-100731
- Full Text:
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- Description: The paralyzed population is increasing worldwide due to stroke, spinal code injury, post-polio, and other related diseases. Different assistive technologies are used to improve the physical and mental health of the affected patients. Exoskeletons have emerged as one of the most promising technology to provide movement and rehabilitation for the paralyzed. But exoskeletons are limited by the constraints of weight, flexibility, and adaptability. To resolve these issues, we propose an adaptive and flexible Brain Energized Full Body Exoskeleton (BFBE) for assisting the paralyzed people. This paper describes the design, control, and testing of BFBE with 15 degrees of freedom (DoF) for assisting the users in their daily activities. The flexibility is incorporated into the system by a modular design approach. The brain signals captured by the Electroencephalogram (EEG) sensors are used for controlling the movements of BFBE. The processing happens at the edge, reducing delay in decision making and the system is further integrated with an IoT module that helps to send an alert message to multiple caregivers in case of an emergency. The potential energy harvesting is used in the system to solve the power issues related to the exoskeleton. The stability in the gait cycle is ensured by using adaptive sensory feedback. The system validation is done by using six natural movements on ten different paralyzed persons. The system recognizes human intensions with an accuracy of 85%. The result shows that BFBE can be an efficient method for providing assistance and rehabilitation for paralyzed patients. © 2013 IEEE. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Venki Balasubramanian” is provided in this record**
AI and IoT-Enabled smart exoskeleton system for rehabilitation of paralyzed people in connected communities
- Jacob, Sunil, Alagirisamy, Mukil, Xi, Chen, Balasubramanian, Venki, Srinivasan, Ram
- Authors: Jacob, Sunil , Alagirisamy, Mukil , Xi, Chen , Balasubramanian, Venki , Srinivasan, Ram
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Access Vol. 9, no. (2021), p. 80340-80350
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- Description: In recent years, the number of cases of spinal cord injuries, stroke and other nervous impairments have led to an increase in the number of paralyzed patients worldwide. Rehabilitation that can aid and enhance the lives of such patients is the need of the hour. Exoskeletons have been found as one of the popular means of rehabilitation. The existing exoskeletons use techniques that impose limitations on adaptability, instant response and continuous control. Also most of them are expensive, bulky, and requires high level of training. To overcome all the above limitations, this paper introduces an Artificial Intelligence (AI) powered Smart and light weight Exoskeleton System (AI-IoT-SES) which receives data from various sensors, classifies them intelligently and generates the desired commands via Internet of Things (IoT) for rendering rehabilitation and support with the help of caretakers for paralyzed patients in smart and connected communities. In the proposed system, the signals collected from the exoskeleton sensors are processed using AI-assisted navigation module, and helps the caretakers in guiding, communicating and controlling the movements of the exoskeleton integrated to the patients. The navigation module uses AI and IoT enabled Simultaneous Localization and Mapping (SLAM). The casualties of a paralyzed person are reduced by commissioning the IoT platform to exchange data from the intelligent sensors with the remote location of the caretaker to monitor the real time movement and navigation of the exoskeleton. The automated exoskeleton detects and take decisions on navigation thereby improving the life conditions of such patients. The experimental results simulated using MATLAB shows that the proposed system is the ideal method for rendering rehabilitation and support for paralyzed patients in smart communities. © 2013 IEEE. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Venki Balasubramanian” is provided in this record**
- Authors: Jacob, Sunil , Alagirisamy, Mukil , Xi, Chen , Balasubramanian, Venki , Srinivasan, Ram
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Access Vol. 9, no. (2021), p. 80340-80350
- Full Text:
- Reviewed:
- Description: In recent years, the number of cases of spinal cord injuries, stroke and other nervous impairments have led to an increase in the number of paralyzed patients worldwide. Rehabilitation that can aid and enhance the lives of such patients is the need of the hour. Exoskeletons have been found as one of the popular means of rehabilitation. The existing exoskeletons use techniques that impose limitations on adaptability, instant response and continuous control. Also most of them are expensive, bulky, and requires high level of training. To overcome all the above limitations, this paper introduces an Artificial Intelligence (AI) powered Smart and light weight Exoskeleton System (AI-IoT-SES) which receives data from various sensors, classifies them intelligently and generates the desired commands via Internet of Things (IoT) for rendering rehabilitation and support with the help of caretakers for paralyzed patients in smart and connected communities. In the proposed system, the signals collected from the exoskeleton sensors are processed using AI-assisted navigation module, and helps the caretakers in guiding, communicating and controlling the movements of the exoskeleton integrated to the patients. The navigation module uses AI and IoT enabled Simultaneous Localization and Mapping (SLAM). The casualties of a paralyzed person are reduced by commissioning the IoT platform to exchange data from the intelligent sensors with the remote location of the caretaker to monitor the real time movement and navigation of the exoskeleton. The automated exoskeleton detects and take decisions on navigation thereby improving the life conditions of such patients. The experimental results simulated using MATLAB shows that the proposed system is the ideal method for rendering rehabilitation and support for paralyzed patients in smart communities. © 2013 IEEE. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Venki Balasubramanian” is provided in this record**
- Yap, Shelley, Ooi, Ean, Foo, Ji, Ooi, Ean Tat
- Authors: Yap, Shelley , Ooi, Ean , Foo, Ji , Ooi, Ean Tat
- Date: 2021
- Type: Text , Journal article
- Relation: Computers in Biology and Medicine Vol. 134, no. (2021), p.
