Reviewerly/exHarmony huggingface dataset:
Integrating Semantic and Temporal Relationships in Facial Action Unit Detection

Facial action unit (AU) detection is a challenging task due to the variety and subtlety of individuals' facial behavior. Facial muscle characteristics such as temporal dependencies and action correlations make AU detection differ from general multi-label classification tasks, and capturing these two characteristics is the key to accurate AU detection. However, there is little work to date taking both of them into consideration concurrently. To capture the AU correlations in an image, we first disentangle the global (image) feature into multiple AU-specific features with an AU contrastive loss, and then we compute the feature for each AU by aggregating the features from the other AUs with a self-attention based transformer. Different from the original transformer, we embed the AU semantic dependency matrix into it to weakly guide the attention learning. We then weighted fuse the AU-wise features to obtain the frame-wise features. We further capture the temporal dependencies among frames by using another attention-based transformer, which achieves information aggregation from the prior frames. Extensive experiments on two benchmark datasets (i.e., BP4D and DISFA) demonstrate that the proposed framework outperforms the state-of-the-art approaches.
Tensions and Mitigations: Understanding Concerns and Values around Smartphone Data Collection for Public Health Emergencies

Smartphones increasingly serve as the source for, or to aggregate, a considerable amount of data that can be relevant in public health emergencies. Hence the sharing and utilisation of mobile health data, for example to help control the spread of communicable diseases, has become a relevant issue, with the COVID-19 pandemic adding a sudden urgency mirrored in debates around contact tracing apps. Building on exploratory work that indicated user perceptions and values around consent, and the notion that smartphones and mobile health data can be perceived as elements of self-embodiment, we present an online study comparing three scenarios of representative diseases undertaken during the first wave lockdown in the UK. Using a mixed-methods analysis of responses from 86 participants, we identify tensions and mitigations in user values and from those present the description of four characteristic user-groups that can inform considerations for design and development activities in this space.
Extracting Feelings of People Regarding COVID-19 by Social Network Mining

In 2020, COVID-19 became one of the most critical concerns in the world. This topic is even still widely discussed on all social networks. Each day, many users publish millions of tweets and comments around this subject, implicitly showing the public’s ideas and points of view regarding this subject. In this regard, to extract the public’s point of view in various countries at the early stages of this outbreak, a dataset of Coronavirus-related tweets in the English language has been collected, which consists of more than two million tweets starting from 23 March until 23 June 2020. To this end, we first use a lexicon-based approach with the GeoNames geographic database to label each tweet with its location. Next, a method based on the recently introduced and widely cited Roberta model is proposed to analyse each tweet’s sentiment. Afterwards, some analysis showing the frequency of the tweets and their sentiments is reported for each country and the world as a whole. We mainly focus on the countries with Coronavirus as a hot topic. Graph analysis shows that the frequency of the tweets for most countries is significantly correlated with the official daily statistics of COVID-19. We also discuss some other extracted knowledge that was implicit in the tweets.
Robust Detection of Malicious URLs with Self-Paced Wide &amp; Deep Learning

As cybercrimes grow in scale with devastating economic costs, it is important to protect potential victims against diverse attacks. It is the uniform resource locators (URLs) that connect vulnerable users with potential attacks. Although numerous solutions (e.g., rule-based solutions and machine learning-based methods) are proposed for malicious URL detection, they can not provide robust performance due to the diversity of cybercrimes and can not cope with the explosive growth of malicious URLs with the evolution of obfuscation strategies. In this paper, we propose a deep learning-based system, dubbed as CyberLen, to detect malicious URLs robustly and effectively. Specifically, we use factorization machine (FM) to learn the latent interaction among lexical features. For the deep structural features, position embedding is introduced for token vectorization to reduce the ambiguity of URL tokens. Meanwhile, temporal convolution network (TCN) is utilized to learn the long-distance dependency among URL tokens. To fuse heterogeneous features, self-paced wide & deep learning strategy is proposed to train a robust model effectively. The proposed solution is evaluated on a large-scale URL dataset. Our experimental results show that position embedding is constructive to reducing the ambiguity of URL tokens, and the self-paced wide & deep learning strategy shows superior performance in terms of F1 score and convergence speed.
Discovering Density-Preserving Latent Space Walks in GANs for Semantic Image Transformations

