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Mechanical performance of tire-derived aggregate permeable pavements under live traffic loads

- Raeesi, Ramin, Soltani, Amin, King, Russell, Disfani, Mahdi

  • Authors: Raeesi, Ramin , Soltani, Amin , King, Russell , Disfani, Mahdi
  • Date: 2022
  • Type: Text , Conference paper
  • Relation: 4th International Conference on Transportation Geotechnics, ICTG 2021 Vol. 164, p. 515-528
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  • Description: Traditional pavements in urban areas are mainly rigid, impervious surfaces, resulting in augmented surface run off during rainfalls, thereby leading to flash-flooding and pollution of waterways. In comparison, permeable pavements permit percolation of water through surface layers, thus alleviating harmful environmental impacts. This study presents the authors’ recent experience in the development of an instrumented large-scale permeable pavement trial site—constructed using a combination of crushed rock (CR) and tire-derived aggregate (TDA), bonded by a polyurethane (PU) binder—located at a car park in South Australia. An area of approximately 400 m2 was paved using different TDA-based mix designs—different CR sizes, colors, and shapes, and different PU contents. Moreover, preliminary field performance monitoring results—including surface deflection measurements by the light weight deflectometer test, and strain measurements using optic fiber and strain gauge sensing techniques—are outlined and discussed in detail. The strength and stiffness of the TDA-based blend was found to be dependent on the TDA content, TDA-to-CR size ratio, CR shape, and PU content. The greater the angularity of the CR particles, the more effective the interlocking of the TDA and CR components, and thus the higher the developed strength and stiffness. The amount of strain generated in the permeable surface layer and at its interface with the lower screening layer were both found to decrease with a decrease in TDA content. Preliminary field observations indicate that the TDA-based system can indeed be a viable (and sustainable) solution to effectively mitigate flash-flooding while sustaining low–medium traffic loads. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

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 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 Exploratory Study on the Employers' Perceptions of ICT Graduate work-readiness

- Faisal, Nadia, Chadhar, Mehmood, Goriss-Hunter, Anitra, Stranieri, Andrew

  • Authors: Faisal, Nadia , Chadhar, Mehmood , Goriss-Hunter, Anitra , Stranieri, Andrew
  • Date: 2021
  • Type: Text , Conference paper
  • Relation: Pacific Asia Conference on Information Systems (PACIS 2021) 12th to 14th July 2021
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  • Description: Drawing on information gathered from scoping interviews with graduate recruiters and industry experts in Australia, this study extends our understanding of how employers, rather than researchers, describe the desired work-ready skills for graduate/entry level roles in the Australian information and communication technology (ICT) industry. Contrary to the developing literature on work-readiness, the findings showed that the skills which contribute to work-readiness should not be limited to field-specific knowledge, skills and cognitive skills, but that they should be extended to include affective skills or personal attributes and behaviors, such as selfefficacy, willingness to learn, disposition, tolerance and integrity. Results have practical implications for developing academic programs aimed at enhancing cognitive and affective skills among IT graduates for employment potential and successful transition into work.

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.

Choosing VET as a post-school activity: What are some influences on non-metropolitan students?

- Smith, Erica, Foley, Annette

  • Authors: Smith, Erica , Foley, Annette
  • Date: 2021
  • Type: Text , Conference paper
  • Relation: AVETRA 21 Virtual conference: recover, rethink, rebuild: all eyes on VET, 19-23 April 2021
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  • Description: This paper draws on data from recently-completed research funded by the Victorian Department of Education and Training (DET) and undertaken in the State of Victoria, in six non-metropolitan communities: three in rural/regional areas and three in peri-urban areas. The rationale for the research was that, despite decades of effort, education outcomes for rural and regional areas in Australia remain well under the Australian average (Napthine et al, 2019), partly because so many young people need to leave home to attend tertiary education (McKenzie, 2014). There is almost no specific research on peri-urban areas. For this paper we have extracted data, from selected phases of the project, specifically to find out why young people may or may not make VET choices. The method for this paper comprised analysis of data from each site, consisting of: • Interviews with VET-sector organisations; • ‘Snapshot surveys’, completed, prior to interviews and focus groups, by 80 young people in schools and 32 in their second-year out; • Publicly-available government ‘On-Track’ data (DET, 2018), of young people in their first year out of school. Recent related literature looks at VET choices in terms of the perceived and actual financial rewards of VET choices (e.g. Norton & Charastidtham, 2019); or in terms of the perceived status of VET choices (e.g. Billett, Choy & Hodge, 2019). Our research showed a complex picture with a number of factors (personal, environmental, cultural background and geographic) influencing choices; and also a perception that VET means apprenticeships, almost to the exclusion of traineeships or full-time VET. The agency of individual schools and of VET providers or apprenticeship organisations was also found to be important. The findings have clear implications for both policy and practice.

