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A brief guide to multi-objective reinforcement learning and planning JAAMAS track

A case for causal loop diagrams to model electronic health records ecosystems

  • Authors: Hashmi, Mustafa , McInnes, Angelique , Sahama, Tony , Stranieri, Andrew
  • Date: 2023
  • Type: Text , Conference paper
  • Relation: 2023 Australasian Computer Science Week, ACSW 2023, Melbourne, Australia, 31 January-3 February 2023, ACSW '23: Proceedings of the 2023 Australasian Computer Science Week p. 238-239
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  • Description: Causal loop diagrams (CLD) that emerged from systems thinking disciplines have been used to simulate complex inter-dependencies between causal factors in diverse phenomena. This paper highlights a process for generating a casual loop diagrams to represent the quality of electronic health record (EHR) ecosystem in a medical context. The quality inherent in the use of electronic health records for specific clinical purposes is taken to depend on factors including data integrity, reliability, relevance, timeliness and completeness. By improving the electronic health record ecosystem quality, health care providers can enhance their data sharing practices, and personalised patient care, while reducing the probabilities of medical errors. Ultimately the CLD can be used to run multiple simulations for several clinical case scenarios to understand the impact of various case phenomena on the quality of the electronic health record ecosystem. © 2023 ACM.

A hybrid PWM strategy for SMES integrated grid-feeding transformerless PV Inverters

A new tour on the subdifferential of the Supremum function

A Novel Common Grounded Type 1-\varphi Five-Level Boost PV Inverter with Reduced Device Count

  • Authors: Ardashir, Jaber , Ghadim, Hadi , Heydari, Dorsa , Hu, Jiefeng
  • Date: 2023
  • Type: Text , Conference paper
  • Relation: 8th International Conference on Technology and Energy Management, ICTEM 2023, Babol, Iran, 8-9 February 2023, 8th International Conference on Technology and Energy Management, ICTEM 2023
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  • Description: Common-grounded multi-level inverters with the input voltage boost ability are more common for grid-tied PV system applications. In this paper, a new five-level inverter topology is introduced that can generate a five-level voltage at the output stage, statically increase the input voltage, fully transfer the reactive power, eliminate the leakage current due to common-grounded capability, reduce the number of the power electronic components, self-balancing capability of switched capacitors voltage based on operation modes, and equality of switched capacitors voltage. The operation of the proposed inverter along with its comparison with other similar inverter topologies and the simulation results of the proposed inverter under different loads are demonstrates in this paper. The results indicate that the performance of the proposed inverter in the grid-connected PV system is best than other compared topologies. Also, experimental results with a prototype have been presented, which validates the simulation results and proves the applicability of the proposed inverter in grid-tied PV systems. © 2023 IEEE.

A robust ensemble regression model for reconstructing genetic networks

  • Authors: Gamage, Hasini , Chetty, Madhu , Lim, Suryani , Hallinan, Jennifer , Nguyen, H.
  • Date: 2023
  • Type: Text , Conference paper
  • Relation: 2023 International Joint Conference on Neural Networks, IJCNN 2023 Vol. 2023-June
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  • Description: Genetic networks contain important information about biological processes, including regulatory relationships and gene-gene interactions. Numerous methods, using high-dimensional gene expression data have been developed to capture these interactions. These gene expression data, generated using high-throughput technologies, are prone to noise. However, most existing network inference methods are unable to cope with noisy data, making genetic network reconstruction challenging. In this paper, we propose a novel ensemble regression model combining quantile regression and cross-validated Ridge regression, RidgeCV, to infer interactions from noisy gene expression data. The application of quantile regression to GRN inference is novel, and its design makes it appropriate for noisy data. RidgeCV also addresses other important issues, such as data overfitting and multicollinearity. First, each regression method is independently applied to gene expression data and the output of these methods, in the form of ranked gene lists, is aggregated using a novel gene score-based method by considering the gene rank and model importance. The model importance score is evaluated based on an adjusted coefficient of determination. This method implicitly includes majority voting by averaging each gene score value across all models. The proposed model was tested on the DREAM4 datasets and publicly available small-scale real-world network datasets. Experiments with noisy datasets showed that the proposed ensemble model is more accurate and efficient than other state-of-the-art methods. © 2023 IEEE.

