MAM : a metaphor-based approach for mental illness detection
- Zhang, Dongyu, Shi, Nan, Peng, Ciyuan, Aziz, Abdul, Zhao, Wenhong, Xia, Feng
- Authors: Zhang, Dongyu , Shi, Nan , Peng, Ciyuan , Aziz, Abdul , Zhao, Wenhong , Xia, Feng
- Date: 2021
- Type: Text , Conference paper
- Relation: 21st International Conference on Computational Science, ICCS 2021 Vol. 12744 LNCS, p. 570-583
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- Description: Among the most disabling disorders, mental illness is one that affects millions of people across the world. Although a great deal of research has been done to prevent mental disorders, detecting mental illness in potential patients remains a considerable challenge. This paper proposes a novel metaphor-based approach (MAM) to determine whether a social media user has a mental disorder or not by classifying social media texts. We observe that the social media texts posted by people with mental illness often contain many implicit emotions that metaphors can express. Therefore, we extract these texts’ metaphor features as the primary indicator for the text classification task. Our approach firstly proposes a CNN-RNN (Convolution Neural Network - Recurrent Neural Network) framework to enable the representations of long texts. The metaphor features are then applied to the attention mechanism for achieving the metaphorical emotions-based mental illness detection. Subsequently, compared with other works, our approach achieves creative results in the detection of mental illnesses. The recall scores of MAM on depression, anorexia, and suicide detection are the highest, with 0.50, 0.70, and 0.65, respectively. Furthermore, MAM has the best F1 scores on depression and anorexia detection tasks, with 0.51 and 0.71. © 2021, Springer Nature Switzerland AG.
- Authors: Zhang, Dongyu , Shi, Nan , Peng, Ciyuan , Aziz, Abdul , Zhao, Wenhong , Xia, Feng
- Date: 2021
- Type: Text , Conference paper
- Relation: 21st International Conference on Computational Science, ICCS 2021 Vol. 12744 LNCS, p. 570-583
- Full Text:
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- Description: Among the most disabling disorders, mental illness is one that affects millions of people across the world. Although a great deal of research has been done to prevent mental disorders, detecting mental illness in potential patients remains a considerable challenge. This paper proposes a novel metaphor-based approach (MAM) to determine whether a social media user has a mental disorder or not by classifying social media texts. We observe that the social media texts posted by people with mental illness often contain many implicit emotions that metaphors can express. Therefore, we extract these texts’ metaphor features as the primary indicator for the text classification task. Our approach firstly proposes a CNN-RNN (Convolution Neural Network - Recurrent Neural Network) framework to enable the representations of long texts. The metaphor features are then applied to the attention mechanism for achieving the metaphorical emotions-based mental illness detection. Subsequently, compared with other works, our approach achieves creative results in the detection of mental illnesses. The recall scores of MAM on depression, anorexia, and suicide detection are the highest, with 0.50, 0.70, and 0.65, respectively. Furthermore, MAM has the best F1 scores on depression and anorexia detection tasks, with 0.51 and 0.71. © 2021, Springer Nature Switzerland AG.
Melanoma classification using efficientnets and ensemble of models with different input resolution
- Karki, Sagar, Kulkarni, Pradnya, Stranieri, Andrew
- Authors: Karki, Sagar , Kulkarni, Pradnya , Stranieri, Andrew
- Date: 2021
- Type: Text , Conference paper
- Relation: 2021 Australasian Computer Science Week Multiconference, ACSW 2021, Virtual, Online, 1-5 February 2021, ACM International Conference Proceeding Series
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- Description: Early and accurate detection of melanoma with data analytics can make treatment more effective. This paper proposes a method to classify melanoma cases using deep learning on dermoscopic images. The method demonstrates that heavy augmentation during training and testing produces promising results and warrants further research. The proposed method has been evaluated on the SIIM-ISIC Melanoma Classification 2020 dataset and the best ensemble model achieved 0.9411 area under the ROC curve on hold out test data. © 2021 ACM.
- Authors: Karki, Sagar , Kulkarni, Pradnya , Stranieri, Andrew
- Date: 2021
- Type: Text , Conference paper
- Relation: 2021 Australasian Computer Science Week Multiconference, ACSW 2021, Virtual, Online, 1-5 February 2021, ACM International Conference Proceeding Series
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- Description: Early and accurate detection of melanoma with data analytics can make treatment more effective. This paper proposes a method to classify melanoma cases using deep learning on dermoscopic images. The method demonstrates that heavy augmentation during training and testing produces promising results and warrants further research. The proposed method has been evaluated on the SIIM-ISIC Melanoma Classification 2020 dataset and the best ensemble model achieved 0.9411 area under the ROC curve on hold out test data. © 2021 ACM.
Open banking and electronic health records
- Stranieri, Andrew, McInnes, Angelique, Hashmi, Mustafa, Sahama, Tony
- Authors: Stranieri, Andrew , McInnes, Angelique , Hashmi, Mustafa , Sahama, Tony
- Date: 2021
- Type: Text , Conference paper
- Relation: 2021 Australasian Computer Science Week Multiconference, ACSW 2021, Virtual, Online, 1-5 February 2021, ACM International Conference Proceeding Series
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- Description: The Open Banking model is a data sharing model emerging in financial services sector that involves technological and regulatory innovations designed to facilitate access to banking records by third party providers such as payment service providers. The model is predicted to disrupt financial services and encourage a wave of new third-party providers offering innovative services that will change the relationship between customers and banks. This article juxtaposes the Open Banking model against models for electronic health records. Providers that could supply innovative third party services with health record data if an Open Banking model were adopted in the health care industry are advanced. © 2021 ACM.
- Authors: Stranieri, Andrew , McInnes, Angelique , Hashmi, Mustafa , Sahama, Tony
- Date: 2021
- Type: Text , Conference paper
- Relation: 2021 Australasian Computer Science Week Multiconference, ACSW 2021, Virtual, Online, 1-5 February 2021, ACM International Conference Proceeding Series
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- Description: The Open Banking model is a data sharing model emerging in financial services sector that involves technological and regulatory innovations designed to facilitate access to banking records by third party providers such as payment service providers. The model is predicted to disrupt financial services and encourage a wave of new third-party providers offering innovative services that will change the relationship between customers and banks. This article juxtaposes the Open Banking model against models for electronic health records. Providers that could supply innovative third party services with health record data if an Open Banking model were adopted in the health care industry are advanced. © 2021 ACM.
