Reviewerly/exHarmony huggingface dataset:
Neural-Network-Based Traffic Sign Detection and Recognition in High-Definition Images Using Region Focusing and Parallelization

Recent trends in the development of autonomous vehicles focus on real-time processing of vast amounts of data from various sensors. The data can be acquired using multiple cameras, lidars, ultrasonic sensors, and radars to collect useful information about the state of the traffic and the surroundings. Significant computational power is required to process the data fast enough, and this is even more pronounced in vehicles that not only assist the driver but are capable of fully autonomous driving. This article proposes speed and accuracy improvement of traffic sign detection and recognition in high-definition images, based on focusing on different regions of interest in traffic images. These regions are determined with efficient and parallelized preprocessing of every traffic image, after which convolutional neural network is applied for detection and recognition in parallel on graphics processing units. We employed different “You Only Look Once” (YOLO) architectures as baseline detectors, due to their speed, straightforward architecture, and high accuracy in general object detection tasks. Several preprocessing procedures were proposed, to achieve real-time performance requirement. Our experiments using a large-scale traffic sign dataset show that we can achieve real-time detection in high-definition images with high recognition accuracy.
Inexpensive Domain Adaptation of Pretrained Language Models: Case Studies on Biomedical NER and Covid-19 QA

Domain adaptation of Pretrained LanguageModels (PTLMs) is typically achieved by unsupervised pretraining on target-domain text.While successful, this approach is expensive in terms of hardware, runtime and CO 2 emissions.Here, we propose a cheaper alternative: We train Word2Vec on target-domain text and align the resulting word vectors with the wordpiece vectors of a general-domain PTLM.We evaluate on eight English biomedical Named Entity Recognition (NER) tasks and compare against the recently proposed BioBERT model.We cover over 60% of the BioBERT -BERT F1 delta, at 5% of BioBERT's CO 2 footprint and 2% of its cloud compute cost.We also show how to quickly adapt an existing generaldomain Question Answering (QA) model to an emerging domain: the Covid-19 pandemic. 1
Emotion Recognition on FER-2013 Face Images Using Fine-Tuned VGG-16

Facial emotion recognition is one among many popular and challenging tasks in the field of computer vision. Numerous researches have been conducted on this task and each proposed either standalone-or ensemble-based processing technique. While many researches strive for better accuracy, this research also attempts to increase the processing efficiency of computer correctly classifying human emotions based on human face by utilizing a single standalone-based neural network. This research proposes the use of standalone-based modified Convolutional Neural Network (CNN) based on VisualGeometry classification model which was pretrained on ImageNet dataset and fine-tuned for emotion classification.The classification is performed on the publicly available FER-2013 dataset of over 35,000 face images with in-the-wild setting for 7 distinct emotions with the provided 80% training, 10% validation, and 10% testing data distributions.The proposed approach outperforms most standalone-based model results with 69.40% accuracy.
A Dynamic Triple-Image Encryption Scheme Based on Chaos, S-Box and Image Compressing

To guarantee the security and high-efficiency of image transmission, a novel triple-image encryption scheme based on chaotic system, S-box and image compressing is proposed in this paper.Firstly, the combination process is performed by compressing three plain images to 25% and combining the compressed images with a stochastic matrix generated by the 2D-LSCM system to construct a new image.This process makes the proposed image encryption scheme have higher image transmission efficiency comparing to that of the stateof-the-art methods.Then, Z-scan and the proposed coded lock scrambling algorithm with low time complexity is used to randomly scramble the positions of pixels in the new construct image.Next, a cipher image is obtained by performing the diffusion operation on the scrambled image through S-box and chaotic sequences.In addition, the added stochastic matrix in the combination process makes the cipher image dynamic.In other words, the generated cipher images are always different to each other even when they are generated by the proposed encrypt scheme with identical plain images under the same secret keys, which can resist chosen-plaintext attacks.Finally, experimental results and simulation analysis are performed, which shows the proposed scheme can effectively resist common kinds of attacks.
Is Context-Aware CNN Ready for the Surroundings? Panoramic Semantic Segmentation in the Wild

