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  • Authors: Regan, Courtney M (University of South Australia, UniSA Business); Connor, Jeff (University of South Australia, UniSA Business);

    Report outlining the role carbon markets may play in promoting plantation forestry expansion Refereed/Peer-reviewed

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  • Authors: Christine Helliar; Vicki Waye; Reza Bradrania;

    This research investigated the drivers, barriers and benefits of technological innovation (including block-chain, Internet of Things (IoT), robotics and Industry 4.0 applications) and the role of management accountants in managing innovation projects within the Australian and Italian wine industry. This report outlines seven steps which can lead to successful project management of digital innovation and finds that incremental innovation across a portfolio of projects works better than a “big-bang” approach. It finds that management accountants, with their focus on risk and financial management, play an important role in the implementation of technical innovation. It also stresses the importance of management accountants understanding digital technology and jargon, sustainability issues and how to measure non-financial performance in order to continue to play a leading role in this area.

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  • Authors: Johannes Sauer; Will Chancellor; Philipp Mennig; Jesús Antón;

    This paper provides detailed farm level data evidence on the dynamics of farm performance from case studies covering crop farms in Australia, France, Italy and the United Kingdom (England and Wales), and dairy farms in the Czech Republic, Denmark and Norway, with different recent sample periods of five to thirty years. An increase in productivity over time is common to all countries and most crop farm classes, but productivity dynamics vary significantly. In Australia, strong productivity growth among the most productive crop farms has led to an increase in the gap between the highest and lowest performing farms; whereas in France, Italy and the United Kingdom, productivity growth was weak among the most productive crop farms and the lowest performing farms closed the productivity gap. Productivity also increased among dairy farms, with an increasing gap between the most and the least productive farm classes in the three sample countries. The impact of policy changes on performance dynamics is analysed for decoupled payments in France and England, and dairy payments in the Czech Republic. The main findings across countries and policy implications are discussed in OECD Food, Agriculture and Fisheries Paper N°164.

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    Authors: Fabrice A. J. DeClerck; Izabella Koziell; A. Sidhu; J. Wirths; +20 Authors
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    Authors: Marie-Agnes Jouanjean; Francesca Casalini; Leanne Wiseman; Emily Gray;

    Agricultural data and their use for better decision-making and innovation are at the core of the digital transformation of agriculture. But fragmented and unclear data governance arrangements may weaken farmers’ willingness to adopt digital solutions. This, in turn, may reduce the availability and accessibility of agricultural data for policymaking, for the agricultural innovation system, and for developing services for farmers. A key challenge for policy makers lies in finding a balance between protecting the privacy and confidentiality of agricultural data, and farmers’ economic interests in those data, while making it possible to leverage their potential for the sector’s growth and innovation. This report focuses on farmers’ concerns around access, sharing and use of agricultural data and explores whether and how existing policy frameworks and other sectoral initiatives can help to foster greater trust.

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    Authors: Pushkar Maitra; Sandip Mitra; Dilip Mookherjee; Sujata Visaria;

    We compare two different methods of appointing a local commission agent as an intermediary for a credit program. In the Trader-Agent Intermediated Lending Scheme (TRAIL), the agent was a randomly selected established private trader, while in the Gram Panchayat-Agent Intermediated-Lending Scheme (GRAIL), he was randomly chosen from nominations by the elected village council. More TRAIL loans were taken up, but repayment rates were similar, and TRAIL loans had larger average impacts on borrowers' farm incomes. The majority of this difference in impacts is due to differences in treatment effects conditional on farmer productivity, rather than differences in borrower selection patterns. The findings can be explained by a model where TRAIL agents increased their middleman profits by helping more able treated borrowers reduce their unit costs and increase output. In contrast, for political reasons GRAIL agents monitored the less able treated borrowers and reduced their default risk.

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  • Authors: Frank Wagner;

    Industrial research and development (R&D) must constantly change and adapt in the coming years in order to successfully offer new products, processes, services, solutions and business models to global markets and international customers.Current geopolitical and economic turbulences destabilize markets, technological trends such as the digital transformation of industries produce new winners and losers. This offers opportunities, especially for small and medium-sized enterprises, which can recognize these potentials and use them for profitable growth. The study "Future R&D" describes current challenges and provides the latest findings on R&D trends, goals and success factors from the perspective of technology-oriented companies as well as case studies from the business practice of numerous R&D experts.

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  • Authors: Frank Wagner;

    This publication is an outcome of the global webinar on the Future of Work in the Digital Economy – Developing Skills for Industry 4.0 on 25 June 2020, which was opened by the Australian Ambassador to Germany and hosted by RMIT Europe.Industry leaders and international academics examine the impact of COVID-19 on the digital economy and analyse the critical skills required to drive this accelerated digital transformation.

