List of Titles

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RMGen : a tri-layer vehicular trajectory data generation model exploring urban region division and mobility pattern

- Kong, Xiangjie, Chen, Qiao, Hou, Mingliang, Rahim, Azizur, Ma, Kai, Xia, Feng

Authors: Kong, Xiangjie , Chen, Qiao , Hou, Mingliang , Rahim, Azizur , Ma, Kai , Xia, Feng
Date: 2022
Type: Text , Journal article
Relation: IEEE Transactions on Vehicular Technology Vol. 71, no. 9 (2022), p. 9225-9238
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Description: As an important branch of the Internet of Things (IoT), the Internet of Vehicles (IoV) has attracted extensive attention in the research field. To deeply study the IoV and build a vehicle spatiotemporal interaction network, it is necessary to use the trajectory data of private cars. However, due to privacy and security protection policies and other reasons, the data set of private cars cannot be obtained, which hinders the research on the social attributes of vehicles in the IoV. Most of the previous work generated the same type of data, and how to generate private car data sets from various existing data sets is a huge challenge. In this paper, we propose a tri-layer framework to solve this problem. First, we propose a novel region division scheme that considers detailed inter-region relations connected by traffic flux. Second, a new spatial-temporal interaction model is developed to estimate the traffic flow between two regions. Third, we devise an evaluation pipeline to validate generation results from microscopic and macroscopic perspectives. Qualitative and quantitative results demonstrate that the data generated in heavy density scenarios can provide strong data support for downstream IoV and mobility research tasks. © 1967-2012 IEEE.

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Robust graph neural networks via ensemble learning

- Lin, Qi, Yu, Shuo, Sun, Ke, Zhao, Wenhong, Alfarraj, Osama, Tolba, Amr, Xia, Feng

Authors: Lin, Qi , Yu, Shuo , Sun, Ke , Zhao, Wenhong , Alfarraj, Osama , Tolba, Amr , Xia, Feng
Date: 2022
Type: Text , Journal article
Relation: Mathematics Vol. 10, no. 8 (2022), p.
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Description: Graph neural networks (GNNs) have demonstrated a remarkable ability in the task of semi-supervised node classification. However, most existing GNNs suffer from the nonrobustness issues, which poses a great challenge for applying GNNs into sensitive scenarios. Some researchers concentrate on constructing an ensemble model to mitigate the nonrobustness issues. Nevertheless, these methods ignore the interaction among base models, leading to similar graph representations. Moreover, due to the deterministic propagation applied in most existing GNNs, each node highly relies on its neighbors, leaving the nodes to be sensitive to perturbations. Therefore, in this paper, we propose a novel framework of graph ensemble learning based on knowledge passing (called GEL) to address the above issues. In order to achieve interaction, we consider the predictions of prior models as knowledge to obtain more reliable predictions. Moreover, we design a multilayer DropNode propagation strategy to reduce each node’s dependence on particular neighbors. This strategy also empowers each node to aggregate information from diverse neighbors, alleviating oversmoothing issues. We conduct experiments on three benchmark datasets, including Cora, Citeseer, and Pubmed. GEL outperforms GCN by more than 5% in terms of accuracy across all three datasets and also performs better than other state-of-the-art baselines. Extensive experimental results also show that the GEL alleviates the nonrobustness and oversmoothing issues. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

Robust piecewise linear L 1-regression via nonsmooth DC optimization

- Bagirov, Adil, Taheri, Sona, Karmitsa, Napsu, Sultanova, Nargiz, Asadi, Soodabeh

Authors: Bagirov, Adil , Taheri, Sona , Karmitsa, Napsu , Sultanova, Nargiz , Asadi, Soodabeh
Date: 2022
Type: Text , Journal article
Relation: Optimization Methods and Software Vol. 37, no. 4 (2022), p. 1289-1309
Relation: http://purl.org/au-research/grants/arc/DP190100580
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Description: Piecewise linear (Formula presented.) -regression problem is formulated as an unconstrained difference of convex (DC) optimization problem and an algorithm for solving this problem is developed. Auxiliary problems are introduced to design an adaptive approach to generate a suitable piecewise linear regression model and starting points for solving the underlying DC optimization problems. The performance of the proposed algorithm as both approximation and prediction tool is evaluated using synthetic and real-world data sets containing outliers. It is also compared with mainstream machine learning regression algorithms using various performance measures. Results demonstrate that the new algorithm is robust to outliers and in general, provides better predictions than the other alternative regression algorithms for most data sets used in the numerical experiments. © 2020 Informa UK Limited, trading as Taylor & Francis Group.

