Reviewerly/exHarmony huggingface dataset:
Recognition of Affective and Grammatical Facial Expressions: A Study for Brazilian Sign Language

Individuals with hearing impairment typically face difficulties in communicating with hearing individuals and during the acquisition of reading and writing skills. Widely adopted by the deaf, Sign Language (SL) has a grammatical structure where facial expressions assume grammatical and affective functions, differentiate lexical items, participate in syntactic construction, and contribute to intensification processes. Automatic Sign Language Recognition (ASLR) technology supports the communication between deaf and hearing individuals, translating sign language gestures into written or spoken sentences of a target language. The recognition of facial expressions can improve ASLR accuracy rates. There are cases where the absence of a facial expression can create wrong translations, making them necessary for the understanding of sign language. This paper presents an approach to facial recognition for sign language. Brazilian Sign Language (Libras) is used as a case study. In our approach, we code Libras’ facial expression using the Facial Action Coding System (FACS). In the paper, we evaluate two convolutional neural networks, a standard CNN and hybrid CNN+LSTM, for AU recognition. We evaluate the models on a challenging real-world video dataset of facial expressions in Libras. The results obtained were 0.87 f1-score average and indicated the potential of the system to recognize Libras’ facial expressions.
How can state regulations over the online sphere continue to respect the freedom of expression? A case study of contemporary ‘fake news’ regulations in Thailand

The fake news crisis has raised questions about the role of state regulation in curbing misinformation, as well as the responsibilities tech companies should shoulder. In engaging with debates on digital governance, this article has two key focuses: first, what are the problems associated with the lack of state regulations in the digital sphere – where technology companies have unprecedented control over online public discourses? Next, how can states ensure that online regulations are measured, and continue to respect important liberties? This essay argues that the lack of regulation over tech firms has led to arbitrary censorship, conflict of interest issues and a legitimacy gap, with adverse societal consequences. Next, through a case study of contemporary fake news responses advanced by the Thai government, it empirically examines concerns associated with state-led initiatives to regulate misinformation on social media. Overall, while advocating for increased regulation over the online sphere, this article argues that regulations which equate the concept of fake news to illegality are not optimal as they are likely to undercut societal liberties. Implementing robust transparency mechanisms over tech companies represents a valuable first step that should be undertaken before further attempts to advance coercive legislation establishing the state as the sole arbiter of truth are made.
Uneven development, crypto-regionalism, and the (un-)tethering of nature in Quebec

Since emerging in 2009, cryptocurrencies, such as bitcoin, have captured the imaginations of many investors and users in accumulating private wealth detached from government control and oversight. This article examines how the rise of bitcoin has particular geographies and trajectories of uneven development across the globe. The generation (or 'mining') of cryptocurrencies is computationally-intensive, requiring computer hardware, cool air and cheap energy. Adopting the case study of Quebec, Canada, we show how these variables interact to produce a relationship between digital currencies, economic imaginaries and space in the regions where cryptomining is clustered. We argue that these new geographies of cryptocurrency 'mining' leave residual marks on the regions where they are located but remain highly mobile – moving from location to location in search of the cheap energy that supports private accumulation. Adopting an illustrative case study of Quebec, Canada, we work to render visible the materiality of cryptocurrencies, such as bitcoin. Far from existing both nowhere and everywhere, the generation of bitcoin is foregrounded in local contexts and regional economic imaginaries with both spatial and social implications for the cities and towns where cryptomining takes place. We conclude with a call for further research into this emergent 'crypto-regionalism' and its consequences.
Computational complexity of flat and generic Assumption-Based Argumentation, with and without probabilities

Reasoning with probabilistic information has recently attracted considerable attention in argumentation, and formalisms of Probabilistic Abstract Argumentation (PAA), Probabilistic Bipolar Argumentation (PBA) and Probabilistic Structured Argumentation (PSA) have been proposed. These foundational advances have been complemented with investigations on the complexity of some approaches to PAA and PBA, but not to PSA. We study the complexity of an existing form of PSA, namely Probabilistic Assumption-Based Argumentation (PABA), a powerful, implemented formalism which subsumes several forms of PAA and other forms of PSA. Specifically, we establish membership (general upper bounds) and completeness (instantiated lower bounds) of reasoning in PABA for the class FP#P (of functions with a #P-oracle for counting the solutions of an NP problem) with respect to newly introduced probabilistic verification, credulous and sceptical acceptance function problems under several ABA semantics. As a by-product necessary to establish PABA complexity results, we provide a comprehensive picture of the ABA complexity landscape (for both flat and generic, possibly non-flat ABA) for the classical decision problems of verification, existence, credulous and sceptical acceptance under those ABA semantics.
Appearance change for colored spun yarn fabric based on image color transfer