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  • This report constitutes the third deliverable for research contract FA5209-18-P-0416 ITC-PAC for the project entitled "Trusted Operations for Robotics Vehicles in Contested Environments.'' It was developed in conjunction with Dr. Mike Del Rose and Steph Roth of the Tank and Automotive Research Development and Engineering Center (TARDEC).The main goal of this project is to introduce a new cyber-security framework for detecting potential robotic cyber-attacks against small military Unmanned Ground Vehicles (UGVs), operating in physically challenging and contested environments. As our experimental platform, we employ a small US Army military (GVR-BOT) unmanned ground vehicle.In this current report, we deliver the third milestone of the project. Progressing from our last report, where we collected a set of experimental data in the form of time series vectors of network traffic data before converting them into RGB images. We further investigate the efficacy of our proposed framework based on real-time experimental data, obtained from real-time cyber-attack experiments. Thus, our major research contributions, addressing the existing research gaps, can be elaborated as follows:We introduce a new cyber-security framework, leveraging the benefits of deep learning Convolutional Neural Network (CNN) to detect potential robotic cyber-attacks in a small military ground vehicle.By means of data-driven modeling (system identification) technique (e.g. The autoregressive-exogenous (ARX) technique), we first derive a high-fidelity mathematical model of our ground robot, describing the dynamics of the two control loops of the GVR-BOT ground robot, namely, the forward velocity and the heading control loops based on our real-time experimental data. We also validate the accuracy of the proposed mathematical model with respect to another set of experimental data. The proposed mathematical models are suitable for the future theoretical study of the system dynamics under Matlab Simulink simulation environments. We perform an experimental real-time cyber-attack (i.e. node isolation attack) on our experimental ground robot, where we successfully isolate a ROS node responsible for receiving and transmitting the control command received from the hand-held transmitter to other nodes. This way, we record the dynamics of the ground robot (network traffic data) under normal and malicious attack conditions. Based on the real-time experimental data, we highlight the efficacy of our cyber-intrusion system, that is, to perform legitimate/malicious (L/M) detection. We split our experimental data into two main parts, namely, for training and validation in our CNN networks. We demonstrate that our proposed cybersecurity framework can be effectively used to detect malicious robotic cyber-attacks with reasonably high accuracy.We also study the efficacy of our deep learning CNN system to recognize four fundamental activities of the GVR-BOT ground robot, namely, moving forward, backward as well as turning left and turning right under normal (legitimate) operating points. We perform a rigorous comparative study with respect to the performance of similar systems fed by overlapping windows techniques. We also demonstrate how the performance of the system (as indicated by e.g. false positive rate) is highly dependant on the amount of the tracking information acquired as given by the length of the network traffic data (epochs) represented in each single input image.The reason to employ the deep learning CNN technique is due to its suitability to situations where data are periodically sampled in one or more dimensions, e.g. audio signals (1D), imagery (2D), or video (3D). The system operates by convolving several adjacent input units with activation functions, whose output defines the next layer in the input. Subsequently, the outcome of each layer is aggregated into the next layer, which contains fewer units. By chaining multiple layers together complex features can be extracted and identified.We use each CNN layer to generate a tensor, represented as a multi-dimensional array, where the dimensions of the input 'image' will be reduced while new dimensionality, which is equal to the number of filters applied to the image will be simultaneously created. Eventually, it will be necessary to transform the tensor into a vector that can be the input to final fully connected layers. The method is a biologically inspired technique for performing object classification. By training the neural network on ROS traffic, both affected and unaffected by a cyberattack, the dynamics of the robot can be constantly monitored at the application level using a combination of anomaly and signature-based detection. This allows us to derive, characterize, and examine models of traffic flow anomalies based on different types of attack with respect to normal traffic flows for a range of `operational' contexts.Our current research demonstrates very promising outcomes for CNNs to be used as a real-time cybersecurity tool and for activity recognition of a military ground robot. The system can achieve highly accurate detection capability within a reasonably short processing time. For instance, for L/M attack detection, our CNN can achieve 1.000 accuracies with 0.000 FPR and 0.000 FNR within 15 epochs, corresponding to 1.5s processing time. Meanwhile, for recognizing four fundamental ground robot activities, namely, moving forward, backward as well as turning left and right; the system can achieve an accuracy of 1.000 for turning left and right and 0.970 with FPRs of 0.039 and 0.000 for forward and backward motions, respectively; with only as short as 2 epoch duration of data using independent window technique, corresponding to only 0.2s processing time.For future work, we will investigate the efficacy of our CNN to detect different types of malicious attacks, namely, man-in-the-middle or service denial of attacks. Under the umbrella of supervised learning, we also intend to compare the relative benefits of CNN with respect to similar detection techniques, such as support vector machines (SVMs) or also known as support-vector networks.