Robustness of vibrational control in the presence of additive disturbances

- Cheng, Xiaoxiao, Tan, Ying, Mareels, Iven

Authors: Cheng, Xiaoxiao , Tan, Ying , Mareels, Iven
Date: 2022
Type: Text , Journal article
Relation: IEEE transactions on automatic control Vol. 67, no. 6 (2022), p. 3112-3119
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Description: Vibrational control seeks to stabilize an unstable system by judiciously injecting a state-free high-frequency dither. This article presents some robustness properties of vibrational control with respect to additive disturbances in a nonlinear system. We assume that, without disturbances, the appropriately averaged system is regionally asymptotically stable. Using perturbation techniques, our first result shows that the stabilization realized through vibrational control is robust with respect to additive disturbances of sufficiently small amplitude. Indeed, the perturbed system of vibrational control has a robustness feature similar to input-to-state stability in a local region. In the case of periodic disturbances, our second result indicates that vibrational control naturally dampens disturbances of sufficiently high frequency, which allows for relatively high amplitude disturbances. A tight bound for the effect of such periodic disturbances on the ultimate deviation of states from the desired equilibrium is presented. Simulation results from a planar manipulator support the theoretic analysis.

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Roles of selective agriculture practices in sustainable agricultural performance : a systematic review

- Ali, Basharat, Dahlhaus, Peter

Authors: Ali, Basharat , Dahlhaus, Peter
Date: 2022
Type: Text , Journal article , Review
Relation: Sustainability (Switzerland) Vol. 14, no. 6 (2022), p.
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Description: Feeding the growing global population while improving the Earth’s economic, environmental, and social values is a challenge recognised in both the United Nations Sustainable Development Goals and the United Nations Framework Convention on Climate Change. Sustaining global agricultural performance requires regular revision of current farming models, attitudes, and practices. In systematically reviewing the international literature through the lens of the sustainability framework, this paper specifically identifies precision conservation agriculture (PCA), digital agriculture (DA), and resilient agriculture (RA) practices as being of value in meeting future challenges. Each of these adaptations carries significantly positive relationships with sustaining agricultural performance, as well as positively mediating and/or moderating each other. While it is clear from the literature that adopting PCA, DA, and RA would substantially improve the sustainability of agricultural performance, the uptake of these adaptations generally lags. More in-depth social science research is required to understand the value propositions that would encourage uptake of these adaptations and the barriers that prevent them. Recommendations are made to explore the specific knowledge gap that needs to be understood to motivate agriculture practitioners to adopt these changes in practice. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

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SAfety and feasibility of EArly resistance training after median sternotomy : the SAFE-ARMS study

- Pengelly, Jacqueline, Boggett, Stuart, Bryant, Adam, Royse, Colin, Royse, Alistair, Williams, Gavin, El-Ansary, Doa El

