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A novel cooperative link selection mechanism for enhancing the robustness in scale-free IoT networks

- Khan, Muhammad, Javaid, Nadeem, Javaid, Sakeena, Khalid, Adia, Nasser, Nidal, Imran, Muhammad

  • Authors: Khan, Muhammad , Javaid, Nadeem , Javaid, Sakeena , Khalid, Adia , Nasser, Nidal , Imran, Muhammad
  • Date: 2020
  • Type: Text , Conference paper
  • Relation: 16th IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2020, Limassol, Cyrprus, 15 to 19 June 2020, 2020 International Wireless Communications and Mobile Computing, IWCMC 2020 p. 2222-2227
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  • Description: In today's world, Internet of Things (IoT) helps people in many fields by enabling smart city projects in health monitoring, smart parking, industrial optimization, home energy management, etc. Daily life objects are connected with the Internet to allow access to their owners to keep an eye on their surroundings. The IoT network is comprised of nodes that are smart enough to perform any function and provide benefits to the people. However, any fault in the network opens up the risk of leaking personal information. The aim is to develop a scale-free network, which controls the effects of malicious attacks and consequently improves the network robustness. In this paper, our prime focus is to mitigate the effect of malicious nodes by providing a robust strategy to maintain the network stability. In this regard, we propose a topology named as a Cooperation based Edge Swap (CES) for improving the network robustness in the scale-free network. The CES uses the edge/link selection mechanism by involving the cooperation using a Rayleigh fading to swap the network topology for improving the network robustness. The simulations' outcome depicts the performance of the CES in terms of improving the network robustness. © 2020 IEEE.

A patient centric agent assisted private blockchain on hyperledger fabric for managing remote patient monitoring

- Wadud, Md Anwar, Amir-Ul-Haque Bhuiyan, T., Uddin, Md Ashraf, Rahman, Md Motiur

  • Authors: Wadud, Md Anwar , Amir-Ul-Haque Bhuiyan, T. , Uddin, Md Ashraf , Rahman, Md Motiur
  • Date: 2020
  • Type: Text , Conference paper
  • Relation: 11th International Conference on Electrical and Computer Engineering, ICECE 2020 p. 194-197
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  • Description: Recently, during the COVID-19 situation, the requirement and importance of tracking patients from a remote location have increased significantly. Most patients now prefer to obtain their doctor's care and check their health status through their mobile phone call, Skype, Facebook Messenger, or other online resources. There is, however, a major concern about the privacy of patients when using online resources. Patients usually choose to keep their information confidential, which should be only accessible to authorized individuals. The most current remote patient monitoring system is organization-centric and patient's privacy and security rely on healthcare providers' mercy. Blockchain technologies have attracted the attention of researchers for designing eHealth applications to provide patients with secure and privacy-preserving health services. Blockchain researchers have recently proposed some models for remote patient monitoring systems. However, most of those researchers have applied public blockchains where health data is available to all participants with the property of data tamper-proof. In this paper, we propose a novel remote patient monitoring model using a decentralized private blockchain to protect patient's privacy and increase the system's efficiency. The private blockchain will be implemented on Hyperledger Fabric where a Patient-centric Agents (PCA) manage patient's data and coordinate authorization to form a secure channel to transmit data to the private blockchain. A hybrid consensus by combining Proof of Integrity (PoI) and Proof of Validity (PoV) is used to protect data privacy and integrity when retrieving data from a blockchain-based cloud database. Finally, the Merkle Tree algorithm was used for data processing and authentication when collecting data and uploading it to a cloud database. © 2020 IEEE.

A trust management system for multi-agent system in smart grids using blockchain technology