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  • Authors: Regan, Courtney M (University of South Australia, UniSA Business); Connor, Jeff (University of South Australia, UniSA Business);

    Report outlining the role carbon markets may play in promoting plantation forestry expansion Refereed/Peer-reviewed

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  • Authors: Christine Helliar; Vicki Waye; Reza Bradrania;

    This research investigated the drivers, barriers and benefits of technological innovation (including block-chain, Internet of Things (IoT), robotics and Industry 4.0 applications) and the role of management accountants in managing innovation projects within the Australian and Italian wine industry. This report outlines seven steps which can lead to successful project management of digital innovation and finds that incremental innovation across a portfolio of projects works better than a “big-bang” approach. It finds that management accountants, with their focus on risk and financial management, play an important role in the implementation of technical innovation. It also stresses the importance of management accountants understanding digital technology and jargon, sustainability issues and how to measure non-financial performance in order to continue to play a leading role in this area.

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  • Authors: Johannes Sauer; Will Chancellor; Philipp Mennig; Jesús Antón;

    This paper provides detailed farm level data evidence on the dynamics of farm performance from case studies covering crop farms in Australia, France, Italy and the United Kingdom (England and Wales), and dairy farms in the Czech Republic, Denmark and Norway, with different recent sample periods of five to thirty years. An increase in productivity over time is common to all countries and most crop farm classes, but productivity dynamics vary significantly. In Australia, strong productivity growth among the most productive crop farms has led to an increase in the gap between the highest and lowest performing farms; whereas in France, Italy and the United Kingdom, productivity growth was weak among the most productive crop farms and the lowest performing farms closed the productivity gap. Productivity also increased among dairy farms, with an increasing gap between the most and the least productive farm classes in the three sample countries. The impact of policy changes on performance dynamics is analysed for decoupled payments in France and England, and dairy payments in the Czech Republic. The main findings across countries and policy implications are discussed in OECD Food, Agriculture and Fisheries Paper N°164.

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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Fabrice A. J. DeClerck; Izabella Koziell; A. Sidhu; J. Wirths; +20 Authors
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    Authors: Marie-Agnes Jouanjean; Francesca Casalini; Leanne Wiseman; Emily Gray;

    Agricultural data and their use for better decision-making and innovation are at the core of the digital transformation of agriculture. But fragmented and unclear data governance arrangements may weaken farmers’ willingness to adopt digital solutions. This, in turn, may reduce the availability and accessibility of agricultural data for policymaking, for the agricultural innovation system, and for developing services for farmers. A key challenge for policy makers lies in finding a balance between protecting the privacy and confidentiality of agricultural data, and farmers’ economic interests in those data, while making it possible to leverage their potential for the sector’s growth and innovation. This report focuses on farmers’ concerns around access, sharing and use of agricultural data and explores whether and how existing policy frameworks and other sectoral initiatives can help to foster greater trust.

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    Authors: Pushkar Maitra; Sandip Mitra; Dilip Mookherjee; Sujata Visaria;

    We compare two different methods of appointing a local commission agent as an intermediary for a credit program. In the Trader-Agent Intermediated Lending Scheme (TRAIL), the agent was a randomly selected established private trader, while in the Gram Panchayat-Agent Intermediated-Lending Scheme (GRAIL), he was randomly chosen from nominations by the elected village council. More TRAIL loans were taken up, but repayment rates were similar, and TRAIL loans had larger average impacts on borrowers' farm incomes. The majority of this difference in impacts is due to differences in treatment effects conditional on farmer productivity, rather than differences in borrower selection patterns. The findings can be explained by a model where TRAIL agents increased their middleman profits by helping more able treated borrowers reduce their unit costs and increase output. In contrast, for political reasons GRAIL agents monitored the less able treated borrowers and reduced their default risk.

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  • Authors: Frank Wagner;

    Industrial research and development (R&D) must constantly change and adapt in the coming years in order to successfully offer new products, processes, services, solutions and business models to global markets and international customers.Current geopolitical and economic turbulences destabilize markets, technological trends such as the digital transformation of industries produce new winners and losers. This offers opportunities, especially for small and medium-sized enterprises, which can recognize these potentials and use them for profitable growth. The study "Future R&D" describes current challenges and provides the latest findings on R&D trends, goals and success factors from the perspective of technology-oriented companies as well as case studies from the business practice of numerous R&D experts.

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  • Authors: Frank Wagner;

    This publication is an outcome of the global webinar on the Future of Work in the Digital Economy – Developing Skills for Industry 4.0 on 25 June 2020, which was opened by the Australian Ambassador to Germany and hosted by RMIT Europe.Industry leaders and international academics examine the impact of COVID-19 on the digital economy and analyse the critical skills required to drive this accelerated digital transformation.