- Full Text: false
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- Description: Switching bipolar radiofrequency ablation (bRFA) is a cancer treatment technique that activates multiple pairs of electrodes alternately based on a predefined criterion. Various criteria can be used to trigger the switch, such as time (ablation duration) and tissue impedance. In a recent study on time-based switching bRFA, it was determined that a shorter switch interval could produce better treatment outcome than when a longer switch interval was used, which reduces tissue charring and roll-off induced cooling. In this study, it was hypothesized that a more efficacious bRFA treatment can be attained by employing impedance-based switching. This is because ablation per pair can be maximized since there will be no interruption to RF energy delivery until roll-off occurs. This was investigated using a two-compartment 3D computational model. Results showed that impedance-based switching bRFA outperformed time-based switching when the switch interval of the latter is 100 s or higher. When compared to the time-based switching with switch interval of 50 s, the impedance-based model is inferior. It remains to be investigated whether the impedance-based protocol is better than the time-based protocol for a switch interval of 50 s due to the inverse relationship between ablation and treatment efficacies. It was suggested that the choice of impedance-based or time-based switching could ultimately be patient-dependent. © 2021 Elsevier Ltd
Wifi-based localisation datasets for No-GPS open areas using smart bins
- Nassar, Mohamed, Hasan, Mahmud, Khan, Md, Sultana, Mirza, Hasan, Md, Luxford, Len, Cole, Peter, Oatley, Giles, Koutsakis, Polychronis
- Authors: Nassar, Mohamed , Hasan, Mahmud , Khan, Md , Sultana, Mirza , Hasan, Md , Luxford, Len , Cole, Peter , Oatley, Giles , Koutsakis, Polychronis
- Date: 2020
- Type: Text , Journal article , Data article
- Relation: Computer Networks Vol. 180, no. (2020), p. 1-5
- Full Text: false
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- Description: In recent years, Wifi-based localisation systems have gained significant interest because of the lack of Global Positioning System (GPS) signal in indoor and certain open areas. Over the past decade, many datasets have been introduced to enable researchers to compare different localisation techniques. Existing datasets, however, have failed to cover open areas such as parks in cases where GPS is still unavailable, and there is a lack of Wifi access points. Also, the existing datasets only focus on getting Wifi fingerprint collected and labelled by users. To the best of our knowledge, no dataset provides Received Signal Strengths (RSS) collected by Wireless Access Points (APs). In this work, we offer two datasets publicly. The first is the Fingerprint dataset in which four users generated 16,032 accurate and consistently labelled WiFi fingerprints for all available Reference Points (RPs) in a central and busy area of Murdoch University, known as Bush Court. The second is the APs dataset that includes 2,450,865 auto-generated records received from 1000 users' devices, including the four users, associated with Wifi signal strengths. To overcome the Wifi coverage problem for the Bush Court, we attached our previously designed Wireless Sensor Nodes (WSNs) to existing garbage bins, enabling them to provide real-time environmental sensing and act as soft APs that sense MAC addresses and Wifi signals from surrounding devices.
Optimal placement of synchronized voltage traveling wave sensors in a radial distribution network
- Tashakkori, Ali, Abu-Siada, Ahmed, Wolfs, Peter, Islam, Syed
- Authors: Tashakkori, Ali , Abu-Siada, Ahmed , Wolfs, Peter , Islam, Syed
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Access Vol. 9, no. (2021), p. 65380-65387
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- Description: A transmission line fault generates transient high frequency travelling waves (TWs) that propagate through the entire network. The fault location can be determined by recording the instants at which the incident waves arrive at various points in the network. In single end-based methods, the incident wave arrival time and its subsequent reflections from the fault point are used to identify the fault location. In heavily branched distribution networks, the magnitude of the traveling wave declines rapidly as it passes through multiple junctions that cause reflection and refraction to the signal. Therefore, detecting the first incident wave from a high impedance fault is a significant challenge in the electrical distribution networks, in particular, subsequent reflections from a temporarily fault may not be possible. Therefore, to identify a high impedance or temporary faults in a distribution network with many branches, loads, switching devices and distributed transformers, multiple observers are required to observe the entire network. A fully observable and locatable network requires at least one observer per branch or spur which is not a cost effective solution. This paper proposes a reasonable number of relatively low-cost voltage TW observers with GPS time-synchronization and radio communication to detect and timestamp the TW arrival at several points in the network. In this regard, a method to optimally place a given number of TW detectors to maximize the network observability and locatability is presented. Results show the robustness of the proposed method to detect high impedance and intermittent faults within distribution networks with a minimum number of observers. © 2013 IEEE.
- Authors: Tashakkori, Ali , Abu-Siada, Ahmed , Wolfs, Peter , Islam, Syed
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Access Vol. 9, no. (2021), p. 65380-65387
- Full Text:
- Reviewed:
- Description: A transmission line fault generates transient high frequency travelling waves (TWs) that propagate through the entire network. The fault location can be determined by recording the instants at which the incident waves arrive at various points in the network. In single end-based methods, the incident wave arrival time and its subsequent reflections from the fault point are used to identify the fault location. In heavily branched distribution networks, the magnitude of the traveling wave declines rapidly as it passes through multiple junctions that cause reflection and refraction to the signal. Therefore, detecting the first incident wave from a high impedance fault is a significant challenge in the electrical distribution networks, in particular, subsequent reflections from a temporarily fault may not be possible. Therefore, to identify a high impedance or temporary faults in a distribution network with many branches, loads, switching devices and distributed transformers, multiple observers are required to observe the entire network. A fully observable and locatable network requires at least one observer per branch or spur which is not a cost effective solution. This paper proposes a reasonable number of relatively low-cost voltage TW observers with GPS time-synchronization and radio communication to detect and timestamp the TW arrival at several points in the network. In this regard, a method to optimally place a given number of TW detectors to maximize the network observability and locatability is presented. Results show the robustness of the proposed method to detect high impedance and intermittent faults within distribution networks with a minimum number of observers. © 2013 IEEE.
Examination of effective VAr with respect to dynamic voltage stability in renewable rich power grids
- Alzahrani, Saeed, Shah, Rakibuzzaman, Mithulananthan, N.
