Reviewerly/exHarmony huggingface dataset:
Telemedicine Meets Augmented Reality: Healthcare Services Delivery and Distance Training

The socio-economic impact of telemedicine is no longer to be proven; the perceived benefits are for the patients themselves, for the different practitioners and more broadly for the whole healthcare system. These advantages include for instance improving health care delivery for remote and/or underserved patients, helping distant physicians to make the right decision through second medical opinions and reducing overall costs of healthcare system. Hence in that field, providing innovative and pragmatic solutions/tools for healthcare professionals is one of the main objectives. Through this work, we present some telemedicine applications using Augmented Reality. We also discuss different aspects existing in these applications aiming to help both patients and physicians. We emphasize especially, fifteen works published in relevant conferences and journals. Our goal is to show the benefit of putting Telemedicine and Augmented Reality technologies together for both healthcare services delivery and remote training activities. In particular, we highlight interactive, immersive and collaborative aspects for medical distance training, one of the fields where using augmented reality is highly beneficial and very promising.
Partially Supervised Multi-Task Network for Single-View Dietary Assessment

Food volume estimation is an essential step in the pipeline of dietary assessment and demands the precise depth estimation of the food surface and table plane. Existing methods based on computer vision require either multi-image input or additional depth maps, reducing convenience of implementation and practical significance. Despite the recent advances in unsupervised depth estimation from a single image, the achieved performance in the case of large texture-less areas needs to be improved. In this paper, we propose a network architecture that jointly performs geometric understanding (i.e., depth prediction and 3D plane estimation) and semantic prediction on a single food image, enabling a robust and accurate food volume estimation regardless of the texture characteristics of the target plane. For the training of the network, only monocular videos with semantic ground truth are required, while the depth map and 3D plane ground truth are no longer needed. Experimental results on two separate food image databases demonstrate that our method performs robustly on texture-less scenarios and is superior to unsupervised networks and structure from motion based approaches, while it achieves comparable performance to fully-supervised methods.
Improving Run Length Encoding by Preprocessing

The binary representation of an arbitrary string does not contain long runs of repeating bits, but, first, reading all most significant bits of all bytes, then all second most significant bits and so on, results in much longer average runs. We use this observation in combination with several preprocessing steps to obtain a lossless RLE based compression algorithm comparable to ZIP: First, the uncompressed byte array is analyzed and for each byte its number of occurrences is counted. In parallel, a bijective Burrows-Wheeler-Scott Transform is applied, which produces a reversible permutation of the input byte array with long repetitions of similar symbols. Afterwards, each byte is remapped, where the most frequent byte values are mapped to the lowest binary values. The resulting byte array is interpreted in a specific way, known as Bit-Layers text representation, where all bits of same significance are read consecutively, starting with the most significant bits, resulting in long average runs of identical bits. On this representation, a run length encoding (RLE) is applied and the runs are counted to generate a Huffman tree. Then, the runs are output with a variable length code, together with the mapping needed to decompress the file.
Semi-Supervised Domain Adaptation via Selective Pseudo Labeling and Progressive Self-Training

Domain adaptation (DA) is a representation learning methodology that transfers knowledge from a label-sufficient source domain to a label-scarce target domain. While most of early methods are focused on unsupervised DA (UDA), several studies on semi-supervised DA (SSDA) are recently suggested. In SSDA, a small number of labeled target images are given for training, and the effectiveness of those data is demonstrated by the previous studies. However, the previous SSDA approaches solely adopt those data for embedding ordinary supervised losses, overlooking the potential usefulness of the few yet informative clues. Based on this observation, in this paper, we propose a novel method that further exploits the labeled target images for SSDA. Specifically, we utilize labeled target images to selectively generate pseudo labels for unlabeled target images. In addition, based on the observation that pseudo labels are inevitably noisy, we apply a label noise-robust learning scheme, which progressively updates the network and the set of pseudo labels by turns. Extensive experimental results show that our proposed method outperforms other previous state-of-the-art SSDA methods.
BGT: A blind image quality evaluator via gradient and texture statistical features