The fabric of colored spun yarn has ever-changing appearances and styles with different fancy yarns. The fabric image is commonly designed by the designer using the software, which needs complex user interactions and difficult image segmentation. In this paper, a modified color transfer method was proposed to generate the fabric appearance of colored spun yarn. Given the color card as the target image, the style fabric image was matched as the reference image based on the dominant luminance. After transferring the two images to lαβ color space, Wavelet transform and luminance sampling were utilized to filter the redundant high-frequency information and select the representative pixels, respectively. Then, the chromatic channels were transferred based on the best matched luminance and the neighborhood relation. Finally, the image after color transfer was reconstructed by wavelet reconstruction. The proposed reference image matching maintained the result to be the ground truth. For the samples selected, the combined methods based on wavelet transform and luminance sampling improved the efficiency and performance of the proposed scheme. Experiments were conducted on different fabrics with different colors and styles. Experiments demonstrated the validity and superiority of the proposed method, which can provide referential assistance for the designer and save considerable amounts of labor.
Layer-Wise Network Compression Using Gaussian Mixture Model

Due to the large number of parameters and heavy computation, the real-time operation of deep learning in low-performance embedded board is still difficult. Network Pruning is one of effective methods to reduce the number of parameters without additional network structure modification. However, the conventional method prunes redundant parameters up to the same rate for all layers. It may cause a bottleneck problem, which leads to the performance degradation, because the minimum number of optimal parameters is different according to the each layer. We propose a layer adaptive pruning method based on the modeling of weight distribution. We can measure the amount of weights close to zero accurately by applying Gaussian Mixture Model (GMM). Until the target compression rate is reached, the layer selection and pruning are iteratively performed. The layer selection in each iteration considers the timing to reach the target compression rate and the degree of weight pruning. We apply the proposed network compression method for image classification and semantic segmentation to show the effectiveness of the proposed method. In the experiments, the proposed method shows higher compression rate during maintaining the accuracy compared with previous methods.
Efficiently Detecting Plausible Locations for Object Placement Using Masked Convolutions

Being able to insert new objects into images is an important problem for both artistic image editing and for data augmentation. For a successful image manipulation, the plausible placement and the blending of the new objects in the image are critical. In this paper, we propose a fast method for the automatic selection of plausible locations for object insertion into images. Like previous work, we approach the object placement problem as a detection problem – given a bounding box, we evaluate whether an object is present inside the box based only on the neighborhood of the box. However, previous work requires a forward pass for each potential bounding box location. We propose instead to make use of masked convolutions to compute featuremaps for left, right, top and bottom contexts just once per image. Combining these features in such a way that no information from inside a bounding box is propagated to the final classifier allows the model to evaluate a grid of proposals on the featuremaps rather than on the image, speeding up inference dramatically. We validate that our model can generate plausible placements using experiments on the COCO dataset and on a user study. Our method trades off speed for performance, as compared to a patch based approach.
Implementation of Embedded Technology-Based English Speech Identification and Translation System

Due to the increase in globalization, communication between different countries has become more and more frequent. Language barriers are the most important issues in communication. Machine translation is limited to texts, and cannot be an adequate substitute for oral communication. In this study, a speech recognition and translation system based on embedded technology was developed for the purpose of English speech recognition and translation. The system adopted the Hidden Markov Model (HMM) and Windows CE operating system. Experiments involving English speech recognition and EnglishChinese translation found that the accuracy of the system in identifying English speech was about 88%, and the accuracy rate of the system in translating English to Chinese was over 85%. The embedded technology-based English speech recognition and translation system demonstrated a level of high accuracy in speech identification and translation, demonstrating its value as a practical application. Therefore, it merits further research and development.
Opioid2FHIR: A system for extracting FHIR-compatible opioid prescriptions from clinical text

