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Browsing Open Research by Subject "08 Information and Computing Sciences"
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- ItemA Comprehensive Multi-functional Controller for Hybrid Energy Storage Systems in DC Microgrids(Institute of Electrical and Electronics Engineers (IEEE), 2023-02-09) Lin, Xin; Zamora, Ramon; Baguley, Craig A
- ItemA Novel Approach for Reconstruction of IMFs of Decomposition and Ensemble Model for Forecasting of Crude Oil Prices(Institute of Electrical and Electronics Engineers (IEEE), 2024-02-26) Naeem, Muhammad; Aamir, Muhammad; Yu, Jian; Albalawi, OlayanIn recent eras, the complexity and fluctuations of the global crude oil prices have affected the economic progress of society. It is therefore, the oil price prediction has hauled the attention of scholars and policymakers. Driven by this critical concern for forecasting of crude oil prices, we introduces a novel hybrid model keeping in mind the primary objective of enhancing prediction accuracy while considering the specific characteristics as inherent in the data. To achieve this achievement, the trend is eliminated, allowing the scrutiny of whether the residual component validates the assurance of a series ran by stochastic trends. Following the removal of the trend, the residual component undergoes rigorous evaluation through autoregressive model following the decomposition model. Then we got support from the support vector machine, autoregressive integrated moving average and long-short term memory. The predictions accuracy can be evaluated by using the various performance metrics. The proposed hybrid model’s robustness and forecasting performance are rigorously evaluated through Diebold-Mariano test in comparison to competing models. Furthermore, the forecasting ability is evaluated via directional forecast. Ultimately, the empirical findings explicitly determine the superior predictive capabilities of the proposed hybrid model over alternative approaches.
- ItemA Survey of Indoor Positioning Systems Based on a Six-Layer Model(Elsevier BV, 2023-09-22) Sartayeva, Yerkezhan; Chan, Henry CB; Ho, Yik Him; Chong, Peter HJIndoor positioning has attracted considerable interest in both the industry and academic communities because of its wide range of applications, such as asset tracking, healthcare and context-aware services like targeted advertisements. While there are many indoor localisation methods, each has its advantages and disadvantages, taking into consideration various factors such as the effect of the indoor environment, ease of implementation, computational cost, positioning accuracy, etc. In other words, no single solution can cater for all different situations. Although many survey papers have been published on indoor positioning, new techniques and methods are proposed every year, so it is important to stay abreast of its latest developments. In addition, each survey has its own classification for indoor positioning systems without a common scheme. Inspired by the well-known OSI model and TCP/IP model, it would be desirable to develop a systematic framework for studying indoor positioning systems. In this paper, we make this new contribution by introducing a systemic survey framework based on a six-layer model to give a comprehensive survey of indoor positioning systems, namely: device layer, communication layer, network layer, data layer, method layer and application layer. Complementing the previous survey papers, this paper provides a survey of the latest research works on indoor positioning based on the six-layer model. Our emphasis is on systematic categorisation, machine learning-based enhancements, collaborative localisation and COVID-19-related applications. The six-layer model should provide a useful framework and new insights for the research community.
- ItemAdvancing Video Data Privacy Preservation in IoT Networks through Video Blockchain(MDPI AG, 2024-03-21) Moolikagedara, Kasun; Nguyen, Minh; Yan, Weiqi; Li, XuejunIn the digital age, where the Internet of Things (IoT) permeates every facet of our lives, the safeguarding of data privacy, especially video data, emerges as a paramount concern. The ubiquity of IoT devices, capable of capturing and disseminating vast quantities of video data, introduces unprecedented challenges in ensuring the privacy and security of such information. This article explores the crucial intersection of video data privacy and blockchain technology within IoT networks. It aims to uncover and articulate the unique challenges video data encounter in the IoT ecosystem, such as susceptibility to unauthorized access and the difficulty in ensuring data integrity and confidentiality. By conducting a thorough literature review, this study not only illuminates the intricate privacy challenges inherent in IoT environments but also showcases the immutable, decentralized nature of blockchain as a potent solution. We systematically explore how blockchain-based methods can be pragmatically implemented to fortify video data privacy, scrutinizing the efficacy of these approaches in the IoT context. Through critical assessment, the paper delineates the strengths and limitations of video blockchain solutions, underscoring the transformative potential of blockchain technology as a cornerstone for enhancing data privacy in IoT networks. Conclusively, this work advocates for blockchain as an indispensable tool in the advancement of data privacy measures for video content, thereby reinforcing trust and security in the increasingly connected fabric of our digital world. As IoT applications burgeon, the fusion of blockchain technology with IoT infrastructures promises a robust framework for protecting sensitive video data, heralding a future of enhanced trust and security in our interconnected ecosystem.