Cost effective annotation framework using zero-shot text classification

- Kasthuriarachchy, Buddhika, Chetty, Madhu, Shatte, Adrian, Walls, Darren

  • Authors: Kasthuriarachchy, Buddhika , Chetty, Madhu , Shatte, Adrian , Walls, Darren
  • Date: 2021
  • Type: Text , Conference paper
  • Relation: 2021 International Joint Conference on Neural Networks, IJCNN 2021 Vol. 2021-July
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  • Description: Manual and high-quality annotation of social media data has enabled companies and researchers to develop improved implementations using natural language processing. However, human text-annotation is expensive and time-consuming. Crowd-sourcing platforms such as Amazon's Mechanical Turk (MTurk) can be leveraged for the creation of large training corpora for text classification tasks using social media data. Nevertheless, the quality of annotations can vary significantly, based on the interpretations and motivations of annotators completing the tasks. Further, the labelling cost of data through MTurk will increase if target messages are small and having a significant amount of noise (e.g. promotional messages on Twitter). In this work, we propose a new annotation framework to create high-quality human-annotated datasets for text classification from social media data. We present a zero-shot text classification based pre-annotation technique reducing the adverse effects arising due to the highly skewed distribution of data across target classes. The proposed framework significantly reduces the cost and time while maintaining the quality of the annotations. Being generic, it can be applied to annotating text data from any discipline. Our experiment with a Twitter data annotation using the proposed annotation framework shows a cost reduction of 80% with no compromise to quality. © 2021 IEEE.

D-optimal design for information driven identification of static nonlinear elements

- Ulapane, Nalika, Thiyagarajan, Karthick, Kodagoda, Sarath, Nguyen, Linh

  • Authors: Ulapane, Nalika , Thiyagarajan, Karthick , Kodagoda, Sarath , Nguyen, Linh
  • Date: 2021
  • Type: Text , Conference paper
  • Relation: 16th IEEE Conference on Industrial Electronics and Applications, ICIEA 2021 p. 492-497
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  • Description: Identification of static nonlinear elements (i.e., nonlinear elements whose outputs depend only on the present value of inputs) is crucial for the success of system identification tasks. Identification of static nonlinear elements though can pose several challenges. Two of the main challenges are: (1) mathematical models describing the elements being unknown and thus requiring black-box identification; and (2) collection of sufficiently informative measurements. With the aim of addressing the two challenges, we propose in this paper a method of predetermining informative measurement points offline (i.e., prior to conducting experiments or seeing any measured data), and using those measurements for online model calibration. Since we deal with an unknown model structure scenario, a high order polynomial model is assumed. Over fit and under fit avoidance are achieved via checking model convergence via an iterative means. Model dependent information maximization is done via a D-optimal design of experiments strategy. Due to experiments being designed offline and being designed prior to conducting measurements, this method eases off the computation burden at the point of conducting measurements. The need for in-the-loop information maximization while conducting measurements is avoided. We conclude by comparing the proposed D-optimal design method with a method of in-the-loop information maximization and point out the pros and cons. The method is demonstrated for the single-input-single-output (SISO) static nonlinear element case. The method can be extended to MISO systems as well. © 2021 IEEE.

Decision behavior based private vehicle trajectory generation towards smart cities

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

  • Authors: Chen, Qiao , Ma, Kai , Hou, Mingliang , Kong, Xiangjie , Xia, Feng
  • Date: 2021
  • Type: Text , Conference paper
  • Relation: 18th International Conference on Web Information Systems and Applications, WISA 2021 Vol. 12999 LNCS, p. 109-120
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  • Description: In contrast with the condition that the trajectory dataset of floating cars (taxis) can be easily obtained from the Internet, it is hard to get the trajectory data of social vehicles (private vehicles) because of personal privacy and government policies. This paper absorbs the idea of game theory, considers the influence of individuals in the group, and proposes a decision behavior based dataset generation (DBDG) model of vehicles to predict future inter-regional traffic. In addition, we adopt simulation tools and generative adversarial networks to train the trajectory prediction model so that the private vehicle trajectory dataset conforming to social rules (e.g., collisionless) is generated. Finally, we construct from macroscopic and microscopic perspectives to verify dataset generation methods proposed in this paper. The results show that the generated data not only has high accuracy and is valuable but can provide strong data support for the Internet of Vehicles and transportation research work. © 2021, Springer Nature Switzerland AG.