A study into the impact of data breaches of electronic health records

A survey on image classification of lightweight convolutional neural network

  • Authors: Liu, Ying , Xiao, Peng , Fang, Jie , Zhang, Dengsheng
  • Date: 2023
  • Type: Text , Conference paper
  • Relation: 19th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2023, Harbin, China, 29-31 July 2023, 2023 19th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)
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  • Description: In recent years, deep neural networks have achieved tremendous success in image classification in both academic and industrial settings. However, the high hardware requirements imposed by their intensive and complex computations pose a challenge for deployment on low-storage devices. To address this challenge, lightweight networks provide a viable solution. This paper provides a detailed review of recent lightweight image classification algorithms, which can be categorized into low-redundancy network model design and neural network compression algorithms. The former reduces network computations by replacing traditional convolution with efficient lightweight convolution, while the latter reduces redundancy in the network by employing methods such as network pruning, knowledge distillation, and parameter quantization. We summarize the experimental results of some classical models and algorithms on ImageNet2012 and CIFAR-10 datasets, and analyze the characteristics, advantages and disadvantages of these models respectively. Finally, future research directions for lightweight algorithms in the field of image classification are identified. © 2023 IEEE.

Artificial intelligence enabled digital twin for predictive maintenance in industrial automation system : a novel framework and case study

  • Authors: Siddiqui, Mustafa , Kahandawa, Gayan , Hewawasam, Hasitha
  • Date: 2023
  • Type: Text , Conference paper
  • Relation: 2023 IEEE International Conference on Mechatronics, ICM 2023, Leicestershire UK, 15-17 March 2023, Proceedings - 2023 IEEE International Conference on Mechatronics, ICM 2023
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  • Description: Industrial automation systems are excessively used in advanced manufacturing environments. These systems are always prone to failure which not only disturbs smooth manufacturing operations but can also cause injuries to operators. Therefore, in this research, a novel predictive maintenance algorithm is proposed that can be used to detect anomalies in the automation system to avoid asset failure. Artificial Intelligence enabled Digital Twin model was used to detect early anomalies to avoid catastrophic effects of equipment failure. Real-time sensor data were used to validate the proposed novel algorithm. The data were recorded via sensors mounted on the physical system. This paper presents the effectiveness of the proposed algorithm to detect anomalies in industrial automation systems under faulty conditions. © 2023 IEEE.

Bilateral insider threat detection : harnessing standalone and sequential activities with recurrent neural networks

Causal deep operator networks for data-driven modeling of dynamical systems

  • Authors: Nghiem, Truong , Nguyen, Thang , Nguyen, Binh , Nguyen, Linh
  • Date: 2023
  • Type: Text , Conference paper
  • Relation: 2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023, Hybrid, Honolulu, 1-4 October 2023, Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics p. 1136-1141
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  • Description: The deep operator network (DeepONet) architecture is a promising approach for learning functional operators, that can represent dynamical systems described by ordinary or partial differential equations. However, it has two major limitations, namely its failures to account for initial conditions and to guarantee the temporal causality - a fundamental property of dynamical systems. This paper proposes a novel causal deep operator network (Causal-DeepONet) architecture for incorporating both the initial condition and the temporal causality into data-driven learning of dynamical systems, overcoming the limitations of the original DeepONet approach. This is achieved by adding an independent root network for the initial condition and independent branch networks conditioned, or switched on/off, by time-shifted step functions or sigmoid functions for expressing the temporal causality. The proposed architecture was evaluated and compared with two baseline deep neural network methods and the original DeepONet method on learning the thermal dynamics of a room in a building using real data. It was shown to not only achieve the best overall prediction accuracy but also enhance substantially the accuracy consistency in multistep predictions, which is crucial for predictive control. © 2023 IEEE.

