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Third party data service providers can enhance patient-provider interactions : insights from a Delphi study

- Hashmi, Mustafa, McInnes, Angelique, Stranieri, Andrew, Sahama, Tony

  • Authors: Hashmi, Mustafa , McInnes, Angelique , Stranieri, Andrew , Sahama, Tony
  • Date: 2022
  • Type: Text , Conference paper
  • Relation: 2022 Australasian Computer Science Week, ACSW 2022, Virtual, Online, 14-17 February 2022, ACM International Conference Proceeding Series p. 224-228
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  • Description: Data sharing between financial services organisations has led to a proliferation of third party data service providers that are not parties to transactions but facilitate interactions between them by analysing, manipulating or storing data related to transactions. This has led to widespread legal, technological and sociocultural changes in that sector broadly described as Open-Banking initiatives. Third party service providers have not emerged in the healthcare sector in the same way. This study reports preliminary results of a Delphi study comprising healthcare and financial experts to explore the extent to which third party providers in healthcare is beneficial and feasible. Ensuring the quality of data service provided by third parties was seen to be a critical success factor. A causal loop model was used to describe the inter-dependent factors underpinning this factor. Further investigations to augment the model with Consumer Data Rights and validate empirically are underway. © 2022 ACM.

Ultimate pit limit optimization by computerized and manual methods for Dadiin Khar Tolgoi – 2 coal mine – a case study

- Purevdavaa, Tuvshinzaya, Khandelwal, Manoj

  • Authors: Purevdavaa, Tuvshinzaya , Khandelwal, Manoj
  • Date: 2022
  • Type: Text , Conference paper
  • Relation: International Conference on Geotechnical challenges in Mining, Tunneling and Underground structures, ICGMTU 2021, Virtual, online, 20-21 December 2021, Lecture Notes in Civil Engineering Vol. 228, p. 97-116
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  • Description: Open-pit mine design and long-term planning are crucial parts of the mining industry. It contains the technical plan that must be followed from the beginning of mine development to mine closure. Every deposit can be mined entirely, however, many things need to be carefully considered before mining the whole deposit. An optimal pit outline is one of the most important parameters that affect both the economy and the safety of mining. Therefore, the purpose of this study is to determine the effectiveness of an optimal pit outline by using both software and hand methods based on cost and revenue. This study investigates Dadiin-Khar Tolgoi2’s optimal pit outline and design to maximize the profit of using Surpac and Whittle software. The study compares the economic gain of both entirely extracted proved (B) and probable (C) reserve mines that were designed on the Surpac software and the hand technique of determining optimal pit outline. Whittle software computed that Pit Shell – 27 of Dadiin-Khar Tolgoi – 2 is the best pit outline regarding profit and safety. Besides, entirely mined Pit outline –B and Pit outline – C gives the loss in economics both

Using analogical proportions for explanations

- Lim, Suryani, Prade, Henri, Richard, Gilles

  • Authors: Lim, Suryani , Prade, Henri , Richard, Gilles
  • Date: 2022
  • Type: Text , Conference paper
  • Relation: 15th International Conference on Scalable Uncertainty Management, SUM 2022, Paris, France, 17-19 October 2022, Scalable Uncertainty Management: 15th International Conference, SUM 2022, Paris, France, October 17-19, 2022 Proceedings Vol. 13562 LNAI, p. 309-325
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  • Description: In this article, we offer an introduction to the notion of analogical explanations. Because analogical reasoning is a widely used type of reasoning, we take the view that analogy-based explanations will be acceptable for humans. The cornerstone of the approach is the concept of analogical proportion (i.e., statements of the form “a is to b as c is to d”), comparing 2 pairs of items. Analogical proportions are not simply based on similarity but also involve differences between items. The approach applies to the explanation of the label of an item in a repository, whether the couple (item, label) belongs to a sample of a given population or the label is predicted via an algorithm. The output can be in terms of abductive/factual explanations (answering a “why?” question and providing examples having the same label) or contrastive/counter-factual (answering a “why not?” question and providing examples having a different label). For preliminary experiments, we build Boolean data sets where relevant attributes are known. Our results show that analogical proportion-based explanations can be effective. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Validation framework of bayesian networks in asset management decision-making