Authors: Pengelly, Jacqueline , Boggett, Stuart , Bryant, Adam , Royse, Colin , Royse, Alistair , Williams, Gavin , El-Ansary, Doa El
Date: 2022
Type: Text , Journal article
Relation: Physical Therapy Vol. 102, no. 7 (2022), p.
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Description: Objective: The purpose of this study was to determine the safety and feasibility of subacute upper limb resistance exercise on sternal micromotion and pain and the reliability of sternal ultrasound assessment following cardiac surgery via median sternotomy. Methods: This experimental study used a pretest-posttest design to investigate the effects of upper limb resistance exercise on the sternum in patients following their first cardiac surgery via median sternotomy. Six bilateral upper limb machine-based exercises were commenced at a base resistance of 20 lb (9 kg) and progressed for each participant. Sternal micromotion was assessed using ultrasound at the mid and lower sternum at 2, 8, and 14 weeks postsurgery. Intrarater and interrater reliability was calculated using intraclass correlation coefficients (ICCs). Participant-reported pain was recorded at rest and with each exercise using a visual analogue scale. Results: Sixteen adults (n = 15 males; 71.3 [SD = 6.2] years of age) consented to participate. Twelve participants completed the study, 2 withdrew prior to the 8-week assessment, and 2 assessments were not completed at 14 weeks due to assessor unavailability. The highest median micromotion at the sternal edges was observed during the bicep curl (median = 1.33 mm; range = -0.8 to 2.0 mm) in the lateral direction and the shoulder pulldown (median = 0.65 mm; range = -0.8 to 1.6 mm) in the anterior-posterior direction. Furthermore, participants reported no increase in pain when performing any of the 6 upper limb exercises. Interrater reliability was moderate to good for both lateral-posterior (ICC = 0.73; 95% CI = 0.58 to 0.83) and anterior-posterior micromotion (ICC = 0.83; 95% CI = 0.73 to 0.89) of the sternal edges. Conclusion: Bilateral upper limb resistance exercises performed on cam-based machines do not result in sternal micromotion exceeding 2.0 mm or an increase in participant-reported pain. Impact: Upper limb resistance training commenced as early as 2 weeks following cardiac surgery via median sternotomy and performed within the safe limits of pain and sternal micromotion appears to be safe and may accelerate postoperative recovery rather than muscular deconditioning. © 2022 The Author(s). Published by Oxford University Press on behalf of the American Physical Therapy Association. All rights reserved.

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Scalar reward is not enough : a response to Silver, Singh, Precup and Sutton (2021)

- Vamplew, Peter, Smith, Benjamin, Källström, Johan, Ramos, Gabriel, Rădulescu, Roxana, Roijers, Diederik, Hayes, Conor, Heintz, Fredrik, Mannion, Patrick, Libin, Pieter, Dazeley, Richard, Foale, Cameron

Authors: Vamplew, Peter , Smith, Benjamin , Källström, Johan , Ramos, Gabriel , Rădulescu, Roxana , Roijers, Diederik , Hayes, Conor , Heintz, Fredrik , Mannion, Patrick , Libin, Pieter , Dazeley, Richard , Foale, Cameron
Date: 2022
Type: Text , Journal article
Relation: Autonomous Agents and Multi-Agent Systems Vol. 36, no. 2 (2022), p.
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Description: The recent paper “Reward is Enough” by Silver, Singh, Precup and Sutton posits that the concept of reward maximisation is sufficient to underpin all intelligence, both natural and artificial, and provides a suitable basis for the creation of artificial general intelligence. We contest the underlying assumption of Silver et al. that such reward can be scalar-valued. In this paper we explain why scalar rewards are insufficient to account for some aspects of both biological and computational intelligence, and argue in favour of explicitly multi-objective models of reward maximisation. Furthermore, we contend that even if scalar reward functions can trigger intelligent behaviour in specific cases, this type of reward is insufficient for the development of human-aligned artificial general intelligence due to unacceptable risks of unsafe or unethical behaviour. © 2022, The Author(s).

Scheduling and power control for connectivity enhancement in multi-hop I2V/V2V networks

- Nguyen, Bach, Ngo, Duy, Dao, Minh, Bao, Vo, Vu, Hai

Authors: Nguyen, Bach , Ngo, Duy , Dao, Minh , Bao, Vo , Vu, Hai
Date: 2022
Type: Text , Journal article
Relation: IEEE Transactions on Intelligent Transportation Systems Vol. 23, no. 8 (2022), p. 10322-10332
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Description: Infrastructure-to-vehicle (I2V) and vehicle-to-vehicle (V2V) communications are often combined to extend the connectivity and coverage in the Intelligent Transportation System (ITS) and its applications, e.g., augmented reality, real-time parking management and online shopping. Through multi-hop I2V and V2V communications, requesting vehicles are always connected to road side units (RSUs) even when they do not reside within the RSUs' coverage range. However, there may be not adequate network resource for several I2V and V2V links when multiple vehicles request services simultaneously. In this paper, we propose a joint frequency scheduling and power control scheme to enhance connectivity in multi-hop I2V/V2V networks. We associate I2V and V2V links with tuple-links, then formulate an NP-hard problem in which a frequency scheduler and a power controller are jointly designed for the tuple-links. The NP-hard problem is decomposed into two separate subproblems by employing the delayed column generation technique. Then, we employ a method for linear programming and a greedy algorithm to address these subproblems. Through numerical experiments with practical parameter settings, we demonstrate the proposed scheme outperforms several existing ones in terms of connectivity enhancement, measured by the service resumption number and average achieved throughput. Furthermore, the efficiency of our scheme is further enhanced when the number of available channels is high, and buffer size equipped to the requesting vehicles is large. © 2000-2011 IEEE.