Blind image quality assessment (BIQA) aims to design a model that can accurately evaluate the quality of the distorted image without any information about its reference image. Previous studies have shown that gradients and textures of image is widely used in image quality evaluation tasks. However, few studies used the joint statistics of gradient and texture information to evaluate image quality. Considering the visual perception characteristics of the human visual system, we develop a novel general-purpose BIQA model via two sets of complementary perception features. Specifically, the joint statistical histograms of gradient and texture are extracted as the first set of features, and the second set of features is extracted using the local binary pattern (LBP) operator. After extracting two groups of complementary quality-aware features, the feature vectors are sent to the support vector regression machine to establish the nonlinear relationship between quality-aware features and quality scores. A large number of experiments on seven large benchmark databases show that the proposed BIQA model has higher accuracy, better generalization properties and lower computational complexity than the relevant state-of-the-art BIQA metrics.
The Effect of Feedback on Estimates of Reaching Ability in Virtual Reality

Immersive virtual environments (VEs) are most useful for training and education when viewers perceive and act accurately within them. Judgments of action capabilities within a VE provide a good measure of perceptual fidelity - the notion of how closely perception and action in the VE match that in the real world - and can also assess how perception for action may be calibrated with visual feedback based on one's own actions. In the current study we tested judgments of action capabilities within a VE for two different reaching behaviors: reaching out and reaching up. Our goal was to assess whether feedback from actual reaching improves judgments and if any recalibration due to feedback differed across reaching behaviors. We first measured participants' actual reaching out and reaching up capabilities so that feedback trials could be scaled to their actual abilities. Participants then completed blocks of alternating perceptual adjustment and feedback trials. In adjustment trials, they adjusted a virtual target to a distance perceived to be just reachable. In feedback trials, they viewed targets that were farther or closer than their actual reach, decided whether the target was reachable, and then reached out to the target to receive visual feedback from a hand-held controller. The first feedback block manipulated the target distance to be 30% over or under actual reach and subsequent blocks decreased the deviation to 20%,10% and 5% of actual reach. We found that for both reaching behaviors, reach was initially overestimated, and then perceptual estimations decreased to become more accurate over feedback blocks. Accuracy in the feedback trials themselves showed that targets just beyond reach were more difficult to judge correctly. This study establishes a straightforward methodology that can be used for calibration of actions in VEs and has implications for applications that depend on accurate reaching within VEs.
Neural Networks Optimally Compress the Sawbridge

Neural-network-based compressors have proven to be remarkably effective at compressing sources, such as images, that are nominally high-dimensional but presumed to be concentrated on a low-dimensional manifold. We consider a continuous-time random process that models an extreme version of such a source, wherein the realizations fall along a one-dimensional “curve” in function space that has infinite-dimensional linear span. We precisely characterize the optimal entropy-distortion tradeoff for this source and show numerically that it is achieved by neural-network-based compressors trained via stochastic gradient descent. In contrast, we show both analytically and experimentally that compressors based on the classical Karhunen-Loeve transform are highly suboptimal at high rates.
Eye-movements reveal semantic interference effects during the encoding of naturalistic scenes in long-term memory

Similarity-based semantic interference (SI) hinders memory recognition. Within long-term visual memory paradigms, the more scenes (or objects) from the same semantic category are viewed, the harder it is to recognize each individual instance. A growing body of evidence shows that overt attention is intimately linked to memory. However, it is yet to be understood whether SI mediates overt attention during scene encoding, and so explain its detrimental impact on recognition memory. In the current experiment, participants watched 372 photographs belonging to different semantic categories (e.g., a kitchen) with different frequency (4, 20, 40 or 60 images), while being eye-tracked. After 10 minutes, they were presented with the same 372 photographs plus 372 new photographs and asked whether they recognized (or not) each photo (i.e., old/new paradigm). We found that the more the SI, the poorer the recognition performance, especially for old scenes of which memory representations existed. Scenes more widely explored were better recognized, but for increasing SI, participants focused on more local regions of the scene in search for its potentially distinctive details. Attending to the centre of the display, or to scene regions rich in low-level saliency was detrimental to recognition accuracy, and as SI increased participants were more likely to rely on visual saliency. The complexity of maintaining faithful memory representations for increasing SI also manifested in longer fixation durations; in fact, a more successful encoding was also associated with shorter fixations. Our study highlights the interdependence between attention and memory during high-level processing of semantic information.
Drawgan: Text to Image Synthesis with Drawing Generative Adversarial Networks