- Samuel, Omaji, Javaid, Nadeem, Khalid, Adia, Imrarn, Muhammad, Nasser, Nidal

  • Authors: Samuel, Omaji , Javaid, Nadeem , Khalid, Adia , Imrarn, Muhammad , Nasser, Nidal
  • Date: 2020
  • Type: Text , Conference paper
  • Relation: 2020 IEEE Global Communications Conference, GLOBECOM 2020, Virtual, Taipei China, 7 to 11 December 2020, 2020 IEEE Global Communications Conference, GLOBECOM 2020 - Proceedings Vol. 2020-January
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  • Description: In a multi-agent system (MAS), the trust of each agent has become hot research issues in the smart grids. The traditional trust systems that use access control and cryptography are not sufficient to handle the dynamic behavior of agents. Also, they are inefficient to solve the computational overhead of the cryptographic primitives. Based on these limitations, this paper proposes a blockchain-based trust management system for MAS. The proposed system consists of two layers: a lower layer that enables an agent to perform direct and indirect trust evaluations of other agents during interactions. Multi-source feedback from the interactions among different aggregators is feed to the blockchain. The upper layer is used to perform trust credibility of agents based on trust distortion, consistency and reliability. The credibility evaluation is used to determine the dynamic behavior of agents and also detect dishonest agents in the system. Trust model and security analysis of the proposed system are provided. Moreover, simulation results evaluate the effectiveness of the proposed trust system while the system is secure against bad-mouthing and on-off attacks. © 2020 IEEE.

A weighted overlook graph representation of eeg data for absence epilepsy detection

- Wang, Jialin, Liang, Shen, Wang, Ye, Zhang, Yanchun, Ma, Jiangang

  • Authors: Wang, Jialin , Liang, Shen , Wang, Ye , Zhang, Yanchun , Ma, Jiangang
  • Date: 2020
  • Type: Text , Conference proceedings , Conference paper
  • Relation: 20th IEEE International Conference on Data Mining, ICDM 2020 Vol. 2020-November, p. 581-590
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  • Description: Absence epilepsy is one of the most common types of epilepsy. The diagnosis of absence epilepsy is among the greatest challenges faced by clinical neurologists due to a lack of easily observable symptoms that are present in conventional epilepsy (e.g. spasm and convulsion), and highly relies on the detection of Spike and Slow Waves (SSWs) in Electroencephalogram (EEG) signals. Recently, graph representations called complex networks have been increasingly applied to characterizing 1D EEG signals. However, existing methods often fail to effectively represent SSWs, struggling to capture the differences between SSW waveforms and their non-SSW counterparts, such as minute differences and distinct shapes. Addressing this issue, in this work, we propose two simple yet effective complex networks, Overlook Graph (OG) and Weighted Overlook Graph (WOG), which have been customized to expressively represent SSWs. Built upon OG and WOG, we then develop a 2D Convolutional Neural Network (2D-CNN) to further learn latent features from the graph representations and accomplish the detection task. Extensive experiments on a real-world absence epilepsy EEG dataset show that the proposed OG/WOG-2D-CNN method can accurately detect SSWs. Additional experiments on the well-known Bonn dataset further show that our method can generalize to the conventional epilepsy seizure detection task with highly competitive performances. © 2020 IEEE. *Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate "Jiangang Ma“ is provided in this record**

Active model selection for positive unlabeled time series classification

- Liang, Shen, Zhang, Yanchun, Ma, Jiangang

  • Authors: Liang, Shen , Zhang, Yanchun , Ma, Jiangang
  • Date: 2020
  • Type: Text , Conference proceedings , Conference paper
  • Relation: 36th IEEE International Conference on Data Engineering, ICDE 2020 Vol. 2020-April, p. 361-372
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  • Description: Positive unlabeled time series classification (PUTSC) refers to classifying time series with a set PL of positive labeled examples and a set U of unlabeled ones. Model selection for PUTSC is a largely untouched topic. In this paper, we look into PUTSC model selection, which as far as we know is the first systematic study in this topic. Focusing on the widely adopted self-training one-nearest-neighbor (ST-1NN) paradigm, we propose a model selection framework based on active learning (AL). We present the novel concepts of self-training label propagation, pseudo label calibration principles and ultimately influence to fully exploit the mechanism of ST-1NN. Based on them, we develop an effective model performance evaluation strategy and three AL sampling strategies. Experiments on over 120 datasets and a case study in arrhythmia detection show that our methods can yield top performance in interactive environments, and can achieve near optimal results by querying very limited numbers of labels from the AL oracle. © 2020 IEEE.
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Adversarial learning-based bias mitigation for fatigue driving detection in fair-intelligent IoV