School ground pedagogies for enriching children’s outdoor learning

- Green, Monica, Rayner, Michelle

Authors: Green, Monica , Rayner, Michelle
Date: 2022
Type: Text , Journal article
Relation: Education 3-13 Vol. 50, no. 2 (2022), p. 238-251
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Description: Despite teacher interest in wanting to engage students in learning beyond the classroom, many don’t have the skills or confidence to do so. Drawing from an earlier study that examined outdoor, environmental and sustainability pedagogies across three Australian primary (elementary) schools, this paper presents and examines two teaching and learning vignettes from one of the study schools to investigate the impact of outdoor pedagogy and children’s learning in a school ground environment. The place-based study used a set of semi-structured interviews with the school’s gardening/environmental teacher as well as children’s reflective workbook entries to explore how outdoor learning was enabled and scaffolded by the teacher. Findings indicate that school ground pedagogies, particularly when framed by self-directed learning tasks increases student autonomy, efficacy and achievement. The implication of the findings is important for increasing teacher capacity to incorporate locally-based curriculum and pedagogy in school ground settings to enrich children’s outdoor learning. © 2020 ASPE.

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Secure IoT data using blockchain

- Dhatrak, Aishwarya, Gong, Mingwei, Naha, Ranesh, Mahanti, Aniket

Authors: Dhatrak, Aishwarya , Gong, Mingwei , Naha, Ranesh , Mahanti, Aniket
Date: 2022
Type: Text , Book chapter
Relation: Emerging real world applications of Internet of things Chapter 1 p. 1-20
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Description: Internet of things (IoT) is a network of physical objects with embedded systems that are able to communicate with each other via the Internet. It leads to a distributed network of devices that can communicate with humans and objects. The implementation of IoT will provide benefits, but it also raises security issues that affect data integrity and privacy of the companies. In order to overcome security issues, blockchain technology is used. This chapter is based on up-to-date research on the blockchain as well as IoT with the purpose of studying the blockchain as potential solutions to secure IoT data. Both blockchain and IoT are new research areas into existing research, which support the use of a qualitative research method. The results indicate that blockchain is used to secure data management, but it is not a complete solution. Most of the security issues are related to devices, and blockchain technology handles information and secures identities, stock traceability and transactions, which are made without human interaction and with automated storage management. There are still some barriers to creating benefits work into reality. This chapter is focused on investigating the integration of IoT and blockchain to analyze and improve security issues.

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Security and privacy aspects of cloud computing : a smart campus case study

- Gill, Sajid, Razzaq, Mirza, Ahmad, Muneer, Almansour, Fahad, Haq, Ikram

Authors: Gill, Sajid , Razzaq, Mirza , Ahmad, Muneer , Almansour, Fahad , Haq, Ikram
Date: 2022
Type: Text , Journal article
Relation: Intelligent Automation and Soft Computing Vol. 31, no. 1 (2022), p. 117-128
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Description: The trend of cloud computing is accelerating along with emerging technologies such as utility computing, grid computing, and distributed computing. Cloud computing is showing remarkable potential to provide flexible, cost- effective, and powerful resources across the internet, and is a driving force in today’s most prominent computing technologies. The cloud offers the means to remotely access and store data while virtual machines access data over a network resource. Furthermore, cloud computing plays a leading role in the fourth industrial revolution. Everyone uses the cloud daily life when accessing Dropbox, various Google services, and Microsoft Office 365. While there are many advantages in such an environment, security issues such as data privacy, data security, access control, cyber-attacks, and data availability, along with performance and reliability issues, exist. Efficient security and privacy measures should be implemented by cloud service providers to ensure the privacy, confidentiality, integrity, and availability of data services. However, cloud service providers have not been providing enough secure and reliable services to end users. Blockchain is a technology that is improving cloud computing. This revolutionary technology offers persuasive data integrity properties and is used to tackle security problems. This research presents a detailed analysis of privacy and security challenges in the cloud. We demonstrate the importance of security challenges in a case study in the context of smart campus security, which will encourage researchers to examine security issues in cloud computing in the future. © 2022, Tech Science Press. All rights reserved. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Ikram Haq” is provided in this record**