Delivery of online electronics and mechatronics labs during lockdowns

- Jayawardena, Amal, Kahandawa, Gayan, Piyathilaka, Lasitha

  • Authors: Jayawardena, Amal , Kahandawa, Gayan , Piyathilaka, Lasitha
  • Date: 2021
  • Type: Text , Conference paper , Conference Paper
  • Relation: 47th Annual Conference of the IEEE Industrial Electronics Society, IECON 2021 Vol. 2021-October
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  • Description: This paper provides a detailed explanation of several approaches that can be used to conduct online labs for electronics/mechatronics engineering courses and explains the results obtained from a survey conducted. The detailed explanations provide information on how to implement the method, benefits of the stated process, possible challenges, and how to overcome those challenges. Furthermore, this paper presents the analyzed results from a survey conducted to capture the student experience in online labs. © 2021 IEEE.

Distributed denial of service attack detection using machine learning and class oversampling

- Shafin, Sakib, Prottoy, Shafin, Abbas, Saif, Hakim, Safayat, Chowdhury, Abdullahi, Rashid, Md Mamanur

  • Authors: Shafin, Sakib , Prottoy, Shafin , Abbas, Saif , Hakim, Safayat , Chowdhury, Abdullahi , Rashid, Md Mamanur
  • Date: 2021
  • Type: Text , Conference paper
  • Relation: First International Conference on Applied Intelligence and Informatics, AII 2021, Nottingham, UK, July 30-31, 2021 Vol. 1435, p. 247-259
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  • Description: Distributed Denial of Services (DDoS) attack, one of the most dangerous types of cyber attack, has been reported to increase during the COVID-19 pandemic. Machine learning techniques have been proposed in the literature to build models to detect DDoS attacks. Existing works in literature tested their models with old datasets where DDoS attacks are not specific. These works mainly focus on detecting the presence of an attack rather than the type of DDoS attacks. However, detection of the attack type is vital for the review and analysis of enterprise-level security policy. Cyber-attacks are inherently an imbalanced data problem, but none of the models treated DDoS attack detection from this perspective. In this work, we present a machine learning model that takes the imbalance nature of the DDoS attack data into consideration for both presence/absence and the type of DDoS attack detection. Extensive experiment analysis with the recent and DDoS attack-specific dataset shows that the proposed technique can effectively identify DDoS attacks. © 2021, Springer Nature Switzerland AG.

Dynamic point cloud compression using a cuboid oriented discrete cosine based motion model

- Ahmmed, Ashek, Paul, Manoranjan, Murshed, Manzur, Taubman, David

  • Authors: Ahmmed, Ashek , Paul, Manoranjan , Murshed, Manzur , Taubman, David
  • Date: 2021
  • Type: Text , Conference paper , Conference Paper
  • Relation: 2021 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2021 Vol. 2021-June, p. 1935-1939
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  • Description: Immersive media representation format based on point clouds has underpinned significant opportunities for extended reality applications. Point cloud in its uncompressed format require very high data rate for storage and transmission. The video based point cloud compression technique projects a dynamic point cloud into geometry and texture video sequences. The projected texture video is then coded using modern video coding standard like HEVC. Since the properties of projected texture video frames are different from traditional video frames, HEVC-based commonality modeling can be inefficient. An improved commonality modeling technique is proposed that employs discrete cosine basis oriented motion models and the domains of such models are approximated by homogeneous regions called cuboids. Experimental results show that the proposed commonality modeling technique can yield savings in bit rate of up to 4.17%. ©2021 IEEE

E-Learning challenges for electronics and mechatronics education during lockdowns

- Jayawardena, Amal, Kahandawa, Gayan, Petty, Mark

  • Authors: Jayawardena, Amal , Kahandawa, Gayan , Petty, Mark
  • Date: 2021
  • Type: Text , Conference paper , Conference Paper
  • Relation: 47th Annual Conference of the IEEE Industrial Electronics Society, IECON 2021 Vol. 2021-October
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  • Description: This paper explains methods used to deliver online electronics and mechatronics courses and clarifies the results obtained by an online survey to understand students' e-learning experience. The purpose of this paper is to outline the challenges of online learning and to explain effective content delivery methods used to overcome those challenges. This study gives detailed descriptions for each content delivery technique, for each online lab delivery method, and for all the online assessment types used while analyzing the effectiveness of those procedures. A case study on the delivery of an online mechatronics project has also been presented. In addition, the paper presents the analyzed results obtained from the survey to improve the future delivery of online electronics and mechatronics courses in a pandemic. © 2021 IEEE.

