Reviewerly/exHarmony huggingface dataset:
A multi-criteria framework for addressing digitalization solutions of medical system under interval-valued T-spherical fuzzy information

With the help of Internet technology, digital reform of the medical system has gradually sprung up and begun to reconstruct the medical and health industry chain and service model, which has brought great change to the medical industry. At present, there are many digitalization solutions of medical system on the market. However, there are many differences in the technical standards, functions, prices and service objects of these solutions, and the objective environment is uncertain. At the same time, decision-makers often have subjective uncertainty when making choices. Therefore, it is difficult for decision-makers to choose the most satisfactory one from many digitalization solutions. To solve these problems, this study proposes the IVT-SFS weighted Muirhead mean (IVTSFWMM) operator, and establishes the evaluation and decision-making method of digitalization solutions of medical system based on this operator. Then we apply this method to cases and select the best performing solution from the four existing digitalization solutions of medical system in the market to prove the effectiveness of the method. The advantage of the method proposed in this study is that it can express subjective uncertainty when the decision-maker gives the evaluation information in the form of interval-valued fuzzy numbers, and the interaction between any number of indicators can be considered when aggregating indicators. The results show that the method proposed in this paper can effectively express the fuzzy evaluation information in complex environments, and provide suggestions for system designers to provide decision support for the evaluation and selection of intelligent medical solutions.
A Blockchain-based Trust and Reputation Model with Dynamic Evaluation Mechanism for IoT

The rapid development of the Internet of Things (IoT) has dramatically increased the number of distributed IoT devices and users. Trust and Reputation Model (TRM) is a well-known technique for improving the security of IoT, which detects malicious attacks by evaluating user behavior. Since traditional distributed TRMs lack secure and reliable data sharing mechanisms, some works have integrated the TRMs into the trusted blockchains. Nevertheless, they have not realized the security requirements of the comprehensive assessment of user behavior and dynamic evaluation of reputation. Therefore, this paper introduces a Blockchain-based Trust and Reputation Model (BTRM), which evaluates user reputation from many aspects and can resist multiple malicious attacks in the distributed network. Second, we propose a novel Dynamic Evaluation Mechanism (DEM), which reduces the number of reputation evaluations without degrading network security and builds a trusting foundation between long-term inactive users and the network. Eventually, we deploy the proposed model DEM-BTRM in a prototype system of Hyperledger Fabric and compare it with existing reputation evaluation methods. The results show that the DEM-BTRM can comprehensively evaluate user behavior and dynamically detect malicious attacks.
Multi-scale YOLACT for instance segmentation

The mainstream instance segmentation is a comprehensive computer vision task, which involves computer vision tasks such as image classification, object detection, and semantic segmentation. Aiming at the Prototype mask for initial segmentation mask with incorrect segmentation, this paper uses YOLACT (You Only Look at CoefficienTs) as the benchmark, in order to improve the network performance in the interfernce situation by enhancing the response of prototype mask, the multi-scale YOLACT for instance segmentation (MS YOLACT) is proposed, which increases the accuracy of segmentation by designing a lightweight network structure. First, the image gets multi-scale features through the residual network and the feature pyramid network. Then, the deep up-sampling and shallow down-sampling in the multi-scale feature layer are realized respectively to the size required by the prototype mask branch input, and all the deep information that has been up-sampled is further learned by convolution. Finally, at the input end of the prototype mask branch, the deep information and the shallow information are sequentially merged in an additive manner to improve the response of the prototype mask, thereby improving the accuracy of the target's mask segmentation. The experimental results show that compared with the benchmark on COCO test-dev, when the speed is reduced by only 1 FPS, the overall segmentation accuracy reaches an improvement of 0.6, and the segmentation accuracy of small and large targets reaches an improvement of 0.4 and 0.7 respectively; the visualization results also show that the segmentation mask of MS YOLACT is more accurate. In addition, MS YOLACT has the advantages of higher speed and lower requirements on equipment.
Neural Point Catacaustics for Novel-View Synthesis of Reflections