Security of Internet of Things devices : ethical hacking a drone and its mitigation strategies

- Karmakar, Gour, Petty, Mark, Ahmed, Hassan, Das, Rajkumar, Kamruzzaman, Joarder

Authors: Karmakar, Gour , Petty, Mark , Ahmed, Hassan , Das, Rajkumar , Kamruzzaman, Joarder
Date: 2022
Type: Text , Conference paper
Relation: 2022 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2022, Gold Coast, Australia, 18-20 December 2022, Proceedings of IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2022
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Description: Internet of Things (IoT) is enabling us to introduce cost-effective, innovative and intelligent services in business, industrial, and government application domains. Despite these huge potential benefits of IoT applications, since the backbone of IoT is Internet and IoT connects numerous heterogeneous devices, IoT is vulnerable to many different attacks and thus has been a honey pot to the cybercriminals and hackers. For this reason, the attacks against IoT devices are increasing sharply in recent years. To prevent and detect these attacks, ethical hacking of different IoT devices are of paramount importance. This is because the lesson learnt from these ethical hackings can be exploited to develop effective and robust strategies and mitigation approaches to protect IoT devices from these attacks. There exist a few ethical hacking techniques reported in the literature such as hacking Android phones, Windows XP virtual machine and a DNS rebinding attack on IoT devices. In this paper, we implement an approach for the ethical hacking of a Drone and then hijack it. As an outcome of lesson learnt, the mitigation approaches on how to reduce the hacking on a drone is presented in this paper. © 2022 IEEE.

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Seeking approval from universities to research the views of their staff : do gatekeepers provide a barrier to ethical research?

- Christian, Katherine, Johnstone, Carolyn, Larkins, Jo-Ann, Wright, Wendy

Authors: Christian, Katherine , Johnstone, Carolyn , Larkins, Jo-Ann , Wright, Wendy
Date: 2022
Type: Text , Journal article
Relation: Journal of Empirical Research on Human Research Ethics Vol. 17, no. 3 (2022), p. 317-328
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Description: A “gatekeeper” controls access to an organization; “gatekeeper approval” is often needed before external research can take place within an organization. We explore the need for gatekeeper approval for research with university staff employing, as a case study, a project which collected data in Australia. This case study addresses known issues, seemingly rarely addressed in the literature. The Human Research Ethics Committee (HREC)'s requirement for approval from individual universities to approach their staff brought significant consequences, exacerbated by the lack of university procedures for such approvals. Simultaneously, since invitations could legitimately be distributed via other avenues, such approval was superfluous. We recommend the HREC's blanket requirement for institutional approval instead be considered on a case-by-case basis depending on the risk of the research, and perhaps waived for low-risk research where participants are able to provide informed consent, and that universities establish processes to deal with requests from external researchers. © The Author(s) 2022.

Seeking choice to fulfill health literacy needs : health literacy opportunities for consumers in hospital waiting areas

- McDonald, Cassie, Granger, Catherine, Said, Catherine, Remedios, Louisa

Authors: McDonald, Cassie , Granger, Catherine , Said, Catherine , Remedios, Louisa
Date: 2022
Type: Text , Journal article
Relation: Qualitative Health Research Vol. 32, no. 2 (2022), p. 345-359
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Description: In this research, we explore and theorize on the potential of hospital outpatient rehabilitation waiting areas to respond and contribute to the health literacy needs of consumers. Constructivist grounded theory informed the sampling and analytical procedures. Thirty-three consumers attending outpatient rehabilitation for a range of health conditions were recruited to this multi-site study. Semi-structured interview and participant observation data were collected and analyzed concurrently using the constant comparison method. The substantive theory of “seeking choice to fulfill health literacy needs” and five interdependent categories were developed. Results indicated that consumers sought choice reflective of their needs; however, the waiting area offered limited choice. Consumers shared ideas to address the lack of choice. Results provide insight into the health literacy needs of consumers in hospital outpatient waiting areas and how health services can appropriately respond to these needs. Future research should investigate the effect of health service environments on health outcomes. © The Author(s) 2021.