Oscillations and periodic solutions in a two-dimensional differential delay model
- Ivanov, Anatoli, Dzalilov, Zari
- Authors: Ivanov, Anatoli , Dzalilov, Zari
- Date: 2021
- Type: Text , Conference paper
- Relation: International Conference on Applied Mathematics, Modeling and Computational Science, AMMCS 2019 Vol. 343, p. 59-70
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- Description: A class of two-dimensional differential systems with delay and overall negative feedback is considered. Sufficient conditions for the existence of periodic solutions are established. The instability of the unique equilibrium together with the one-sided boundedness of one of the two nonlinearities lead to the existence of periodic solutions. Systems of this type appear in various applications in engineering and natural sciences, in particular in mathematical biology and physiology as models of circadian rhythm generator and glucose-insulin regulation models in humans. © 2021, Springer Nature Switzerland AG.
- Authors: Ivanov, Anatoli , Dzalilov, Zari
- Date: 2021
- Type: Text , Conference paper
- Relation: International Conference on Applied Mathematics, Modeling and Computational Science, AMMCS 2019 Vol. 343, p. 59-70
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- Description: A class of two-dimensional differential systems with delay and overall negative feedback is considered. Sufficient conditions for the existence of periodic solutions are established. The instability of the unique equilibrium together with the one-sided boundedness of one of the two nonlinearities lead to the existence of periodic solutions. Systems of this type appear in various applications in engineering and natural sciences, in particular in mathematical biology and physiology as models of circadian rhythm generator and glucose-insulin regulation models in humans. © 2021, Springer Nature Switzerland AG.
Predicting mental health problems with personality, behavior, and social networks
- Zhang, Dongyu, Guo, Teng, Han, Shiyu, Vahabli, Sadaf, Naseriparsa, Mehdi, Xia, Feng
- Authors: Zhang, Dongyu , Guo, Teng , Han, Shiyu , Vahabli, Sadaf , Naseriparsa, Mehdi , Xia, Feng
- Date: 2021
- Type: Text , Conference paper
- Relation: 2021 IEEE International Conference on Big Data, Big Data 2021, virtual online, 15-18 December 2021, Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021 p. 4537-4546
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- Description: Mental health is an integral part of human health and well-being. Unhealthy mentality leads to serious consequences such as self-mutilation and suicide, especially for college students. While the literature focused on analysing the relationship between mental health and a single factor such as personality or behavior, accurate prediction is yet to be achieved due to the lack of cross-dimensional analysis and multi-dimensional joint prediction. To this end, this work proposes leveraging multiple factors from three crucial dimensions of mental health: behaviors, personality, and social networks. We recruited 490 college students, and collected their behavioral records from smart cards. In addition, we extracted their psychological traits from questionnaires, and social networks by conducting the survey on the nominating community members. We created a neural network-based model to integrate behavioral, psychological, and social network factors to predict mental health problems. The experimental results verify the efficacy of the proposed model, and demonstrate that the classification model of various factors effectively predicts the students' mental issues. © 2021 IEEE.
- Authors: Zhang, Dongyu , Guo, Teng , Han, Shiyu , Vahabli, Sadaf , Naseriparsa, Mehdi , Xia, Feng
- Date: 2021
- Type: Text , Conference paper
- Relation: 2021 IEEE International Conference on Big Data, Big Data 2021, virtual online, 15-18 December 2021, Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021 p. 4537-4546
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- Description: Mental health is an integral part of human health and well-being. Unhealthy mentality leads to serious consequences such as self-mutilation and suicide, especially for college students. While the literature focused on analysing the relationship between mental health and a single factor such as personality or behavior, accurate prediction is yet to be achieved due to the lack of cross-dimensional analysis and multi-dimensional joint prediction. To this end, this work proposes leveraging multiple factors from three crucial dimensions of mental health: behaviors, personality, and social networks. We recruited 490 college students, and collected their behavioral records from smart cards. In addition, we extracted their psychological traits from questionnaires, and social networks by conducting the survey on the nominating community members. We created a neural network-based model to integrate behavioral, psychological, and social network factors to predict mental health problems. The experimental results verify the efficacy of the proposed model, and demonstrate that the classification model of various factors effectively predicts the students' mental issues. © 2021 IEEE.
Solving ESL sentence completion questions via pre-trained neural language models
- Liu, Qiongqiong, Liu, Tianqiao, Zhao, Jiafu, Fang, Qiang, Ding, Wenbiao, Wu, Zhongqin, Xia, Feng, Tang, Jiliang, Liu, Zitao
- Authors: Liu, Qiongqiong , Liu, Tianqiao , Zhao, Jiafu , Fang, Qiang , Ding, Wenbiao , Wu, Zhongqin , Xia, Feng , Tang, Jiliang , Liu, Zitao
- Date: 2021
- Type: Text , Conference paper
- Relation: 22nd International Conference on Artificial Intelligence in Education, AIED 2021, Virtual, Online, 14-18 June 2021, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 12749 LNAI, p. 256-261
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- Description: Sentence completion (SC) questions present a sentence with one or more blanks that need to be filled in, three to five possible words or phrases as options. SC questions are widely used for students learning English as a Second Language (ESL) and building computational approaches to automatically solve such questions is beneficial to language learners. In this work, we propose a neural framework to solve SC questions in English examinations by utilizing pre-trained language models. We conduct extensive experiments on a real-world K-12 ESL SC question dataset and the results demonstrate the superiority of our model in terms of prediction accuracy. Furthermore, we run precision-recall tradeoff analysis to discuss the practical issues when deploying it in real-life scenarios. To encourage reproducible results, we make our code publicly available at https://github.com/AIED2021/ESL-SentenceCompletion. © Springer Nature Switzerland AG 2021.
- Authors: Liu, Qiongqiong , Liu, Tianqiao , Zhao, Jiafu , Fang, Qiang , Ding, Wenbiao , Wu, Zhongqin , Xia, Feng , Tang, Jiliang , Liu, Zitao
- Date: 2021
- Type: Text , Conference paper
- Relation: 22nd International Conference on Artificial Intelligence in Education, AIED 2021, Virtual, Online, 14-18 June 2021, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 12749 LNAI, p. 256-261
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- Description: Sentence completion (SC) questions present a sentence with one or more blanks that need to be filled in, three to five possible words or phrases as options. SC questions are widely used for students learning English as a Second Language (ESL) and building computational approaches to automatically solve such questions is beneficial to language learners. In this work, we propose a neural framework to solve SC questions in English examinations by utilizing pre-trained language models. We conduct extensive experiments on a real-world K-12 ESL SC question dataset and the results demonstrate the superiority of our model in terms of prediction accuracy. Furthermore, we run precision-recall tradeoff analysis to discuss the practical issues when deploying it in real-life scenarios. To encourage reproducible results, we make our code publicly available at https://github.com/AIED2021/ESL-SentenceCompletion. © Springer Nature Switzerland AG 2021.