- ItemAn Adaptive Deep Learning Neural Network Model to Enhance Machine-Learning-Based Classifiers for Intrusion Detection in Smart Grids(MDPI AG, 2023-06-02) Li, Xue Jun; Ma, Maode; Sun, YihanModern smart grids are built based on top of advanced computing and networking technologies, where condition monitoring relies on secure cyberphysical connectivity. Over the network infrastructure, transported data containing confidential information, must be protected as smart grids are vulnerable and subject to various cyberattacks. Various machine learning based classifiers were proposed for intrusion detection in smart grids. However, each of them has respective advantage and disadvantages. Aiming to improve the performance of existing machine learning based classifiers, this paper proposes an adaptive deep learning algorithm with a data pre-processing module, a neural network pre-training module and a classifier module, which work together classify intrusion data types using their high-dimensional data features. The proposed Adaptive Deep Learning (ADL) algorithm obtains the number of layers and the number of neurons per layer by determining the characteristic dimension of the network traffic. With transfer learning, the proposed ADL algorithm can extract the original data dimensions and obtain new abstract features. By combining deep learning models with traditional machine learning-based classification models, the performance of classification of network traffic data is significantly improved. By using the Network Security Laboratory-Knowledge Discovery in Databases (NSL-KDD) dataset, experimental results show that the proposed ADL algorithm improves the effectiveness of existing intrusion detection methods and reduces the training time, indicating a promising candidate to enhance network security in smart grids.
- ItemClinical Information System (CIS) Implementation in Developing Countries: Requirements, Success Factors, and Recommendations(Oxford University Press (OUP), 2023-02-07) Tun, Soe Ye Yint; Madanian, SamanehObjective Clinical Information System (CIS) usage can reduce healthcare costs over time, improve the quality of medical care and safety, and enhance clinical efficiency. However, CIS implementation in developing countries poses additional, different challenges from the developed countries. Therefore, this research aimed to systematically review the literature, gathering and integrating research findings on Success Factors (SFs) in CIS implementation for developing countries. This helps to integrate past knowledge and develop a set of recommendations, presented as a framework, for implementing CIS in developing countries. Materials and Methods A systematic literature review was conducted, followed by qualitative data analysis on the published articles related to requirements and SF for CIS implementation. Eighty-three articles met the inclusion criteria and were included in the data analysis. Thematic analysis and cross-case analysis were applied to identify and categorize the requirements and SF for CIS implementation in developing countries. Results Six major requirement categories were identified including project management, financial resources, government involvement and support, human resources, organizational, and technical requirements. Subcategories related to SF are classified under each major requirement. A set of recommendations is provided, presented in a framework, based on the project management lifecycle approach. Conclusion The proposed framework could support CIS implementations in developing countries while enhancing their rate of success. Future studies should focus on identifying barriers to CIS implementation in developing countries. The country-specific empirical studies should also be conducted based on this research’s findings to match the local context.