Clinically prioritized data visualization in remote patient monitoring

Comprehensive analysis of feature extraction techniques and runtime performance evaluation for phishing detection

Data-efficient graph learning meets ethical challenges

Distributed formation trajectory planning for multi-vehicle systems

  • Authors: Nguyen, Binh , Nghiem, Truong , Nguyen, Linh , Nguyen, Tung , La, Hung , Sookhak, Mehdi , Nguyen, Thang
  • Date: 2023
  • Type: Text , Conference paper
  • Relation: 2023 American Control Conference, ACC 2023 Vol. 2023-May, p. 1325-1330
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  • Description: This paper addresses the problem of distributed formation trajectory planning for multi-vehicle systems with collision avoidance among vehicles. Unlike some previous distributed formation trajectory planning methods, our proposed approach offers great flexibility in handling computational tasks for each vehicle when the global formation of all the vehicles changes. It affords the system the ability to adapt to the computational capabilities of the vehicles. Furthermore, global formation constraints can be handled at any selected vehicles. Thus, any formation change can be effectively updated without recomputing all local formations at all the vehicles. To guarantee the above features, we first formulate a dynamic consensus-based optimization problem to achieve desired formations while guaranteeing collision avoidance among vehicles. Then, the optimization problem is effectively solved by ADMM-based or alternating projection-based algorithms, which are also presented. Theoretical analysis is provided not only to ensure the convergence of our method but also to show that the proposed algorithm can surely be implemented in a fully distributed manner. The effectiveness of the proposed method is illustrated by a numerical example of a 9-vehicle system. © 2023 American Automatic Control Council.

Dynamic trust boundary identification for the secure communications of the entities via 6G

  • Authors: Basri, Rabeya , Karmakar, Gour , Kamruzzaman, Joarder , Newaz, S. H. Shah , Nguyen, Linh , Usman, Muhammad
  • Date: 2023
  • Type: Text , Conference paper
  • Relation: 18th International Conference on Information Security Practice and Experience (ISPEC), 24-25 August 2023, Copenhagen, Denmark, International Conference on Information Security Practice and Experience: 18th International Conference, ISPEC 2023, Copenhagen, Denmark, August 24–25, 2023, Proceedings Vol. 14341, p. 194-208
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  • Description: 6G is more likely prone to a range of known and unknown cyber-attacks because of its highly distributive nature. Current literature and research prove that a trust boundary can be used as a security door (e.g., gateway/firewall) to validate entities and applications attempting to access 6G networks. Trust boundaries allow these entities to connect or work with entities of other trust boundaries via 6G by dynamically monitoring their interactions, behaviors, and data transmissions. The importance of trust boundaries in security protection mechanisms demands a dynamic multi-trust boundary identification. There exists an automatic trust boundary identification for IoT data. However, it is a binary trust boundary classification and the dataset used in the approach is not suitable for dynamic trust boundary identification. Motivated by these facts, to provide automatic security protection for entities in 6G, in this paper, we propose a mechanism to identify dynamic and multiple trust boundaries based on trust values and geographical location coordinates of 6G communication entities. Our proposed mechanism uses unsupervised clustering and splitting and merging techniques. The experimental results show that entities can dynamically change their boundary location if their trust values and locations change over time. We also analyze the trust boundary identification accuracy in terms of our defined two performance metrics, i.e., trust consistency and the degree of gateway coverage. The proposed scheme allows us to distinguish between entities and control their access to the 6G network based on their trust levels to ensure secure and resilient communication.