Semantic segmentation, unifying most navigational perception tasks at the pixel level has catalyzed striking progress in the field of autonomous transportation. Modern Convolution Neural Networks (CNNs) are able to perform semantic segmentation both efficiently and accurately, particularly owing to their exploitation of wide context information. However, most segmentation CNNs are benchmarked against pinhole images with limited Field of View (FoV). Despite the growing popularity of panoramic cameras to sense the surroundings, semantic segmenters have not been comprehensively evaluated on omnidirectional wide-FoV data, which features rich and distinct contextual information. In this paper, we propose a concurrent horizontal and vertical attention module to leverage width-wise and height-wise contextual priors markedly available in the panoramas. To yield semantic segmenters suitable for wide-FoV images, we present a multi-source omni-supervised learning scheme with panoramic domain covered in the training via data distillation. To facilitate the evaluation of contemporary CNNs in panoramic imagery, we put forward the Wild PAnoramic Semantic Segmentation (WildPASS) dataset, comprising images from all around the globe, as well as adverse and unconstrained scenes, which further reflects perception challenges of navigation applications in the real world. A comprehensive variety of experiments demonstrates that the proposed methods enable our high-efficiency architecture to attain significant accuracy gains, outperforming the state of the art in panoramic imagery domains.
A Novel $\mathcal {H}_{\infty }$ Control for T–S Fuzzy Systems With Membership Functions Online Optimization Learning

This article investigatesthe optimization <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {H}_{\infty }$</tex-math></inline-formula> nonparallel distribution compensation (non-PDC) control issue for nonlinear systems under Takagi–Sugeno (T–S) fuzzy framework. First, sufficient conditions of designing fuzzy non-PDC controller to assure asymptotic stability while maintaining <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {H}_{\infty }$</tex-math></inline-formula> performance for studied systems are presented. Afterward, in the case of guaranteeing performance requirements, based on the feasible region of controller membership functions, a novel membership functions online learning algorithm utilizing gradient decent strategy is first proposed to adjust controller membership functions in real time to achieve a superior <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {H}_{\infty }$</tex-math></inline-formula> performance. Compared with conventional non-PDC fuzzy control scheme, the actual response of interference attenuation performance can be decreased efficaciously. In the light of Lyapunov stability theory, sufficient condition is derived to ensure the error convergence of cost function. At last, two illustrative examples are provided to demonstrate the effectiveness and usefulness of the proposed online learning algorithm.
Integrating contextual sentiment analysis in collaborative recommender systems

Recently. recommender systems have become a very crucial application in the online market and e-commerce as users are often astounded by choices and preferences and they need help finding what the best they are looking for. Recommender systems have proven to overcome information overload issues in the retrieval of information, but still suffer from persistent problems related to cold-start and data sparsity. On the flip side, sentiment analysis technique has been known in translating text and expressing user preferences. It is often used to help online businesses to observe customers’ feedbacks on their products as well as try to understand customer needs and preferences. However, the current solution for embedding traditional sentiment analysis in recommender solutions seems to have limitations when involving multiple domains. Therefore, an issue called domain sensitivity should be addressed. In this paper, a sentiment-based model with contextual information for recommender system was proposed. A novel solution for domain sensitivity was proposed by applying a contextual information sentiment-based model for recommender systems. In evaluating the contributions of contextual information in sentiment-based recommendations, experiments were divided into standard rating model, standard sentiment model and contextual information model. Results showed that the proposed contextual information sentiment-based model illustrates better performance as compared to the traditional collaborative filtering approach.
A Performance-Optimized Consensus Mechanism for Consortium Blockchains Consisting of Trust-Varying Nodes

Blockchain technology has wide applications in the fields of finance, public welfare, and the Internet of Things. Owing to a blockchain's characteristics, which include decentralization, openness, autonomy, immutability, and anonymity, it is difficult to quickly reach a reliable consensus result among its nodes. This work proposes a performance-optimized consensus mechanism based on node classification. Nodes are classified into accounting, validating, and propagating ones based on their trust values. All accounting nodes form an accounting node group, from which one is selected as the current accounting node to package transactions into a block, and the remaining nodes in the accounting node group can be used to validate the block quickly, owing to their high trust values. Validating and propagating nodes are responsible for validating and propagating transactions, respectively. All nodes' trust values are dynamically updated according to their behavior and performance. Corresponding algorithms are designed to realize the proposed consensus mechanism. The experimental results show that the proposed consensus mechanism provides higher throughput, lower consumption, and higher fault tolerance than some popularly used methods, thereby advancing the field of consortium blockchains.
Continuous 3D Multi-Channel Sign Language Production via Progressive Transformers and Mixture Density Networks

Abstract Sign languages are multi-channel visual languages, where signers use a continuous 3D space to communicate. Sign language production (SLP), the automatic translation from spoken to sign languages, must embody both the continuous articulation and full morphology of sign to be truly understandable by the Deaf community. Previous deep learning-based SLP works have produced only a concatenation of isolated signs focusing primarily on the manual features, leading to a robotic and non-expressive production. In this work, we propose a novel Progressive Transformer architecture, the first SLP model to translate from spoken language sentences to continuous 3D multi-channel sign pose sequences in an end-to-end manner. Our transformer network architecture introduces a counter decoding that enables variable length continuous sequence generation by tracking the production progress over time and predicting the end of sequence. We present extensive data augmentation techniques to reduce prediction drift, alongside an adversarial training regime and a mixture density network (MDN) formulation to produce realistic and expressive sign pose sequences. We propose a back translation evaluation mechanism for SLP, presenting benchmark quantitative results on the challenging PHOENIX14T dataset and setting baselines for future research. We further provide a user evaluation of our SLP model, to understand the Deaf reception of our sign pose productions.
Determining the Efficiency of Drugs Under Special Conditions From Users’ Reviews on Healthcare Web Forums