- ItemConstructing New Backbone Networks via Space-Frequency Interactive Convolution for Deepfake Detection(Institute of Electrical and Electronics Engineers (IEEE), 2023-10-16) Guo, Zhiqing; Jia, Zhenhong; Wang, Liejun; Wang, Dewang; Yang, Gaobo; Kasabov, NikolaThe serious concerns over the negative impacts of Deepfakes have attracted wide attentions in the community of multimedia forensics. The existing detection works achieve deepfake detection by improving the traditional backbone networks to capture subtle manipulation traces. However, there is no attempt to construct new backbone networks with different structures for Deepfake detection by improving the internal feature representation of convolution. In this work, we propose a novel Space-Frequency Interactive Convolution (SFIConv) to efficiently model the manipulation clues left by Deepfake. To obtain high-frequency features from tampering traces, a Multichannel Constrained Separable Convolution (MCSConv) is designed as the component of the proposed SFIConv, which learns space-frequency features via three stages, namely generation, interaction and fusion. In addition, SFIConv can replace the vanilla convolution in any backbone networks without changing the network structure. Extensive experimental results show that seamlessly equipping SFIConv into the backbone network greatly improves the accuracy for Deepfake detection. In addition, the space-frequency interaction mechanism does benefit to capturing common artifact features, thus achieving better results in cross-dataset evaluation. Our code will be available at https://github.com/EricGzq/SFIConv.
- ItemDevelopment and High-Fidelity Simulation of Trajectory Tracking Control Schemes of a UUV for Fish Net-Pen Visual Inspection in Offshore Aquaculture(Institute of Electrical and Electronics Engineers (IEEE), 2023-11-30) Tun, Thein Than; Huang, Loulin; Preece, Mark AnthonyOffshore aquaculture fish farming faces labor shortage, safety, productivity and high operating cost issues. Unmanned underwater vehicles (UUVs) are being deployed to mitigate these issues. One of their applications is the fish net-pen visual inspection. This paper aims to develop and simulate with high-fidelity several trajectory tracking control schemes for a UUV to visually inspect a fish net-pen in a standard task scenario in offshore aquaculture under 0.0 m/s, 0.5 m/s and 0.9 m/s underwater current disturbances. Three controllers, namely 1) Proportional-Derivative control with restoring force & moment compensation (Compensated-PD), 2) Proportional-Integral-Derivative control with restoring force & moment compensation (Compensated-PID), and 3) computed torque (or) inverse dynamics control (CTC/IDC) were conducted on a 6 degrees-of-freedom (DoF) BlueROV2 Heavy Configuration dealing with 12 error states (pose and twist). A standard task scenario for the controllers was formulated based on the Blue Endeavour project of the New Zealand King Salmon company located 5 kilometres due north of Cape Lambert, in northern Marlborough. This simulated experimental study gathered and applied many available and physically quantifiable parameters of the fish farm and a UUV called BlueROV2 Heavy Configuration. Results show that while utilizing the minimum thrust, CTC/IDC outperforms Compensated-PID and Compensated-PD in overall trajectory tracking under different underwater current disturbances. Numerical results measured with root-mean-square-error (RMSE), mean-absolute-error (MAE) and root-sum-squared (RSS) are reported for comparison, and simulation results in the form of histograms, bar charts, plots, and video recordings are provided. Future work will explore into advanced controllers, with a specific emphasis on energy-optimal control schemes, accompanied by comprehensive stability and robustness analyses applied to linear and nonlinear UUV models.
