Reviewerly/exHarmony huggingface dataset:
Parameter Sharing Exploration and Hetero-Center Triplet Loss for Visible-Thermal Person Re-Identification

This paper focuses on the visible-thermal cross-modality person re-identification (VT Re-ID) task, whose goal is to match person images between the daytime visible modality and the nighttime thermal modality. The two-stream network is usually adopted to address the cross-modality discrepancy, the most challenging problem for VT Re-ID, by learning the multi-modality person features. In this paper, we explore how many parameters a two-stream network should share, which is still not well investigated in the existing literature. By splitting the ResNet50 model to construct the modality-specific feature extraction network and modality-sharing feature embedding network, we experimentally demonstrate the effect of parameter sharing of two-stream network for VT Re-ID. Moreover, in the framework of part-level person feature learning, we propose the hetero-center triplet loss to relax the strict constraint of traditional triplet loss by replacing the comparison of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">anchor to all the other samples</i> by the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">anchor center to all the other centers</i> . With extremely simple means, the proposed method can significantly improve the VT Re-ID performance. The experimental results on two datasets show that our proposed method distinctly outperforms the state-of-the-art methods by large margins, especially on the RegDB dataset achieving superior performance, rank1/mAP/mINP 91.05%/83.28%/68.84%. It can be a new baseline for VT Re-ID, with a simple but effective strategy.
HoloLens and mobile augmented reality in medical and health science education: A randomised controlled trial

Abstract Due to increasing demands in the amount of content to be learned within a medical and health sciences curriculum, there are benefits towards exploring options for new and effective delivery modes. Augmented reality technology has the potential to enhance learning in physiology and anatomy, where students require a three‐dimensional knowledge of human organ systems and structures. This study aimed to assess the effectiveness of learning when an identical lesson was delivered through augmented reality using either the Microsoft HoloLens or a mobile handheld tablet device. Thirty‐eight pre‐clinical undergraduate participants completed a lesson detailing the physiology and anatomy of the brain. Pre‐ and post‐intervention tests were provided to evaluate acquired knowledge. After the activity, participants also completed a Likert‐style questionnaire to evaluate adverse health effects experienced and assess their perceptions of the module. There were no significant differences between test scores from lesson delivery in either the HoloLens or mobile‐based augmented reality. However, a significant increase was reported in dizziness when using the HoloLens (25% higher, n = 19, p = .04). No other adverse health effects, such as nausea, disorientation or fatigue were observed. Both modes were effective for learning, providing evidence to support educators and developers wishing to adopt an augmented reality method of delivery in health sciences and medicine. A video abstract of this article can be viewed at https://youtu.be/GSayCmopGZg Practitioner Notes What is already known about this topic Modern technology continues to disrupt the way we teach in higher education. Teaching through virtual and augmented reality has shown great benefit in enhancing learning and the student experience in health sciences and medicine. There is the potential for new and upcoming delivery modes to continue this trend, including the introduction of both mobile and head‐mounted display based augmented reality. What this study contributes Investigates the potential for augmented reality to be used as a teaching tool, and supports its implementation in tertiary education. Identifies that although slight dizziness was reported in some participants from using the HoloLens, this does not appear to impact learning or student perceptions of the technology. Presents the HoloLens and mobile‐based augmented reality as novel and evidence‐based methods of instruction in health sciences and medicine. Implications for practice and/or policy Augmented reality is an effective delivery mode which can enhance learning. Students consider augmented reality through both the HoloLens and mobile‐based devices to be enjoyable and engaging. This novel method of instruction is useful to supplement learning in a tertiary education programme.
Monocular Expressive Body Regression Through Body-Driven Attention

To understand how people look, interact, or perform tasks, we need to quickly and accurately capture their 3D body, face, and hands together from an RGB image. Most existing methods focus only on parts of the body. A few recent approaches reconstruct full expressive 3D humans from images using 3D body models that include the face and hands. These methods are optimization-based and thus slow, prone to local optima, and require 2D keypoints as input. We address these limitations by introducing ExPose (EXpressive POse and Shape rEgression), which directly regresses the body, face, and hands, in SMPL-X format, from an RGB image. This is a hard problem due to the high dimensionality of the body and the lack of expressive training data. Additionally, hands and faces are much smaller than the body, occupying very few image pixels. This makes hand and face estimation hard when body images are downscaled for neural networks. We make three main contributions. First, we account for the lack of training data by curating a dataset of SMPL-X fits on in-the-wild images. Second, we observe that body estimation localizes the face and hands reasonably well. We introduce body-driven attention for face and hand regions in the original image to extract higher-resolution crops that are fed to dedicated refinement modules. Third, these modules exploit part-specific knowledge from existing face- and hand-only datasets. ExPose estimates expressive 3D humans more accurately than existing optimization methods at a small fraction of the computational cost. Our data, model and code are available for research at https://expose.is.tue.mpg.de .
Comparative study of deep learning models for analyzing online restaurant reviews in the era of the COVID-19 pandemic