Background: The opioid crisis is a national public health emergency in US. Especially, prescription opioids contributed significantly to drug overdose deaths. To improve the surveillance of prescription opioid overdose, it is critical to accurately collect prescription opioid information and calculate morphine milligram equivalents (MMEs). However, plenty of detailed information is only contained in the free text Sig component of electronic health record (EHR) prescriptions and need to be extracted first. Moreover, it is also indispensable to normalize opioid information extracted from multiple heath care facilities to clinical data standards such as Fast Healthcare Interoperability Resources (FHIR) for efficient clinical decision support. However, few efforts are spent in this direction at present. Methods: In this study, we designed and implemented a system that can automatically extract opioid information from free text in Sig and map them to FHIR. The system, named as Opioid2FHIR, applied multiple natural language processing (NLP) techniques for opioid information extraction and normalization. In order to reduce manual efforts, a general-purpose medication IE model was first leveraged. Based on 1, 000 opioid prescription records randomly selected from MIMICIII, post-processing rules were designed to adapt the IE model to opioid medications. Concept normalization models were also built to transform and map the extracted medication elements to fine-granular standard concepts in FHIR. The system was evaluated on another 1, 000 opioid prescription records in MIMICIII. Results: Opioid2FHIR obtained an F-measure of 0.963 for medication information extraction and an accuracy of 0.987 for medical concept normalization. Conclusions: A clinical NLP application to EHR opioid scripts would fill a current gap in available batch script processing tools and would greatly enhance individual prescription processing limitations of prescription drug monitoring programs and clinical MME calculators.
Performance Analysis of Surface Reconstruction Algorithms in Vertical Scanning Interferometry Based on Coherence Envelope Detection

Optical interferometry plays an important role in the topographical surface measurement and characterization in precision/ultra-precision manufacturing. An appropriate surface reconstruction algorithm is essential in obtaining accurate topography information from the digitized interferograms. However, the performance of a surface reconstruction algorithm in interferometric measurements is influenced by environmental disturbances and system noise. This paper presents a comparative analysis of three algorithms commonly used for coherence envelope detection in vertical scanning interferometry, including the centroid method, fast Fourier transform (FFT), and Hilbert transform (HT). Numerical analysis and experimental studies were carried out to evaluate the performance of different envelope detection algorithms in terms of measurement accuracy, speed, and noise resistance. Step height standards were measured using a developed interferometer and the step profiles were reconstructed by different algorithms. The results show that the centroid method has a higher measurement speed than the FFT and HT methods, but it can only provide acceptable measurement accuracy at a low noise level. The FFT and HT methods outperform the centroid method in terms of noise immunity and measurement accuracy. Even if the FFT and HT methods provide similar measurement accuracy, the HT method has a superior measurement speed compared to the FFT method.
Object Detection Models and Research Directions

Object detection is the task of detecting different objects in images and videos. In this paper, a comprehensive review for the classical models is given first. Then the object detection performance in UAV images, as well as the design of lightweight and small-object detection models, are discussed as new directions for object detection.
A visually secure image encryption method based on integer wavelet transform and rhombus prediction

<abstract> Traditional image encryption technology usually encrypts a normal image into a noise matrix, which can protect the image in a certain extent, but noise appearance is easy to arouse the suspicion of attackers. To avoid this problem, a method of encrypting image into carrier image with visual meaning is proposed. Inspired by the existing visually secure encryption technique, we proposed an improved method based on the integer wavelet transform (IWT) and prediction scheme. The secret image is hidden in the high frequency coefficients of IWT to achieve good invisibility, and prediction error are used to replace the pixels of the carrier image to improve the final image quality. Experimental results and analysis show that the quality of the encrypted image is 3.5 dB better than that of the previous ones. </abstract>
Implementation of quantum random walk on a real quantum computer

Abstract A random walk is one of the widely adopted random processes for simulation and approximation in multiple areas of science and engineering. Quantum random walk is an analog version of the classical random walk. It was first introduced in 1993 by Y. Aharonov et al. They presented that, with the nature of quantum characteristic, the average length of the walking path on a line is possible to larger than a classical random walk can produce. Therefore, the quantum random walk can be used as a tool to construct many other quantum algorithms. Moreover, it can solve graph problems that many real-world problems can be formulated. However, quantum algorithms are only useful in practice if we can implement them efficiently on a quantum computer. In this study, we are then interested in designing and implementing a quantum circuit that can be run on a real quantum computing device. We firstly focus on the most straightforward, which is a one-dimension, quantum random walk algorithm. Then, the quantum circuits are developed and implemented on a real quantum computer and a quantum computing simulator using software development kits provided by IBM. Lastly, the performance and results of the quantum circuits tested on both computing platforms are presented.
Proposed big data architecture for facial recognition using machine learning