- ItemDual Knowledge Distillation on Multiview Pseudo Labels for Unsupervised Person Re-Identification(Institute of Electrical and Electronics Engineers (IEEE), 2024) Zhu, Wenjie; Peng, Bo; Yan, Wei QiUnsupervised person re-identification (Re-ID) has made significant progress by leveraging valuable pseudo labels from completely unlabeled data. However, the predominant use of pseudo labels heavily relies on clustering results, which may lead to the accumulation of supervision deviation due to inevitable noise. In this paper, we propose a novel framework, namely Dual Knowledge Distillation on Multiview Pseudo Labels (DKD-MPL), to address this challenge. Specifically, the proposed DKD-MPL framework consists of two modules: Global Knowledge Distillation (GKD) and Self-Knowledge Distillation (SKD). In the GKD module, the pseudo labels obtained from the epoch-wise clustering procedure serve as the logits for the teacher model, while the mini-batch query images' pseudo labels act as the logits for the student model. Within the SKD module, we facilitate self-knowledge distillation by considering the pseudo labels generated by positive anchors and query images as two augmentations of the mini-batch data. As a result, DKD-MPL facilitates the exploitation of both global and local complementary knowledge across different views of pseudo labels, thereby mitigating supervision deviation. To demonstrate the effectiveness of DKD-MPL, we provide a theoretical analysis of the proposed loss and conduct extensive experiments on four popular datasets, e.g., Market-1501, DukeMTMC-reID, MSMT17, and VeRi-776. The results indicate that our method surpasses unsupervised approaches and achieves comparable performance to supervised person Re-ID methods.
- ItemDynamic- Structured Reservoir Spiking Neural Network in Sound Localization(Institute of Electrical and Electronics Engineers (IEEE), 2024) Roozbehi, Zahra; Narayanan, Ajit; Mohaghegh, Mahsa; Saeedinia, Samaneh-AlsadatSound source localization is a critical problem in various fields, including communication, security, and entertainment. Binaural cues are a natural technique used by mammalian ears for efficient sound source localization. Spiking neural networks (SNNs) have emerged as a promising tool for implementing binaural sound source localization approaches. However, optimizing the topology and size of SNNs is crucial to reduce computational costs while maintaining accuracy. This paper proposes a real-time structure of a reservoir SNN (rSNN) called Adaptive-Resonance-Theory-based rSNN (ART-rSNN) for localizing sound sources in the time domain by integrating an energy-based localization method. The dataset used in this work is recorded by two different omnidirectional microphones from a real environment. The dataset includes various sound events such as speech, music, and environmental sounds. The proposed ART-rSNN architecture can dynamically adjust the location of its neurons to amplify estimated energy near the sound source, resulting in higher localization accuracy. Our proposed method outperforms several conventional and state of the art algorithms in terms of accuracy and is able to detect the front and back direction of azimuth angle. This work demonstrates the potential of dynamic neuron arrangements in SNNs for improving sound source localization in practical applications.
- ItemElectron Beam Powder Bed Fusion Additive Manufacturing of Ti6Al4V Alloy Lattice Structures: Orientation-Dependent Compressive Strength and Fracture Behavior(Springer, 2024-04-09) Huang, Yawen; Chen, ZW; Wan, ARO; Schmidt, K; Sefont, P; Singamneni, SHigh porosity level lattice structures made using electron beam powder bed fusion additive manufacturing (EBPBF) need to be sufficiently strong and the understanding of the mechanical anisotropy of the structures is important for the design of orthopedic implants. In this work, the combined effects of loading direction (LD), cell orientation, and strut irregularity associated with EBPBF of Ti6Al4V alloy lattices on the mechanical behavior of the lattices under compressive loading have been studied. Three groups of simple cubic unit cell lattices were EBPBF made, compressively tested, and examined. The three groups were [001]//LD lattices, [011]//LD lattices, and [111]//LD lattices. Simulation has also been conducted. Yield strength (σy-L) values of all lattices determined experimentally have been found to be comparable to the values predicted by simulation; thus, EBPBF surface defects do not affect σy-L. σy-L of [001]//LD lattices is 1.8–2.0 times higher than those of [011]//LD and [111]//LD lattices. The reason for this is shown to be due to the high stress concentrations in non-[001]//LD samples, causing yielding at low loading levels. Furthermore, plastic strain (εp) at ultimate compression strength of [001]//LD samples has been determined to be 4–6 times higher than the values of non-[001]//LD samples. Examining the tested samples has shown cracks more readily propagating from EBPBF micro-notches in non-[001]//LD samples, resulting in low εp.