- Morey, Stephen, Chattopadhyay, Gopinath, Larkins, Jo-ann

  • Authors: Morey, Stephen , Chattopadhyay, Gopinath , Larkins, Jo-ann
  • Date: 2022
  • Type: Text , Conference paper
  • Relation: International Congress and Workshop on Industrial AI, IAI 2021, Virtual online, 6-7 October 2021, published in Lecture Notes in Mechanical Engineering p. 360-369
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  • Description: Capital-intensive industries are under increasing pressure from capital constraints to extend the life of long-life assets and to defer asset renewals. Assets in most of those industries have complex life-cycle management challenges in aspects of design, manufacture, maintenance and service contracts, the usage environment, and changes in support personnel over the asset life. A significant challenge is the availability and quality of relevant data for informed decision-making in assuring reliability, availability and safety. There is a need for better-informed maintenance decisions and cost-effective interventions in managing the risk and assuring performance of those assets. Bayesian networks have been considered in asset management applications in recent years for addressing these challenges, by modelling of reliability, maintenance decisions, life extension and prognostics, across a wide range of technological domains of complex assets. However, models of long-life assets are challenging to validate, particularly due to issues with data scarcity and quality. A literature review on Bayesian networks in asset management in this paper shows that there is a need for further work in this area. This paper discusses the issues and challenges of validation of Bayesian network models in asset management and draws on findings from literature research to propose a preliminary validation framework for Bayesian network models in life-cycle management applications of capital-intensive long-life assets. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Vulnerability assessment framework for a smart grid

- Rashed, Muhammad, Kamruzzaman, Joarder, Gondal, Iqbal, Islam, Syed

  • Authors: Rashed, Muhammad , Kamruzzaman, Joarder , Gondal, Iqbal , Islam, Syed
  • Date: 2022
  • Type: Text , Conference paper
  • Relation: 4th IEEE Global Power, Energy and Communication Conference, GPECOM 2022, Cappadocia, Turkey, 14-17 June 2022, Proceedings - 2022 IEEE 4th Global Power, Energy and Communication Conference, GPECOM 2022 p. 449-454
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  • Description: The increasing demand for the interconnected IoT based smart grid is facing threats from cyber-attacks due to inherent vulnerability in the smart grid network. There is a pressing need to evaluate and model these vulnerabilities in the network to avoid cascading failures in power systems. In this paper, we propose and evaluate a vulnerability assessment framework based on attack probability for the protection and security of a smart grid. Several factors were taken into consideration such as the probability of attack, propagation of attack from a parent node to child nodes, effectiveness of basic metering system, Kalman estimation and Advanced Metering Infrastructure (AMI). The IEEE-300 bus smart grid was simulated using MATPOWER to study the effectiveness of the proposed framework by injecting false data injection attacks (FDIA); and studying their propagation. Our results show that the use of severity assessment standards such as Common Vulnerability Scoring System (CVSS), AMI measurements and Kalman estimates were very effective for evaluating the vulnerability assessment of smart grid in the presence of FDIA attack scenarios. © 2022 IEEE.

A design of haptic hand exoskeleton for virtual reality applications

- Le, Duy, Nguyen, Linh

A Fast and Reliable Blocked Bus Bar Protection Scheme Leveraging on Sampled Value and GOOSE Protection based on IEC 61850 Architecture

- Kumar, Shantanu, Abu-Siada, Ahmed, Das, Narottam, Islam, Syed

  • Authors: Kumar, Shantanu , Abu-Siada, Ahmed , Das, Narottam , Islam, Syed
  • Date: 2021
  • Type: Text , Conference paper
  • Relation: 31st Australasian Universities Power Engineering Conference, AUPEC 2021, Virtual, Online 26 to 30 September 2021, Proceedings of 2021 31st Australasian Universities Power Engineering Conference, AUPEC 2021
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  • Description: Substation Automation System (SAS) is in a matured state and promises to replace conventional protection scheme. It has created a new paradigm in its application for future smart grids. The multifunction features of IEC 61850 facilitate this technology to reliably monitor, protect and control critical assets. Mostly agree, the intelligent electronic devices (IEDs) and substation automation features have the same effect as 'html' had in developing web technology on IT system in 1990's. However, in order to be acceptable completely, this technology needs to gain confidence of end user, it needs to be researched further using multivendor equipment and experimented in a network comprising of complex secondary protection schemes. In this paper, a practical Blocked Busbar Protection (BBP) scheme have been implemented and tested for a single bus bar scheme, leveraging on Sampled Values (SV) and Generic Object Oriented Substation Event (GOOSE) at laboratory to prove its reliability and improvement in technology. This experiment has been carried out for three scenarios in a simulated substation network related to a process plant industry. Furthermore, this laboratory-based experiment with multivendor IEDs evaluated performance of an Ethernet and FO based process bus network to validate overall performance of BBP protection scheme. It concluded with a comparison between digital and conventional protection scheme with recommendation for future applications. © 2021 IEEE.

