Reviewerly/exHarmony huggingface dataset:
Infrared Image Super Resolution Using GAN With Infrared Image Prior

Super-resolution reconstruction technology based on deep-learning is rarely used in the field of infrared image. This paper will apply the Generative Adversarial Network super-resolution approach to the infrared super-resolution task. The natural image gradient prior is introduced into the super-resolution algorithm, and the visible image of the corresponding scene and the field of view is innovatively used as the style map, and the corresponding shallow network perceptual loss and deep network perceptual loss are added to the super-resolution objective function. The reconstructed image is more abundant and more detailed in the subjective visual reconstruction of the image texture than the existing algorithm in the simulation experiment.
Dataset of Pages from Early Printed Books with Multiple Font Groups

Based on contemporary scripts, early printers developed a large variety of different fonts. While fonts may slightly differ from one printer to another, they can be divided into font groups, such as Textura, Antiqua, or Fraktur. The recognition of font groups is important for computer scientists to select adequate OCR models, and of high interest to humanities scholars studying early printed books and the history of fonts. In this paper, we introduce a new, public dataset for the recognition of font groups in early printed books, and evaluate several state-of-the-art CNNs for the font group recognition task. The dataset consists of more than 35 600 page images, each page showing up to five different font groups, of which ten are considered in this dataset.
Robust optimal graph clustering

Most graph-based clustering methods separate the graph construction and clustering into two independent processes. The manually pre-constructed graph may not be suitable for the subsequent clustering. Moreover, as real world data generally contains noises and outliers, the similarity graph directly learned from them will be unreliable and further impair the subsequent clustering performance. To tackle the problems, in this paper, we propose a novel clustering framework where a robust graph is learned with noise removal, and simultaneously, with desirable clustering structure. To this end, we first learn a discriminative representation of data samples via sparse reconstruction. Then, a robust graph is automatically constructed with adaptive neighbors to each data sample. Simultaneously, a reasonable rank constraint is imposed on the Laplacian matrix of similarity graph to pursue the ideal clustering structure, where the number of connected components in the learned graph is exactly equal to the number of clusters. We finally derive an alternate optimization algorithm guaranteed with convergence to solve the formulated unified learning framework to achieve better prediction accuracy. Experiments on both synthetic and real datasets demonstrate the superior performance of the proposed method compared with several state-of-the-art clustering techniques.
Measuring semantic similarity of clinical trial outcomes using deep pre-trained language representations

Outcomes are variables monitored during a clinical trial to assess the impact of an intervention on humans' health.Automatic assessment of semantic similarity of trial outcomes is required for a number of tasks, such as detection of outcome switching (unjustified changes of pre-defined outcomes of a trial) and implementation of Core Outcome Sets (minimal sets of outcomes that should be reported in a particular medical domain). We aimed at building an algorithm for assessing semantic similarity of pairs of primary and reported outcomes.We focused on approaches that do not require manually curated domain-specific resources such as ontologies and thesauri. We tested several approaches, including single measures of similarity (based on strings, stems and lemmas, paths and distances in an ontology, and vector representations of phrases), classifiers using a combination of single measures as features, and a deep learning approach that consists in fine-tuning pre-trained deep language representations.We tested language models provided by BERT (trained on general-domain texts), BioBERT and SciBERT (trained on biomedical and scientific texts, respectively).We explored the possibility of improving the results by taking into account the variants for referring to an outcome (e.g.the use of a measurement tool name instead on the outcome name; the use of abbreviations).We release an open corpus with annotation for similarity of pairs of outcomes. Classifiers using a combination of single measures as features outperformed the single measures, while deep learning algorithms using BioBERT and SciBERT models outperformed the classifiers.BioBERT reached the best F-measure of 89.75%.The addition of variants of outcomes did not improve the results for the best-performing single measures nor for the classifiers, but it improved the performance of deep learning algorithms: BioBERT achieved an F-measure of93.38%. Deep learning approaches using pre-trained language representations outperformed other approaches for similarity assessment of trial outcomes, without relying on any manually curated domain-specific resources (ontologies and other lexical resources). Addition of variants of outcomes further improved the performance of deep learning algorithms.
Attention Transfer (ANT) Network for View-invariant Action Recognition