Generative adversarial network (GAN)-based models possess superior capability of high-fidelity image synthesis. There are a wide range of semantically meaningful directions in the latent representation space of well-trained GANs, and the corresponding latent space walks are meaningful for semantic controllability in the synthesized images. To explore the underlying organization of a latent space, we propose an unsupervised Density-Preserving Latent Semantics Exploration model (DP-LaSE). The important latent directions are determined by maximizing the variations in intermediate features, while the correlation between the directions is minimized. Considering that latent codes are sampled from a prior distribution, we adopt a density-preserving regularization approach to ensure latent space walks are maintained in iso-density regions, since moving to a higher/lower density region tends to cause unexpected transformations. To further refine semantics-specific transformations, we perform subspace learning over intermediate feature channels, such that the transformations are limited to the most relevant subspaces. Extensive experiments on a variety of benchmark datasets demonstrate that DP-LaSE is able to discover interpretable latent space walks, and specific properties of synthesized images can thus be precisely controlled.
Semantically Guided Multi-View Stereo for Dense 3D Road Mapping

Compared to widely used LiDAR-based mapping in autonomous driving field, image-based mapping method has the advantages of low cost, high resolution, and no need for complex calibration. However, the image-based 3D mapping depends heavily on the texture richness and always leaves holes and outliers in low-textured areas, such as the road surface. To this end, this paper proposed a novel semantically guided Multi-View Stereo method for dense 3D road mapping, which integrates semantic information into PatchMatch-based MVS pipeline and uses image semantic segmentation as soft constraints in neighbor views selection, depth-map initialization, depth propagation, and depth-map completion. Experimental results on public and our own datasets show that, with the help of semantics, the proposed method achieves superior completeness with comparable accuracy for 3D road mapping compared to state-of-the-art MVS methods.
A Predictive and Preventive Model for Onset of Alzheimer's Disease

Alzheimer's Disease (AD) is a neurodegenerative irreversible brain disorder that gradually wipes out the memory, thinking skills and eventually the ability to carry out day-to-day tasks. The amount of AD patients is rapidly increasing due to several lifestyle changes that affect biological functions. Detection of AD at its early stages helps in the treatment of patients. In this paper, a predictive and preventive model that uses biomarkers such as the amyloid-beta protein is proposed to detect, predict, and prevent AD onset. A Convolution Neural Network (CNN) based model is developed to predict AD at its early stages. The results obtained proved that the proposed model outperforms the traditional Machine Learning (ML) algorithms such as Logistic Regression, Support Vector Machine, Decision Tree Classifier, and K Nearest Neighbor algorithms.
Real-Time Detection Method for Center and Attitude Precise Positioning of Cross Laser-Pattern

Optical metrology has experienced a fast development in recent years—cross laser-pattern has become a common cooperative measuring marker in optical metrology equipment, such as infrared imaging equipment or visual 3D measurement system. The rapid and accurate extraction of the center point and attitude of the cross-marker image is the first prerequisite to ensure the measurement speed and accuracy. In this paper, a cross laser-pattern is used as a cooperative marker, in view of the high resolution of the cross laser-pattern image in the project and the vulnerability to adverse environmental effects, such as stray light, smoke, water mist and other interference in the environment, resulting in poor contrast, low signal-to-noise ratio (SNR), uneven energy distribution. As a result, a method is proposed to detect the center point and attitude of cross laser-pattern image based on Gaussian fitting and least square fitting. Firstly, the distortion of original image is corrected in real time, the corrected image is smoothed by median filter, and the noise is suppressed while preserving the edge sharpness and detail of the image. In order to adapt to different environments, the maximum inter-class variance method of threshold automatic selection is used to determine the threshold of image segmentation to eliminate the background interference caused by different illumination intensities. To improve the real-time performance of the algorithm, the four cross laser edge pixels are obtained by line search, and then fitted by least square. With the edge lines, the transverse and portrait line of the cross-laser image are separated, then we calculate Gaussian center points of all Gaussian sections of transverse and portrait lines based on Gaussian fitting method, respectively. Based on the traditional line fitting method, the sub-pixel center of the transverse and portrait laser strip images are fitted by removing the Outlying Points, and the center coordinates and attitude information of the cross laser-pattern are calculated by using the center equation of the laser strip, realizing cross laser-pattern center and attitude accurate positioning. The results show that the method is robust, the center positioning accuracy is better than 0.6 pixels, the attitude positioning accuracy is better than ±15” under smoke and water mist environment and the processing speed is better than 0.1 s, which meets the real-time requirements of the project.
Daff-Net: Dual Attention Feature Fusion Network for Aircraft Detection in Remote Sensing Images

Aircraft detection in remote sensing images has always been a research hotspot which has great significance in both civil and military applications. Due to the variations of aircraft types, poses, sizes and complex backgrounds, it is still difficult to effectively and accurately detect aircrafts in remote sensing images. This paper proposes DAFF-Net (Dual Attention Feature Fusion Network), which makes full use of the semantic information of the high-level feature map and the location information of the shallow feature map, and integrates the local features with its global dependency adaptively. Experiments on RSOD aircraft dataset have been implemented, and the results have proved that the detection accuracy of aircraft objects with different scales and densities can all be improved.
Protecting Data Privacy in Federated Learning Combining Differential Privacy and Weak Encryption