- Authors: Alzahrani, Saeed , Shah, Rakibuzzaman , Mithulananthan, N.
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Access Vol. 9, no. (2021), p. 75494-75508
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- Description: High penetrations of inverter-based renewable resources (IBRs) diminish the resilience that traditional power systems had due to constant research and developments for many years. In particular, dynamic voltage stability becomes one of the major concerns for transmission system operators due to the limited capabilities of IBRs (i.e., voltage and frequency regulation). A heavily loaded renewable-rich network is susceptible to fault-induced delayed voltage recovery (FIDVR) due to insufficient effective reactive power (E-VAr) in power grids. Hence, it is crucial to thoroughly scrutinize each VAr resources' participation in E-VAr under various operating conditions. Moreover, it is essential to investigate the influence of E-VAr on system post-fault performance. The E-VAr investigation would help in determining the optimal location and sizing of grid-connected IBRs and allow more renewable energy integration. Furthermore, it would enrich decision-making about adopting additional grid support devices. In this paper, a comprehensive assessment framework is utilized to assess the E-VAr of a power system with a large-scale photovoltaic power. Plant under different realistic operating conditions. Several indices quantifying the contribution of VAr resources and load bus voltage recovery assists to explore the transient response and voltage trajectories. The recovery indices help have a better understanding of the factors affecting E-VAr. The proposed framework has been tested in the New England (IEEE 39 bus system) through simulation by DIgSILENT Power Factory. © 2013 IEEE.
Examination of effective VAr with respect to dynamic voltage stability in renewable rich power grids
- Authors: Alzahrani, Saeed , Shah, Rakibuzzaman , Mithulananthan, N.
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Access Vol. 9, no. (2021), p. 75494-75508
- Full Text:
- Reviewed:
- Description: High penetrations of inverter-based renewable resources (IBRs) diminish the resilience that traditional power systems had due to constant research and developments for many years. In particular, dynamic voltage stability becomes one of the major concerns for transmission system operators due to the limited capabilities of IBRs (i.e., voltage and frequency regulation). A heavily loaded renewable-rich network is susceptible to fault-induced delayed voltage recovery (FIDVR) due to insufficient effective reactive power (E-VAr) in power grids. Hence, it is crucial to thoroughly scrutinize each VAr resources' participation in E-VAr under various operating conditions. Moreover, it is essential to investigate the influence of E-VAr on system post-fault performance. The E-VAr investigation would help in determining the optimal location and sizing of grid-connected IBRs and allow more renewable energy integration. Furthermore, it would enrich decision-making about adopting additional grid support devices. In this paper, a comprehensive assessment framework is utilized to assess the E-VAr of a power system with a large-scale photovoltaic power. Plant under different realistic operating conditions. Several indices quantifying the contribution of VAr resources and load bus voltage recovery assists to explore the transient response and voltage trajectories. The recovery indices help have a better understanding of the factors affecting E-VAr. The proposed framework has been tested in the New England (IEEE 39 bus system) through simulation by DIgSILENT Power Factory. © 2013 IEEE.
A new global index for short term voltage stability assessment
- Alshareef, Abdulrhman, Shah, Rakibuzzaman, Mithulananthan, Nadarajah, Alzahrani, Saeed
- Authors: Alshareef, Abdulrhman , Shah, Rakibuzzaman , Mithulananthan, Nadarajah , Alzahrani, Saeed
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Access Vol. 9, no. (2021), p. 36114-36124
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- Description: The utility scale of non-conventional generators (NCGs), such as wind and photovoltaic (PV) plants, are competitive alternatives to synchronous machines (SMs) for power generation. Higher penetration of NCGs has been respondent of causing several recent incidents leading up to voltage collapse in power systems due to the distinct characteristics of NCGs under different operating conditions. Consequently, the so-called system strength has been reduced with higher NCGs penetration. A number of indices have been developed to quantify system strength from the short-term voltage stability (STVS) perspective. None of the indices capture the overall performances of power systems on dynamic voltage recovery. In this paper, an improvement in one of the STVS indices namely, the Voltage Recovery Index (VRI), is proposed to overcome shortcomings in the original index. Moreover, the improved index is globalized to establish a new index defined as system voltage recovery index (VRIsys) to quantify STVS at the system level. The amended VRI and developed VRIsys are used in systematic simulations to quantify the impact and interaction of various factors that could affect system strength. The assessment was conducted using time-domain simulation with direct connected induction motors (DCIMs) and a proliferation of converter-based technologies on both the generation and load sides, namely, NCGs and Variable Speed Drives (VSDs), respectively. © 2013 IEEE.
- Authors: Alshareef, Abdulrhman , Shah, Rakibuzzaman , Mithulananthan, Nadarajah , Alzahrani, Saeed
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Access Vol. 9, no. (2021), p. 36114-36124
- Full Text:
- Reviewed:
- Description: The utility scale of non-conventional generators (NCGs), such as wind and photovoltaic (PV) plants, are competitive alternatives to synchronous machines (SMs) for power generation. Higher penetration of NCGs has been respondent of causing several recent incidents leading up to voltage collapse in power systems due to the distinct characteristics of NCGs under different operating conditions. Consequently, the so-called system strength has been reduced with higher NCGs penetration. A number of indices have been developed to quantify system strength from the short-term voltage stability (STVS) perspective. None of the indices capture the overall performances of power systems on dynamic voltage recovery. In this paper, an improvement in one of the STVS indices namely, the Voltage Recovery Index (VRI), is proposed to overcome shortcomings in the original index. Moreover, the improved index is globalized to establish a new index defined as system voltage recovery index (VRIsys) to quantify STVS at the system level. The amended VRI and developed VRIsys are used in systematic simulations to quantify the impact and interaction of various factors that could affect system strength. The assessment was conducted using time-domain simulation with direct connected induction motors (DCIMs) and a proliferation of converter-based technologies on both the generation and load sides, namely, NCGs and Variable Speed Drives (VSDs), respectively. © 2013 IEEE.