Online reviews remain important during the COVID-19 pandemic as they help customers make safe dining decisions. To help restaurants better understand customers' needs and sustain their business under current circumstance, this study extracts restaurant features that are cared for by customers in current circumstance. This study also introduces deep learning methods to examine customers' opinions about restaurant features and to detect reviews with mismatched ratings. By analyzing 112,412 restaurant reviews posted during January-June 2020 on Yelp.com, four frequently mentioned restaurant features (e.g., service, food, place, and experience) along with their associated sentiment scores were identified. Findings also show that deep learning algorithms (i.e., Bidirectional LSTM and Simple Embedding + Average Pooling) outperform traditional machine learning algorithms in sentiment classification and review rating prediction. This study strengthens the extant literature by empirically analyzing restaurant reviews posted during the COVID-19 pandemic and discovering suitable deep learning algorithms for different text mining tasks.
Explaining black-box classifiers using post-hoc explanations-by-example: The effect of explanations and error-rates in XAI user studies

In this paper, we describe a post-hoc explanation-by-example approach to eXplainable AI (XAI), where a black-box, deep learning system is explained by reference to a more transparent, proxy model (in this situation a case-based reasoner), based on a feature-weighting analysis of the former that is used to find explanatory cases from the latter (as one instance of the so-called Twin Systems approach). A novel method (COLE-HP) for extracting the feature-weights from black-box models is demonstrated for a convolutional neural network (CNN) applied to the MNIST dataset; in which extracted feature-weights are used to find explanatory, nearest-neighbours for test instances. Three user studies are reported examining people's judgements of right and wrong classifications made by this XAI twin-system, in the presence/absence of explanations-by-example and different error-rates (from 3-60%). The judgements gathered include item-level evaluations of both correctness and reasonableness, and system-level evaluations of trust, satisfaction, correctness, and reasonableness. Several proposals are made about the user's mental model in these tasks and how it is impacted by explanations at an item- and system-level. The wider lessons from this work for XAI and its user studies are reviewed.
AP-CNN: Weakly Supervised Attention Pyramid Convolutional Neural Network for Fine-Grained Visual Classification

Classifying the sub-categories of an object from the same super-category (e.g., bird species and cars) in fine-grained visual classification (FGVC) highly relies on discriminative feature representation and accurate region localization. Existing approaches mainly focus on distilling information from high-level features. In this article, by contrast, we show that by integrating low-level information (e.g., color, edge junctions, texture patterns), performance can be improved with enhanced feature representation and accurately located discriminative regions. Our solution, named Attention Pyramid Convolutional Neural Network (AP-CNN), consists of 1) a dual pathway hierarchy structure with a top-down feature pathway and a bottom-up attention pathway, hence learning both high-level semantic and low-level detailed feature representation, and 2) an ROI-guided refinement strategy with ROI-guided dropblock and ROI-guided zoom-in operation, which refines features with discriminative local regions enhanced and background noises eliminated. The proposed AP-CNN can be trained end-to-end, without the need of any additional bounding box/part annotation. Extensive experiments on three popularly tested FGVC datasets (CUB-200-2011, Stanford Cars, and FGVC-Aircraft) demonstrate that our approach achieves state-of-the-art performance. Models and code are available at https://github.com/PRIS-CV/AP-CNN_Pytorch-master.
Federated Learning in Vehicular Networks: Opportunities and Solutions

The emerging advances in personal devices and privacy concerns have given the rise to the concept of Federated Learning. Federated Learning proves its effectiveness and privacy preservation through collaborative local training and updating a shared machine learning model while protecting the individual data-sets. This article investigates a new type of vehicular network concept, namely a Federated Vehicular Network (FVN), which can be viewed as a robust distributed vehicular network. Compared to traditional vehicular networks, an FVN has centralized components and utilizes both DSRC and mmWave communication to achieve more scalable and stable performance. As a result, FVN can be used to support data-/computation-intensive applications such as distributed machine learning and Federated Learning. The article first outlines the enabling technologies of FVN. Then, we briefly discuss the high-level architecture of FVN and explain why such an architecture is adequate for Federated Learning. In addition, we use auxiliary Blockchain-based systems to facilitate transactions and mitigate malicious behaviors. Next, we discuss in detail one key component of FVN, a federated vehicular cloud (FVC), that is used for sharing data and models in FVN. In particular, we focus on the routing inside FVCs and present our solutions and preliminary evaluation results. Finally, we point out open problems and future research directions of this disruptive technology.
A privacy image encryption algorithm based on piecewise coupled map lattice with multi dynamic coupling coefficient

