Browsing by Author "Wang R"

Now showing 1 - 11 of 11
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    A Blockchain Based Data Monitoring and Sharing Approach for Smart Grids
    (IEEE, 2019-11-11) Yang Y; Liu M; Zhou Q; Zhou H; Wang R
    With the development of science and technology, human beings cannot live without electricity. The introduction of smart grid systems brings new ideas to break the shackle of existing electricity systems. This paper proposes a mechanism with data monitoring and sharing capabilities based on the consortium blockchain, realizing comprehensive monitoring of smart devices, and promoting the effective sharing of electrical data in smart grids. When a smart device is out of order, the smart contract connected to it will be triggered, and the users can check the running status through the smart phone. This approach allows nodes in the consortium blockchain to request transactions, using the prepaid payment smart contract with time-lock script to protect the consumer right of request nodes. In addition, we use a (t, n) -threshold secret sharing scheme to realize multiparty sharing of electrical data. Paillier encryption arithmetic is used to guarantee the confidentiality of messages in node transaction.
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    A label noise filtering and label missing supplement framework based on game theory
    (Elsevier B.V. on behalf of KeAi Communications Co Ltd for the Chongqing University of Posts and Telecommunications, 2023-08-31) Liu Y; Yao R; Jia S; Wang F; Wang R; Ma R; Qi L
    Labeled data is widely used in various classification tasks. However, there is a huge challenge that labels are often added artificially. Wrong labels added by malicious users will affect the training effect of the model. The unreliability of labeled data has hindered the research. In order to solve the above problems, we propose a framework of Label Noise Filtering and Missing Label Supplement (LNFS). And we take location labels in Location-Based Social Networks (LBSN) as an example to implement our framework. For the problem of label noise filtering, we first use FastText to transform the restaurant's labels into vectors, and then based on the assumption that the label most similar to all other labels in the location is most representative. We use cosine similarity to judge and select the most representative label. For the problem of label missing, we use simple common word similarity to judge the similarity of users' comments, and then use the label of the similar restaurant to supplement the missing labels. To optimize the performance of the model, we introduce game theory into our model to simulate the game between the malicious users and the model to improve the reliability of the model. Finally, a case study is given to illustrate the effectiveness and reliability of LNFS.
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    A study of design change management for infrastructure development projects in New Zealand
    (MDPI AG, 19/09/2022) Wang R; Samarasinghe DAS; Skelton L; Rotimi JOB
    Design changes seem to be an inevitable part of engineering, procurement and construction EPC projects. Such changes create a need for a proactive approach to adjusting project scope, cost and time (the triple constraints) for efficiency and effectiveness in overall delivery. This study investigates the causes and implications of design changes in order to improve design change management practices. Data for the study were obtained through online interviews with New Zealand industry practitioners. Thematic analysis was used to collate the results into meaningful data. The study found that design changes were predominantly caused by clients’ inadequate strategic planning, insufficient attention to design, EPC contractors’ inadequate design ability, and on-site variations. There were three categories of such design changes: direct impact on the project, the reciprocal and complementary effect on stakeholders, and the far-reaching impact on the community. The study concludes by suggesting improvements, such as strengthening the integration of project teams to enhance design quality, strategic alignment of stakeholders at the planning stage, early contractor involvement (ECI) between the planning and design phases, and improving collaboration between design and construction teams. Further, a combination of high technical skills (e.g., design ability) and soft skills (can-do attitude, interpersonal skills, problem-solving skills, documentation skills, etc.) are needed to generate the desired improvement in design change management.
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    A Study of Design Change Management for Infrastructure Development Projects in New Zealand
    (MDPI (Basel, Switzerland), 2022-09-19) Wang R; Samarasinghe DAS; Skelton L; Rotimi JOB
    Design changes seem to be an inevitable part of engineering, procurement and construction EPC projects. Such changes create a need for a proactive approach to adjusting project scope, cost and time (the triple constraints) for efficiency and effectiveness in overall delivery. This study investigates the causes and implications of design changes in order to improve design change management practices. Data for the study were obtained through online interviews with New Zealand industry practitioners. Thematic analysis was used to collate the results into meaningful data. The study found that design changes were predominantly caused by clients’ inadequate strategic planning, insufficient attention to design, EPC contractors’ inadequate design ability, and on-site variations. There were three categories of such design changes: direct impact on the project, the reciprocal and complementary effect on stakeholders, and the far-reaching impact on the community. The study concludes by suggesting improvements, such as strengthening the integration of project teams to enhance design quality, strategic alignment of stakeholders at the planning stage, early contractor involvement (ECI) between the planning and design phases, and improving collaboration between design and construction teams. Further, a combination of high technical skills (e.g., design ability) and soft skills (can-do attitude, interpersonal skills, problem-solving skills, documentation skills, etc.) are needed to generate the desired improvement in design change management.