TOSNet : a topic-based optimal subnetwork identification in academic networks
- Bedru, Hayat, Zhao, Wenhong, Alrashoud, Mubarak, Tolba, Amr, Guo, He, Xia, Feng
- Authors: Bedru, Hayat , Zhao, Wenhong , Alrashoud, Mubarak , Tolba, Amr , Guo, He , Xia, Feng
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Access Vol. 8, no. (2020), p. 201015-201027
- Full Text:
- Reviewed:
- Description: Subnetwork identification plays a significant role in analyzing, managing, and comprehending the structure and functions in big networks. Numerous approaches have been proposed to solve the problem of subnetwork identification as well as community detection. Most of the methods focus on detecting communities by considering node attributes, edge information, or both. This study focuses on discovering subnetworks containing researchers with similar or related areas of interest or research topics. A topic- aware subnetwork identification is essential to discover potential researchers on particular research topics and provide qualitywork. Thus, we propose a topic-based optimal subnetwork identification approach (TOSNet). Based on some fundamental characteristics, this paper addresses the following problems: 1)How to discover topic-based subnetworks with a vigorous collaboration intensity? 2) How to rank the discovered subnetworks and single out one optimal subnetwork? We evaluate the performance of the proposed method against baseline methods by adopting the modularity measure, assess the accuracy based on the size of the identified subnetworks, and check the scalability for different sizes of benchmark networks. The experimental findings indicate that our approach shows excellent performance in identifying contextual subnetworks that maintain intensive collaboration amongst researchers for a particular research topic. © 2020 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.
- Authors: Bedru, Hayat , Zhao, Wenhong , Alrashoud, Mubarak , Tolba, Amr , Guo, He , Xia, Feng
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Access Vol. 8, no. (2020), p. 201015-201027
- Full Text:
- Reviewed:
- Description: Subnetwork identification plays a significant role in analyzing, managing, and comprehending the structure and functions in big networks. Numerous approaches have been proposed to solve the problem of subnetwork identification as well as community detection. Most of the methods focus on detecting communities by considering node attributes, edge information, or both. This study focuses on discovering subnetworks containing researchers with similar or related areas of interest or research topics. A topic- aware subnetwork identification is essential to discover potential researchers on particular research topics and provide qualitywork. Thus, we propose a topic-based optimal subnetwork identification approach (TOSNet). Based on some fundamental characteristics, this paper addresses the following problems: 1)How to discover topic-based subnetworks with a vigorous collaboration intensity? 2) How to rank the discovered subnetworks and single out one optimal subnetwork? We evaluate the performance of the proposed method against baseline methods by adopting the modularity measure, assess the accuracy based on the size of the identified subnetworks, and check the scalability for different sizes of benchmark networks. The experimental findings indicate that our approach shows excellent performance in identifying contextual subnetworks that maintain intensive collaboration amongst researchers for a particular research topic. © 2020 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.
- Pramod, Aladurthi, Hirshikesh, Natarajan, Sundararajan, Ooi, Ean Tat
- Authors: Pramod, Aladurthi , Hirshikesh , Natarajan, Sundararajan , Ooi, Ean Tat
- Date: 2021
- Type: Text , Journal article
- Relation: International Journal of Computational Methods Vol. 18, no. 3 (2021), p.
- Full Text: false
- Reviewed:
- Description: In this paper, an adaptive phase-field scaled boundary finite element method for fracture in functionally graded material (FGM) is presented. The model accounts for spatial variation in the material and fracture properties. The quadtree decomposition is adopted for refinement, and the refinement is based on an error indicator evaluated directly from the solutions of the scaled boundary finite element method. This combination makes it a suitable choice to study fracture using the phase field method, as it reduces the mesh burden. A few standard benchmark numerical examples are solved to demonstrate the improvement in computational efficiency in terms of the number of degrees of freedom. © 2021 World Scientific Publishing Company.
An efficient hybrid system for anomaly detection in social networks
- Rahman, Md Shafiur, Halder, Sajal, Uddin, Ashraf, Acharjee, Uzzal
- Authors: Rahman, Md Shafiur , Halder, Sajal , Uddin, Ashraf , Acharjee, Uzzal
- Date: 2021
- Type: Text , Journal article
- Relation: Cybersecurity Vol. 4, no. 1 (2021), p.
- Full Text:
- Reviewed:
- Description: Anomaly detection has been an essential and dynamic research area in the data mining. A wide range of applications including different social medias have adopted different state-of-the-art methods to identify anomaly for ensuring user’s security and privacy. The social network refers to a forum used by different groups of people to express their thoughts, communicate with each other, and share the content needed. This social networks also facilitate abnormal activities, spread fake news, rumours, misinformation, unsolicited messages, and propaganda post malicious links. Therefore, detection of abnormalities is one of the important data analysis activities for the identification of normal or abnormal users on the social networks. In this paper, we have developed a hybrid anomaly detection method named DT-SVMNB that cascades several machine learning algorithms including decision tree (C5.0), Support Vector Machine (SVM) and Naïve Bayesian classifier (NBC) for classifying normal and abnormal users in social networks. We have extracted a list of unique features derived from users’ profile and contents. Using two kinds of dataset with the selected features, the proposed machine learning model called DT-SVMNB is trained. Our model classifies users as depressed one or suicidal one in the social network. We have conducted an experiment of our model using synthetic and real datasets from social network. The performance analysis demonstrates around 98% accuracy which proves the effectiveness and efficiency of our proposed system. © 2021, The Author(s).
- Authors: Rahman, Md Shafiur , Halder, Sajal , Uddin, Ashraf , Acharjee, Uzzal
- Date: 2021
- Type: Text , Journal article
- Relation: Cybersecurity Vol. 4, no. 1 (2021), p.