This paper proposes a new tent-multi dynamic piecewise coupled mapping lattice (TMDPCML). Through the comprehensive analysis of the performance test results, TMDPCML system increases the correlation dynamics of spatiotemporal behavior and improves the efficiency of energy diffusion between lattices. Moreover, TMDPCML system has larger parameter space, better chaos and outstanding cryptographic characteristics. Therefore, this paper proposes a privacy image encryption algorithm combined with TMDPCML system. The application of TMDPCML system in private images encryption further proves that TMDPCML system has good chaotic behavior and meets the requirements of cryptography.
DeepWukong

Static bug detection has shown its effectiveness in detecting well-defined memory errors, e.g., memory leaks, buffer overflows, and null dereference. However, modern software systems have a wide variety of vulnerabilities. These vulnerabilities are extremely complicated with sophisticated programming logic, and these bugs are often caused by different bad programming practices, challenging existing bug detection solutions. It is hard and labor-intensive to develop precise and efficient static analysis solutions for different types of vulnerabilities, particularly for those that may not have a clear specification as the traditional well-defined vulnerabilities. This article presents D eep W ukong , a new deep-learning-based embedding approach to static detection of software vulnerabilities for C/C++ programs. Our approach makes a new attempt by leveraging advanced recent graph neural networks to embed code fragments in a compact and low-dimensional representation, producing a new code representation that preserves high-level programming logic (in the form of control- and data-flows) together with the natural language information of a program. Our evaluation studies the top 10 most common C/C++ vulnerabilities during the past 3 years. We have conducted our experiments using 105,428 real-world programs by comparing our approach with four well-known traditional static vulnerability detectors and three state-of-the-art deep-learning-based approaches. The experimental results demonstrate the effectiveness of our research and have shed light on the promising direction of combining program analysis with deep learning techniques to address the general static code analysis challenges.
Applications of Blockchain Technology in Sustainable Manufacturing and Supply Chain Management: A Systematic Review

Developing sustainable products and processes is essential for the survival of manufacturers in the current competitive market and the industry 4.0 era. The activities of manufacturers and their supply chain partners should be aligned with sustainable development goals. Manufacturers have faced many barriers and challenges in implementing sustainable practices along the entire supply chain due to globalisation, outsourcing, and offshoring. Blockchain technology has the potential to address the challenges of sustainability. This study aims to explain the applications of blockchain technology to sustainable manufacturing. We conducted a systematic literature review and explained the potential contributions of blockchain technology to the economic, environmental, and social performances of manufacturers and their supply chains. The findings of the study extend our understanding of the blockchain applications in sustainable manufacturing and sustainable supply chains. Furthermore, the study explains how blockchain can influence the sustainable performance of manufacturers by creating transparency, traceability, real-time information sharing, and security of the data capabilities.
CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flows

Unsupervised anomaly detection with localization has many practical applications when labeling is infeasible and, moreover, when anomaly examples are completely missing in the train data. While recently proposed models for such data setup achieve high accuracy metrics, their complexity is a limiting factor for real-time processing. In this paper, we propose a real-time model and analytically derive its relationship to prior methods. Our CFLOW-AD model is based on a conditional normalizing flow frame- work adopted for anomaly detection with localization. In particular, CFLOW-AD consists of a discriminatively pretrained encoder followed by a multi-scale generative de- coders where the latter explicitly estimate likelihood of the encoded features. Our approach results in a computationally and memory-efficient model: CFLOW-AD is faster and smaller by a factor of 10× than prior state-of-the-art with the same input setting. Our experiments on the MVTec dataset show that CFLOW-AD outperforms previous methods by 0.36% AUROC in detection task, by 1.12% AUROC and 2.5% AUPRO in localization task, respectively. We open-source our code with fully reproducible experiments <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Counterfactual Attention Learning for Fine-Grained Visual Categorization and Re-identification