- ItemInformation Security and Privacy Challenges of Cloud Computing for Government Adoption: A Systematic Review(Springer Science and Business Media LLC, 2024-01-03) Ukeje, Ndukwe; Gutierrez, Jairo; Petrova, KrassieThe advent of new technologies and applications coupled with the COVID-19 pandemic tremendously increased cloud computing adoption in private and public institutions (government) and raised the demand for communication and access to a shared pool of resources and storage capabilities. Governments across the globe are moving to the cloud to improve services, reduce costs, and increase effectiveness and efficiency while fostering innovation and citizen engagement. However, information security and privacy concerns raised in the past remain significant to government adoption and utilisation of cloud computing. The study conducts a systematic literature review (SLR) using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach to examine information security and privacy as the fundamental challenges to government intention to adopt cloud computing. This study screened 758 articles and included 33 articles that revealed information security and privacy as critical factors and barriers to adopting cloud computing through a systematic evaluation (PRISMA approach). The combined two factors contributed 70% of the significant gaps to the cloud computing adoption challenges. In contrast, the individual contribution of information security and privacy as a significant gap to the challenges of cloud adoption yielded 9% and 12%, respectively. Furthermore, 9% of the authors recognised the need for a framework to address the challenges but could not attempt to develop the framework. The study contributes to the information security body of knowledge, PRISMA studies and provides direction in proposing strategies and frameworks to tackle information security and privacy challenges as future research.
- ItemMotif-Based Graph Attentional Neural Network for Web Service Recommendation(Elsevier BV, 2023-03-27) Wang, Guiling; Yu, Jian; Nguyen, Mo; Zhang, Yuqi; Yongchareon, Sira; Han, YanboDeep Neural Networks (DNN) based collaborative filtering has been successful in recommending services by effectively generalizing graph-structured data. However, most existing approaches focus on first-order interactions. Although recent approaches have utilized high-order connectivity, they still limit themselves to simple interactions and ignore the pattern of structural sub-graphs/motifs. In this study, we first explore the commonly used motifs in the Mashup-API interaction bipartite graph and propose a dedicated algorithm to generate the motif adjacency matrix. We then propose a Motif-based Graph Attention Network for service recommendation (MGSR) that utilizes a motif-based attention mechanism to capture the high-order information of various motifs, and a Collaborative Filtering model to generate the recommendation prediction. We have conducted extensive experiments on ProgrammableWeb dataset and our results demonstrate the superior performance of our proposed framework over some state-of-the-art approaches.
- ItemOn Fusing Artificial and Convolutional Neural Network Features for Automatic Bug Assignments(Institute of Electrical and Electronics Engineers (IEEE), 2023-05-08) Dipongkor, Atish Kumar; Islam, Md Saiful; Hussain, Ishtiaque; Yongchareon, Sira; Mistry, SajibAutomated bug report assignment is critical for large-scale software projects where reported bugs are frequent and expert developers are required to fix them on time. Finding an appropriate developer with the necessary skill sets and prior experience in fixing similar bugs is difficult and can be an expensive process, depending on the severity of the reported bug. To address this issue, researchers have proposed several machine learning and deep learning-based automated bug report assignment techniques that make use of historical data on reported bugs as well as fixer information. However, there is still room for improvement in the performance of these techniques. In this paper, we propose a novel deep learning-based approach that utilizes two sets of features from the reported bugs’ textual data, namely contextual information and the occurrence of repeating keywords. We develop convolutional neural network and artificial neural network modules to mine these features. We then fuse these two sets of extracted features to assign a bug to an appropriate developer. We conduct extensive experiments on eight benchmark datasets of open-source, real-world software projects to assess the effectiveness of our approach. The experimental results demonstrate that our information fusion-based approach outperforms previous models and improves automated bug report assignment. Furthermore, we debug the errors of our proposed model and publish all source code so that future researchers can contribute to this problem.