Synchronous learning and teaching in engineering education in response to COVID situations
- Authors: Phung, Truong
- Date: 2021
- Type: Text , Conference paper
- Relation: 47th Annual Conference of the IEEE Industrial Electronics Society, IECON 2021 Vol. 2021-October
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- Description: COVID-19 and the government restrictions in place have seriously affected the face-to-face (F2F) mode of delivery in education and higher education has been one of the hardest hit. Universities around the world had to implement the transition from F2F to online on very short notice. In this paper, the author presents a case study that demonstrates the challenges, steps, and adjustments taken to bring a course that has significant components of communication, teamwork, and project management from a full F2F mode to fully online whilst maintaining as much learner-learner and learner-teacher interactions and students engagement as possible. This has been achieved, not without drawbacks and challenges, via synchronous delivery mode and with private channels and breakout room function to promote intra-team and inter-team communications, and teamwork. © 2021 IEEE.
- Authors: Phung, Truong
- Date: 2021
- Type: Text , Conference paper
- Relation: 47th Annual Conference of the IEEE Industrial Electronics Society, IECON 2021 Vol. 2021-October
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- Description: COVID-19 and the government restrictions in place have seriously affected the face-to-face (F2F) mode of delivery in education and higher education has been one of the hardest hit. Universities around the world had to implement the transition from F2F to online on very short notice. In this paper, the author presents a case study that demonstrates the challenges, steps, and adjustments taken to bring a course that has significant components of communication, teamwork, and project management from a full F2F mode to fully online whilst maintaining as much learner-learner and learner-teacher interactions and students engagement as possible. This has been achieved, not without drawbacks and challenges, via synchronous delivery mode and with private channels and breakout room function to promote intra-team and inter-team communications, and teamwork. © 2021 IEEE.
The agoraphilic navigation algorithm under dynamic environment with a moving goal
- Hewawasam, Hasitha, Ibrahim, Yousef, Kahandawa, Gayan, Choudhury, Tanveer
- Authors: Hewawasam, Hasitha , Ibrahim, Yousef , Kahandawa, Gayan , Choudhury, Tanveer
- Date: 2021
- Type: Text , Conference paper
- Relation: 30th IEEE International Symposium on Industrial Electronics, ISIE 2021 Vol. 2021-June
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- Description: This paper presents a research conducted in developing a new navigation algorithm to navigate robots under dynamically cluttered environments with a moving goal. There are only a few navigation algorithms capable of navigating robots under dynamic environments compared to static environments. The inability to track and reach a moving goal/target is a one of the common weakness of existing navigating algorithms operating in dynamic environments. The existing free space attraction (Agoraphilic) based navigation algorithms also suffer from this common problem. The proposed algorithm, in this paper was developed to overcome this issue. Agoraphilic Navigation Algorithm under Dynamic Environment, 'ANADE' consists of eight main modules. These modules are iteratively used to create the robot's driving force which pulls the robot towards the moving goal. An obstacle tracking module is used to identify the time varying free spaces by tracking moving obstacles. Furthermore, a tracking system is also used to track the moving goal. The capacity of the ANADE was further strengthen by obstacle and goal path prediction modules. Future location prediction allowed the algorithm to make decision considering future environments around the robot. The proposed algorithm was tested under dynamic environment. These tests were focused on testing the behavior of the algorithm under the challenge of reaching a moving goal. Furthermore, the test results demonstrate that ANADE is successful in reaching a moving goal under an unknown dynamically cluttered environment. © 2021 IEEE.
- Authors: Hewawasam, Hasitha , Ibrahim, Yousef , Kahandawa, Gayan , Choudhury, Tanveer
- Date: 2021
- Type: Text , Conference paper
- Relation: 30th IEEE International Symposium on Industrial Electronics, ISIE 2021 Vol. 2021-June
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- Description: This paper presents a research conducted in developing a new navigation algorithm to navigate robots under dynamically cluttered environments with a moving goal. There are only a few navigation algorithms capable of navigating robots under dynamic environments compared to static environments. The inability to track and reach a moving goal/target is a one of the common weakness of existing navigating algorithms operating in dynamic environments. The existing free space attraction (Agoraphilic) based navigation algorithms also suffer from this common problem. The proposed algorithm, in this paper was developed to overcome this issue. Agoraphilic Navigation Algorithm under Dynamic Environment, 'ANADE' consists of eight main modules. These modules are iteratively used to create the robot's driving force which pulls the robot towards the moving goal. An obstacle tracking module is used to identify the time varying free spaces by tracking moving obstacles. Furthermore, a tracking system is also used to track the moving goal. The capacity of the ANADE was further strengthen by obstacle and goal path prediction modules. Future location prediction allowed the algorithm to make decision considering future environments around the robot. The proposed algorithm was tested under dynamic environment. These tests were focused on testing the behavior of the algorithm under the challenge of reaching a moving goal. Furthermore, the test results demonstrate that ANADE is successful in reaching a moving goal under an unknown dynamically cluttered environment. © 2021 IEEE.
The implementation of Blockchain framework in MOOCs to support a freedom of learning in Indonesia
- Febrinanto, Falih, Dafik, Nisviasari, R.
- Authors: Febrinanto, Falih , Dafik , Nisviasari, R.
- Date: 2021
- Type: Text , Conference paper
- Relation: 4th International Conference on Combinatorics, Graph Theory, and Network Topology, ICCGANT 2020 Vol. 1836
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- Description: A freedom of learning program has been released by the Indonesian Ministry and Culture this year 2020. There are three ways for students to earn their credits, namely take the subject course in face-to-face based class, virtual based class or under Massive Open Online Courses (MOOCs). MOOCs is a model that is developed to help people to learn about certain skills through the online platform, without any limitation in the audience. MOOCs aim to enhance broad collaboration between individuals in creating learning environments that have high scalability and can be accessed by anyone and anywhere. The complexity arises when students undertake a subject course through MOOCs, how to certify the completion of their program in which the certification can be gained easily, and the last how secure the obtained certificate? Blockchain technology can help to improve the quality of MOOCs by providing control of academic records as evidence that someone has completed a learning process on MOOCs. Academic records generated will be stored in one place forever and safely stored in the Blockchain environment. This article will explore how the possible to implement the Blockchain framework in MOOCs to support a freedom of learning in Indonesia. © 2021 Published under licence by IOP Publishing Ltd.