E-Learning in Industrial Electronics during Covid-19

- Dunai, Larisa, Martins, Joao, Umetani, Kazuhiro, Lucia, Oscar, Ibrahim, Yousef, Appuhamillage, G. K.

  • Authors: Dunai, Larisa , Martins, Joao , Umetani, Kazuhiro , Lucia, Oscar , Ibrahim, Yousef , Appuhamillage, G. K.
  • Date: 2021
  • Type: Text , Conference paper
  • Relation: 22nd IEEE International Conference on Industrial Technology, ICIT 2021 Vol. 2021-March, p. 1227-1233
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  • Description: The educational methodologies employed in Industrial Electronics have been affected by Covid-19. In many cases, conventional learning methods relying on face-to-face lectures have been replaced by online methodologies. The whole process has required a fast adaptation and development of the e-learning tools to ensure a quality of theoretical, practical and laboratory lectures, as well as the development of new methods for the reliable assessment of the learning process. From this perspective, the present paper deals with the different strategies that have been implemented in institutions of several countries located in different geographical areas, including Portugal, Spain, Japan and Australia. It is shown that the use of methodologies, such as flip teaching, has provided a wide variety of possibilities to adapt to the new educational context. Moreover, for Industrial Electronics degrees, the use of virtual or remote laboratories, portable learning tools and advanced information and communication technologies have also risen as valuable resources. The paper also reports the problems arising during the development of the e-learning tools, their implementation constraints, and the evaluation of their results. © 2021 IEEE.

Factors affecting the organizational adoption of blockchain technology : an Australian perspective

- Malik, Saleem, Chadhar, Mehmood, Chetty, Madhu

  • Authors: Malik, Saleem , Chadhar, Mehmood , Chetty, Madhu
  • Date: 2021
  • Type: Text , Conference paper
  • Relation: 54th Annual Hawaii International Conference on System Sciences, HICSS 2021 Vol. 2020-January, p. 5597-5606
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  • Description: Blockchain Technology (BCT) is a novel innovation that has the potential to transform industries, for instance, supply chain, energy, finance, and healthcare. However, despite the potential and the wide range of benefits reported, organizational adoption of BCT is low in several countries including Australia. Some studies investigated the adoption of BCT in different countries, however, there is a lack of research that examines the organizational adoption of BCT in Australia. This study fills this gap by exploring the factors, which influence BCT adoption among Australian organizations. To achieve this, we used an interpretative qualitative research approach based on the Technology, Organization, and Environment (TOE) framework and the Institutional Theory. The findings show that organizational adoption of BCT in Australia is influenced by perceived novelty, complexity, cost, and disintermediation feature of BCT; top management knowledge and support; government support, customer pressure, trading partner readiness, and consensus among trading partners. © 2021 IEEE Computer Society. All rights reserved.

In your face : sentiment analysis of metaphor with facial expressive features

- Zhang, Dongyu, Zhang, Minghao, Guo, Teng, Peng, Ciyuan, Saikrishna, Vidya, Xia, Feng

  • Authors: Zhang, Dongyu , Zhang, Minghao , Guo, Teng , Peng, Ciyuan , Saikrishna, Vidya , Xia, Feng
  • Date: 2021
  • Type: Text , Conference paper
  • Relation: 2021 International Joint Conference on Neural Networks, IJCNN 2021 Vol. 2021-July
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  • Description: Metaphor plays an important role in human communication, which often conveys and evokes sentiments. Numerous approaches to sentiment analysis of metaphors have thus gained attention in natural language processing (NLP). The primary focus of these approaches is on linguistic features and text rather than other modal information and data. However, visual features such as facial expressions also play an important role in expressing sentiments. In this paper, we present a novel neural network approach to sentiment analysis of metaphorical expressions that combines both linguistic and visual features and refer to it as the multimodal model approach. For this, we create a Chinese dataset, containing textual data from metaphorical sentences along with visual data on synchronized facial images. The experimental results indicate that our multimodal model outperforms several other linguistic and visual models, and also outperforms the state-of-the-art methods. The contribution is realized in terms of novelty of the approach and creation of a new, sizeable, and scarce dataset with linguistic and synchronized facial expressive image data. The dataset is particularly useful in languages other than English and the approach addresses one of the most challenging NLP issue: sentiment analysis in metaphor. © 2021 IEEE.