- ItemOnline Low-Light Sand-Dust Video Enhancement Using Adaptive Dynamic Brightness Correction and a Rolling Guidance Filter(Institute of Electrical and Electronics Engineers (IEEE), 2023-07-07) Ni, Dongdong; Jia, Zhenhong; Yang, Jie; Kasabov, NikolaSand-dust videos obtained in a low-light environment are characterized by low contrast, nonuniform illumination, color cast, and considerable noise. To realize sand-dust removal and brightness enhancement simultaneously, this paper proposes an online low-light sand-dust video enhancement method using adaptive dynamic brightness correction and a rolling guidance filter. The proposed dual-threshold interframe detection strategy involves two methods to treat low-light sand-dust video frames. The first method involves two components: an adaptive dynamic brightness correction algorithm to correct the color deviation of the low-light video frame and improve its brightness and a rolling guidance filter combined with guided image filtering to enhance the frame details. The second method enhances the quality of the incoming frame by reducing the amount of calculation. The first frame of the video is processed using the first method. The processing method of each subsequent frame is determined according to its interframe detection value with the buffer frame. Through qualitative and quantitative comprehensive experiments on low-light sand-dust images and videos, the performance of the proposed method is compared with those of state-of-the-art methods. The proposed method for frame quality improvement achieves the best visual effect in enhancing the quality of low-light sand-dust images, as indicated by the best objective evaluation indicators. Moreover, compared with the framewise enhancement method, the video processing efficiency associated with the dual-threshold interframe detection strategy is 2.77 times higher.
- ItemPattern Based Mobility Management in 5G Networks with a Game Theoretic-Jump Markov Linear System Approach(Institute of Electrical and Electronics Engineers (IEEE), 2023-10-10) Chiputa, Masoto; Zhang, Minglong; Chong, Peter Han JooThe fifth generation (5G) mobile communication adopted the usage of Millimeter Wave (mmWave) bands to ignite prospects of gigabit data rates in mobile networks. However, mmWave propagation is highly susceptible to competing factors of user and topographic dynamics: they formulate irregular cell patterns. The irregularities in mmWave cell patterns cause unreliable connectivity and can instigate unnecessary Handoffs (HOs). This behavior ultimately increases the risk of 5G link failures. To improve mmWave link connectivity hence guarantee continuous connectivity in 5G mobile communication, this paper proposes a HO scheme that predicts target link deterioration patterns to select the most reliable mmWave link for a mobile user. The scheme is based on Game Theory (GT) and Jump Markov Linear Systems (JMLS). JMLSs are known to account for abrupt/erratic changes in system dynamic predictions. We amalgamate GT with JMLS capability to predict target mmWave link pattern/behavior after the HO execution. Specifically, given channel gain and received power variation over distance, the GT-JMLS HO scheme predicts the sustainability of the signal-interference-noise ratio (SINR) pattern of a target link above threshold. This is paramount to reducing the selection of mmWave links that prematurely fail or require multiple HOs to sustain connectivity over a short distance or period. Our simulation results show that our proposed HO scheme offers target links with higher: throughput, energy efficiency, reliability, and longer dwell time between HOs than classical HO schemes.
- ItemPOI Recommendation for Occasional Groups Based on Hybrid Graph Neural Networks(Elsevier BV, 2023-09-19) Meng, L; Liu, Z; Chu, D; Sheng, QZ; Yu, J; Song, XRecently, POI (Point-of-interest) recommendation for groups has become a critical challenge when helping groups to discover potentially interesting new places, and some effective recommendation models have been proposed to address this issue. However, most existing research focuses on POI recommendation for fixed groups, few studies have been conducted on POI recommendation for occasional groups. To tackle this issue, we propose a POI recommendation model for occasional groups based on Hybrid Graph Neural Networks (termed as PROG-HGNN) which combines excellent graph neural networks models. Firstly, PROG-HGNN generates the fitted representation of the occasional group based on the Node Influence Indicator (INF) method and Graph Attention Networks (GAT) model. Then, PROG-HGNN learns POIs’ representations containing members’ POI interaction preferences and members’ POI transfer preferences with the Signed Bipartite Graph Neural Networks (SBGNN) model and the Session-based Graph Neural Networks (SRGNN) model, respectively. Finally, PROG-HGNN recommends the potential POIs for the occasional group based on the fitted representation of the occasional group and the learned representations of POIs. We verify our proposed model on three public benchmark datasets (Foursquare, Gowalla and Yelp), which contains 124,933 to 860,888 POI check-in records. The comparison between our proposed model and the twelve baseline models demonstrates the outstanding performance of PROG-HGNN. In terms of Precision@K and Recall@K, our model achieves about 32.92% and 19.67% improvement compared with the best baseline models on the three benchmark datasets averagely. Adequate ablation experiments prove the effectiveness of the members’ POI interaction preferences learning module and POI transfer preferences learning module.