- Authors: Febrinanto, Falih , Dafik , Nisviasari, R.
- Date: 2021
- Type: Text , Conference paper
- Relation: 4th International Conference on Combinatorics, Graph Theory, and Network Topology, ICCGANT 2020 Vol. 1836
- Full Text:
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- Description: A freedom of learning program has been released by the Indonesian Ministry and Culture this year 2020. There are three ways for students to earn their credits, namely take the subject course in face-to-face based class, virtual based class or under Massive Open Online Courses (MOOCs). MOOCs is a model that is developed to help people to learn about certain skills through the online platform, without any limitation in the audience. MOOCs aim to enhance broad collaboration between individuals in creating learning environments that have high scalability and can be accessed by anyone and anywhere. The complexity arises when students undertake a subject course through MOOCs, how to certify the completion of their program in which the certification can be gained easily, and the last how secure the obtained certificate? Blockchain technology can help to improve the quality of MOOCs by providing control of academic records as evidence that someone has completed a learning process on MOOCs. Academic records generated will be stored in one place forever and safely stored in the Blockchain environment. This article will explore how the possible to implement the Blockchain framework in MOOCs to support a freedom of learning in Indonesia. © 2021 Published under licence by IOP Publishing Ltd.
Time-expanded method improving throughput in dynamic renewable networks
- Zhang, Jianhui, Guan, Siqi, Wang, Jiacheng, Liu, Liming, Wang, HanXiang, Xia, Feng
- Authors: Zhang, Jianhui , Guan, Siqi , Wang, Jiacheng , Liu, Liming , Wang, HanXiang , Xia, Feng
- Date: 2021
- Type: Text , Conference paper
- Relation: 29th IEEE/ACM International Symposium on Quality of Service, IWQOS 2021
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- Description: In the Dynamic Rechargeable Networks (DRNs), the existing studies usually consider the spatio-temporal dynamics of the harvested energy so as to maximize the throughput by efficient energy allocation. However, the network dynamics have seldom been considered simultaneously including the time variable link quality, communication power and battery charge efficiency. Furthermore, the wireless interference brings extra challenge. To take these dynamics into account together, this paper studies the quite challenging problem, the network throughput maximization in the DRNs, by proper energy allocation while considering the additional affection of wireless interference. We introduce the Time-Expanded Graph (TEG) to describe the above dynamics in a feasible easy way, and then look into the scenario where there is only one pair of source-target firstly. To maximize the throughput, this paper designs the Single Pair Throughput maximization (SPT) algorithm based on TEG while considering the wireless interference. In the case of multiple pairs of source-targets, it's quite complex to solve the network throughput maximization problem directly. This paper introduces the Garg and Könemanns framework and then designs the Multiple Pairs Throughput (MPT) algorithm to maximize the overall throughput of all pairs. MPT is a fast approximation solution with the ratio of 1-3ϵ, where 0 < ϵ < 1 is a small positive constant. This paper also conducts the extensive numerical evaluation based on the simulated data and the data collected by our real system. The numerical simulation results demonstrate the throughput improvement of our algorithms. © 2021 IEEE.
- Authors: Zhang, Jianhui , Guan, Siqi , Wang, Jiacheng , Liu, Liming , Wang, HanXiang , Xia, Feng
- Date: 2021
- Type: Text , Conference paper
- Relation: 29th IEEE/ACM International Symposium on Quality of Service, IWQOS 2021
- Full Text:
- Reviewed:
- Description: In the Dynamic Rechargeable Networks (DRNs), the existing studies usually consider the spatio-temporal dynamics of the harvested energy so as to maximize the throughput by efficient energy allocation. However, the network dynamics have seldom been considered simultaneously including the time variable link quality, communication power and battery charge efficiency. Furthermore, the wireless interference brings extra challenge. To take these dynamics into account together, this paper studies the quite challenging problem, the network throughput maximization in the DRNs, by proper energy allocation while considering the additional affection of wireless interference. We introduce the Time-Expanded Graph (TEG) to describe the above dynamics in a feasible easy way, and then look into the scenario where there is only one pair of source-target firstly. To maximize the throughput, this paper designs the Single Pair Throughput maximization (SPT) algorithm based on TEG while considering the wireless interference. In the case of multiple pairs of source-targets, it's quite complex to solve the network throughput maximization problem directly. This paper introduces the Garg and Könemanns framework and then designs the Multiple Pairs Throughput (MPT) algorithm to maximize the overall throughput of all pairs. MPT is a fast approximation solution with the ratio of 1-3ϵ, where 0 < ϵ < 1 is a small positive constant. This paper also conducts the extensive numerical evaluation based on the simulated data and the data collected by our real system. The numerical simulation results demonstrate the throughput improvement of our algorithms. © 2021 IEEE.
Towards a formal framework for partial compliance of business processes
- Lam, Ho-Pun, Hashmi, Mustafa, Kumar, Akhil
- Authors: Lam, Ho-Pun , Hashmi, Mustafa , Kumar, Akhil
- Date: 2021
- Type: Text , Conference paper
- Relation: International Workshops on AI Approaches to the Complexity of Legal Systems, AICOL 2018 and AICOL 2020, held jointly with the International Workshop on Explainable and Responsible AI and Law, XAILA 2020, Virtual online, 9 December 2020 Vol. 13048 LNAI, p. 90-105
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- Description: Binary “YES-NO” notions of process compliance are not very helpful to managers for assessing the operational performance of their company because a large number of cases fall in the grey area of partial compliance. Hence, it is necessary to have ways to quantify partial compliance in terms of metrics and be able to classify actual cases by assigning a numeric value of compliance to them. In this paper, we formulate an evaluation framework to quantify the level of compliance of business processes across different levels of abstraction (such as task, trace and process level) and across multiple dimensions of each task (such as temporal, monetary, role-, data-, and quality-related) to provide managers more useful information about their operations and to help them improve their decision making processes. Our approach can also add social value by making social services provided by local, state and federal governments more flexible and improving the lives of citizens. © 2021, Springer Nature Switzerland AG.