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    An asset subset-constrained minimax optimization framework for online portfolio selection
    (Elsevier Ltd, 2024-11-15) Yin J; Zhong A; Xiao X; Wang R; Huang JZ
    Effective online portfolio selection necessitates seamless integration of three key properties: diversity, sparsity, and risk control. However, existing algorithms often prioritize one property at the expense of the others due to inherent conflicts. To address this issue, we propose an asset subset-constrained minimax (ASCM) optimization framework, which generates optimal portfolios from diverse investment strategies represented as asset subsets. ASCM consists of: (i) a minimax optimization model that focuses on risk control by considering a set of loss functions constrained by different asset subsets; (ii) the construction of asset subsets via price-feature clipping, which effectively reduces redundant assets in the portfolio; (iii) a state-based estimation of price trends that guides all ASCM loss functions, facilitating the generation of sparse solutions. We solve the ASCM minimax model using an efficient iterative updating formula derived from the projected subgradient method. Furthermore, we achieve near O(1) time complexity through a novel initialization scheme. Experimental results demonstrate that ASCM outperforms eight representative algorithms, including the best constant rebalanced portfolio in hindsight (BCRP) on five out of the six real-world financial datasets. Notably, ASCM achieves a 67-fold improvement over BCRP in cumulative wealth on the TSE dataset.
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    Anisotropic span embeddings and the negative impact of higher-order inference for coreference resolution: An empirical analysis
    (Cambridge University Press, 2024-01-25) Hou F; Wang R; Ng S-K; Zhu F; Witbrock M; Cahan SF; Chen L; Jia X
    Coreference resolution is the task of identifying and clustering mentions that refer to the same entity in a document. Based on state-of-the-art deep learning approaches, end-to-end coreference resolution considers all spans as candidate mentions and tackles mention detection and coreference resolution simultaneously. Recently, researchers have attempted to incorporate document-level context using higher-order inference (HOI) to improve end-to-end coreference resolution. However, HOI methods have been shown to have marginal or even negative impact on coreference resolution. In this paper, we reveal the reasons for the negative impact of HOI coreference resolution. Contextualized representations (e.g., those produced by BERT) for building span embeddings have been shown to be highly anisotropic. We show that HOI actually increases and thus worsens the anisotropy of span embeddings and makes it difficult to distinguish between related but distinct entities (e.g., pilots and flight attendants). Instead of using HOI, we propose two methods, Less-Anisotropic Internal Representations (LAIR) and Data Augmentation with Document Synthesis and Mention Swap (DSMS), to learn less-anisotropic span embeddings for coreference resolution. LAIR uses a linear aggregation of the first layer and the topmost layer of contextualized embeddings. DSMS generates more diversified examples of related but distinct entities by synthesizing documents and by mention swapping. Our experiments show that less-anisotropic span embeddings improve the performance significantly (+2.8 F1 gain on the OntoNotes benchmark) reaching new state-of-the-art performance on the GAP dataset.
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    k-NN attention-based video vision transformer for action recognition
    (Elsevier B.V,, 2024-03-14) Sun W; Ma Y; Wang R
    Action Recognition aims to understand human behavior and predict a label for each action. Recently, Vision Transformer (ViT) has achieved remarkable performance on action recognition, which models the long sequences token over spatial and temporal index in a video. The fully-connected self-attention layer is the fundamental key in the vanilla Transformer. However, the redundant architecture of the vision Transformer model ignores the locality of video frame patches, which involves non-informative tokens and potentially leads to increased computational complexity. To solve this problem, we propose a k-NN attention-based Video Vision Transformer (k-ViViT) network for action recognition. We adopt k-NN attention to Video Vision Transformer (ViViT) instead of original self-attention, which can optimize the training process and neglect the irrelevant or noisy tokens in the input sequence. We conduct experiments on the UCF101 and HMDB51 datasets to verify the effectiveness of our model. The experimental results illustrate that the proposed k-ViViT achieves superior accuracy compared to several state-of-the-art models on these action recognition datasets.