- Full Text:
- Reviewed:
- Description: Anomaly detection has been an essential and dynamic research area in the data mining. A wide range of applications including different social medias have adopted different state-of-the-art methods to identify anomaly for ensuring user’s security and privacy. The social network refers to a forum used by different groups of people to express their thoughts, communicate with each other, and share the content needed. This social networks also facilitate abnormal activities, spread fake news, rumours, misinformation, unsolicited messages, and propaganda post malicious links. Therefore, detection of abnormalities is one of the important data analysis activities for the identification of normal or abnormal users on the social networks. In this paper, we have developed a hybrid anomaly detection method named DT-SVMNB that cascades several machine learning algorithms including decision tree (C5.0), Support Vector Machine (SVM) and Naïve Bayesian classifier (NBC) for classifying normal and abnormal users in social networks. We have extracted a list of unique features derived from users’ profile and contents. Using two kinds of dataset with the selected features, the proposed machine learning model called DT-SVMNB is trained. Our model classifies users as depressed one or suicidal one in the social network. We have conducted an experiment of our model using synthetic and real datasets from social network. The performance analysis demonstrates around 98% accuracy which proves the effectiveness and efficiency of our proposed system. © 2021, The Author(s).
Shifu2 : a network representation learning based model for advisor-advisee relationship mining
- Liu, Jiaying, Xia, Feng, Wang, Lei, Xu, Bo, Kong, Xiangjie
- Authors: Liu, Jiaying , Xia, Feng , Wang, Lei , Xu, Bo , Kong, Xiangjie
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Transactions on Knowledge and Data Engineering Vol. 33, no. 4 (2021), p. 1763-1777
- Full Text:
- Reviewed:
- Description: The advisor-advisee relationship represents direct knowledge heritage, and such relationship may not be readily available from academic libraries and search engines. This work aims to discover advisor-advisee relationships hidden behind scientific collaboration networks. For this purpose, we propose a novel model based on Network Representation Learning (NRL), namely Shifu2, which takes the collaboration network as input and the identified advisor-advisee relationship as output. In contrast to existing NRL models, Shifu2 considers not only the network structure but also the semantic information of nodes and edges. Shifu2 encodes nodes and edges into low-dimensional vectors respectively, both of which are then utilized to identify advisor-advisee relationships. Experimental results illustrate improved stability and effectiveness of the proposed model over state-of-the-art methods. In addition, we generate a large-scale academic genealogy dataset by taking advantage of Shifu2. © 1989-2012 IEEE.
- Authors: Liu, Jiaying , Xia, Feng , Wang, Lei , Xu, Bo , Kong, Xiangjie
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Transactions on Knowledge and Data Engineering Vol. 33, no. 4 (2021), p. 1763-1777
- Full Text:
- Reviewed:
- Description: The advisor-advisee relationship represents direct knowledge heritage, and such relationship may not be readily available from academic libraries and search engines. This work aims to discover advisor-advisee relationships hidden behind scientific collaboration networks. For this purpose, we propose a novel model based on Network Representation Learning (NRL), namely Shifu2, which takes the collaboration network as input and the identified advisor-advisee relationship as output. In contrast to existing NRL models, Shifu2 considers not only the network structure but also the semantic information of nodes and edges. Shifu2 encodes nodes and edges into low-dimensional vectors respectively, both of which are then utilized to identify advisor-advisee relationships. Experimental results illustrate improved stability and effectiveness of the proposed model over state-of-the-art methods. In addition, we generate a large-scale academic genealogy dataset by taking advantage of Shifu2. © 1989-2012 IEEE.
A critical review of intrusion detection systems in the internet of things : techniques, deployment strategy, validation strategy, attacks, public datasets and challenges
- Khraisat, Ansam, Alazab, Ammar
- Authors: Khraisat, Ansam , Alazab, Ammar
- Date: 2021
- Type: Text , Journal article
- Relation: Cybersecurity Vol. 4, no. 1 (2021), p.
- Full Text:
- Reviewed:
- Description: The Internet of Things (IoT) has been rapidly evolving towards making a greater impact on everyday life to large industrial systems. Unfortunately, this has attracted the attention of cybercriminals who made IoT a target of malicious activities, opening the door to a possible attack on the end nodes. To this end, Numerous IoT intrusion detection Systems (IDS) have been proposed in the literature to tackle attacks on the IoT ecosystem, which can be broadly classified based on detection technique, validation strategy, and deployment strategy. This survey paper presents a comprehensive review of contemporary IoT IDS and an overview of techniques, deployment Strategy, validation strategy and datasets that are commonly applied for building IDS. We also review how existing IoT IDS detect intrusive attacks and secure communications on the IoT. It also presents the classification of IoT attacks and discusses future research challenges to counter such IoT attacks to make IoT more secure. These purposes help IoT security researchers by uniting, contrasting, and compiling scattered research efforts. Consequently, we provide a unique IoT IDS taxonomy, which sheds light on IoT IDS techniques, their advantages and disadvantages, IoT attacks that exploit IoT communication systems, corresponding advanced IDS and detection capabilities to detect IoT attacks. © 2021, The Author(s).
- Authors: Khraisat, Ansam , Alazab, Ammar
- Date: 2021
- Type: Text , Journal article
- Relation: Cybersecurity Vol. 4, no. 1 (2021), p.