- Authors: Lam, Ho-Pun , Hashmi, Mustafa , Kumar, Akhil
- Date: 2021
- Type: Text , Conference paper
- Relation: International Workshops on AI Approaches to the Complexity of Legal Systems, AICOL 2018 and AICOL 2020, held jointly with the International Workshop on Explainable and Responsible AI and Law, XAILA 2020, Virtual online, 9 December 2020 Vol. 13048 LNAI, p. 90-105
- Full Text:
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- Description: Binary “YES-NO” notions of process compliance are not very helpful to managers for assessing the operational performance of their company because a large number of cases fall in the grey area of partial compliance. Hence, it is necessary to have ways to quantify partial compliance in terms of metrics and be able to classify actual cases by assigning a numeric value of compliance to them. In this paper, we formulate an evaluation framework to quantify the level of compliance of business processes across different levels of abstraction (such as task, trace and process level) and across multiple dimensions of each task (such as temporal, monetary, role-, data-, and quality-related) to provide managers more useful information about their operations and to help them improve their decision making processes. Our approach can also add social value by making social services provided by local, state and federal governments more flexible and improving the lives of citizens. © 2021, Springer Nature Switzerland AG.
Tropical cyclone-induced extreme winds in climate datasets: East coast of Australia
- Bell, Samuel, Chand, Savin, Dowdy, Andrew, Ramsay, Hamish, Deo, Anil
- Authors: Bell, Samuel , Chand, Savin , Dowdy, Andrew , Ramsay, Hamish , Deo, Anil
- Date: 2021
- Type: Text , Conference paper
- Relation: 24th International Congress on Modelling and Simulation; Sydney, NSW; Australia, 5 to 10 December 2021 in mssanz.org.au/modsim2021
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- Description: Extreme wind speeds, which are typically induced by tropical cyclones (TCs) in coastal regions of tropical Australia, are an important hazard to consider in the context of climate change. Here, a range of climate datasets based on direct observations, reanalyses and regional climate model simulations are used to examine trends in TC-related extreme winds over coastal Eastern Australia. Wind gust speed estimates from best-track data and automatic weather station (AWS) observations are used to calibrate reanalysis wind gusts from the Bureau of Meteorology (BoM) Atmospheric high-resolution Regional Reanalysis for Australia (BARRA) and from the global ERA5 reanalysis. Together, these different datasets provide complementary lines of evidence in relation to historical changes in extreme wind gust speeds. Differences between the occurrence frequency of TC-related wind gusts reaching Category 4/5 on the Australian TC intensity scale and the return periods of TC-related wind gusts over three decades (1990–2019) are presented. Lognormal and Weibull curves are fitted to the extreme value wind speeds and used to provide estimates of associated return periods. Results indicate that the East coast has likely experienced a slightly increased frequency of extreme wind gusts from TCs over the more recent time period (2005–2019), noting considerable uncertainties around these extremes given the limitations of the available data, including that of rapidly evolving observational practices and short time period. Projection results from climate models provide can provide a more homogenous evaluation of the impacts of climate change over a longer time period than is currently available, despite having their own limitations such as model biases and inaccurate representation of certain climate processes. The same experimental methods applied to the observational datasets, are here applied to future projections based on several regional climate model (RCM) simulations under high emission scenarios: NSW and ACT Regional Climate Modelling (NARCliM), CSIRO Conformal Cubic Atmospheric Model (CCAM) and BoM’s Atmospheric Regional Projections for Australia (BARPA). NARCliM results are downscaled from a selection CMIP3 models and use a mean wind speed rather than a gust, while CCAM and BARPA results are downscaled from a selection of CMIP5 models. Results from these projections on extreme wind speeds are generally inconclusive for climate trends on the East coast but indicated that an increase in intensity would be more likely than a decrease in a warmer world. Small sample size and considerable interannual variability in landfalling severe TCs means that there are considerable uncertainties around long-term observed trends in their occurrence. However, a small increase in the observed occurrence frequency of severe TCs for the East coast is noted here based on observations, such that an increase in the more damaging wind gust speeds associated with severe TCs (i.e., rare events with higher return period values) is a plausible outcome for the future climate of Eastern Australia. For example, the return period projections from the regional climate models generally suggest an increase is more likely than a decrease for the most extreme wind gust speeds. We note that whether a change in long-term return periods of wind gusts or a change in the frequency of TC landfalls of any intensity is more important is likely specific to the region or application being considered. Although there are considerable uncertainties around this topic of extreme wind gusts and TCs in a changing climate, our findings are intended to help contribute to the range of guidance available in relation to managing climate risk in Eastern Australia.
- Authors: Bell, Samuel , Chand, Savin , Dowdy, Andrew , Ramsay, Hamish , Deo, Anil
- Date: 2021
- Type: Text , Conference paper
- Relation: 24th International Congress on Modelling and Simulation; Sydney, NSW; Australia, 5 to 10 December 2021 in mssanz.org.au/modsim2021
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- Description: Extreme wind speeds, which are typically induced by tropical cyclones (TCs) in coastal regions of tropical Australia, are an important hazard to consider in the context of climate change. Here, a range of climate datasets based on direct observations, reanalyses and regional climate model simulations are used to examine trends in TC-related extreme winds over coastal Eastern Australia. Wind gust speed estimates from best-track data and automatic weather station (AWS) observations are used to calibrate reanalysis wind gusts from the Bureau of Meteorology (BoM) Atmospheric high-resolution Regional Reanalysis for Australia (BARRA) and from the global ERA5 reanalysis. Together, these different datasets provide complementary lines of evidence in relation to historical changes in extreme wind gust speeds. Differences between the occurrence frequency of TC-related wind gusts reaching Category 4/5 on the Australian TC intensity scale and the return periods of TC-related wind gusts over three decades (1990–2019) are presented. Lognormal and Weibull curves are fitted to the extreme value wind speeds and used to provide estimates of associated return periods. Results indicate that the East coast has likely experienced a slightly increased frequency of extreme wind gusts from TCs over the more recent time period (2005–2019), noting considerable uncertainties around these extremes given the limitations of the available data, including that of rapidly evolving observational practices and short time period. Projection results from climate models provide can provide a more homogenous evaluation of the impacts of climate change over a longer time period than is currently available, despite having their own limitations such as model biases and inaccurate representation of certain climate processes. The same experimental methods applied to the observational datasets, are here applied to future projections based on several regional climate model (RCM) simulations under high emission scenarios: NSW and ACT Regional Climate Modelling (NARCliM), CSIRO Conformal Cubic Atmospheric Model (CCAM) and BoM’s Atmospheric Regional Projections for Australia (BARPA). NARCliM results are downscaled from a selection CMIP3 models and use a mean wind speed rather than a gust, while CCAM and BARPA results are downscaled from a selection of CMIP5 models. Results from these projections on extreme wind speeds are generally inconclusive for climate trends on the East coast but indicated that an increase in intensity would be more likely than a decrease in a warmer world. Small sample size and considerable interannual variability in landfalling severe TCs means that there are considerable uncertainties around long-term observed trends in their occurrence. However, a small increase in the observed occurrence frequency of severe TCs for the East coast is noted here based on observations, such that an increase in the more damaging wind gust speeds associated with severe TCs (i.e., rare events with higher return period values) is a plausible outcome for the future climate of Eastern Australia. For example, the return period projections from the regional climate models generally suggest an increase is more likely than a decrease for the most extreme wind gust speeds. We note that whether a change in long-term return periods of wind gusts or a change in the frequency of TC landfalls of any intensity is more important is likely specific to the region or application being considered. Although there are considerable uncertainties around this topic of extreme wind gusts and TCs in a changing climate, our findings are intended to help contribute to the range of guidance available in relation to managing climate risk in Eastern Australia.