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    Learning and integration of adaptive hybrid graph structures for multivariate time series forecasting
    (Elsevier Inc., 2023-11-01) Guo T; Hou F; Pang Y; Jia X; Wang Z; Wang R
    Recent status-of-the-art methods for multivariate time series forecasting can be categorized into graph-based approach and global-local approach. The former approach uses graphs to represent the dependencies among variables and apply graph neural networks to the forecasting problem. The latter approach decomposes the matrix of multivariate time series into global components and local components to capture the shared information across variables. However, both approaches cannot capture the propagation delay of the dependencies among individual variables of a multivariate time series, for example, the congestion at intersection A has delayed effects on the neighboring intersection B. In addition, graph-based forecasting methods cannot capture the shared global tendency across the variables of a multivariate time series; and global-local forecasting methods cannot reflect the nonlinear inter-dependencies among variables of a multivariate time series. In this paper, we propose to combine the advantages of both approaches by integrating Adaptive Global-Local Graph Structure Learning with Gated Recurrent Units (AGLG-GRU). We learn a global graph to represent the shared information across variables. And we learn dynamic local graphs to capture the local randomness and nonlinear dependencies among variables. We apply diffusion convolution and graph convolution operations to global and dynamic local graphs to integrate the information of graphs and update gated recurrent unit for multivariate time series forecasting. The experimental results on seven representative real-world datasets demonstrate that our approach outperforms various existing methods.
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    Real and synthetic Punjabi speech datasets for automatic speech recognition
    (Elsevier Inc, 2024-02) Singh S; Hou F; Wang R
    Automatic speech recognition (ASR) has been an active area of research. Training with large annotated datasets is the key to the development of robust ASR systems. However, most available datasets are focused on high-resource languages like English, leaving a significant gap for low-resource languages. Among these languages is Punjabi, despite its large number of speakers, Punjabi lacks high-quality annotated datasets for accurate speech recognition. To address this gap, we introduce three labeled Punjabi speech datasets: Punjabi Speech (real speech dataset) and Google-synth/CMU-synth (synthesized speech datasets). The Punjabi Speech dataset consists of read speech recordings captured in various environments, including both studio and open settings. In addition, the Google-synth dataset is synthesized using Google's Punjabi text-to-speech cloud services. Furthermore, the CMU-synth dataset is created using the Clustergen model available in the Festival speech synthesis system developed by CMU. These datasets aim to facilitate the development of accurate Punjabi speech recognition systems, bridging the resource gap for this important language.
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    Recent Advances in Pulse-Coupled Neural Networks with Applications in Image Processing
    (MDPI (Basel, Switzerland), 2022-10-11) Liu H; Liu M; Li D; Zheng W; Yin L; Wang R; Song BC
    This paper surveys recent advances in pulse-coupled neural networks (PCNNs) and their applications in image processing. The PCNN is a neurology-inspired neural network model that aims to imitate the information analysis process of the biological cortex. In recent years, many PCNN-derived models have been developed. Research aims with respect to these models can be divided into three categories: (1) to reduce the number of manual parameters, (2) to achieve better real cortex imitation performance, and (3) to combine them with other methodologies. We provide a comprehensive and schematic review of these novel PCNN-derived models. Moreover, the PCNN has been widely used in the image processing field due to its outstanding information extraction ability. We review the recent applications of PCNN-derived models in image processing, providing a general framework for the state of the art and a better understanding of PCNNs with applications in image processing. In conclusion, PCNN models are developing rapidly, and it is projected that more applications of these novel emerging models will be seen in future.
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    WBNet: Weakly-supervised salient object detection via scribble and pseudo-background priors
    (Elsevier Ltd, 2024-10) Wang Y; Wang R; He X; Lin C; Wang T; Jia Q; Fan X
    Weakly supervised salient object detection (WSOD) methods endeavor to boost sparse labels to get more salient cues in various ways. Among them, an effective approach is using pseudo labels from multiple unsupervised self-learning methods, but inaccurate and inconsistent pseudo labels could ultimately lead to detection performance degradation. To tackle this problem, we develop a new multi-source WSOD framework, WBNet, that can effectively utilize pseudo-background (non-salient region) labels combined with scribble labels to obtain more accurate salient features. We first design a comprehensive salient pseudo-mask generator from multiple self-learning features. Then, we pioneer the exploration of generating salient pseudo-labels via point-prompted and box-prompted Segment Anything Models (SAM). Then, WBNet leverages a pixel-level Feature Aggregation Module (FAM), a mask-level Transformer-decoder (TFD), and an auxiliary Boundary Prediction Module (EPM) with a hybrid loss function to handle complex saliency detection tasks. Comprehensively evaluated with state-of-the-art methods on five widely used datasets, the proposed method significantly improves saliency detection performance. The code and results are publicly available at https://github.com/yiwangtz/WBNet.