Sentiment analysis is the extraction and categorization of sentiments that have been expressed in text data using text analysis techniques.Manifested by earlier studies, sentiment analysis of drug reviews has a large potential for providing valuable insights to assist healthcare professionals and companies for evaluating the safety of drugs after it has been marketed.Such insights help safeguard patients and increase their trust in medical companies.The existing systems either follow a lexicon-based approach or a learning-based approach for sentiment analysis in the medical domain.Learning-based techniques require annotated data while lexicon-based techniques tend to be domain-specific which restricts their wide use.This research embarks on a hybrid technique that utilizes both learning-based and lexicon-based approaches to achieve better results.General-purpose sentiment lexicons, such as AFFIN, TextBlob, and VADER, are used for annotating the reviews.Furthermore, several feature engineering techniques, such as term frequency (TF), term frequency-inverse document frequency (TF-IDF), and union of TF and TF-IDF (TF U TF-IDF) have been incorporated for the extraction of useful features.Finally, the learning models including logistic regression (LR), AdaBoost classifier (AB), random forest (RF), extra tree classifier (ETC), and multilayer perceptron (MLP) are used to classify sentiments of the reviews.The performance of the proposed hybrid approach is evaluated using accuracy, precision, recall, and F1-score.Experimental results indicate that the combination of learning-based and lexicon-based approaches provide improved results than their individual use.Moreover, TextBlob has shown promising results giving an accuracy of 96% with MLP when used with TF-IDF and with LR when used with TF U TF-IDF.
Blind Image Super-Resolution: A Survey and Beyond

Blind image super-resolution (SR), aiming to super-resolve low-resolution images with unknown degradation, has attracted increasing attention due to its significance in promoting real-world applications. Many novel and effective solutions have been proposed recently, especially with powerful deep learning techniques. Despite years of efforts, it still remains as a challenging research problem. This paper serves as a systematic review on recent progress in blind image SR, and proposes a taxonomy to categorize existing methods into three different classes according to their ways of degradation modelling and the data used to solve the SR model. This taxonomy helps summarize and distinguish among existing methods. We hope to provide insights into current research states, as well as revealing novel research directions worth exploring. In addition, we make a summary on commonly used datasets and previous competitions related to blind image SR. Last but not least, a comparison among different methods is provided with detailed analysis on their merits and demerits using both synthetic and real testing images.
Portable 3D Human Pose Estimation for Human-Human Interaction using a Chest-Mounted Fisheye Camera

We propose a system that estimates the 3D body pose of other parties using a single RGB chest-mounted ultra-wide fisheye camera. Although the fisheye camera can capture a wide field of view, it is difficult to apply image processing for perspective images because of its strong distortion. In our method, the input fisheye image is converted to an equirectangular image to detect another person and their 2D keypoints, and then convert them to a 3D pose. In order to adapt to the distortion of equirectangular images, we generate a synthetic dataset and fine-tune the model. We also estimate the location of the other person so that we can reconstruct the absolute camera-centered global pose. We evaluate the accuracy on real-world data and show that the fine-tuned model performs best.
Deep Learning Fast Screening Approach on Cytological Whole Slides for Thyroid Cancer Diagnosis

Thyroid cancer is the most common cancer in the endocrine system, and papillary thyroid carcinoma (PTC) is the most prevalent type of thyroid cancer, accounting for 70 to 80% of all thyroid cancer cases. In clinical practice, visual inspection of cytopathological slides is an essential initial method used by the pathologist to diagnose PTC. Manual visual assessment of the whole slide images is difficult, time consuming, and subjective, with a high inter-observer variability, which can sometimes lead to suboptimal patient management due to false-positive and false-negative. In this study, we present a fully automatic, efficient, and fast deep learning framework for fast screening of papanicolaou-stained thyroid fine needle aspiration (FNA) and ThinPrep (TP) cytological slides. To the authors’ best of knowledge, this work is the first study to build an automated deep learning framework for identification of PTC from both FNA and TP slides. The proposed deep learning framework is evaluated on a dataset of 131 WSIs, and the results show that the proposed method achieves an accuracy of 99%, precision of 85%, recall of 94% and F1-score of 87% in segmentation of PTC in FNA slides and an accuracy of 99%, precision of 97%, recall of 98%, F1-score of 98%, and Jaccard-Index of 96% in TP slides. In addition, the proposed method significantly outperforms the two state-of-the-art deep learning methods, i.e., U-Net and SegNet, in terms of accuracy, recall, F1-score, and Jaccard-Index (p&lt;0.001). Furthermore, for run-time analysis, the proposed fast screening method takes 0.4 min to process a WSI and is 7.8 times faster than U-Net and 9.1 times faster than SegNet, respectively.
Falling in Love with Virtual Reality Art: A New Perspective on 3D Immersive Virtual Reality for Future Sustaining Art Consumption