A secured real-time IoMT application for monitoring isolated COVID-19 patients using edge computing

- Balasubramanian, Venki, Sulthana, Rehena, Stranieri, Andrew, Manoharan, G., Arora, Teena, Srinivasan, Ram, Mahalakshmi, K., Menon, Varun

A study on the use of machine learning methods to improve reciprocating compressor reliability via torque tailoring

- Lu, Kui, Sultan, Ibrahim, Phung, Truong

  • Authors: Lu, Kui , Sultan, Ibrahim , Phung, Truong
  • Date: 2021
  • Type: Text , Conference paper
  • Relation: 2021 International Conference on Maintenance and Intelligent Asset Management, ICMIAM 2021, Ballarat, Australia, 12-15 December 2021, 2021 International Conference on Maintenance and Intelligent Asset Management, ICMIAM 2021
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  • Description: Reciprocating compressors have found popularity in applications where compressed air is required at high pressure levels with moderate flow rates. The mechanical drives used for these compressors are based on the traditional slider-crank linkage which, despite its simplicity, does not lend itself to optimization effort aimed at improving the compressor reliability and performance. The work presented in this paper adopts the notion that the mechanical reliability of the compressor drive is limited by the level and cyclical variability of the loads transmitted through its members and the effectiveness of its cooling system. In the paper, machine learning methods will be employed to craft an objective function suitable to use in a Bayesian optimization effort undertaken to produce a more reliable compressor drive. A numerical example is presented to prove the validity of the presented method and its suitability for use in real life compressor designs. © 2021 IEEE.

A3Graph : adversarial attributed autoencoder for graph representation learning

- Hou, Mingliang, Wang, Lei, Liu, Jiaying, Kong, Xiangjie, Xia, Feng

  • Authors: Hou, Mingliang , Wang, Lei , Liu, Jiaying , Kong, Xiangjie , Xia, Feng
  • Date: 2021
  • Type: Text , Conference paper
  • Relation: 36th Annual ACM Symposium on Applied Computing, SAC 2021 p. 1697-1704
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  • Description: Recent years have witnessed a proliferation of graph representation techniques in social network analysis. Graph representation aims to map nodes in the graph into low-dimensional vector space while preserving as much information as possible. However, most existing methods ignore the robustness of learned latent vectors, which leads to inferior representation results due to sparse and noisy data in graphs. In this paper, we propose a novel framework, named A3Graph, which aims to improve the robustness and stability of graph representations. Specifically, we first construct an aggregation matrix by the combining positive point-wise mutual information matrix with the attribute matrix. Then, we enforce the autoencoder to reconstruct the aggregation matrix instead of the input attribute matrix. The enhancement autoencoder can incorporate structural and attributed information in a joint learning way to improve the noise-resilient during the learning process. Furthermore, an adversarial learning component is leveraged in our framework to impose a prior distribution on learned representations has been demonstrated as an effective mechanism in improving the robustness and stability in representation learning. Experimental studies on real-world datasets have demonstrated the effectiveness of the proposed A3Graph. © 2021 ACM.

Adaptive Dynamic Programming based Control Scheme for Uncertain Two-Wheel Robots

- Van Nguyen, Thien, Le, Hai, Tran, Hoang, Nguyen, Duc, Nguyen, Minh, Nguyen, Linh

An efficient adaptive sampling approach for mobile robotic sensor networks using proximal ADMM

- Le, Viet-Anh, Nguyen, Linh, Nghiem, Truong

  • Authors: Le, Viet-Anh , Nguyen, Linh , Nghiem, Truong
  • Date: 2021
  • Type: Text , Conference paper
  • Relation: 2021 American Control Conference, ACC 2021 Vol. 2021-May, p. 1101-1106
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  • Description: Adaptive sampling in a resource-constrained mobile robotic sensor network for monitoring a spatial phenomenon is a fundamental but challenging problem. In applications where a Gaussian Process is employed to model a spatial field and then to predict the field at unobserved locations, the adaptive sampling problem can be formulated as minimizing the negative log determinant of a predicted covariance matrix, which is a non-convex and highly complex function. Consequently, this optimization problem is typically addressed in a grid-based discrete domain, although it is combinatorial NP-hard and only a near-optimal solution can be obtained. To overcome this challenge, we propose using a proximal alternating direction method of multipliers (Px-ADMM) technique to solve the adaptive sampling optimization problem in a continuous domain. Numerical simulations using a real-world dataset demonstrate that the proposed PxADMM-based method outperforms a commonly used grid-based greedy method in the final model accuracy. © 2021 American Automatic Control Council.