In this paper, we propose a novel drawing generative adversarial networks (DrawGAN) for text-to-image synthesis. The whole model divides the image synthesis into three stages by imitating the process of drawing. The first stage synthesizes the simple contour image based on the text description, the second stage generates the foreground image with detailed information, and the third stage synthesizes the final result. Through the step by step synthesis process from simple to complex and easy to difficult, the model can draw the corresponding results step by step and finally achieve the higher-quality image synthesis effect. Our method is validated on the Caltech-UCSD Birds 200 (CUB) dataset and the Microsoft Common Objects in Context (MS COCO) dataset. The experimental results demonstrate the effectiveness and superiority of our method. In terms of both subjective and objective evaluation, our method’s results surpass the existing state-of-the-art methods.
Lecture quality assessment based on the audience reactions using machine learning and neural networks

The assessment of the academic behavior and lecture quality in educational environments is of great interest and importance. This assessment has generally been done through external observers or questionnaires. Recently some attempts have been made to solve this problem with intelligent systems. In this paper, we try to solve this problem with the help of machine learning and neural networks. A dataset of lectures in real classrooms is collected. Four methods based on extracting spatio-temporal features from the videos and classifying them using different classifiers to measure the audience's attention and consequently, lecture quality are presented. Features are extracted from sequences of each person's face frames using machine vision algorithms or neural networks. Among the proposed methods, using 3D convolutional neural networks resulted in the best accuracy of 82% and the best improvement comparing to the majority baseline accuracy of 53%.
Noise Level Limited Sub-Modeling for Diffusion Probabilistic Vocoders

Although diffusion probabilistic vocoders WaveGrad and DiffWave can realize real-time high-fidelity speech synthesis with a simple loss function in training, all noise components with over the full range of noise levels are predicted by one model in all iterations. This paper proposes a simple but effective noise level-limited sub-modeling framework for diffusion probabilistic vocoders Sub-WaveGrad and Sub-DiffWave. In the proposed method, DiffWave conditioned on a continuous noise level like WaveGrad, and spectral enhancement post-filtering are also provided. The proposed Sub-WaveGrad and Sub-DiffWave models are realized using 10 sub-models. These models are separately trained with different noise level limits, and only necessary sub-models are used according to the noise schedule during inference. The results of experiments using a Japanese female speech corpus indicate that both the proposed Sub-WaveGrad and Sub-DiffWave outperform vanilla WaveGrad and DiffWave in terms of the model accuracy and synthesis quality while retaining the inference speed.
reVISit: Looking Under the Hood of Interactive Visualization Studies

Quantifying user performance with metrics such as time and accuracy does not show the whole picture when researchers evaluate complex, interactive visualization tools. In such systems, performance is often influenced by different analysis strategies that statistical analysis methods cannot account for. To remedy this lack of nuance, we propose a novel analysis methodology for evaluating complex interactive visualizations at scale. We implement our analysis methods in reVISit, which enables analysts to explore participant interaction performance metrics and responses in the context of users’ analysis strategies. Replays of participant sessions can aid in identifying usability problems during pilot studies and make individual analysis processes salient. To demonstrate the applicability of reVISit to visualization studies, we analyze participant data from two published crowdsourced studies. Our findings show that reVISit can be used to reveal and describe novel interaction patterns, to analyze performance differences between different analysis strategies, and to validate or challenge design decisions.
An End-To-End Actor-Critic-Based Neural Coreference Resolution System

The target of a coreference resolution system is to cluster all mentions that refer to the same entity in a given context. All coreference resolution systems need to solve two subtasks; one task is to detect all of the potential mentions, and the other is to learn the linking of an antecedent for each possible mention. In this paper, we propose an actor-critic-based neural coreference resolution system, which can achieve both mention detection and mention clustering by leveraging an actor-critic deep reinforcement learning technique and a joint training algorithm. We experiment on the BERT model to generate different input span representations. Our model with the BERT span representation achieves the state-of-the-art performance among the models on the CoNLL-2012 Shared Task English Test Set.
Methods of privacy-preserving genomic sequencing data alignments

Genomic data alignment, a fundamental operation in sequencing, can be utilized to map reads into a reference sequence, query on a genomic database and perform genetic tests. However, with the reduction of sequencing cost and the accumulation of genome data, privacy-preserving genomic sequencing data alignment is becoming unprecedentedly important. In this paper, we present a comprehensive review of secure genomic data comparison schemes. We discuss the privacy threats, including adversaries and privacy attacks. The attacks can be categorized into inference, membership, identity tracing and completion attacks and have been applied to obtaining the genomic privacy information. We classify the state-of-the-art genomic privacy-preserving alignment methods into three different scenarios: large-scale reads mapping, encrypted genomic datasets querying and genetic testing to ease privacy threats. A comprehensive analysis of these approaches has been carried out to evaluate the computation and communication complexity as well as the privacy requirements. The survey provides the researchers with the current trends and the insights on the significance and challenges of privacy issues in genomic data alignment.
Let’s Frets! Mastering Guitar Playing with Capacitive Sensing and Visual Guidance