SEeMS : advanced artificial neural networks for employee learning motivation prediction

- Sin, Audrey, Islam, Sardar, Prentice, Catherine, Xia, Feng

Authors: Sin, Audrey , Islam, Sardar , Prentice, Catherine , Xia, Feng
Date: 2022
Type: Text , Conference paper
Relation: 7th IEEE International conference for Convergence in Technology, I2CT 2022, Pune, India, 7-9 April 2022, Proceedings 2022 IEEE 7th International conference for Convergence in Technology (I2CT)
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Description: Employee learning motivation is vital for employee professional development and organisational success. However, worldwide statistics show that employees are generally unmotivated to learn. This study aims to examine employee learning motivation signals to determine the best-fit model for early intervention. In this paper, we present SEeMS a Smart Employee learning Motivation System to predict employee learning motivation autonomously. An Advanced Artificial Neural Networks (AANN) with a blended activation function of Sigmoid and ReLu (bSigReLu) is proposed and compared with other learning models. Experimental results demonstrate that the proposed model outperformed conventional state-of-art models. This novel study contributes to the field of organisational behaviour and data science by extending the usage of kernels and customised activation functions to solve the employee learning motivation problem. The superiority of the algorithm makes SEeMS ideal for practical deployment. According to the predictions, organisations could design better strategies to improve employee learning motivation for targeted employees. It is the first step towards achieving an eco-system of self-motivated employee learning that contributes to employee job satisfaction, performance, and well-being, indirectly contributing to employer competitiveness. © 2022 IEEE.

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Self-assembled peptide habitats to model tumor metastasis

- Al Balushi, Noora, Boyd-Moss, Mitchell, Samarasinghe, Rasika, Rifai, Aaqil, Franks, Stephanie, Firipis, Kate, Long, Benjamin, Darby, Ian, Nisbet, David, Pouniotis, Dodie, Williams, Richard

Authors: Al Balushi, Noora , Boyd-Moss, Mitchell , Samarasinghe, Rasika , Rifai, Aaqil , Franks, Stephanie , Firipis, Kate , Long, Benjamin , Darby, Ian , Nisbet, David , Pouniotis, Dodie , Williams, Richard
Date: 2022
Type: Text , Journal article
Relation: Gels Vol. 8, no. 6 (2022), p.
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Description: Metastatic tumours are complex ecosystems; a community of multiple cell types, including cancerous cells, fibroblasts, and immune cells that exist within a supportive and specific microenvironment. The interplay of these cells, together with tissue specific chemical, structural and temporal signals within a three-dimensional (3D) habitat, direct tumour cell behavior, a subtlety that can be easily lost in 2D tissue culture. Here, we investigate a significantly improved tool, consisting of a novel matrix of functionally programmed peptide sequences, self-assembled into a scaffold to enable the growth and the migration of multicellular lung tumour spheroids, as proof-of-concept. This 3D functional model aims to mimic the biological, chemical, and contextual cues of an in vivo tumor more closely than a typically used, unstructured hydrogel, allowing spatial and temporal activity modelling. This approach shows promise as a cancer model, enhancing current understandings of how tumours progress and spread over time within their microenvironment. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

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Sensitivity analysis for vulnerability mitigation in hybrid networks