An efficient approach for SIMO systems using adaptive fuzzy hierarchical sliding mode control

- Van Nguyen, Thien, Le, Hai, Tran, Hoang, Nguyen, Duc, Nguyen, Minh, Nguyen, Linh

  • Authors: Van Nguyen, Thien , Le, Hai , Tran, Hoang , Nguyen, Duc , Nguyen, Minh , Nguyen, Linh
  • Date: 2021
  • Type: Text , Conference paper
  • Relation: 2021 IEEE International Conference on Autonomous Robot Systems and Competitions, ICARSC 2021 p. 85-90
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  • Description: The paper addresses the problem of efficiently controlling a class of single input multiple output (SIMO) under-Actuated robotic systems such as a two dimensional inverted pendulum cart or a two dimensional overhead crane. It is first proposed to employ the hierarchical sliding mode control approach to design a control law, which guarantees stability and anti-swing of the vehicle when it is driven on a predefined trajectory. More importantly, the unknown and uncertain parameters of the system caused by its actuator nonlinearity and external disturbances are adaptively estimated and inferred by the proposed fuzzy logic mechanism, which results in the efficient operation of the SIMO under-Actuated system in real time. The proposed algorithm was then implemented in the synthetic environment, where the obtained results demonstrate its effectiveness. © 2021 IEEE.

An efficient boolean modelling approach for genetic network inference

- Gamage, Hasini, Chetty, Madhu, Shatte, Arian, Hallinan, Jennifer

  • Authors: Gamage, Hasini , Chetty, Madhu , Shatte, Arian , Hallinan, Jennifer
  • Date: 2021
  • Type: Text , Conference paper
  • Relation: 2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2021, Virtual, Online, 13-15 October 2021, 2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2021
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  • Description: The inference of Gene Regulatory Networks (GRNs) from time series gene expression data is an effective approach for unveiling important underlying gene-gene relationships and dynamics. While various computational models exist for accurate inference of GRNs, many are computationally inefficient, and do not focus on simultaneous inference of both network topology and dynamics. In this paper, we introduce a simple, Boolean network model-based solution for efficient inference of GRNs. First, the microarray expression data are discretized using the average gene expression value as a threshold. This step permits an experimental approach of defining the maximum indegree of a network. Next, regulatory genes, including the self-regulations for each target gene, are inferred using estimated multivariate mutual information-based Min-Redundancy Max-Relevance Criterion, and further accurate inference is performed by a swapping operation. Subsequently, we introduce a new method, combining Boolean network regulation modelling and Pearson correlation coefficient to identify the interaction types (inhibition or activation) of the regulatory genes. This method is utilized for the efficient determination of the optimal regulatory rule, consisting AND, OR, and NOT operators, by defining the accurate application of the NOT operation in conjunction and disjunction Boolean functions. The proposed approach is evaluated using two real gene expression datasets for an Escherichia coli gene regulatory network and a fission yeast cell cycle network. Although the Structural Accuracy is approximately the same as existing methods (MIBNI, REVEAL, Best-Fit, BIBN, and CST), the proposed method outperforms all these methods with respect to efficiency and Dynamic Accuracy. © 2021 IEEE.

An efficient multi-vehicle routing strategy for goods delivery services

- Le, Duy, Men, Ying, Luo, Yunkang, Zhou, Yixuan, Nguyen, Linh

  • Authors: Le, Duy , Men, Ying , Luo, Yunkang , Zhou, Yixuan , Nguyen, Linh
  • Date: 2021
  • Type: Text , Conference paper
  • Relation: 2021 IEEE International Conference on Advanced Robotics and Its Social Impacts, ARSO 2021 Vol. 2021-July, p. 188-193
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  • Description: The paper addresses the problem of efficiently planning routes for multiple ground vehicles used in goods delivery services. Given popularity of today's e-commerce, particularly under the COVID-19 pandemic conditions, goods delivery services have been booming than ever, dominated by small-scaled (electric) bikes and promised by autonomous vehicles. However, finding optimal routing paths for multiple delivery vehicles operating simultaneously in order to minimize transportation cost is a fundamental but challenging problem. In this paper, it is first proposed to exploit the mixed integer programming paradigm to model the delivery routing optimization problem (DROP) for multiple simultaneously-operating vehicles given their energy constraints. The routing optimization problem is then solved by the multi-chromosome genetic algorithm, where the number of delivery vehicles can be optimized. The proposed approach was evaluated in a realworld experiment in which goods were expected to be delivered from a depot to 26 suburb locations in Canberra, Australia. The obtained results demonstrate effectiveness of the proposed algorithm. © 2021 IEEE.