Mastering the guitar requires regular exercise to develop new skills and maintain existing abilities. We present Let's Frets - a modular guitar support system that provides visual guidance through LEDs that are integrated into a capacitive fretboard to support the practice of chords, scales, melodies, and exercises. Additional feedback is provided through a 3D-printed fretboard that senses the finger positions through capacitive sensing. We envision Let's Frets as an integrated guitar support system that raises the awareness of guitarists about their playing styles, their training progress, the composition of new pieces, and facilitating remote collaborations between teachers as well as guitar students. This interactivity demonstrates Let's Frets with an augmented fretboard and supporting software that runs on a mobile device.
Character Recognition System for Devanagari Script Using Machine Learning Approach

It is a very difficult task to manually process the handwritten documents due to varieties of handwritten scripts and lack of associated language dictionary to interpret documents. Most of the large companies as well as small-scale industries want to automate the process of script recognition. The big challenge is to make machines recognize the hand-printed scripts. Humans can recognize handwritten or hand-printed words after gaining knowledge of a specific language. In the same way, machines should be trained to recognize the handwritten scripts. This process of transferring human knowledge to computers should be automated. The proposed research work attempts to automate the character recognition system for Devanagari script using various machine learning classifiers like Decision Tree classifier, Nearest Centroid classifier, K Nearest Neighbors classifier, Extra Trees classifiers and Random Forest classifier. The performance of all the classifiers is evaluated using accuracy parameter as success criteria. The Extra Trees classifiers and Random Forest classifier is proved to better than other classifiers with 78% and 77% of accuracy respectively. The robustness to picture quality, writing style, font size is the novelty of the OCR system which makes it ideal to use.
Relating Reading, Visualization, and Coding for New Programmers: A Neuroimaging Study

Understanding how novices reason about coding at a neurological level has implications for training the next generation of software engineers. In recent years, medical imaging has been increasingly employed to investigate patterns of neural activity associated with coding activity. However, such studies have focused on advanced undergraduates and professionals. In a human study of 31 participants, we use functional near-infrared spectroscopy to measure the neural activity associated with introductory programming. In a controlled, contrast-based experiment, we relate brain activity when coding to that of reading natural language or mentally rotating objects (a spatial visualization task). Our primary result is that all three tasks-coding, prose reading, and mental rotation-are mentally distinct for novices. However, while those tasks are neurally distinct, we find more significant differences between prose and coding than between mental rotation and coding. Intriguingly, we generally find more activation in areas of the brain associated with spatial ability and task difficulty for novice coding compared to that reported in studies with more expert developers. Finally, in an exploratory analysis, we also find a neural activation pattern predictive of programming performance 11 weeks later. While preliminary, these findings both expand on previous results (e.g., relating expertise to a similarity between coding and prose reading) and also provide a new understanding of the cognitive processes underlying novice programming.
A novel weighted total variation model for image denoising

Image denoising is a very important problem in image processing field. In order to improve denoising effects and meanwhile keep image structures, a novel weighted total variation (WTV) model is proposed in this paper. The WTV model consists of data fidelity and ℓ 1 norm based regularisation terms. In the WTV model, a weight function w in exponential form is incorporated into the regularisation term, which only depends on the given image itself without extra parameters. The nonlinearly monotone formulation of w helps to increase gaps between lower and higher frequencies of images, which is effective to highlight edges and keep textures. For solving the proposed model, the alternating direction method of multipliers is explored and the according convergence is analysed. Compared experiments of TV, HOTV, ATV and TV p models are conducted and the results show the effectiveness and efficiency of the proposed model.
Fall Detection by Human Pose Estimation and Kinematic Theory

In a society with increasing age, the understanding of human falls it is of paramount importance. This paper presents a Decision Support System whose pipeline is designed to extract and compute physical domain's features achieving the state of the art accuracy on the Le2i and UR fall detection datasets. The paper uses the Kinematic Theory of Rapid Human Movement and its sigma-lognormal model together with classic physical features to achieve 98% and 99% of accuracy in automatic fall detection on respectively Le2i and URFD datasets. The effort made in the design of this work is toward recognition of falls by using physical models whose laws are clear and understandable.