View-dependent effects such as reflections pose a substantial challenge for image-based and neural rendering algorithms. Above all, curved reflectors are particularly hard, as they lead to highly non-linear reflection flows as the camera moves. We introduce a new point-based representation to compute Neural Point Catacaustics allowing novel-view synthesis of scenes with curved reflectors, from a set of casually-captured input photos. At the core of our method is a neural warp field that models catacaustic trajectories of reflections, so complex specular effects can be rendered using efficient point splatting in conjunction with a neural renderer. One of our key contributions is the explicit representation of reflections with a reflection point cloud which is displaced by the neural warp field, and a primary point cloud which is optimized to represent the rest of the scene. After a short manual annotation step, our approach allows interactive high-quality renderings of novel views with accurate reflection flow. Additionally, the explicit representation of reflection flow supports several forms of scene manipulation in captured scenes, such as reflection editing, cloning of specular objects, reflection tracking across views, and comfortable stereo viewing. We provide the source code and other supplemental material on https://repo-sam.inria.fr/ fungraph/neural_catacaustics/
Beyond the Parts: Learning Coarse-to-Fine Adaptive Alignment Representation for Person Search

Person search is a time-consuming computer vision task that entails locating and recognizing query people in scenic pictures. Body components are commonly mismatched during matching due to position variation, occlusions, and partially absent body parts, resulting in unsatisfactory person search results. Existing approaches for extracting local characteristics of the human body using keypoint information are unable to handle the search job when distinct body parts are misaligned, ignoring to exploit multiple granularities, which is crucial in the person search process. Moreover, the alignment learning methods learn body part features with fixed and equal weights, ignoring the beneficial contextual information, e.g., the umbrella carried by the pedestrian, which supplements compelling clues for identifying the person. In this paper, we propose a Coarse-to-Fine Adaptive Alignment Representation (CFA 2 R) network for learning multiple granular features in misaligned person search in the coarse-to-fine perspective. To exploit more beneficial body parts and related context of the cropped pedestrians, we design a Part-Attentional Progressive Module (PAPM) to guide the network to focus on informative body parts and positive accessorial regions. Besides, we propose a Re-weighting Alignment Module (RAM) shedding light on more contributive parts instead of treating them equally. Specifically, adaptive re-weighted but not fixed part features are reconstructed by Re-weighting Reconstruction module, considering that different parts serve unequally during image matching. Extensive experiments conducted on CUHK-SYSU and PRW datasets demonstrate competitive performance of our proposed method.
Python in Heliophysics Community (PyHC): Current status and future outlook

It’s been four years since the formation of the Python in Heliophysics Community (PyHC). In that time, the community has made great strides towards embodying and implementing the ideals of a “Heliophysics Framework” put forth by Burrell et al. (2018). Specifically, the components of such a framework include: 1) centralization of current Python packages, 2) increasing accessibility and connectivity of these projects, 3) consideration of software attribution issues, and 4) the establishment and implementation of best practices and standards for code development. We describe the manner in which, and to what extent, PyHC has realized these four tenants. We then set forth suggestions for advancing PyHC’s efforts, including ways in which we can improve our information architecture, how we can grow our community, both in terms of project sustainability and usage, as well as the social component of the community itself, how we can improve PyHC package integration, and finally, non-Python library considerations. The suggested improvements and additions therein advance PyHC’s mission and strategic goals, while helping better integrate PyHC into the broader Heliophysics and Space Weather community efforts.
A Surveillance Video Real-Time Object Detection System Based on Edge-Cloud Cooperation in Airport Apron

Airport apron carries a lot of preparations for flight operation, and the advancement of its various tasks is of great significance to the flight operation. In order to build a more intelligent and easy-to-deploy airport apron operation analysis guarantee system, it is necessary to study a low-cost, fast, and real-time object detection scheme. In this article, a real-time object detection solution based on edge cloud system for airport apron operation surveillance video is proposed, which includes lightweight detection model Edge-YOLO, edge video detection acceleration strategy, and a cloud-based detection results verification mechanism. Edge-YOLO reduces the amounts of parameters and computational complexity by using model lightweight technology, which can achieve better detection speed performance on edge-end embedded devices with weak computing power, and adds an attention mechanism to compensate for accuracy loss. Edge video detection acceleration strategy achieves further detection acceleration for Edge-YOLO by utilizing the motion information of objects in the video to achieve real-time detection. Cloud-based detection results verification mechanism verifies and corrects the detection results generated by the edge through a multi-level intervention mechanism to improve the accuracy of the detection results. Through this solution, we can achieve reliable and real-time monitoring of airport apron video on edge devices with the support of a small amount of cloud computing power.
Intelligent Arabic Handwriting Recognition Using Different Standalone and Hybrid CNN Architectures