Attention mechanism has demonstrated great potential in fine-grained visual recognition tasks. In this paper, we present a counterfactual attention learning method to learn more effective attention based on causal inference. Unlike most existing methods that learn visual attention based on conventional likelihood, we propose to learn the attention with counterfactual causality, which provides a tool to measure the attention quality and a powerful supervisory signal to guide the learning process. Specifically, we analyze the effect of the learned visual attention on network prediction through counterfactual intervention and maximize the effect to encourage the network to learn more useful attention for fine-grained image recognition. Empirically, we evaluate our method on a wide range of fine-grained recognition tasks where attention plays a crucial role, including fine-grained image categorization, person re-identification, and vehicle re-identification. The consistent improvement on all benchmarks demonstrates the effectiveness of our method. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Image-to-Image Translation: Methods and Applications

Image-to-image translation (I2I) aims to transfer images from a source domain to a target domain while preserving the content representations. I2I has drawn increasing attention and made tremendous progress in recent years because of its wide range of applications in many computer vision and image processing problems, such as image synthesis, segmentation, style transfer, restoration, and pose estimation. In this paper, we provide an overview of the I2I works developed in recent years. We will analyze the key techniques of the existing I2I works and clarify the main progress the community has made. Additionally, we will elaborate on the effect of I2I on the research and industry community and point out remaining challenges in related fields.
Sentiment Analysis and Topic Modeling on Tweets about Online Education during COVID-19

Amid the worldwide COVID-19 pandemic lockdowns, the closure of educational institutes leads to an unprecedented rise in online learning. For limiting the impact of COVID-19 and obstructing its widespread, educational institutions closed their campuses immediately and academic activities are moved to e-learning platforms. The effectiveness of e-learning is a critical concern for both students and parents, specifically in terms of its suitability to students and teachers and its technical feasibility with respect to different social scenarios. Such concerns must be reviewed from several aspects before e-learning can be adopted at such a larger scale. This study endeavors to investigate the effectiveness of e-learning by analyzing the sentiments of people about e-learning. Due to the rise of social media as an important mode of communication recently, people’s views can be found on platforms such as Twitter, Instagram, Facebook, etc. This study uses a Twitter dataset containing 17,155 tweets about e-learning. Machine learning and deep learning approaches have shown their suitability, capability, and potential for image processing, object detection, and natural language processing tasks and text analysis is no exception. Machine learning approaches have been largely used both for annotation and text and sentiment analysis. Keeping in view the adequacy and efficacy of machine learning models, this study adopts TextBlob, VADER (Valence Aware Dictionary for Sentiment Reasoning), and SentiWordNet to analyze the polarity and subjectivity score of tweets’ text. Furthermore, bearing in mind the fact that machine learning models display high classification accuracy, various machine learning models have been used for sentiment classification. Two feature extraction techniques, TF-IDF (Term Frequency-Inverse Document Frequency) and BoW (Bag of Words) have been used to effectively build and evaluate the models. All the models have been evaluated in terms of various important performance metrics such as accuracy, precision, recall, and F1 score. The results reveal that the random forest and support vector machine classifier achieve the highest accuracy of 0.95 when used with Bow features. Performance comparison is carried out for results of TextBlob, VADER, and SentiWordNet, as well as classification results of machine learning models and deep learning models such as CNN (Convolutional Neural Network), LSTM (Long Short Term Memory), CNN-LSTM, and Bi-LSTM (Bidirectional-LSTM). Additionally, topic modeling is performed to find the problems associated with e-learning which indicates that uncertainty of campus opening date, children’s disabilities to grasp online education, and lagging efficient networks for online education are the top three problems.
Dynamic DETR: End-to-End Object Detection with Dynamic Attention

In this paper, we present a novel Dynamic DETR (Detection with Transformers) approach by introducing dynamic attentions into both the encoder and decoder stages of DETR to break its two limitations on small feature resolution and slow training convergence. To address the first limitation, which is due to the quadratic computational complexity of the self-attention module in Transformer encoders, we propose a dynamic encoder to approximate the Transformer encoder’s attention mechanism using a convolution-based dynamic encoder with various attention types. Such an encoder can dynamically adjust attentions based on multiple factors such as scale importance, spatial importance, and representation (i.e., feature dimension) importance. To mitigate the second limitation of learning difficulty, we introduce a dynamic decoder by replacing the cross-attention module with a ROI-based dynamic attention in the Transformer decoder. Such a decoder effectively assists Transformers to focus on region of interests from a coarse-to-fine manner and dramatically lowers the learning difficulty, leading to a much faster convergence with fewer training epochs. We conduct a series of experiments to demonstrate our advantages. Our Dynamic DETR significantly reduces the training epochs (by 14×), yet results in a much better performance (by 3.6 on mAP). Meanwhile, in the standard 1× setup with ResNet-50 backbone, we archive a new state-of-the-art performance that further proves the learning effectiveness of the proposed approach.