With wide applications in surveillance and human-robot interaction, view-invariant human action recognition is critical, however, challenging, due to the action occlusion and information loss caused by view change. Current methods mainly seek for a common feature space for different views. However, such solutions become invalid when there exist few common features, e.g. large view change. To tackle the problem, we propose an AttentioN Transfer (ANT) Network for view-invariant action recognition. Other than transferring features, ANT transfers attention from the reference view to arbitrary views, which correctly emphasize crucial body joints and their relations for view-invariant representation. In addition, the attention calculation method taking into account both recognition contribution and reliability of skeleton joints generates effective attention. Experiments showed its effectiveness for correctly locating crucial body joints in action sequences. We exhaustively evaluate our approach on the UESTC and the NTU dataset with three types of view-invariant evaluations, i.e. X-view, X-sub, and Arbitrary-view evaluation. Experiment results demonstrate its superiority in view-invariant representation and recognition.
Comp-GAN

Facial expression is important in understanding our social interaction. Thus the ability to recognize facial expression enables the novel multimedia applications. With the advance of recent deep architectures, research on facial expression recognition has achieved great progress. However, these models are still suffering from the problems of lacking sufficient and diverse high quality training faces, vulnerability to the facial variations, and recognizing a limited number of basic types of emotions. To tackle these problems, this paper proposes a novel end-to-end Compositional Generative Adversarial Network (Comp-GAN) that is able to synthesize new face images with specified poses and desired facial expressions; and such synthesized images can be further utilized to help train a robust and generalized expression recognition model. Essentially, Comp-GAN can dynamically change the expression and pose of faces according to the input images while keeping the identity information. Specifically, the generator has two major components: one for generating images with desired expression and the other for changing the pose of faces. Furthermore, a face reconstruction learning process is applied to re-generate the input image and constrains the generator for preserving the key information such as facial identity. For the first time, various one/zero-shot facial expression recognition tasks have been created. We conduct extensive experiments to show that the images generated by Comp-GAN are helpful to improve the performance of one/zero-shot facial expression recognition.
Computing AES related-key differential characteristics with constraint programming

Cryptanalysis aims at testing the properties of encryption processes, and this usually implies solving hard optimization problems. In this paper, we focus on related-key differential attacks for the Advanced Encryption Standard (AES), which is the encryption standard for block ciphers. To mount these attacks, cryptanalysts need to solve the optimal related-key differential characteristic problem. Dedicated approaches do not scale well for this problem, and need weeks to solve its hardest instances. In this paper, we improve existing Constraint Programming (CP) approaches for computing optimal related-key differential characteristics: we add new constraints that detect inconsistencies sooner, and we introduce a new decomposition of the problem in two steps. These improvements allow us to compute all optimal related-key differential characteristics for AES-128, AES-192 and AES-256 in a few hours.
Texture features extraction based on GLCM for face retrieval system

Texture features play an important role in most image retrieval techniques to obtain results of high accuracy. In this work, the face image retrieval method considering texture analysis and statistical features has been proposed. Textile features can also be extracted using the GLCM tool. In this research, the GLCM calculation method involves two phases, first: some of the previous image processing techniques work together to get the best results to determine the big object of the face image (center of face image) then, the gray level co-occurrence matrix GLCM is computed for gray face image and then some statistical texture features with second-order are extracted. In the second phase, the facial texture features are retrieved by finding the minimum distance between texture features of an unknown face image with the texture features of face images that are stored in the database system. The experimental results show that the proposed method is capable to achieve high accuracy degree in face image retrieval.
A Hybrid Key Agreement Scheme for Smart Homes Using the Merkle Puzzle

Cryptographic keys should be established for the smart home devices in order to secure home area networks. In certain smart home applications, however, the devices might be produced by different factories. As a result, it becomes impractical to assume devices are preloaded with secrets before leaving factories. Moreover, in some scenarios, smart home devices have no access to an online trusted third party. These problems make conventional key agreement schemes inapplicable for these devices. It is investigated that devices can extract secrets from received signal strength (RSS) measurements at the physical layer. However, the bit extraction rate is low. The Merkle puzzle is introduced to design the key agreement scheme by expanding the low entropy seed to high entropy secret key. However, it introduces considerable time and computation costs. To alleviate these problems, in this article, we design a hybrid key agreement scheme for smart homes. Namely, smart home devices first extract short random keys at the physical layer. Then, they establish secret communication keys at higher layers by making use of the Merkle puzzle. In this way, secret keys can be established without using any preloaded secrets or online trusted third party. We prove the security of the new scheme and present a prototype implementation using Ralink WiFi cards. Moreover, we evaluate the performance of our scheme and compare it with other related schemes. The analysis shows that comparing with the related schemes, in our scheme, the time cost is at least one order of magnitude lower, the computation cost is at least five orders of magnitude lower, and the extra communication cost is moderate.
Emotion sharing in remote patient monitoring of patients with chronic kidney disease