As a typical application of decentralization, federated learning prevents privacy leakage of crowdsourcing data for various training tasks. Instead of transmitting actual data, federated learning only updates model parameters of server by learning multiple sub-models from clients. However, these parameters may be leaked during transmission and further used by attackers to restore client data. Existing technologies used to protect parameters from privacy leakage do not achieve the sufficient protection of parameter information. In this paper, we propose a novel and efficient privacy protection method, which perturbs the privacy information contained in the parameters and completes its ciphertext representation in transmission. Regarding to the perturbation part, differential privacy is utilized to perturb the real parameters, which can minimize the privacy information contained in the parameters. To further camouflage the parameters, the weak encryption keeps the ciphertext form of the parameters as they are transmitted from the client to the server. As a result, neither the server nor any middle attacker can obtain the real information of the parameter directly. The experiments show that our method effectively resists attacks from both malicious clients and malicious server.
Lawful Sequence of Events and Cryptocurrency Anomalies: An Empirical Investigation

The structural variations related to legal tender of cryptocurrencies operating across the world markets has been instable since their inception. There have been many changes incorporated for their status to be recognized as a legal financial instrument for investment purposes over the virtual financial markets. The concept of anomalies associated with the popular efficient market hypotheses given by Eugene Fama existed for Bitcoin and few other cryptocurrencies over a period of time. The present work attempts to contribute to the existing literature on cryptocurrency studies. A focused investigation has been carried for cryptocurrencies to find different behaviour of returns on these currencies over a varied response from various countries with respect to their permissible manoeuvres. The study has used independent sample t-test, one-way ANOVA and dummy regression analysis to examine the day of the week effect for a time period between 2014–2020 split into multiple sub-periods. Anomalies have been found for cryptocurrencies across multiple sub-periods with varied magnitude.
Existence of primitive normal pairs with one prescribed trace over finite fields

Given $$m, n, q\in \mathbb {N}$$ such that q is a prime power and $$m\ge 3$$ , $$a\in \mathbb {F}_q$$ , we establish a sufficient condition for the existence of a primitive pair $$(\alpha , f(\alpha ))$$ in $$\mathbb {F}_{q^m}$$ such that $$\alpha $$ is normal over $$\mathbb {F}_q$$ and $$\text {Tr}_{\mathbb {F}_{q^m}/\mathbb {F}_q}(\alpha ^{-1})=a$$ , where $$f(x)\in \mathbb {F}_{q^m}(x)$$ is a rational function of degree sum n. Further, when $$n=2$$ and $$q=5^k$$ for some $$k\in \mathbb {N}$$ , such a pair definitely exists for all (q, m) apart from at most 20 choices.
Highway-Based Local Graph Convolution Network for Aspect Based Sentiment Analysis

Aspect-level sentiment analysis is a fine-grained task in sentiment analysis, whose target is to identify the sentiment polarity of a specific aspect in a sentence. Due to the complexity of the human language, the widely-applied syntactic-based neural network methods have deficiencies in precisely capturing the relation between aspects and opinion words, and thus results in the misunderstanding of the sentiment. To address such issue, we focus on optimizing the encoding of syntactic information. To start with, the sub-dependency trees, from the basic dependency tree, are constructed in line with the syntactic distance. Further, we propose a novel Highway-Based Local Graph Convolution Network (HL-GCN) to capture the more-related information and thus facilitate the sentiment classification. Substantial experiments on a variety of datasets are performed. Comparing to the state-of-arts, the proposed model shows the effectiveness in eliminating the noise from the dependency tree, which results in an even higher classification accuracy.
Siamese Network's Performance for Face Recognition

Performance of Siamese network for real-time face recognition software in a one-shot learning setting is discussed in the paper. Two loss functions for the Siamese network are also compared, which are the contrastive loss and the triplet loss. Initially, a multitask cascaded neural network detects faces from a webcam, and the Siamese network matches the detected faces to the user's registered face. In the experiment evaluation, we find that the Siamese network with contrastive loss achieves better performance. The accuracy is 0.8875. However, the model with triplet loss has an accuracy of 0.85.
Dual Lightweight Network with Attention and Feature Fusion for Semantic Segmentation of High-Resolution Remote Sensing Images

Semantic segmentation of high-resolution remote sensing (HRRS) images has been a long-term research topic in the field of remote sensing. Nowdays, many excellent networks based on deep learning have been applied in various remote sensing fields. However, these networks always have a large number of network parameters and rely on extensive computing resources. To solve the above problems, we propose a lightweight dual branches network with the attention modules and the feature fusion module. The backbone networks of dual branches, which have fewer parameters, are used to obtain the detail information and the context information respectively. The attention modules are used to establish full-image dependencies over the local feature representations. The feature fusion module is used to fuse the low-leve features and the high-leve features effectively. Compared to other popular networks, our network has better results evaluated on ISPRS Vaihingen Dataset while with fewer parameters(8M).
Children’s perception of phobogenic stimuli in virtual reality