Handwritten character recognition is a computer-vision-system problem that is still critical and challenging in many computer-vision tasks. With the increased interest in handwriting recognition as well as the developments in machine-learning and deep-learning algorithms, researchers have made significant improvements and advances in developing English-handwriting-recognition methodologies; however, Arabic handwriting recognition has not yet received enough interest. In this work, several deep-learning and hybrid models were created. The methodology of the current study took advantage of machine learning in classification and deep learning in feature extraction to create hybrid models. Among the standalone deep-learning models trained on the two datasets used in the experiments performed, the best results were obtained with the transfer-learning model on the MNIST dataset, with 0.9967 accuracy achieved. The results for the hybrid models using the MNIST dataset were good, with accuracy measures exceeding 0.9 for all the hybrid models; however, the results for the hybrid models using the Arabic character dataset were inferior.
Deep Learning for Encrypted Traffic Classification and Unknown Data Detection

Despite the widespread use of encryption techniques to provide confidentiality over Internet communications, mobile device users are still susceptible to privacy and security risks. In this paper, a novel Deep Neural Network (DNN) based on a user activity detection framework is proposed to identify fine-grained user activities performed on mobile applications (known as in-app activities) from a sniffed encrypted Internet traffic stream. One of the challenges is that there are countless applications, and it is practically impossible to collect and train a DNN model using all possible data from them. Therefore, in this work, we exploit the probability distribution of a DNN output layer to filter the data from applications that are not considered during the model training (i.e., unknown data). The proposed framework uses a time window-based approach to divide the traffic flow of activity into segments so that in-app activities can be identified just by observing only a fraction of the activity-related traffic. Our tests have shown that the DNN-based framework has demonstrated an accuracy of 90% or above in identifying previously trained in-app activities and an average accuracy of 79% in identifying previously untrained in-app activity traffic as unknown data when this framework is employed.
Adaptive Anti-Bottleneck Multi-Modal Graph Learning Network for Personalized Micro-video Recommendation

Micro-video recommendation has attracted extensive research attention with the increasing popularity of micro-video sharing platforms. There exists a substantial amount of excellent efforts made to the micro-video recommendation task. Recently, homogeneous (or heterogeneous) GNN-based approaches utilize graph convolutional operators (or meta-path based similarity measures) to learn meaningful representations for users and micro-videos and show promising performance for the micro-video recommendation task. However, these methods may suffer from the following problems: (1) fail to aggregate information from distant or long-range nodes; (2) ignore the varying intensity of users' preferences for different items in micro-video recommendations; (3) neglect the similarities of multi-modal contents of micro-videos for recommendation tasks. In this paper, we propose a novel Adaptive Anti-Bottleneck Multi-Modal Graph Learning Network for personalized micro-video recommendation. Specifically, we design a collaborative representation learning module and a semantic representation learning module to fully exploit user-video interaction information and the similarities of micro-videos, respectively. Furthermore, we utilize an anti-bottleneck module to automatically learn the importance weights of short-range and long-range neighboring nodes to obtain more expressive representations of users and micro-videos. Finally, to consider the varying intensity of users' preferences for different micro-videos, we design and optimize an adaptive recommendation loss to train our model in an end-to-end manner. We evaluate our method on three real-world datasets and the results demonstrate that the proposed model outperforms the baselines.
Query Prior Matters

Multimodal named entity recognition (MNER) is a vision-language task where the system is required to detect entity spans and corresponding entity types given a sentence-image pair. Existing methods capture text-image relations with various attention mechanisms that only obtain implicit alignments between entity types and image regions. To locate regions more accurately and better model cross-/within-modal relations, we propose a machine reading comprehension based framework for MNER, namely MRC-MNER. By utilizing queries in MRC, our framework can provide prior information about entity types and image regions. Specifically, we design two stages, Query-Guided Visual Grounding and Multi-Level Modal Interaction, to align fine-grained type-region information and simulate text-image/inner-text interactions respectively. For the former, we train a visual grounding model via transfer learning to extract region candidates that can be further integrated into the second stage to enhance token representations. For the latter, we design text-image and inner-text interaction modules along with three sub-tasks for MRC-MNER. To verify the effectiveness of our model, we conduct extensive experiments on two public MNER datasets, Twitter2015 and Twitter2017. Experimental results show that MRC-MNER outperforms the current state-of-the-art models on Twitter2017, and yields competitive results on Twitter2015.
Learning Action-guided Spatio-temporal Transformer for Group Activity Recognition