A coarse representation of frames oriented video coding by leveraging cuboidal partitioning of image data
- Ahmmed, Ashe, Paul, Manoranjan, Murshed, Manzur, Taubman, David
- Authors: Ahmmed, Ashe , Paul, Manoranjan , Murshed, Manzur , Taubman, David
- Date: 2020
- Type: Text , Conference paper
- Relation: 22nd IEEE International Workshop on Multimedia Signal Processing, MMSP 2020, Virtual Tampere, Finland 21-24 September 2020
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- Description: Video coding algorithms attempt to minimize the significant commonality that exists within a video sequence. Each new video coding standard contains tools that can perform this task more efficiently compared to its predecessors. In this work, we form a coarse representation of the current frame by minimizing commonality within that frame while preserving important structural properties of the frame. The building blocks of this coarse representation are rectangular regions called cuboids, which are computationally simple and has a compact description. Then we propose to employ the coarse frame as an additional source for predictive coding of the current frame. Experimental results show an improvement in bit rate savings over a reference codec for HEVC, with minor increase in the codec computational complexity. © 2020 IEEE.
- Authors: Ahmmed, Ashe , Paul, Manoranjan , Murshed, Manzur , Taubman, David
- Date: 2020
- Type: Text , Conference paper
- Relation: 22nd IEEE International Workshop on Multimedia Signal Processing, MMSP 2020, Virtual Tampere, Finland 21-24 September 2020
- Full Text:
- Reviewed:
- Description: Video coding algorithms attempt to minimize the significant commonality that exists within a video sequence. Each new video coding standard contains tools that can perform this task more efficiently compared to its predecessors. In this work, we form a coarse representation of the current frame by minimizing commonality within that frame while preserving important structural properties of the frame. The building blocks of this coarse representation are rectangular regions called cuboids, which are computationally simple and has a compact description. Then we propose to employ the coarse frame as an additional source for predictive coding of the current frame. Experimental results show an improvement in bit rate savings over a reference codec for HEVC, with minor increase in the codec computational complexity. © 2020 IEEE.
An approach to map geography mark-up language data to resource description framework schema
- Faqir, Ammara, Mahmood, Aqsa, Qazi, Kiran, Malik, Saleem
- Authors: Faqir, Ammara , Mahmood, Aqsa , Qazi, Kiran , Malik, Saleem
- Date: 2020
- Type: Text , Conference paper
- Relation: 2nd International Conference on Intelligent Technologies and Applications, INTAP 2019 Vol. 1198, p. 343-354
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- Description: GML serves as premier modeling language used to represent data of geographic information related to geography locations. However, a problem of GML is its ability to integrate with a variety of geographical and GPS applications. Since, GML saves data in coordinates and in topology for the purpose to integrate data with variety of applications on semantic web, data be mapped to Resource Description Framework (RDF) and Resource Description Framework Schema (RDFS). An approach of mapping GML metadata to RDFS is presented in this paper. This study focuses on the methodology to convert GML data in semantics to represent in extended and enriched form such as RDFS as representation in RDF is not sufficient over semantic web. Firstly, we have GML script from case study and parse it using GML parser and get XML file. XML file parse using Java and get text file to extract GML features and then get a graph form of these features. After that we designed methodology of prototype tool to map GML features to RDFS. Tool performed features by features mapping and extracted results are represented in the tabular form of mapping GML metadata to RDFS. © 2020, Springer Nature Singapore Pte Ltd.
- Description: E1
- Authors: Faqir, Ammara , Mahmood, Aqsa , Qazi, Kiran , Malik, Saleem
- Date: 2020
- Type: Text , Conference paper
- Relation: 2nd International Conference on Intelligent Technologies and Applications, INTAP 2019 Vol. 1198, p. 343-354
- Full Text:
- Reviewed:
- Description: GML serves as premier modeling language used to represent data of geographic information related to geography locations. However, a problem of GML is its ability to integrate with a variety of geographical and GPS applications. Since, GML saves data in coordinates and in topology for the purpose to integrate data with variety of applications on semantic web, data be mapped to Resource Description Framework (RDF) and Resource Description Framework Schema (RDFS). An approach of mapping GML metadata to RDFS is presented in this paper. This study focuses on the methodology to convert GML data in semantics to represent in extended and enriched form such as RDFS as representation in RDF is not sufficient over semantic web. Firstly, we have GML script from case study and parse it using GML parser and get XML file. XML file parse using Java and get text file to extract GML features and then get a graph form of these features. After that we designed methodology of prototype tool to map GML features to RDFS. Tool performed features by features mapping and extracted results are represented in the tabular form of mapping GML metadata to RDFS. © 2020, Springer Nature Singapore Pte Ltd.