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  • This report constitutes the third deliverable for research contract FA5209-18-P-0416 ITC-PAC for the project entitled "Trusted Operations for Robotics Vehicles in Contested Environments.'' It was developed in conjunction with Dr. Mike Del Rose and Steph Roth of the Tank and Automotive Research Development and Engineering Center (TARDEC).The main goal of this project is to introduce a new cyber-security framework for detecting potential robotic cyber-attacks against small military Unmanned Ground Vehicles (UGVs), operating in physically challenging and contested environments. As our experimental platform, we employ a small US Army military (GVR-BOT) unmanned ground vehicle.In this current report, we deliver the third milestone of the project. Progressing from our last report, where we collected a set of experimental data in the form of time series vectors of network traffic data before converting them into RGB images. We further investigate the efficacy of our proposed framework based on real-time experimental data, obtained from real-time cyber-attack experiments. Thus, our major research contributions, addressing the existing research gaps, can be elaborated as follows:We introduce a new cyber-security framework, leveraging the benefits of deep learning Convolutional Neural Network (CNN) to detect potential robotic cyber-attacks in a small military ground vehicle.By means of data-driven modeling (system identification) technique (e.g. The autoregressive-exogenous (ARX) technique), we first derive a high-fidelity mathematical model of our ground robot, describing the dynamics of the two control loops of the GVR-BOT ground robot, namely, the forward velocity and the heading control loops based on our real-time experimental data. We also validate the accuracy of the proposed mathematical model with respect to another set of experimental data. The proposed mathematical models are suitable for the future theoretical study of the system dynamics under Matlab Simulink simulation environments. We perform an experimental real-time cyber-attack (i.e. node isolation attack) on our experimental ground robot, where we successfully isolate a ROS node responsible for receiving and transmitting the control command received from the hand-held transmitter to other nodes. This way, we record the dynamics of the ground robot (network traffic data) under normal and malicious attack conditions. Based on the real-time experimental data, we highlight the efficacy of our cyber-intrusion system, that is, to perform legitimate/malicious (L/M) detection. We split our experimental data into two main parts, namely, for training and validation in our CNN networks. We demonstrate that our proposed cybersecurity framework can be effectively used to detect malicious robotic cyber-attacks with reasonably high accuracy.We also study the efficacy of our deep learning CNN system to recognize four fundamental activities of the GVR-BOT ground robot, namely, moving forward, backward as well as turning left and turning right under normal (legitimate) operating points. We perform a rigorous comparative study with respect to the performance of similar systems fed by overlapping windows techniques. We also demonstrate how the performance of the system (as indicated by e.g. false positive rate) is highly dependant on the amount of the tracking information acquired as given by the length of the network traffic data (epochs) represented in each single input image.The reason to employ the deep learning CNN technique is due to its suitability to situations where data are periodically sampled in one or more dimensions, e.g. audio signals (1D), imagery (2D), or video (3D). The system operates by convolving several adjacent input units with activation functions, whose output defines the next layer in the input. Subsequently, the outcome of each layer is aggregated into the next layer, which contains fewer units. By chaining multiple layers together complex features can be extracted and identified.We use each CNN layer to generate a tensor, represented as a multi-dimensional array, where the dimensions of the input 'image' will be reduced while new dimensionality, which is equal to the number of filters applied to the image will be simultaneously created. Eventually, it will be necessary to transform the tensor into a vector that can be the input to final fully connected layers. The method is a biologically inspired technique for performing object classification. By training the neural network on ROS traffic, both affected and unaffected by a cyberattack, the dynamics of the robot can be constantly monitored at the application level using a combination of anomaly and signature-based detection. This allows us to derive, characterize, and examine models of traffic flow anomalies based on different types of attack with respect to normal traffic flows for a range of `operational' contexts.Our current research demonstrates very promising outcomes for CNNs to be used as a real-time cybersecurity tool and for activity recognition of a military ground robot. The system can achieve highly accurate detection capability within a reasonably short processing time. For instance, for L/M attack detection, our CNN can achieve 1.000 accuracies with 0.000 FPR and 0.000 FNR within 15 epochs, corresponding to 1.5s processing time. Meanwhile, for recognizing four fundamental ground robot activities, namely, moving forward, backward as well as turning left and right; the system can achieve an accuracy of 1.000 for turning left and right and 0.970 with FPRs of 0.039 and 0.000 for forward and backward motions, respectively; with only as short as 2 epoch duration of data using independent window technique, corresponding to only 0.2s processing time.For future work, we will investigate the efficacy of our CNN to detect different types of malicious attacks, namely, man-in-the-middle or service denial of attacks. Under the umbrella of supervised learning, we also intend to compare the relative benefits of CNN with respect to similar detection techniques, such as support vector machines (SVMs) or also known as support-vector networks.

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