Internet-of-Things (IoTs) entity discovery plays an important role in the Industrial IoTs, especially with the rapidly increasing and updating of IoT sensors in the industrial environment driven by the era of Industry...
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Internet-of-Things (IoTs) entity discovery plays an important role in the Industrial IoTs, especially with the rapidly increasing and updating of IoT sensors in the industrial environment driven by the era of Industry 4.0 and intelligent manufacturing. However, large numbers of nonsmart sensors are required in the industrial environment, causing IoT entity discovery challenges. Unlike the smart sensor, the nonsmart sensor with limited computation and communication ability is hard to discover and recognize by traditional IoT platforms. Aiming at the challenge, this work proposes a novel IoT entity discovery middleware for nonsmart sensor discovery in the industrial environment. The proposed middleware combines both sensor knowledge graphs and sensor data values to build an IoT entity discovery and recognition model. A knowledge-data fused learning network is proposed for the model to identify the data type, function, and other information of the nonsmart sensor. At last, a prototype middleware with the discovery and recognition model is produced to implement nonsmart sensor discovery. In the experimental evaluations, the prototype middleware tests various nonsmart sensors and achieves 87.6% recognition accuracy. In real-world case studies, the prototype middleware proves the feasibility and effectiveness of nonsmart sensor discovery in the industrial environment.
In this paper we proposed a Graph-Based conspiracy source detection method for the MediaEval task 2022 FakeNews: Corona Virus and Conspiracies Multimedia Analysis Task. The goal of this study was to apply SOTA graph n...
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Given the escalating magnitude and intricacy of software systems, software measurement data often contains irrelevant and redundant features, resulting in significant resource and storage requirements for software def...
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Given the escalating magnitude and intricacy of software systems, software measurement data often contains irrelevant and redundant features, resulting in significant resource and storage requirements for software defect prediction (SDP). Feature selection (FS) has a vital impact on the initial data preparation phase of SDP. Nonetheless, existing FS methods suffer from issues such as insignificant dimensionality reduction, low accuracy in classifying chosen optimal feature sets, and neglect of complex interactions and dependencies between defect data and features as well as between features and classes. To tackle the aforementioned problems, this paper proposes a many-objective SDPFS (MOSDPFS) model and the binary many-objective PSO algorithm with adaptive enhanced selection strategy (BMaOPSO-AR2) is proposed within this paper. MOSDPFS selects F1 score, the number of features within subsets, and correlation and redundancy measures based on mutual information (MI) as optimization objectives. BMaOPSO-AR2 constructs a binary version of MaOPSO using transfer functions specifically for binary classification. Adaptive update formulas and the introduction of the R2 indicator are employed to augment the variety and convergence of algorithm. Additionally, performance of MOSDPFS and BMaOPSO-AR2 are tested on the NASA-MDP and PROMISE datasets. Numerical results prove that a proposed model and algorithm effectively reduces feature count while enhancing predictive accuracy and minimizing model complexity.
Air pollution has become one of the most dreadful threats to the whole living thing. There are numerous global researches giving lots of attention to the impact of air pollution on health and the environment. One of t...
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Depth estimation is a fundamental task in many vision applications. With the popularity of omnidirectional cameras, it becomes a new trend to tackle this problem in the spherical space. In this paper, we propose a lea...
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Depth estimation is a fundamental task in many vision applications. With the popularity of omnidirectional cameras, it becomes a new trend to tackle this problem in the spherical space. In this paper, we propose a learning-based method for predicting dense depth values of a scene from a monocular omnidirectional image. An omnidirectional image has a full field-of-view, providing much more complete descriptions of the scene than perspective images. However, fully-convolutional networks that most current solutions rely on fail to capture rich global contexts from the panorama. To address this issue and also the distortion of equirectangular projection in the panorama, we propose Cubemap Vision Transformers (CViT), a new transformer-based architecture that can model long-range dependencies and extract distortion-free global features from the panorama. We show that cubemap vision transformers have a global receptive field at every stage and can provide globally coherent predictions for spherical signals. As a general architecture, it removes any restriction that has been imposed on the panorama in many other monocular panoramic depth estimation methods. To preserve important local features, we further design a convolution-based branch in our pipeline (dubbed GLPanoDepth) and fuse global features from cubemap vision transformers at multiple scales. This global-to-local strategy allows us to fully exploit useful global and local features in the panorama, achieving state-of-the-art performance in panoramic depth estimation.
Tremors are a prevalent movement disorder due to a nervous system condition that leads to involuntary muscle movements observed in patients. This paper converts the tremorous anatomical human arm model to a single deg...
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In this paper, we introduce a new dataset for air conditioner refrigerant leak smoke detection, called ACRL-10K. The dataset is designed to develop algorithms for detecting refrigerant leak smoke faults during air con...
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Introducing learnable prompts into CLIP and fine-tuning them have demonstrated excellent performance across many downstream tasks. However, existing methods have insufficient interaction between modalities and neglect...
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Introducing learnable prompts into CLIP and fine-tuning them have demonstrated excellent performance across many downstream tasks. However, existing methods have insufficient interaction between modalities and neglect the importance of hierarchical contextual information, leading to ineffective alignment in both the visual and textual representation spaces. Additionally, CLIP is highly sensitive to prompts, making learnable prompts prone to overfitting on seen classes, which results in the forgetting of general knowledge of CLIP and severely impair generalization ability on unseen classes. To address these issues, we propose an original Dual-Guidance Prompts Generation (DGPrompt) method that promotes alignment between visual and textual spaces while ensuring the continuous retention of general knowledge. The main ideas of DGPrompt are as follows: 1) The extraction of image and text embeddings are guided mutually by generating visual and textual prompts, making full use of complementary information from both modalities to align visual and textual spaces. 2) The prompt-tuning process is restrained by a retention module, reducing the forgetting of general knowledge. Extensive experiments conducted in settings of base-to-new class generalization and few-shot learning demonstrate the superiority of the proposed method. Compared with the baseline method CLIP and the state-of-the-art method MaPLe, DGPrompt exhibits favorable performance and achieves an absolute gain of 7.84% and 0.99% on overall harmonic mean, averaged over 11 diverse image recognition datasets.
In order to provide more comprehensive medical services and personalized health monitoring according to individual needs, Body Area Networks (BANs) have been extensively studied by many researchers. As BANs involve th...
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In the cloud server, ranked searchable encryption allows the cloud server to search for the first k most relevant documents based on the correlation scores between the query keyword and the document. OPE (Orderpreserv...
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