- Description: E1
An exploratory study of the adoption of blockchain technology among Australian organizations : a theoretical model
- Malik, Saleem, Chadhar, Mehmood, Chetty, Madhu, Vatanasakdakul, Savanid
- Authors: Malik, Saleem , Chadhar, Mehmood , Chetty, Madhu , Vatanasakdakul, Savanid
- Date: 2020
- Type: Text , Conference paper
- Relation: 17th European, Mediterranean, and Middle Eastern Conference on Information Systems, EMCIS 2020; Dubai; 25-26 November 2020 Vol. 402, p. 205-220
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- Description: Scholarly and commercial literature indicates several applications of Blockchain Technology (BCT) in different industries e.g. health, finance, supply chain, government, and energy. Despite abundant benefits reported and growing prominence, BCT has been facing various challenges across the globe, including low adoption by organizations. There is a dearth of studies that examined the organizational adoption of blockchain technology, particularly in Australia. This lack of uptake provides the rationale to initiate this research to identify the factors influencing the Australian organizations to adopt BCT. To achieve this, we conducted a qualitative study based on the Technology, Organization, Environment (TOE) framework. The study proposes a theoretical model grounded on the findings of semi-structured interviews of blockchain experts in Australia. The proposed model shows that the organizational adoption of blockchain is influenced by perceived benefits, compatibility, and complexity, organization innovativeness, organizational learning capability, competitive intensity, government support, trading partner readiness, and standards uncertainty. © 2020, Springer Nature Switzerland AG.
- Authors: Malik, Saleem , Chadhar, Mehmood , Chetty, Madhu , Vatanasakdakul, Savanid
- Date: 2020
- Type: Text , Conference paper
- Relation: 17th European, Mediterranean, and Middle Eastern Conference on Information Systems, EMCIS 2020; Dubai; 25-26 November 2020 Vol. 402, p. 205-220
- Full Text:
- Reviewed:
- Description: Scholarly and commercial literature indicates several applications of Blockchain Technology (BCT) in different industries e.g. health, finance, supply chain, government, and energy. Despite abundant benefits reported and growing prominence, BCT has been facing various challenges across the globe, including low adoption by organizations. There is a dearth of studies that examined the organizational adoption of blockchain technology, particularly in Australia. This lack of uptake provides the rationale to initiate this research to identify the factors influencing the Australian organizations to adopt BCT. To achieve this, we conducted a qualitative study based on the Technology, Organization, Environment (TOE) framework. The study proposes a theoretical model grounded on the findings of semi-structured interviews of blockchain experts in Australia. The proposed model shows that the organizational adoption of blockchain is influenced by perceived benefits, compatibility, and complexity, organization innovativeness, organizational learning capability, competitive intensity, government support, trading partner readiness, and standards uncertainty. © 2020, Springer Nature Switzerland AG.
Capability building through workplace based learning in maintenance and reliability engineering (MRE) postgraduate programmes
- Chattopadhyay, Gopinath, Larkins, Jo-ann
- Authors: Chattopadhyay, Gopinath , Larkins, Jo-ann
- Date: 2020
- Type: Text , Conference paper
- Relation: 31st Annual Conference of the Australasian Association for Engineering Education (AAEE 2020) : Disrupting Business as Usual in Engineering Education
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- Authors: Chattopadhyay, Gopinath , Larkins, Jo-ann
- Date: 2020
- Type: Text , Conference paper
- Relation: 31st Annual Conference of the Australasian Association for Engineering Education (AAEE 2020) : Disrupting Business as Usual in Engineering Education
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Characterizations of robust and stable duality for linearly perturbed uncertain optimization problems
- Dinh, Nguyen, Goberna, Miguel, López, Marco, Volle, Michel
- Authors: Dinh, Nguyen , Goberna, Miguel , López, Marco , Volle, Michel
- Date: 2020
- Type: Text , Conference paper
- Relation: Jonathan Borwein Commemorative Conference, JBCC 2017 Vol. 313, p. 43-74
- Relation: http://purl.org/au-research/grants/arc/DP180100602
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- Description: We introduce a robust optimization model consisting in a family of perturbation functions giving rise to certain pairs of dual optimization problems in which the dual variable depends on the uncertainty parameter. The interest of our approach is illustrated by some examples, including uncertain conic optimization and infinite optimization via discretization. The main results characterize desirable robust duality relations (as robust zero-duality gap) by formulas involving the epsilon-minima or the epsilon-subdifferentials of the objective function. The two extreme cases, namely, the usual perturbational duality (without uncertainty), and the duality for the supremum of functions (duality parameter vanishing) are analyzed in detail. © Springer Nature Switzerland AG 2020.
- Authors: Dinh, Nguyen , Goberna, Miguel , López, Marco , Volle, Michel
- Date: 2020
- Type: Text , Conference paper
- Relation: Jonathan Borwein Commemorative Conference, JBCC 2017 Vol. 313, p. 43-74
- Relation: http://purl.org/au-research/grants/arc/DP180100602
- Full Text:
- Reviewed:
- Description: We introduce a robust optimization model consisting in a family of perturbation functions giving rise to certain pairs of dual optimization problems in which the dual variable depends on the uncertainty parameter. The interest of our approach is illustrated by some examples, including uncertain conic optimization and infinite optimization via discretization. The main results characterize desirable robust duality relations (as robust zero-duality gap) by formulas involving the epsilon-minima or the epsilon-subdifferentials of the objective function. The two extreme cases, namely, the usual perturbational duality (without uncertainty), and the duality for the supremum of functions (duality parameter vanishing) are analyzed in detail. © Springer Nature Switzerland AG 2020.
Conservation, agriculture, sustainable, development and strong communities
- Authors: Martin, Jennifer
- Date: 2020
- Type: Text , Conference paper
- Relation: Strategies for the promotion of conservation agriculture in Central Asia, Proceedings of the International Conference, Tashkent, Uzbekistan, 5–7 September 2018 p. 278-287
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- Description: Four decades have passed since the introduction of Conservation Agriculture research and development. We also mark forty years since the introduction of the Declaration of Alma-Ata at the International Conference on Primary Health Care, Alma-Ata, Kazakhstan, 6–12 September 1978. Furthermore, of significance to the future development of sustainable agriculture practices and healthy communities, is the introduction of the United Nations Sustainable Development Goals in January 2016. These follow on from the Millennium Development goals that will guide the United Nations Development Program policy and development unti l 2030. An Australian case study on Conservation Agriculture is presented examining the relationship between Conservation Agriculture, health and wellbeing and sustainable development. It is argued that an ecosystems approach is useful for strategic sustainable development to understand the connectedness and inter-relationship between climate change agricultural practices, sense of place, identity, health and wellbeing. Community development processes can assist to build strong communities through collaboration between farmers, farmer organizations, local experts, and national and regional public and private institutions.