- Full Text:
- Reviewed:
- Description: The Internet of Things (IoT) has been rapidly evolving towards making a greater impact on everyday life to large industrial systems. Unfortunately, this has attracted the attention of cybercriminals who made IoT a target of malicious activities, opening the door to a possible attack on the end nodes. To this end, Numerous IoT intrusion detection Systems (IDS) have been proposed in the literature to tackle attacks on the IoT ecosystem, which can be broadly classified based on detection technique, validation strategy, and deployment strategy. This survey paper presents a comprehensive review of contemporary IoT IDS and an overview of techniques, deployment Strategy, validation strategy and datasets that are commonly applied for building IDS. We also review how existing IoT IDS detect intrusive attacks and secure communications on the IoT. It also presents the classification of IoT attacks and discusses future research challenges to counter such IoT attacks to make IoT more secure. These purposes help IoT security researchers by uniting, contrasting, and compiling scattered research efforts. Consequently, we provide a unique IoT IDS taxonomy, which sheds light on IoT IDS techniques, their advantages and disadvantages, IoT attacks that exploit IoT communication systems, corresponding advanced IDS and detection capabilities to detect IoT attacks. © 2021, The Author(s).
A secured framework for SDN-based edge computing in IoT-enabled healthcare system
- Li, Junxia, Cai, Jinjin, Khan, Fazlullah, Rehman, Ateeq, Balasubramanian, Venki
- Authors: Li, Junxia , Cai, Jinjin , Khan, Fazlullah , Rehman, Ateeq , Balasubramanian, Venki
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Access Vol. 8, no. (2020), p. 135479-135490
- Full Text:
- Reviewed:
- Description: The Internet of Things (IoT) consists of resource-constrained smart devices capable to sense and process data. It connects a huge number of smart sensing devices, i.e., things, and heterogeneous networks. The IoT is incorporated into different applications, such as smart health, smart home, smart grid, etc. The concept of smart healthcare has emerged in different countries, where pilot projects of healthcare facilities are analyzed. In IoT-enabled healthcare systems, the security of IoT devices and associated data is very important, whereas Edge computing is a promising architecture that solves their computational and processing problems. Edge computing is economical and has the potential to provide low latency data services by improving the communication and computation speed of IoT devices in a healthcare system. In Edge-based IoT-enabled healthcare systems, load balancing, network optimization, and efficient resource utilization are accurately performed using artificial intelligence (AI), i.e., intelligent software-defined network (SDN) controller. SDN-based Edge computing is helpful in the efficient utilization of limited resources of IoT devices. However, these low powered devices and associated data (private sensitive data of patients) are prone to various security threats. Therefore, in this paper, we design a secure framework for SDN-based Edge computing in IoT-enabled healthcare system. In the proposed framework, the IoT devices are authenticated by the Edge servers using a lightweight authentication scheme. After authentication, these devices collect data from the patients and send them to the Edge servers for storage, processing, and analyses. The Edge servers are connected with an SDN controller, which performs load balancing, network optimization, and efficient resource utilization in the healthcare system. The proposed framework is evaluated using computer-based simulations. The results demonstrate that the proposed framework provides better solutions for IoT-enabled healthcare systems. © 2013 IEEE. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Venki Balasubramaniam” is provided in this record**
- Authors: Li, Junxia , Cai, Jinjin , Khan, Fazlullah , Rehman, Ateeq , Balasubramanian, Venki
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Access Vol. 8, no. (2020), p. 135479-135490
- Full Text:
- Reviewed:
- Description: The Internet of Things (IoT) consists of resource-constrained smart devices capable to sense and process data. It connects a huge number of smart sensing devices, i.e., things, and heterogeneous networks. The IoT is incorporated into different applications, such as smart health, smart home, smart grid, etc. The concept of smart healthcare has emerged in different countries, where pilot projects of healthcare facilities are analyzed. In IoT-enabled healthcare systems, the security of IoT devices and associated data is very important, whereas Edge computing is a promising architecture that solves their computational and processing problems. Edge computing is economical and has the potential to provide low latency data services by improving the communication and computation speed of IoT devices in a healthcare system. In Edge-based IoT-enabled healthcare systems, load balancing, network optimization, and efficient resource utilization are accurately performed using artificial intelligence (AI), i.e., intelligent software-defined network (SDN) controller. SDN-based Edge computing is helpful in the efficient utilization of limited resources of IoT devices. However, these low powered devices and associated data (private sensitive data of patients) are prone to various security threats. Therefore, in this paper, we design a secure framework for SDN-based Edge computing in IoT-enabled healthcare system. In the proposed framework, the IoT devices are authenticated by the Edge servers using a lightweight authentication scheme. After authentication, these devices collect data from the patients and send them to the Edge servers for storage, processing, and analyses. The Edge servers are connected with an SDN controller, which performs load balancing, network optimization, and efficient resource utilization in the healthcare system. The proposed framework is evaluated using computer-based simulations. The results demonstrate that the proposed framework provides better solutions for IoT-enabled healthcare systems. © 2013 IEEE. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Venki Balasubramaniam” is provided in this record**
Big data and predictive analytics in healthcare in Bangladesh: regulatory challenges
- Hassan, Shafiqul, Dhali, Mohsin, Zaman, Fazluz, Tanveer, Muhammad
- Authors: Hassan, Shafiqul , Dhali, Mohsin , Zaman, Fazluz , Tanveer, Muhammad
- Date: 2021
- Type: Text , Journal article
- Relation: Vol. 7, no. 6 (2021), p.