- Ur‐rehman, Attiq, Gondal, Iqbal, Kamruzzaman, Joarder, Jolfaei, Alireza

Authors: Ur‐rehman, Attiq , Gondal, Iqbal , Kamruzzaman, Joarder , Jolfaei, Alireza
Date: 2022
Type: Text , Journal article
Relation: Electronics (Switzerland) Vol. 11, no. 2 (2022), p.
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Description: The development of cyber‐assured systems is a challenging task, particularly due to the cost and complexities associated with the modern hybrid networks architectures, as well as the recent advancements in cloud computing. For this reason, the early detection of vulnerabilities and threat strategies are vital for minimising the risks for enterprise networks configured with a variety of node types, which are called hybrid networks. Existing vulnerability assessment techniques are unable to exhaustively analyse all vulnerabilities in modern dynamic IT networks, which utilise a wide range of IoT and industrial control devices (ICS). This could lead to having a less optimal risk evaluation. In this paper, we present a novel framework to analyse the mitigation strategies for a variety of nodes, including traditional IT systems and their dependability on IoT devices, as well as industrial control systems. The framework adopts avoid, reduce, and manage as its core principles in characterising mitigation strategies. Our results confirmed the effectiveness of our mitigation strategy framework, which took node types, their criticality, and the network topology into account. Our results showed that our proposed framework was highly effective at reducing the risks in dynamic and resource constraint environments, in contrast to the existing techniques in the literature. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

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Sequence-to-sequence learning-based conversion of pseudo-code to source code using neural translation approach

- Acharjee, Uzzal, Arefin, Minhazul, Hossen, Kazi, Uddin, Mohammed, Uddin, Md Ashraf, Islam, Linta

Authors: Acharjee, Uzzal , Arefin, Minhazul , Hossen, Kazi , Uddin, Mohammed , Uddin, Md Ashraf , Islam, Linta
Date: 2022
Type: Text , Journal article
Relation: IEEE Access Vol. 10, no. (2022), p. 26730-26742
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Description: Pseudo-code refers to an informal means of representing algorithms that do not require the exact syntax of a computer programming language. Pseudo-code helps developers and researchers represent their algorithms using human-readable language. Generally, researchers can convert the pseudo-code into computer source code using different conversion techniques. The efficiency of such conversion methods is measured based on the converted algorithm's correctness. Researchers have already explored diverse technologies to devise conversion methods with higher accuracy. This paper proposes a novel pseudo-code conversion learning method that includes natural language processing-based text preprocessing and a sequence-to-sequence deep learning-based model trained with the SPoC dataset. We conducted an extensive experiment on our designed algorithm using descriptive bilingual understudy scoring and compared our results with state-of-the-art techniques. Result analysis shows that our approach is more accurate and efficient than other existing conversion methods in terms of several performances metrics. Furthermore, the proposed method outperforms the existing approaches because our method utilizes two Long-Short-Term-Memory networks that might increase the accuracy. © 2013 IEEE.

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Severe tropical cyclones over southwest Pacific Islands : economic impacts and implications for disaster risk management

- Deo, Anil, Chand, Savin, McIntosh, R. Duncan, Prakash, Bipen, Holbrook, Neil, Magee, Andrew, Haruhiru, Alick, Malsale, Philip

Authors: Deo, Anil , Chand, Savin , McIntosh, R. Duncan , Prakash, Bipen , Holbrook, Neil , Magee, Andrew , Haruhiru, Alick , Malsale, Philip
Date: 2022
Type: Text , Journal article
Relation: Climatic Change Vol. 172, no. 3-4 (2022), p.
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Description: Tropical cyclones (TCs) are amongst the costliest natural hazards for southwest Pacific (SWP) Island nations. Extreme winds coupled with heavy rainfall and related coastal hazards, such as large waves and high seas, can have devastating consequences for life and property. Effects of anthropogenic climate change are likely to make TCs even more destructive in the SWP (as that observed particularly over Fiji) and elsewhere around the globe, yet TCs may occur less often. However, the underpinning science of quantifying future TC projections amid multiple uncertainties can be complex. The challenge for scientists is how to turn such technical knowledge framed around uncertainties into tangible products to inform decision-making in the disaster risk management (DRM) and disaster risk reduction (DRR) sector. Drawing on experiences from past TC events as analogies to what may happen in a warming climate can be useful. The role of science-based climate services tailored to the needs of the DRM and DRR sector is critical in this context. In the first part of this paper, we examine cases of historically severe TCs in the SWP and quantify their socio-economic impacts. The second part of this paper discusses a decision-support framework developed in collaboration with a number of agencies in the SWP, featuring science-based climate services that inform different stages of planning in national-level risk management strategies. © 2022, The Author(s).

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