Many art activities have been trialed in 360-degree virtual reality (VR) and this has become an increasingly representative way toward sustainable consumption of art. However, understanding audience responses to the VR art remains a matter of debate and investigation. By employing the uses and gratifications (U&G) approach, this study explores motives for and consequences of watching 360-degree VR art. The identified motives are three dimensions: pursuing learning from entertainment, pursuing social conformity, and pursuing convenience. Among the three use motives, it was found that pursuing learning from entertainment is the most significant factor influencing audiences’ transportation experience while watching 360-degree VR art, and audiences’ innovativeness functions as a moderator in the relationship between transportation and the attitude toward the VR content. The theoretical and practical implications of this study are discussed.
Trans4Trans: Efficient Transformer for Transparent Object and Semantic Scene Segmentation in Real-World Navigation Assistance

Transparent objects, such as glass walls and doors, constitute architectural obstacles hindering the mobility of people with low vision or blindness. For instance, the open space behind glass doors is inaccessible, unless it is correctly perceived and interacted with. However, traditional assistive technologies rarely cover the segmentation of these safety-critical transparent objects. In this paper, we build a wearable system with a novel dual-head Transformer for Transparency (Trans4Trans) perception model, which can segment general- and transparent objects. The two dense segmentation results are further combined with depth information in the system to help users navigate safely and assist them to negotiate transparent obstacles. We propose a lightweight Transformer Parsing Module (TPM) to perform multi-scale feature interpretation in the transformer-based decoder. Benefiting from TPM, the double decoders can perform joint learning from corresponding datasets to pursue robustness, meanwhile maintain efficiency on a portable GPU, with negligible calculation increase. The entire Trans4Trans model is constructed in a symmetrical encoder-decoder architecture, which outperforms state-of-the-art methods on the test sets of Stanford2D3D and Trans10K-v2 datasets, obtaining mIoU of 45.13% and 75.14%, respectively. Through a user study and various pre-tests conducted in indoor and outdoor scenes, the usability and reliability of our assistive system have been extensively verified. Meanwhile, the Tran4Trans model has outstanding performances on driving scene datasets. On Cityscapes, ACDC, and DADA-seg datasets corresponding to common environments, adverse weather, and traffic accident scenarios, mIoU scores of 81.5%, 76.3%, and 39.2% are obtained, demonstrating its high efficiency and robustness for real-world transportation applications.
GAN-based spatial image steganography with cross feedback mechanism

Steganography has been widely used to realize covert communication with multimedia content. By simulating the competition between a generator and a discriminator in a generative adversarial network (GAN), good distortion measurement can be automatically learnt to help designing high undetectable steganography. Nevertheless, existing GAN-based steganography always uses linear convolutional neural networks to construct generators, where the modification information of pixel in deep layers cannot be real-time transmitted to the neurons of the shallow layers, resulting in a low undetectable performance due to insufficient network training. To address the problem, we propose a new GAN-based spatial steganographic scheme. Different from existing GAN-based steganography, we build multiple cross feedback channels between the contraction path and the expansion path of the generator, which allows that the down-sampling information is directly sent to the expansion layer through these feedback channels. Since the detailed information from deep layers can be captured effectively by referring to the information from cover image, the generator can finally learn a sophisticated probability map to guide high undetectable steganography. Comprehensive experimental results demonstrate that, with the same steganalyzers, our scheme is superior to existing GAN-based steganographic schemes in terms of anti-steganalysis capability.
Human pose estimation for mitigating false negatives in weapon detection in video-surveillance

Applying CNN-based object detection models to the task of weapon detection in video-surveillance is still producing a high number of false negatives. In this context, most existing works focus on one type of weapons, mainly firearms, and improve the detection using different pre- and post-processing strategies. One interesting approach that has not been explored in depth yet is the exploitation of the human pose information for improving weapon detection. This paper proposes a top-down methodology that first determines the hand regions guided by the human pose estimation then analyzes those regions using a weapon detection model. For an optimal localization of each hand region, we defined a new factor, called Adaptive pose factor, that takes into account the distance of the body from the camera. Our experiments show that this top-down Weapon Detection over Pose Estimation (WeDePE) methodology is more robust than the alternative bottom-up approach and state-of-the art detection models in both indoor and outdoor video-surveillance scenarios.