Effect of wood/binder ratio, slag/binder ratio, and alkaline dosage on the compressive strength of wood-geopolymer composites

  • Authors: Gigar, Firensenay , Khennane, Amar , Liow, Jong-leng , Tekle, Biruk
  • Date: 2023
  • Type: Text , Conference paper
  • Relation: International Symposium of the International Federation for Structural Concrete, fib Symposium 2023, Istanbul, 5-7 June 2023, Building for the Future: Durable, Sustainable, Resilient: Vol. 349 LNCE, p. 658-667
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  • Description: The impact of building construction on the environment is significant. Occupying large land areas (urban footprint), buildings are one of the most important consumers of resources and raw materials. They are responsible for 38% of greenhouse gas (GHG) emissions in both developed and developing countries. Therefore, incorporating sustainability and resilience into all aspects of urban infrastructure has become necessary. To curb emissions, part of the answer lies in the use of construction and building materials made from recycled materials. Bio-sourced materials, like wood chips, combined with a cementitious matrix, offer an alternative to conventional materials. They are sustainable, lightweight, and have good thermal insulation. However, because of their inferior mechanical strength, they have limited use as load-bearing structural parts. Furthermore, the use of Portland cement as a binder still poses some challenges due to its high carbon footprint. This study investigates the potential of wood-geopolymer composites for better mechanical performance and environmental sustainability. A 6x2x2x2 fractional factorial-based experimental design was used to simultaneously study the effect of slag content, wood binder ratio, and alkaline on the compressive strength of the wood-geopolymer composite. The experiments showed encouraging results for developing ambient cured wood geopolymer composites. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Efficient graph learning for anomaly detection systems

Elastic step DDPG : multi-step reinforcement learning for improved sample efficiency

  • Authors: Ly, Adrian , Dazeley, Richard , Vamplew, Peter , Cruz, Francisco , Aryal, Sunil
  • Date: 2023
  • Type: Text , Conference paper
  • Relation: 2023 International Joint Conference on Neural Networks, IJCNN 2023 Vol. 2023-June
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  • Description: A major challenge in deep reinforcement learning is that it requires more data to converge to an policy for complex problems. One way to improve sample efficiency is to use n-step updates to reduce the number of samples required to converge to a good policy. However n-step updates are known to be brittle and difficult to tune. Elastic Step DQN has shown that it is possible to automate the value of n in DQN to solve problems involving discrete action spaces, however the efficacy of the technique when applied on more complex problems and against problems with continuous action spaces is yet to be shown. In this paper we adapt the innovations proposed by Elastic Step DQN onto the DDPG algorithm and show empirically that Elastic Step DDPG is able to achieve a much stronger final training policy and is more sample efficient than DDPG. © 2023 IEEE.

Embodied carbon footprint analysis of signage industry : insights from two case studies

  • Authors: Paresi, Prudvireddy , Javidan, Fatemeh , Sparks, Paul
  • Date: 2023
  • Type: Text , Conference paper
  • Relation: International Conference on Green Building, ICoGB 2023, Malmo, Sweden, 19-21 May 2023, Proceedings of 2023 International Conference on Green Building p. 69-76
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  • Description: Embodied carbon has recently become a hot topic among environmentalists and designers, especially after the Paris Agreement on climate change. Embodied carbon refers to the carbon emissions associated with the manufacturing and transportation of building materials and the process of construction. The “Global Status Report for Buildings and Construction” report estimated that the building and construction sector alone contributed nearly 37–39% of global carbon emissions in 2017–2020. To tackle embodied carbon, the World Green Building Council (WorldGBC) has set a bold vision to reduce it by at least 40% by 2030 and achieve net-zero operating carbon in all new buildings. The signage industry plays a significant role in the building industry, as signages are a key component of buildings. Signages serve multiple purposes, such as providing information, enhancing brand identity, and promoting safety. Therefore, it is essential to understand the embodied carbon emissions associated with signage materials used to minimise the overall carbon emissions of construction projects. The present paper aims to study the embodied carbon footprint of the signage industry with the help of two case studies. The embodied carbon factors required while estimating the overall footprint of the signages are taken from Environmental Performance in Construction (EPiC) database. The study identifies the aluminum as the major contributor of the embodied emissions in the signage projects. This study provides insight into the other sources of embodied carbon and makes more informed decisions while selecting signage materials used in designs to create sustainable and economic projects. This information helps to increase sustainability and reduce the carbon footprint of signage projects in the early decision-making stages. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.