There has been a growing interest in treatments involving exposure in virtual reality (VR) for a number of anxiety disorders and phobias. Contrary to expectations, this technique has proven less popular with phobic children and is less appealing to them than to adults. The hypothesis is that children may be more apprehensive about virtual aversive stimuli than adults. In Study 1, the perception of virtual phobogenic stimuli was assessed in 523 participants between the ages of 7 and 60. To analyze to what extent anxiety and phobic fear predict apprehension, 38 school-aged children took part in Study 2. The results show that children apprehend phobic virtual stimulus more than adults and that apprehension is associated with specific phobic fear rather than a general anxiety state. Results are discussed in terms of clinical interest, children's cognitive development, and a priori fear of virtual reality stimuli.
OpenCL-based FPGA Accelerator for Semi-Global Approximate String Matching Using Diagonal Bit-Vectors

An FPGA accelerator for the computation of the semi-global Levenshtein distance between a pattern and a reference text is presented. The accelerator provides an important benefit to reduce the execution time of read-mappers used in short-read genomic sequencing. Previous attempts to solve the same problem in FPGA use the Myers algorithm following a column approach to compute the dynamic programming table. We use an approach based on diagonals that allows for some resource savings while maintaining a very high throughput of 1 alignment per clock cycle. The design is implemented in OpenCL and tested on two FPGA accelerators. The maximum performance obtained is 91.5 MPairs/s for 100 × 120 sequences and 47 MPairs/s for 300 × 360 sequences, the highest ever reported for this problem.
Trkic G00gle: Why and How Users Game Translation Algorithms

Individuals interact with algorithms in various ways. Users even game and circumvent algorithms so as to achieve favorable outcomes. This study aims to come to an understanding of how various stakeholders interact with each other in tricking algorithms, with a focus towards online review communities. We employed a mixed-method approach in order to explore how and why users write machine non-translatable reviews as well as how those encrypted messages are perceived by those receiving them. We found that users are able to find tactics to trick the algorithms in order to avoid censoring, to mitigate interpersonal burden, to protect privacy, and to provide authentic information for enabling the formation of informative review communities. They apply several linguistic and social strategies in this regard. Furthermore, users perceive encrypted messages as both more trustworthy and authentic. Based on these findings, we discuss implications for online review community and content moderation algorithms.
Real Time Feature Extraction Deep-CNN for Mask Detection

COVID-19 pandemic outbreak became one of the serious threats to humans. As there is no cure yet for this virus, we have to control the spread of Coronavirus through precautions. One of the effective precautions as announced by the World Health Organization is mask wearing. Surveillance systems in crowded places can lead to detection of people wearing masks. Therefore, it is highly urgent for computerized mask detection methods that can operate in real-time. As for now, most countries demand mask-wearing in public places to avoid the spreading of this virus. In this paper, we are presenting an object detection technique using a single camera, which presents real-time mask detection in closed places. Our contributions are as follows: 1) presenting a real time feature extraction module to improve the detection computational time; 2) enhancing the extracted features learned from the deep convolutional neural network models to improve small objects detection. The proposed model is a lightweight backbone CNN which ensures real time mask detection. The accuracy is also enhanced by utilizing the feature enhancement module after some of the convolution layers in the CNN. We performed extensive experiments comparing our model to the single-shot detector (SDD) and YoloV3 neural network models, which are the state-of-the-art models in the literature. The comparison shows that the result of our proposed model achieves 95.9% accuracy which is 21% higher than SSD and 17.7% higher than YoloV3 accuracy. We also conducted experiments testing the mask detection speed. It was found that our model achieves average detection time of 0.85s for images of size 1024 × 1024 pixels, which is better than the speed achieved by SSD but slightly less than the speed of YoloV3.
infOrigami: A Computer-aided Design Method for Introducing Traditional Perforated Boneless Lantern Craft to Everyday Interfaces

The traditional perforated boneless lantern craft is a three-dimensional (3D) lamp fabrication method in which paper sheets are folded without any skeleton, and often accompanied by hole-like patterns. It has been extensively applied throughout history using various means and is favored because of its low cost and environmental friendliness. However, the traditional craft requires a complicated manual production process and accumulated craft expertise, which limits its aesthetics and daily use-value. We propose a computer-aided design method to customize a 3D lamp with decorative patterns from paper pieces. The key idea is to establish an automatic workflow for producing 3D objects with visual information that is similar to the lantern tradition but without overloaded manual efforts. Our user tests and results demonstrate the potential for DIY everyday interfaces for creating personalized aesthetics, enhancing the visualization of information, and promoting multi-person handicraft activity.