An IIoT-Based Networked Industrial Control System Architecture to Secure Industrial Applications

- Sen, Sachin, Song, Lei

  • Authors: Sen, Sachin , Song, Lei
  • Date: 2021
  • Type: Text , Conference paper
  • Relation: 2nd IEEE Industrial Electronics and Applications Conference, IEACon 2021, Virtual, Online,22-23 November 2021, IEACon 2021 - 2021 IEEE Industrial Electronics and Applications Conference p. 280-285
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  • Description: Behind the great success of the current internet, Open Systems Interconnect (OSI) and Transport Control Protocol/Internet Protocol (TCP/IP) standards play the most important role. Whereas, due to a lack of standard architectures, industrial internet is lagging behind. This makes industrial internet applications experience increased security risks due to their integration with the information technology and exposure to the public internet. In this research, we propose a layered architecture for industrial internet of things (IIoT) based networked industrial control systems (n-ICS). Layer-wise functionality of this architecture could be useful in identifying necessary security protocols for each layer. Subsequently, this might assist in allocating resources towards the secure operation of industrial applications. To validate the proposed architecture, we modelled a water flow control system, where we demonstrated a data deception attack on its operation at the physical layer. This demonstration validates that from within the close proximity of networked control systems, threat actors can launch possible attacks to deceive physical industrial applications. Our proposed system includes a network communication architecture and a corresponding security architecture aligning with the network architecture. This will facilitate the design of security suites and/or the allocation of security resources on the basis of layered network functionalities. © 2021 IEEE.

An inspection robot for detecting and tracking welding seam

- Giang, Huynh, Anh, Nguyen, Quang, Nguyen, Nguyen, Linh

  • Authors: Giang, Huynh , Anh, Nguyen , Quang, Nguyen , Nguyen, Linh
  • Date: 2021
  • Type: Text , Conference paper
  • Relation: 2021 Innovations in Intelligent Systems and Applications Conference, ASYU 2021, Elazig, Turkey, 6 to 8 October 2021, Proceedings - 2021 Innovations in Intelligent Systems and Applications Conference, ASYU 2021
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  • Description: The paper introduces a new inspection robotic system for effectively detecting and tracking welding seam on a metal surface. Frequently inspecting and assessing conditions of weld joints are critical in many ageing infrastructures. Given advancement in the robotic research, it is proposed to develop an autonomous inspection system where a robot with an embedded camera sensor is used to conduct the inspections. More specifically, images of a metal surface are in situ captured and then processed by exploiting the state-of-the-art computer vision algorithms, which results in location of the weld on the corresponding surface. Information of the welding seam is then forwarded to the controllers of the robot to accurately drive it along the weld. Moreover, the wheels of the robot are fabricated with magnets that allows it to operate on different surfaces positioned at different inclination angles. The fully developed robot was experimentally evaluated in a real-world welded steel plate, where the obtained results are highly promising. © 2021 IEEE.

An online peer assessment method in computational-based engineering courses : combining theoretical and computer tools