- Full Text:
- Reviewed:
- Description: Big data analytics and artificial intelligence are revolutionizing the global healthcare industry. As the world accumulates unfathomable volumes of data and health technology grows more and more critical to the advancement of medicine, policymakers and regulators are faced with tough challenges around data security and data privacy. This paper reviews existing regulatory frameworks for artificial intelligence-based medical devices and health data privacy in Bangladesh. The study is legal research employing a comparative approach where data is collected from primary and secondary legal materials and filtered based on policies relating to medical data privacy and medical device regulation of Bangladesh. Such policies are then compared with benchmark policies of the European Union and the USA to test the adequacy of the present regulatory framework of Bangladesh and identify the gaps in the current regulation. The study highlights the gaps in policy and regulation in Bangladesh that are hampering the widespread adoption of big data analytics and artificial intelligence in the industry. Despite the vast benefits that big data would bring to Bangladesh's healthcare industry, it lacks the proper data governance and legal framework necessary to gain consumer trust and move forward. Policymakers and regulators must work collaboratively with clinicians, patients and industry to adopt a new regulatory framework that harnesses the potential of big data but ensures adequate privacy and security of personal data. The article opens valuable insight to regulators, academicians, researchers and legal practitioners regarding the present regulatory loopholes in Bangladesh involving exploiting the promise of big data in the medical field. The study concludes with the recommendation for future research into the area of privacy as it relates to artificial intelligence-based medical devices should consult the patients' perspective by employing quantitative analysis research methodology. © 2021 The Author(s)
- Authors: Hassan, Shafiqul , Dhali, Mohsin , Zaman, Fazluz , Tanveer, Muhammad
- Date: 2021
- Type: Text , Journal article
- Relation: Vol. 7, no. 6 (2021), p.
- Full Text:
- Reviewed:
- Description: Big data analytics and artificial intelligence are revolutionizing the global healthcare industry. As the world accumulates unfathomable volumes of data and health technology grows more and more critical to the advancement of medicine, policymakers and regulators are faced with tough challenges around data security and data privacy. This paper reviews existing regulatory frameworks for artificial intelligence-based medical devices and health data privacy in Bangladesh. The study is legal research employing a comparative approach where data is collected from primary and secondary legal materials and filtered based on policies relating to medical data privacy and medical device regulation of Bangladesh. Such policies are then compared with benchmark policies of the European Union and the USA to test the adequacy of the present regulatory framework of Bangladesh and identify the gaps in the current regulation. The study highlights the gaps in policy and regulation in Bangladesh that are hampering the widespread adoption of big data analytics and artificial intelligence in the industry. Despite the vast benefits that big data would bring to Bangladesh's healthcare industry, it lacks the proper data governance and legal framework necessary to gain consumer trust and move forward. Policymakers and regulators must work collaboratively with clinicians, patients and industry to adopt a new regulatory framework that harnesses the potential of big data but ensures adequate privacy and security of personal data. The article opens valuable insight to regulators, academicians, researchers and legal practitioners regarding the present regulatory loopholes in Bangladesh involving exploiting the promise of big data in the medical field. The study concludes with the recommendation for future research into the area of privacy as it relates to artificial intelligence-based medical devices should consult the patients' perspective by employing quantitative analysis research methodology. © 2021 The Author(s)
A Survey on Behavioral Pattern Mining from Sensor Data in Internet of Things
- Rashid, Md Mamunur, Kamruzzaman, Joarder, Hassan, Mohammad, Shahriar Shafin, Sakib, Bhuiyan, Md Zakirul
- Authors: Rashid, Md Mamunur , Kamruzzaman, Joarder , Hassan, Mohammad , Shahriar Shafin, Sakib , Bhuiyan, Md Zakirul
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Access Vol. 8, no. (2020), p. 33318-33341
- Full Text:
- Reviewed:
- Description: The deployment of large-scale wireless sensor networks (WSNs) for the Internet of Things (IoT) applications is increasing day-by-day, especially with the emergence of smart city services. The sensor data streams generated from these applications are largely dynamic, heterogeneous, and often geographically distributed over large areas. For high-value use in business, industry and services, these data streams must be mined to extract insightful knowledge, such as about monitoring (e.g., discovering certain behaviors over a deployed area) or network diagnostics (e.g., predicting faulty sensor nodes). However, due to the inherent constraints of sensor networks and application requirements, traditional data mining techniques cannot be directly used to mine IoT data streams efficiently and accurately in real-time. In the last decade, a number of works have been reported in the literature proposing behavioral pattern mining algorithms for sensor networks. This paper presents the technical challenges that need to be considered for mining sensor data. It then provides a thorough review of the mining techniques proposed in the recent literature to mine behavioral patterns from sensor data in IoT, and their characteristics and differences are highlighted and compared. We also propose a behavioral pattern mining framework for IoT and discuss possible future research directions in this area. © 2013 IEEE.
- Authors: Rashid, Md Mamunur , Kamruzzaman, Joarder , Hassan, Mohammad , Shahriar Shafin, Sakib , Bhuiyan, Md Zakirul
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Access Vol. 8, no. (2020), p. 33318-33341
- Full Text:
- Reviewed:
- Description: The deployment of large-scale wireless sensor networks (WSNs) for the Internet of Things (IoT) applications is increasing day-by-day, especially with the emergence of smart city services. The sensor data streams generated from these applications are largely dynamic, heterogeneous, and often geographically distributed over large areas. For high-value use in business, industry and services, these data streams must be mined to extract insightful knowledge, such as about monitoring (e.g., discovering certain behaviors over a deployed area) or network diagnostics (e.g., predicting faulty sensor nodes). However, due to the inherent constraints of sensor networks and application requirements, traditional data mining techniques cannot be directly used to mine IoT data streams efficiently and accurately in real-time. In the last decade, a number of works have been reported in the literature proposing behavioral pattern mining algorithms for sensor networks. This paper presents the technical challenges that need to be considered for mining sensor data. It then provides a thorough review of the mining techniques proposed in the recent literature to mine behavioral patterns from sensor data in IoT, and their characteristics and differences are highlighted and compared. We also propose a behavioral pattern mining framework for IoT and discuss possible future research directions in this area. © 2013 IEEE.
Venue topic model-enhanced joint graph modelling for citation recommendation in scholarly big data
- Wang, Wei, Gong, Zhiguo, Ren, Jing, Xia, Feng, Lv, Zhihan, Wei, Wei
- Authors: Wang, Wei , Gong, Zhiguo , Ren, Jing , Xia, Feng , Lv, Zhihan , Wei, Wei
- Date: 2021
- Type: Text , Journal article
- Relation: ACM Transactions on Asian and Low-Resource Language Information Processing Vol. 20, no. 1 (2021), p.