Learning spatial and temporal relations among people plays an important role in recognizing group activity. Recently, transformer-based methods have become popular solutions due to the proposal of self-attention mechanism. However, the person-level features are fed directly into the self-attention module without any refinement. Moreover, group activity in a clip often involves unbalanced spatio-temporal interactions, where only a few persons with special actions are critical to identifying different activities. It is difficult to learn the spatio-temporal interactions due to the lack of elaborately modeling the action dependencies among all people. In this paper, a novel Action-guided Spatio-Temporal transFormer (ASTFormer) is proposed to capture the interaction relations for group activity recognition by learning action-centric aggregation and modeling spatio-temporal action dependencies. Specifically, ASTFormer starts with assigning all persons in each frame to the latent actions, while an action-centric aggregation strategy is performed by weighting the sum of residuals for each latent action under the supervision of global action information. Then, a dual-branch transformer is proposed to refine the inter- and intra-frame action-level features, where two encoders with the self-attention mechanism are employed to select important tokens. Next, a semantic action graph is explicitly devised to model the dynamic action-wise dependencies. Finally, our model is capable of boosting group activity recognition by fusing these important cues, while only requiring video-level action labels. Extensive experiments on two popular benchmarks (Volleyball and Collective Activity) demonstrate the superior performance of our method in comparison with the state-of-the-art methods using only raw RGB frames as input.
VMRF: View Matching Neural Radiance Fields

Neural Radiance Fields (NeRF) has demonstrated very impressive performance in novel view synthesis via implicitly modelling 3D representations from multi-view 2D images. However, most existing studies train NeRF models with either reasonable camera pose initialization or manually-crafted camera pose distributions which are often unavailable or hard to acquire in various real-world data. We design VMRF, an innovative view matching NeRF that enables effective NeRF training without requiring prior knowledge in camera poses or camera pose distributions. VMRF introduces a view matching scheme, which exploits unbalanced optimal transport to produce a feature transport plan for mapping a rendered image with randomly initialized camera pose to the corresponding real image. With the feature transport plan as the guidance, a novel pose calibration technique is designed which rectifies the initially randomized camera poses by predicting relative pose transformations between the pair of rendered and real images. Extensive experiments over a number of synthetic and real datasets show that the proposed VMRF outperforms the state-of-the-art qualitatively and quantitatively by large margins.
Two-Stream Transformer for Multi-Label Image Classification

Multi-label image classification is a fundamental yet challenging task in computer vision that aims to identify multiple objects from a given image. Recent studies on this task mainly focus on learning cross-modal interactions between label semantics and high-level visual representations via an attention operation. However, these one-shot attention based approaches generally perform poorly in establishing accurate and robust alignments between vision and text due to the acknowledged semantic gap. In this paper, we propose a two-stream transformer (TSFormer) learning framework, in which the spatial stream focuses on extracting patch features with a global perception, while the semantic stream aims to learn vision-aware label semantics as well as their correlations via a multi-shot attention mechanism. Specifically, in each layer of TSFormer, a cross-modal attention module is developed to aggregate visual features from spatial stream into semantic stream and update label semantics via a residual connection. In this way, the semantic gap between two streams gradually narrows as the procedure progresses layer by layer, allowing the semantic stream to produce sophisticated visual representations for each label towards accurate label recognition. Extensive experiments on three visual benchmarks, including Pascal VOC 2007, Microsoft COCO and NUS-WIDE, consistently demonstrate that our proposed TSFormer achieves state-of-the-art performance on the multi-label image classification task.
Source-Free Active Domain Adaptation via Energy-Based Locality Preserving Transfer

Unsupervised domain adaptation (UDA) aims at transferring knowledge from one labeled source domain to a related but unlabeled target domain. Recently, active domain adaptation (ADA) has been proposed as a new paradigm which significantly boosts performance of UDA with minor additional labeling. However, existing ADA methods require source data to explicitly measure the domain gap between the source domain and the target domain, which is restricted in many real-world scenarios. In this work, we handle ADA with only a source-pretrained model and unlabeled target data, proposing a new setting named source-free active domain adaptation. Specifically, we propose a Locality Preserving Transfer (LPT) framework which preserves and utilizes locality structures on target data to achieve adaptation without source data. Meanwhile, a label propagation strategy is adopted to improve the discriminability for better adaptation. After LPT, unique samples with insignificant locality structure are identified by an energy-based approach for active annotation. An energy-based pseudo labeling strategy is further applied to generate labels for reliable samples. Finally, with supervision from the annotated samples and pseudo labels, a well adapted model is obtained. Extensive experiments on three widely used UDA benchmarks show that our method is comparable or superior to current state-of-the-art active domain adaptation methods even without access to source data.