- Han, Mingzhe, Wu, Jun, Bashir, Ali, Yang, Wu, Imran, Muhammad, Nasser, Nidal

  • Authors: Han, Mingzhe , Wu, Jun , Bashir, Ali , Yang, Wu , Imran, Muhammad , Nasser, Nidal
  • Date: 2020
  • Type: Text , Conference paper
  • Relation: 2020 IEEE Global Communications Conference, GLOBECOM 2020, Virtual, Taipei, China, 7 to 11 December 2020, 2020 IEEE Global Communications Conference, GLOBECOM 2020 - Proceedings
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  • Description: Fatigue driving is one of main causes of traffic accidents. To avoid such traffic accidents, divers' fatigue detection has been used in Intelligent Internet of Vehicles (IIoV). IIoV usually dynamically allocate computing resources according to drivers' fatigue degree to improve the real-time of fatigue detection model. However, the traditional fatigue detection model may have bias on certain groups, which would further cause unfair resource allocation. To solve the problem, this paper proposes an improved IIoV framework, named Fair-Intelligent Internet of Vehicles (FIIoV). Compared with IIoV, we improve two layers in FIIoV, i.e., the detection layer and the normalization layer. The detection layer uses Convolutional Neural Network (CNN) to detect drivers' fatigue degree, and then uses adversarial network to achieve fairness of detection models. The normalization layer achieves the distribution of different sensitive feature values from historical detection results generated in the detection layer, and then uses the distribution to normalize the output of the detection layer to improve the fairness and accuracy of fatigue detection models. Simulation results show that both accuracy and fairness of FIIoV is improved compared with the original IIoV. © 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

An Enhanced Local Texture Descriptor for Image Segmentation

- Tania, Sheikh, Murshed, Manzur, Teng, Shyh, Karmakar, Gour

  • Authors: Tania, Sheikh , Murshed, Manzur , Teng, Shyh , Karmakar, Gour
  • Date: 2020
  • Type: Text , Conference paper
  • Relation: 2020 IEEE International Conference on Image Processing, ICIP 2020 Vol. 2020-October, p. 1526-1530
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  • Description: Texture is an indispensable property to develop many vision based autonomous applications. Compared to colour, feature dimension in a local texture descriptor is quite large as dense texture features need to represent the distribution of pixel intensities in the neighbourhood of each pixel. Large dimensional features require additional time for further processing that often restrict real-time applications. In this paper, a robust local texture descriptor is enhanced by reducing feature dimension by three folds without compromising the accuracy in region-based image segmentation applications. Reduction in feature dimension is achieved by exploiting the mean of neighbourhood pixel intensities radially along lines across a certain radius, which eliminates the need for sampling intensity distribution at three scales. Both the results of benchmark metrics and computational time are promising when the enhanced texture feature is used in a region-based hierarchical segmentation algorithm, a recent state-of-the-art technique. © 2020 IEEE.

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.

An improved energy and cost minimization scheme for Home Energy Management (HEM) in the smart grid framework

- Ur Rashid, Md Mamun, Hossain, Md Alamgir, Shah, Rakibuzzaman, Alam, Md Shaiul, Karmaker, Ashish, Rahman, Muaz

  • Authors: Ur Rashid, Md Mamun , Hossain, Md Alamgir , Shah, Rakibuzzaman , Alam, Md Shaiul , Karmaker, Ashish , Rahman, Muaz
  • Date: 2020
  • Type: Text , Conference paper
  • Relation: 2020 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices, ASEMD 2020; Virtual, Tianjin; 16 to 18 October 2020
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  • Description: In the current energy perspective, home energy management (HEM) is an essential requirement for residential consumers due to excessive energy utilization by the household appliances. This paper proposes an improved energy and cost minimization scheme for residential consumers to shape peak load and utility tariffs. An efficient power management algorithm (PMA) has been developed with renewable energy sources (RESs) and energy storage systems (ESS). The PMA is executed using C++ software. The benefits of the RESs and ESS with the proposed PMA techniques are analyzed using three different scenarios. Extensive case studies are carried out to validate the effectiveness of the proposed energy and cost minimization scheme. It is demonstrated that the proposed method may save energy and costs up to 34% and 45% compared to the existing methods. © 2020 IEEE.