<abstract> With the abundance of raw data generated from various sources including social networks, big data has become essential in acquiring, processing, and analyzing heterogeneous data from multiple sources for real-time applications. In this paper, we propose a big data framework suitable for pre‑processing and classification of image as well as text analytics by employing two key workflows, called big data (BD) pipeline and machine learning (ML) pipeline. Our unique end-to-end workflow integrates data cleansing, data integration, data transformation and data reduction processes, followed by various analytics using suitable machine learning techniques. Further, our model is the first of its kind to augment facial recognition with sentiment analysis in a distributed big data framework. The implementation of our model uses state-of-the-art distributed technologies to ingest, prepare, process and analyze big data for generating actionable data insights by employing relevant ML algorithms such as k-NN, logistic regression and decision tree. In addition, we demonstrate the application of our big data framework to facial recognition system using open sources by developing a prototype as a use case. We also employ sentiment analysis on non-repetitive semi structured public data (text) such as user comments, image tagging, and other information associated with the facial images. We believe our work provides a novel approach to intersect Big Data, ML and Face Recognition and would create new research to alleviate some of the challenges associated with big data processing in real world applications. </abstract>
An experimental eye-tracking study of text adaptation for readers with dyslexia: effects of visual support and word frequency

Easy-to-read guidelines recommend visual support and lexical simplification to facilitate text processing, but few empirical studies confirm a positive effect from these recommendations in individuals with dyslexia. This study examined the influence of the visual support and lexical simplification on sentence processing through eye movements at both the text- and word-level, and the differences between readers with and without dyslexia. Furthermore, we explored the influence of reading experience and vocabulary, as control variables. We tested 20 young adults with dyslexia and 20 chronological age-matched controls. Participants read 60 sentences in total. Half the sentences contained an image and the other half did not, and half contained a low-frequency word and half a high-frequency word. Results showed that visual support and lexical simplification facilitated sentence processing, potentially by jointly facilitating lexical semantic access. We also found that participants with lower print exposure and lower vocabulary benefited more from word-level lexical simplification. We conclude that both adaptations could benefit readers with low print exposure and smaller vocabularies, and therefore, to many dyslexic readers who show these characteristics.
Brain MR Image Super-resolution using 3D Feature Attention Network

Magnetic resonance images (MRI) with high spatial resolution provide detailed anatomical information for accurate disease diagnosis and quantitative analysis. However, the resolution of clinical MRI is restricted by hardware limitation and cost. Recently, convolutional neural networks (CNNs) are utilized to improve the spatial resolution of MRI. Whereas, current CNNbased super-resolution (SR) methods treat different types and levels of features equally, which hinders the representation ability of network. In this paper, we propose a novel feature attention super-resolution (FASR) network to adaptively capture different informative features. FASR uses parallel channel and spatial attention to enhance valuable features and suppress redundant information. The refined features are upsampled using a subpixel convolution layer, and then fused to predict the missing high-resolution details. To accelerate the training and generate realistic MRI, we introduce cross-scale residual and adversarial training to train FASR. Experimental results on brain MRI datasets of healthy subjects and gliomas show that the proposed FASR achieves a new state-of-the-art MRI SR performance.
A Power Side-Channel Attack on the CCA2-Secure HQC KEM

The Hamming Quasi-Cyclic (HQC) proposal is a promising candidate in the second round of the NIST Post-Quantum Cryptography Standardization project. It features small public key sizes, precise estimation of its decryption failure rates and contrary to most of the code-based systems, its security does not rely on hiding the structure of an error-correcting code. In this paper, we propose the first power side-channel attack on the Key Encapsulation Mechanism (KEM) version of HQC. Our attack utilizes a power side-channel to build an oracle that outputs whether the BCH decoder in HQC’s decryption algorithm corrects an error for a chosen ciphertext. Based on the decoding algorithm applied in HQC, it is shown how to design queries such that the output of the oracle allows to retrieve a large part of the secret key. The remaining part of the key can then be determined by an algorithm based on linear algebra. It is shown in experiments that less than 10000 measurements are sufficient to successfully mount the attack on the HQC reference implementation running on an ARM Cortex-M4 microcontroller.
Digital signature based on ISRSAC

Digital signature has recently played an increasingly important role in cyberspace security. Most of them are based on the public key cryptography. Public key cryptography is a mainstream cryptographic algorithm system that has been widely used in cyberspace security in recent years. The most classic public key cryptography algorithm is RSA and its difficulty is based on the large integer decomposition problem. In 2017, ISRSAC was proposed by M.Thangaval. ISRSAC has made security improvements to the RSA algorithm by increasing the complexity in factoring the value of modulus `n'. A digital signature algorithm based on ISRSAC algorithm was completed in this paper, and furthermore, a proxy signature algorithm based on ISRSAC and two kinds of multi-signature algorithms were presented, which include sequential multi-signature and broadcasting multi-signature.