- Authors: Martin, Jennifer
- Date: 2020
- Type: Text , Conference paper
- Relation: Strategies for the promotion of conservation agriculture in Central Asia, Proceedings of the International Conference, Tashkent, Uzbekistan, 5–7 September 2018 p. 278-287
- Full Text:
- Reviewed:
- Description: Four decades have passed since the introduction of Conservation Agriculture research and development. We also mark forty years since the introduction of the Declaration of Alma-Ata at the International Conference on Primary Health Care, Alma-Ata, Kazakhstan, 6–12 September 1978. Furthermore, of significance to the future development of sustainable agriculture practices and healthy communities, is the introduction of the United Nations Sustainable Development Goals in January 2016. These follow on from the Millennium Development goals that will guide the United Nations Development Program policy and development unti l 2030. An Australian case study on Conservation Agriculture is presented examining the relationship between Conservation Agriculture, health and wellbeing and sustainable development. It is argued that an ecosystems approach is useful for strategic sustainable development to understand the connectedness and inter-relationship between climate change agricultural practices, sense of place, identity, health and wellbeing. Community development processes can assist to build strong communities through collaboration between farmers, farmer organizations, local experts, and national and regional public and private institutions.
DEFINE: friendship detection based on node enhancement
- Pan, Hanxiao, Guo, Teng, Bedru, Hayat, Qing, Qing, Zhang, Dongyu, Xia, Feng
- Authors: Pan, Hanxiao , Guo, Teng , Bedru, Hayat , Qing, Qing , Zhang, Dongyu , Xia, Feng
- Date: 2020
- Type: Text , Conference paper
- Relation: 31st Australasian Database Conference, ADC 2019 Vol. 12008 LNCS, p. 81-92
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- Description: Network representation learning (NRL) is a matter of importance to a variety of tasks such as link prediction. Learning low-dimensional vector representations for node enhancement based on nodes attributes and network structures can improve link prediction performance. Node attributes are important factors in forming networks, like psychological factors and appearance features affecting friendship networks. However, little to no work has detected friendship using the NRL technique, which combines students’ psychological features and perceived traits based on facial appearance. In this paper, we propose a framework named DEFINE (No enhancement based r e dship D tection) to detect students’ friend relationships, which combines with students’ psychological factors and facial perception information. To detect friend relationships accurately, DEFINE uses the NRL technique, which considers network structure and the additional attributes information for nodes. DEFINE transforms them into low-dimensional vector spaces while preserving the inherent properties of the friendship network. Experimental results on real-world friendship network datasets illustrate that DEFINE outperforms other state-of-art methods. © 2020, Springer Nature Switzerland AG.
- Description: E1
- Authors: Pan, Hanxiao , Guo, Teng , Bedru, Hayat , Qing, Qing , Zhang, Dongyu , Xia, Feng
- Date: 2020
- Type: Text , Conference paper
- Relation: 31st Australasian Database Conference, ADC 2019 Vol. 12008 LNCS, p. 81-92
- Full Text:
- Reviewed:
- Description: Network representation learning (NRL) is a matter of importance to a variety of tasks such as link prediction. Learning low-dimensional vector representations for node enhancement based on nodes attributes and network structures can improve link prediction performance. Node attributes are important factors in forming networks, like psychological factors and appearance features affecting friendship networks. However, little to no work has detected friendship using the NRL technique, which combines students’ psychological features and perceived traits based on facial appearance. In this paper, we propose a framework named DEFINE (No enhancement based r e dship D tection) to detect students’ friend relationships, which combines with students’ psychological factors and facial perception information. To detect friend relationships accurately, DEFINE uses the NRL technique, which considers network structure and the additional attributes information for nodes. DEFINE transforms them into low-dimensional vector spaces while preserving the inherent properties of the friendship network. Experimental results on real-world friendship network datasets illustrate that DEFINE outperforms other state-of-art methods. © 2020, Springer Nature Switzerland AG.
- Description: E1
Discovery of small group interactions and performance from project emails
- Ivkovic, Sasha, Oseni, Taiwo, Chadhar, Mehmood, Firmin, Sally
- Authors: Ivkovic, Sasha , Oseni, Taiwo , Chadhar, Mehmood , Firmin, Sally
- Date: 2020
- Type: Text , Conference paper
- Relation: 24th Pacific Asia Conference on Information Systems: Information Systems (IS) for the Future, PACIS 2020
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- Description: Despite latest advances in small group research, discovery of group interactions and performance from analysis of small group communication, such as project emails, is still minimally represented. This paper presents a novel approach of studying small groups through analysis of the participants' emails sent to the project manager. We examined 1,105 email messages from managers' email in-boxes across five distinct ICT projects from the personal, social, collaborative, and engaging perspective of the email senders and link the findings to group performance. The study provides theoretical evidence that analysis of incoming communication from project managers' email in-box can be used to measure a group's success. For project managers the approach has the potential to be highly beneficial for monitoring of indicators for the state of project health. © Proceedings of the 24th Pacific Asia Conference on Information Systems: Information Systems (IS) for the Future, PACIS 2020. All rights reserved.
- Authors: Ivkovic, Sasha , Oseni, Taiwo , Chadhar, Mehmood , Firmin, Sally
- Date: 2020
- Type: Text , Conference paper
- Relation: 24th Pacific Asia Conference on Information Systems: Information Systems (IS) for the Future, PACIS 2020
- Full Text:
- Reviewed:
- Description: Despite latest advances in small group research, discovery of group interactions and performance from analysis of small group communication, such as project emails, is still minimally represented. This paper presents a novel approach of studying small groups through analysis of the participants' emails sent to the project manager. We examined 1,105 email messages from managers' email in-boxes across five distinct ICT projects from the personal, social, collaborative, and engaging perspective of the email senders and link the findings to group performance. The study provides theoretical evidence that analysis of incoming communication from project managers' email in-box can be used to measure a group's success. For project managers the approach has the potential to be highly beneficial for monitoring of indicators for the state of project health. © Proceedings of the 24th Pacific Asia Conference on Information Systems: Information Systems (IS) for the Future, PACIS 2020. All rights reserved.