An incentive scheme for VANETs based on traffic event validation using blockchain

- Iftikhar, Muhammad, Javaid, Nadeem, Samuel, Omaji, Shoaib, Muhammad, Imran, Muhammad

  • Authors: Iftikhar, Muhammad , Javaid, Nadeem , Samuel, Omaji , Shoaib, Muhammad , Imran, Muhammad
  • Date: 2020
  • Type: Text , Conference paper
  • Relation: 16th IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2020, Limassol, Cyprus, 15 to 19 June 2020, 2020 International Wireless Communications and Mobile Computing, IWCMC 2020 p. 2133-2137
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  • Description: A large amount of data is involved in an effective and timely exchange of traffic information between vehicles in Vehicular Ad-hoc Networks (VANETs), which ensures efficiency and reliability. VANETs assist in sharing traffic information effectively and timely to improve traffic efficiency and reliability. However, less storage capability and selfish behavior of the vehicles are important issues that need to be tackled. Moreover, traditional storage mechanisms require the involvement of third parties, which are insecure, untrustworthy, non-transparent, and unreliable. To overcome these issues, we proposed a blockchain-based data storage scheme for VANETs by exploiting the benefits of the Interplanetary File System (IPFS), which is deployed on Road Side Units (RSUs). Furthermore, RSUs are able to receive the aggregation packet comprising of the event information acquired from the vehicles. After receiving and verifying the aggregation packet, the RSU stores the event's information in IPFS and the reputation values of vehicles in blockchain. Moreover, we proposed an incentive mechanism in this work, in which monetary incentives are given to the repliers who agree with the vehicle regarding the event information. The incentives are given by the initiator after verifying the signatures of the repliers. All the transactions involved in the incentive process are stored in blockchain. The simulation results prove the efficiency of the proposed scheme in terms of transaction cost and storage savings in VANETs. © 2020 IEEE.

An investigation of compensation networks for three-coil wireless power transfer

- Wang, H. S., Cheng, K. W. E., Hu, Jiefeng

API based discrimination of ransomware and benign cryptographic programs

- Black, Paul, Sohail, Ammar, Gondal, Iqbal, Kamruzzaman, Joarder, Vamplew, Peter, Watters, Paul

Capability building through workplace based learning in maintenance and reliability engineering (MRE) postgraduate programmes

- Chattopadhyay, Gopinath, Larkins, Jo-ann

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.

CNN and GRU based deep neural network for electricity theft detection to secure smart grid

- Ullah, Ashraf, Javaid, Nadeem, Samuel, Omaji, Imran, Muhammad, Shoaib, Muhammad

  • Authors: Ullah, Ashraf , Javaid, Nadeem , Samuel, Omaji , Imran, Muhammad , Shoaib, Muhammad
  • Date: 2020
  • Type: Text , Conference paper
  • Relation: 16th IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2020, Limassol, Cyprus, 15 to 19 June 2020, 2020 International Wireless Communications and Mobile Computing, IWCMC 2020 p. 1598-1602
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  • Description: In this paper, a Hybrid Deep Neural Network (HDNN) is proposed in this work, which is the combination of Convolutional Neural Network (CNN), Gated Recurrent Unit (GRU) and Particle Swarm Optimization (PSO), termed as CNN-GRU-PSO HDNN. In this paper, real time electricity consumption data of consumers is used, which is taken from an easily available online source, named as State Grid Corporation of China (SGCC). The original dataset consists of actual values along with the erroneous and missing values. The pre-processing steps are performed initially to refine the data. After that, feature selection and extraction are performed using CNN, which reduce both the dimensionality and the redundancy present in the dataset. Furthermore, the classification of provided data into honest and fake consumers is done using GRU-PSO technique. The proposed HDNN model's performance is then compared with various benchmark techniques like Logistic Regression (LR), Support Vector Machine (SVM), Long Short Term Memory (LSTM) and GRU. The efficiency of the proposed model is validated using various performance parameters like Area Under the Curve (AUC), precision, accuracy, recall and F1-Score. The simulation results show that the proposed model outperforms the existing techniques in terms of ETD and class imbalanced issues. Moreover, the proposed model is also more robust and accurate than the existing methods. © 2020 IEEE.

Collisionless fast pattern formation mechanism for dynamic number of UAVs

- Raja, Gunasekaran, Saran, V., Anbalagan, Sudha, Bashir, Ali, Imran, Muhammad, Nasser, Nidal