- Javidan, Fatemeh

  • Authors: Javidan, Fatemeh
  • Date: 2021
  • Type: Text , Conference paper
  • Relation: 9th Research in Engineering Education Symposium and 32nd Australasian Association for Engineering Education Conference: Engineering Education Research Capability Development, REES AAEE 2021, Perth, Australia, 5-8 December 2021, 9th Research in Engineering Education Symposium and 32nd Australasian Association for Engineering Education Conference, REES AAEE 2021: Engineering Education Research Capability Development Vol. 1, p. 282-289
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  • Description: CONTEXT As its name suggests, peer assessment involves each student to take active part in providing feedback and in some cases, evaluation of the learning outcome of their peers. Peer assessment can introduce several advantages to the learning process such as increasing the student motivation, critical thinking and development of qualitative and quantitative arguments. This research proposes a peer assessment method for computational-based assignments and describes the process of implementing it in an online “Structural Analysis” subject. PURPOSE OR GOAL Some experts have criticised peer assessment procedures and questioned the ability of students to provide reliable evaluation. There are concerns raised in the literature on the usefulness and reliability of student peer reviews including inconsistency in the feedback and quantitative assessment marks. This study aims to implement an online tool to increase the engagement and partnership of students in the virtual environment and replace some of the lengthy computational processes with computer-based tools. The goal of the proposed method is also to increase the reliability of peer assessment activity by providing evidence-based evaluations. APPROACH OR METHODOLOGY/METHODS The design of the peer assessment task has been implemented in a second year “Structural Analysis” subject on the topic of “Analysing Indeterminate Structures”. The delivery of this task was examined in a methodological approach as well as an executional approach. In the methodological approach, benefits were investigated, and comparison was made with previous peer-assessment procedures. The execution of this task which includes a combination of manual calculations and computer methods is outlined using available LMS tools. ACTUAL OR ANTICIPATED OUTCOMES The method proposed in this study introduces benefits to student learning and engagement in theoretical computational-based topics. This method is built on suggestions to mitigate some of the downsides of peer assessment reported in previous literature. For instance, to escape the double volume of computational effort, to reduce the reluctancy of students and to eliminate the potential errors they make in evaluating the computational work of their peers, the assessment phase is proposed to be done using a “Structural Analysis” computer software. CONCLUSIONS/RECOMMENDATIONS/SUMMARY Online peer assessment combining theoretical methods and computer-based approaches has provided a means to overcome some of the shortcomings traditionally associated with this approach. These improvements include an increase in the level of consistency and reliability of peer-assessment results compared to traditional approaches. The implementation of the method also shows approximately 25% increase in student active participation. Copyright © Fatemeh Javidan, 2021.

Analogies between sentences : theoretical aspects - preliminary experiments

- Afantenos, Stergos, Kunze, Tarek, Lim, Suryani, Prade, Henri, Richard, Gilles

  • Authors: Afantenos, Stergos , Kunze, Tarek , Lim, Suryani , Prade, Henri , Richard, Gilles
  • Date: 2021
  • Type: Text , Conference paper
  • Relation: 16th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty, ECSQARU 2021 Vol. 12897 LNAI, p. 3-18
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  • Description: Analogical proportions hold between 4 items a, b, c, d insofar as we can consider that “a is to b as c is to d”. Such proportions are supposed to obey postulates, from which one can derive Boolean or numerical models that relate vector-based representations of items making a proportion. One basic postulate is the preservation of the proportion by permuting the central elements b and c. However this postulate becomes debatable in many cases when items are words or sentences. This paper proposes a weaker set of postulates based on internal reversal, from which new Boolean and numerical models are derived. The new system of postulates is used to extend a finite set of examples in a machine learning perspective. By embedding a whole sentence into a real-valued vector space, we tested the potential of these weaker postulates for classifying analogical sentences into valid and non-valid proportions. It is advocated that identifying analogical proportions between sentences may be of interest especially for checking discourse coherence, question-answering, argumentation and computational creativity. The proposed theoretical setting backed with promising preliminary experimental results also suggests the possibility of crossing a real-valued embedding with an ontology-based representation of words. This hybrid approach might provide some insights to automatically extract analogical proportions in natural language corpora. © 2021, Springer Nature Switzerland AG.

ANSWER : generating information dissemination network on campus

- Qing, Qing, Guo, Teng, Zhang, Dongyu, Xia, Feng

  • Authors: Qing, Qing , Guo, Teng , Zhang, Dongyu , Xia, Feng
  • Date: 2021
  • Type: Text , Conference paper
  • Relation: 32nd Australasian Database Conference, ADC 2021 Vol. 12610 LNCS, p. 74-86
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  • Description: Information dissemination matters, both on an individual and group level. For college students who are physically and mentally immature, they are more sensitive and susceptible to unnormal information like rumors. However, current researches focus on large-scale online message sharing networks like Facebook and Twitter, rather than profile the information dissemination on campus, which fail to provide any references for daily campus management. Against this background, we propose a framework to generate the information dissemination network on campus, named ANSWER (cAmpus iNformation diSsemination netWork gEneRation), based on multimodal data including behavior data, appearance data, and psychological data. The construction of the ANSWER is listed as four steps. First, we use a convolutional autoencoder to extract the students’ facial features. Second, we process the behavior data to construct a friendship network. Third, heterogeneous information is embedded in the low-dimensional vector space by using network representation learning to obtain embedding vectors. Fourth, we use the deep learning model to predict. The experiment results show that ANSWER outperforms other methods in multiple feature fusion and prediction of information dissemination relationship performance. © 2021, Springer Nature Switzerland AG.