- ItemSyntax-Enhanced Aspect-Based Sentiment Analysis with Multi-Layer Attention(Elsevier BV, 2023-08-23) Shi, Jingli; Li, Weihua; Bai, Quan; Yang, Yi; Jiang, JianhuaAs a key task of fine-grained sentiment analysis, aspect-based sentiment analysis aims to analyse people’s opinions at the aspect level from user-generated texts. Various sub-tasks have been defined according to different scenarios, extracting aspect terms, opinion terms, and the corresponding sentiment. However, most existing studies merely focus on a specific sub-task or a subset of sub-tasks, having many complicated models designed and developed. This hinders the practical applications of aspect-based sentiment analysis. Therefore, some unified frameworks are proposed to handle all the subtasks, but most of them suffer from two limitations. First, the syntactic features are neglected, but such features have been proven effective for aspect-based sentiment analysis. Second, very few efficient mechanisms are developed to leverage important syntactic features, e.g., dependency relations, dependency relation types, and part-of-speech tags. To address these challenges, in this paper, we propose a novel unified framework to handle all defined sub-tasks for aspect-based sentiment analysis. Specifically, based on the graph convolutional network, a multi-layer semantic model is designed to capture the semantic relations between aspect and opinion terms. Moreover, a multi-layer syntax model is proposed to learn explicit dependency relations from different layers. To facilitate the sub-tasks, the learned semantic features are propagated to the syntax model with better semantic guidance to learn the syntactic representations comprehensively. Different from the conventional syntactic model, the proposed framework introduces two attention mechanisms. One is to model dependency relation and type, and the other is to encode part-of-speech tags for detecting aspect and opinion term boundaries. Extensive experiments are conducted to evaluate the proposed novel unified framework, and the experimental results on four groups of real-world datasets explicitly demonstrate the superiority of the proposed framework over a range of baselines.
- ItemWhen Harry, the Human, Met Sally, the Software Robot: Metaphorical Sensemaking and Sensegiving Around an Emergent Digital Technology(SAGE Publications, 2023-01-30) Techatassanasoontorn, Angsana A; Waizenegger, Lena; Doolin, BillRobotic process automation (RPA) is often used in organisational digitalisation efforts to automate work processes. RPA, and the software robots at its heart, is an equivocal and contentious technology Adopting the products of theorising approach, this study views metaphors as central sensemaking and sensegiving devices that shape the interpretation of RPA among stakeholders towards a preferred reality of ways of seeing and experiencing software robots. The empirical materials are drawn from research in three Australasian organisations that have implemented RPA. Grounding our analysis in the domains-interaction model, we identified three root metaphors: person, robot, and tool, their constitutive conceptual metaphors, and intended use as heuristics devices. Our findings show that metaphor is a powerful device that employees rely on to make sense of their experiences with a new digital technology that can potentially shape their roles, work practices and job design. In addition, managers and automation team members intentionally leverage metaphors to shape others’ perceptions of a software robot’s capabilities and limitations, its implication for human work, and its expanding benefits for organisations over time, among others. Metaphor as a precursor to more formal theory provides scholars with a vocabulary to understand disparate experiences with an emergent automation technology that can be further developed to generate a theory of seeing automation and working with automated agents.