  • Authors: Raja, Gunasekaran , Saran, V. , Anbalagan, Sudha , Bashir, Ali , Imran, Muhammad , Nasser, Nidal
  • Date: 2020
  • Type: Text , Conference paper
  • Relation: 2020 IEEE Global Communications Conference, GLOBECOM 2020, Virtual, Taipei, China, 7 to 11 December 2020, 2020 IEEE Global Communications Conference, GLOBECOM 2020 - Proceedings
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  • Description: Unmanned Aerial Vehicle (UAV) is an emerging technology that assists in various automated activities where human involvement is minimal. Though individual UAVs are extremely useful entities, their productivity can further be increased by deploying multi-UAVs. Pattern formation among multi-UAVs is one of the key functionalities in a swarm environment that is essential for several UAV missions namely military expedition, search and rescue operations, drone based delivery mechanisms etc. In this paper, to facilitate pattern formation among UAVs in an effective manner, a Time-Interleaved Pattern Formation (TIPF) Mechanism is proposed. The existing systems work for a fixed number of drones whose pattern switching mechanisms are preprogrammed. However, the TIPF mechanism enables switching patterns among dynamic number of drones (UAVs) on the fly by inducing a small delay between each UAV movement. The TIPF mechanism avoids collision, which occurs due to the simultaneous movement of UAVs. The proposed TIPF mechanism encompasses a Centralised Coordinate Calculation (CCC) algorithm to easily calculate the coordinates of UAVs in a given pattern. Further, this mechanism has also been simulated and tested in our proposed virtual IP based Software In The Loop (V-SITL) environment. This proposed V-SITL environment offers increased scalability on account of the entire UAV system being simulated in a single computer. The TIPF mechanism has been simulated for 8 drones in a dynamic manner for square and triangle patterns. The simulation results show that the pattern formation time avoids collision in a time interleaving rate of 52.63%. © 2020 IEEE.

Conditional anonymity enabled blockchain-based ad dissemination in vehicular ad-hoc network

- Javed, Muhammad, Jamal, Abid, Javaid, Nadeem, Haider, Noman, Imran, Muhammad

  • Authors: Javed, Muhammad , Jamal, Abid , Javaid, Nadeem , Haider, Noman , Imran, Muhammad
  • Date: 2020
  • Type: Text , Conference paper
  • Relation: 16th IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2020, Limassol, Cyprus, 15 to 19 June 2020, 2020 International Wireless Communications and Mobile Computing, IWCMC 2020 p. 2149-2153
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  • Description: I. Absract Advertisement sharing in vehicular network through vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication is a fascinating in-vehicle service for advertisers and the users due to multiple reasons. It enable advertisers to promote their product or services in the region of their interest. Also the users get to receive more relevant ads. Usually, users tend to contribute in dissemination of ads if their privacy is preserved and if some incentive is provided. Recent researches have focused on enabling both of the parameters for the users by developing fair incentive mechanism which preserves privacy by using Zero-Knowledge Proof of Knowledge (ZKPoK) [2]. However, the anonymity provided by ZKPoK can introduce internal attacker scenarios in the network due to which authenticated users can disseminate fake ads in the network without payment. As the existing scheme uses certificate-less cryptography, due to which malicious users cannot be removed from the network. In order to resolve these challenges, we employed conditional anonymity and introduced Monitoring Authority (MA) in the system. In our proposed scheme, the pseudonyms are assigned to the vehicles while their real identities are stored in Certification Authority (CA) in encrypted form. The pseudonyms are updated after a pre-defined time threshold to prevent behavioural privacy leakage. We performed security and performance analysis to show the efficiency of our proposed system. © 2020 IEEE.

Conservation, agriculture, sustainable, development and strong communities

- Martin, Jennifer

  • 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.

CRI : measuring city infection risk amid COVID-19

- Liu, Mingliang, Yu, Shuo, Chu, Xinbei, Xia, Feng

  • Authors: Liu, Mingliang , Yu, Shuo , Chu, Xinbei , Xia, Feng
  • Date: 2020
  • Type: Text , Conference paper
  • Relation: 2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2020
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  • Description: The outbreak of COVID-19 has brought incalculable economy and life losses. Accurately assessing the risk of a certain city can help formulate effective measures to prevent and control COVID-19 in time. It will be of great significance for us to measure city risk in infection amid epidemics. City risk in infection is related to many factors. To address this problem, this paper proposes city risk index (CRI) to measure city risk in infection, considering the following four perspectives: Economy (i.e., GDP and FCI), technology (i.e., education and innovation), population, and geographical position (i.e., latitude and longitude). The experimental results show that CRI can be effectively employed to measure city risk in infection amid COVID-19 as well as other similar epidemics. The proposed CRI can be used to guide policymakers for better emergency management policies making when coping with COVID-19. © 2020 IEEE.