- Full Text:
- Reviewed:
- Description: Natural language processing technologies, such as topic models, have been proven to be effective for scholarly recommendation tasks with the ability to deal with content information. Recently, venue recommendation is becoming an increasingly important research task due to the unprecedented number of publication venues. However, traditional methods focus on either the author's local network or author-venue similarity, where the multiple relationships between scholars and venues are overlooked, especially the venue-venue interaction. To solve this problem, we propose an author topic model-enhanced joint graph modeling approach that consists of venue topic modeling, venue-specific topic influence modeling, and scholar preference modeling. We first model the venue topic with Latent Dirichlet Allocation. Then, we model the venue-specific topic influence in an asymmetric and low-dimensional way by considering the topic similarity between venues, the top-influence of venues, and the top-susceptibility of venues. The top-influence characterizes venues' capacity of exerting topic influence on other venues. The top-susceptibility captures venues' propensity of being topically influenced by other venues. Extensive experiments on two real-world datasets show that our proposed joint graph modeling approach outperforms the state-of-The-Art methods. © 2020 ACM.
- Authors: Wang, Wei , Gong, Zhiguo , Ren, Jing , Xia, Feng , Lv, Zhihan , Wei, Wei
- Date: 2021
- Type: Text , Journal article
- Relation: ACM Transactions on Asian and Low-Resource Language Information Processing Vol. 20, no. 1 (2021), p.
- Full Text:
- Reviewed:
- Description: Natural language processing technologies, such as topic models, have been proven to be effective for scholarly recommendation tasks with the ability to deal with content information. Recently, venue recommendation is becoming an increasingly important research task due to the unprecedented number of publication venues. However, traditional methods focus on either the author's local network or author-venue similarity, where the multiple relationships between scholars and venues are overlooked, especially the venue-venue interaction. To solve this problem, we propose an author topic model-enhanced joint graph modeling approach that consists of venue topic modeling, venue-specific topic influence modeling, and scholar preference modeling. We first model the venue topic with Latent Dirichlet Allocation. Then, we model the venue-specific topic influence in an asymmetric and low-dimensional way by considering the topic similarity between venues, the top-influence of venues, and the top-susceptibility of venues. The top-influence characterizes venues' capacity of exerting topic influence on other venues. The top-susceptibility captures venues' propensity of being topically influenced by other venues. Extensive experiments on two real-world datasets show that our proposed joint graph modeling approach outperforms the state-of-The-Art methods. © 2020 ACM.
Cyberattack triage using incremental clustering for intrusion detection systems
- Taheri, Sona, Bagirov, Adil, Gondal, Iqbal, Brown, Simon
- Authors: Taheri, Sona , Bagirov, Adil , Gondal, Iqbal , Brown, Simon
- Date: 2020
- Type: Text , Journal article
- Relation: International Journal of Information Security Vol. 19, no. 5 (2020), p. 597-607
- Relation: http://purl.org/au-research/grants/arc/DP190100580
- Full Text:
- Reviewed:
- Description: Intrusion detection systems (IDSs) are devices or software applications that monitor networks or systems for malicious activities and signals alerts/alarms when such activity is discovered. However, an IDS may generate many false alerts which affect its accuracy. In this paper, we develop a cyberattack triage algorithm to detect these alerts (so-called outliers). The proposed algorithm is designed using the clustering, optimization and distance-based approaches. An optimization-based incremental clustering algorithm is proposed to find clusters of different types of cyberattacks. Using a special procedure, a set of clusters is divided into two subsets: normal and stable clusters. Then, outliers are found among stable clusters using an average distance between centroids of normal clusters. The proposed algorithm is evaluated using the well-known IDS data sets—Knowledge Discovery and Data mining Cup 1999 and UNSW-NB15—and compared with some other existing algorithms. Results show that the proposed algorithm has a high detection accuracy and its false negative rate is very low. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.
- Description: This research was conducted in Internet Commerce Security Laboratory (ICSL) funded by Westpac Banking Corporation Australia. In addition, the research by Dr. Sona Taheri and A/Prof. Adil Bagirov was supported by the Australian Government through the Australian Research Council’s Discovery Projects funding scheme (DP190100580).
- Authors: Taheri, Sona , Bagirov, Adil , Gondal, Iqbal , Brown, Simon
- Date: 2020
- Type: Text , Journal article
- Relation: International Journal of Information Security Vol. 19, no. 5 (2020), p. 597-607
- Relation: http://purl.org/au-research/grants/arc/DP190100580
- Full Text:
- Reviewed:
- Description: Intrusion detection systems (IDSs) are devices or software applications that monitor networks or systems for malicious activities and signals alerts/alarms when such activity is discovered. However, an IDS may generate many false alerts which affect its accuracy. In this paper, we develop a cyberattack triage algorithm to detect these alerts (so-called outliers). The proposed algorithm is designed using the clustering, optimization and distance-based approaches. An optimization-based incremental clustering algorithm is proposed to find clusters of different types of cyberattacks. Using a special procedure, a set of clusters is divided into two subsets: normal and stable clusters. Then, outliers are found among stable clusters using an average distance between centroids of normal clusters. The proposed algorithm is evaluated using the well-known IDS data sets—Knowledge Discovery and Data mining Cup 1999 and UNSW-NB15—and compared with some other existing algorithms. Results show that the proposed algorithm has a high detection accuracy and its false negative rate is very low. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.
- Description: This research was conducted in Internet Commerce Security Laboratory (ICSL) funded by Westpac Banking Corporation Australia. In addition, the research by Dr. Sona Taheri and A/Prof. Adil Bagirov was supported by the Australian Government through the Australian Research Council’s Discovery Projects funding scheme (DP190100580).