Abstract Objective To investigate the relationship between emotion sharing and technically troubled dialysis (TTD) in a remote patient monitoring (RPM) setting. Materials and Methods A custom software system was developed for home hemodialysis patients to use in an RPM setting, with focus on emoticon sharing and sentiment analysis of patients’ text data. We analyzed the outcome of emoticon and sentiment against TTD. Logistic regression was used to assess the relationship between patients’ emotions (emoticon and sentiment) and TTD. Results Usage data were collected from January 1, 2015 to June 1, 2018 from 156 patients that actively used the app system, with a total of 31 159 dialysis sessions recorded. Overall, 122 patients (78%) made use of the emoticon feature while 146 patients (94%) wrote at least 1 or more session notes for sentiment analysis. In total, 4087 (13%) sessions were classified as TTD. In the multivariate model, when compared to sessions with self-reported very happy emoticons, those with sad emoticons showed significantly higher associations to TTD (aOR 4.97; 95% CI 4.13–5.99; P = &amp;lt; .001). Similarly, negative sentiments also revealed significant associations to TTD (aOR 1.56; 95% CI 1.22–2; P = .003) when compared to positive sentiments. Discussion The distribution of emoticons varied greatly when compared to sentiment analysis outcomes due to the differences in the design features. The emoticon feature was generally easier to understand and quicker to input while the sentiment analysis required patients to manually input their personal thoughts. Conclusion Patients on home hemodialysis actively expressed their emotions during RPM. Negative emotions were found to have significant associations with TTD. The use of emoticons and sentimental analysis may be used as a predictive indicator for prolonged TTD.
Gen Z Nursing Students' Usage, Perception and Satisfaction With Facebook for Educational Purposes: Tool for Learning or Distraction

Introduction: Gen Z has started to enter the portals of nursing classrooms. They are technology and socially-savvy consumers who grew up in the world of internet and smartphones. However, little is known about this generation, particularly among nursing students and how they use Facebook for academic purposes. The purpose of this study was to determine nursing students’ usage, perception, and satisfaction with Facebook for educational purposes Methods:A descriptive, cross-sectional research design was used. A total of 113 conveniently chosen nursing students in a Nursing College in the Philippines participated in the survey. Data were collected using an adopted questionnaire administered via online survey. Descriptive and inferential statistical tools were used to analyze the data. Results: Results indicated that majority of nursing students moderately (M=3.58) use Facebook for educational purposes to communicate, collaborate, and share academic or learning materials. They generallyhad a good perception of (M=4.36) and were highly satisfied (Mean=3.95) with using Facebook for educational purposes. The main challenge reported by 81.42% of nursing students in the use of Facebook was distraction from other Facebook contents. There was a significant positive relationship between usage and perception (p=.000) likewise between satisfaction and recommendation in the use of Facebook for educational purposes (p=.000). Conclusions:While Facebook showcases promising potential as a learning tool, challenges associated with its use in nursingeducation must be properly addressed. Careful consideration and thoughtful planning are necessary for nursing schools to maximize the advantages of Facebook as a supplemental tool for learning.
Fuzzy clustering and fuzzy c-means partition cluster analysis and validation studies on a subset of citescore dataset

A hard partition clustering algorithm assigns equally distant points to one of the clusters, where each datum has the probability to appear in simultaneous assignment to further clusters. The fuzzy cluster analysis assigns membership coefficients of data points which are equidistant between two clusters so the information directs have a place toward in excess of one cluster in the meantime. For a subset of CiteScore dataset, fuzzy clustering (fanny) and fuzzy c-means (fcm) algorithms were implemented to study the data points that lie equally distant from each other. Before analysis, clusterability of the dataset was evaluated with Hopkins statistic which resulted in 0.4371, a value &amp;lt; 0.5, indicating that the data is highly clusterable. The optimal clusters were determined using NbClust package, where it is evidenced that 9 various indices proposed 3 cluster solutions as best clusters. Further, appropriate value of fuzziness parameter &lt;em&gt;m&lt;/em&gt; was evaluated to determine the distribution of membership values with variation in &lt;em&gt;m&lt;/em&gt; from 1 to 2. Coefficient of variation (CV), also known as relative variability was evaluated to study the spread of data. The time complexity of fuzzy clustering (fanny) and fuzzy c-means algorithms were evaluated by keeping data points constant and varying number of clusters.
Beyond English-Only Reading Comprehension: Experiments in Zero-Shot Multilingual Transfer for Bulgarian

Recently, reading comprehension models achieved near-human performance on large-scale datasets such as SQuAD, CoQA, MS Macro, RACE, etc.This is largely due to the release of pre-trained contextualized representations such as BERT and ELMo, which can be fine-tuned for the target task.Despite those advances and the creation of more challenging datasets, most of the work is still done for English.Here, we study the effectiveness of multilingual BERT fine-tuned on large-scale English datasets for reading comprehension (e.g., for RACE), and we apply it to Bulgarian multiple-choice reading comprehension.We propose a new dataset containing 2,221 questions from matriculation exams for twelfth grade in various subjects -history, biology, geography and philosophy-, and 412 additional questions from online quizzes in history.While the quiz authors gave no relevant context, we incorporate knowledge from Wikipedia, retrieving documents matching the combination of question + each answer option.Moreover, we experiment with different indexing and pre-training strategies.The evaluation results show accuracy of 42.23%, which is well above the baseline of 24.89%.
Optimal digital color image correlation

Within the context of Digital Image Correlation (DIC), the optimal treatment of color images is considered. The mathematical bses of a weighted 3-field image correlation are first introduced, which are relevant for RGB encoded images. In this framework, noise characterization methods are developed as noise properties dictate the best suited metric to compare images. Consistent ways to process an image from elementary Bayer matrices are derived. Last, a case study on uncertainty quantification is performed.
Dance Movement Learning for Labanotation Generation Based on Motion-Captured Data

Labanotation is a widely used notation system for recording body movements, especially dances. It has wide applications in choreography preservation, dance archiving, and so on. However, the manual creation of Labanotation scores is rather difficult and time-consuming. Therefore, research on the generation of Labanotation scores is of great interest. In this paper, we aim to generate Labanotation scores based on the motion-captured data obtained from real-world dance performances. First, to deal with challenges such as various dance movement patterns, different dancer shapes, and noises in the motion-captured data, we propose a novel feature that is invariant to anthropometric variation and body orientation. Then, we generate the notations of both lower-limb movements and upper-limb gestures. On the one hand, we utilize the hidden Markov model (HMM) to analyze the temporal dynamic characteristics of limb movements and map each lower limb movement to a corresponding dance notation. On the other hand, for upper limbs, we train a multi-class classifier based on the extremely randomized trees (Extra-Trees) to identify the notations for arm gestures. Finally, we generate the Labanotation symbols based on the above movement analysis and thus create Labanotation scores. The proposed methods can generate the spatial symbols describing directions and levels in both the support column and arm column based on motion-captured data. The generated scores are clear and reliable. Experimental results show an average recognizing accuracy of over 92% for the generated notations, which is significantly better than previous work.
Embedding Learning with Triple Trustiness on Noisy Knowledge Graph

Embedding learning on knowledge graphs (KGs) aims to encode all entities and relationships into a continuous vector space, which provides an effective and flexible method to implement downstream knowledge-driven artificial intelligence (AI) and natural language processing (NLP) tasks. Since KG construction usually involves automatic mechanisms with less human supervision, it inevitably brings in plenty of noises to KGs. However, most conventional KG embedding approaches inappropriately assume that all facts in existing KGs are completely correct and ignore noise issues, which brings about potentially serious errors. To address this issue, in this paper we propose a novel approach to learn embeddings with triple trustiness on KGs, which takes possible noises into consideration. Specifically, we calculate the trustiness value of triples according to the rich and relatively reliable information from large amounts of entity type instances and entity descriptions in KGs. In addition, we present a cross-entropy based loss function for model optimization. In experiments, we evaluate our models on KG noise detection, KG completion and classification. Through extensive experiments on three datasets, we demonstrate that our proposed model can learn better embeddings than all baselines on noisy KGs.
HRCM: An Efficient Hybrid Referential Compression Method for Genomic Big Data

With the maturity of genome sequencing technology, huge amounts of sequence reads as well as assembled genomes are generating. With the explosive growth of genomic data, the storage and transmission of genomic data are facing enormous challenges. FASTA, as one of the main storage formats for genome sequences, is widely used in the Gene Bank because it eases sequence analysis and gene research and is easy to be read. Many compression methods for FASTA genome sequences have been proposed, but they still have room for improvement. For example, the compression ratio and speed are not so high and robust enough, and memory consumption is not ideal, etc. Therefore, it is of great significance to improve the efficiency, robustness, and practicability of genomic data compression to reduce the storage and transmission cost of genomic data further and promote the research and development of genomic technology. In this manuscript, a hybrid referential compression method (HRCM) for FASTA genome sequences is proposed. HRCM is a lossless compression method able to compress single sequence as well as large collections of sequences. It is implemented through three stages: sequence information extraction, sequence information matching, and sequence information encoding. A large number of experiments fully evaluated the performance of HRCM. Experimental verification shows that HRCM is superior to the best-known methods in genome batch compression. Moreover, HRCM memory consumption is relatively low and can be deployed on standard PCs.