Reviewerly/exHarmony huggingface dataset:
Realtime 3D 360-Degree Telepresence With Deep-Learning-Based Head-Motion Prediction

The acceptance and the dissemination of 360° telepresence systems are severely restricted by the appearance of motion sickness. Such systems consist of a client-side, where the user wears a head-mounted display, a server-side, which provides a 3D 360° visual representation of the remote scene, and a communication network in between. Due to the physically unavoidable latency, there is often a noticeable lag between head motion and visual response. If the sensory information from the visual system is not consistent with the perceived egomotion of the user, and the emergence of visual discomfort is inevitable. In this paper, we present a delay-compensating 3D 360° vision system, which provides omnistereoscopic vision and a significant reduction of the perceived latency. We formally describe the underlying problem and provide an algebraic description of the amount of achievable delay compensation both for perspective and fisheye camera systems, considering all the three degrees of freedom. Furthermore, we propose a generic approach that is agnostic to the underlying camera system. In addition, a novel deep-learning-based head motion prediction algorithm is presented to further improve the compensation rate. Using the naive approach, where no compensation and prediction is applied, we obtain a mean compensation rate of 72.8% for investigated latencies from 0.1 to 1.0 s. Our proposed generic delay-compensation approach, combined with our novel deep-learning-based head-motion prediction approach, manages to achieve a mean compensation rate of 97.3%. The proposed technique also substantially outperforms prior head-motion prediction techniques, both for traditional and deep learning-based methods.
Integration of remote sensing and geographic information systems for geological fault detection on the island of Crete, Greece

Abstract. Fracture systems are of great importance in the field of structural geology. Faults commonly afford easy passage to groundwater and fluids such as hydrothermal fluids and magmas or even contribute to earthquake hazard monitoring. For a geologist, it is not always easy to discern such morphotectonic structures at close range. Both remote sensing techniques and spatial modeling permit the recognition and better understanding of the brittle tectonics in an area. This study was an effort to delineate the tectonic structures on Crete by combining Sentinel-2 satellite data and spatial data. For the enhancement and better discrimination of photolineaments primarily recognized on satellite imagery, a variety of enhancement techniques has been applied. The evaluation of a photolineament as a potential fracture zone was based on several factors: the DEM of the study area, the shaded relief, the slopes and corresponding aspects, the drainage network, the geology and general observations on vegetative coverage appearance. The application of these methods revealed several fracture zones, which we recommend be certified by field investigations. Fault-mapping results may be used for a variety of geological and hydrogeological studies. Indicative places of a large concentration of groundwater are of vital importance for subsequent exploitation by areas of need. The present work may provide useful information for further analysis by geophysicists and seismologists.
An Evaluation of Depth and Size Perception on a Spherical Fish Tank Virtual Reality Display

Fish Tank Virtual Reality (FTVR) displays create a compelling 3D spatial effect by rendering to the perspective of the viewer with head-tracking. Combining FTVR with a spherical display enhances the 3D experience with unique properties of the spherical screen such as the enclosing shape, consistent curved surface, and borderless views from all angles around the display. The ability to generate a strong 3D effect on a spherical display with head-tracked rendering is promising for increasing user's performance in 3D tasks. An unanswered question is whether these natural affordances of spherical FTVR displays can improve spatial perception in comparison to traditional flat FTVR displays. To investigate this question, we conducted an experiment to see whether users can perceive the depth and size of virtual objects better on a spherical FTVR display compared to a flat FTVR display on two tasks. Using the spherical display, we found significantly that users had 1cm depth accuracy compared to 6.5cm accuracy using the flat display on a depth-ranking task. Likewise, their performance on a size-matching task was also significantly better with the size error of 2.3mm on the spherical display compared to 3.1mm on the flat display. Furthermore, the perception of size-constancy is stronger on the spherical display than the flat display. This study indicates that the natural affordances provided by the spherical form factor improve depth and size perception in 3D compared to a flat display. We believe that spherical FTVR displays have potential as a 3D virtual environment to provide better task performance for various 3D applications such as 3D designs, scientific visualizations, and virtual surgery.
Accelerating Self-Play Learning in Go

By introducing several improvements to the AlphaZero process and architecture, we greatly accelerate self-play learning in Go, achieving a 50x reduction in computation over comparable methods. Like AlphaZero and replications such as ELF OpenGo and Leela Zero, our bot KataGo only learns from neural-net-guided Monte Carlo tree search self-play. But whereas AlphaZero required thousands of TPUs over several days and ELF required thousands of GPUs over two weeks, KataGo surpasses ELF's final model after only 19 days on fewer than 30 GPUs. Much of the speedup involves non-domain-specific improvements that might directly transfer to other problems. Further gains from domain-specific techniques reveal the remaining efficiency gap between the best methods and purely general methods such as AlphaZero. Our work is a step towards making learning in state spaces as large as Go possible without large-scale computational resources.
Single Image Haze Removal Using Conditional Wasserstein Generative Adversarial Networks

We present a method to restore a clear image from a haze-affected image using a Wasserstein generative adversarial network. As the problem is ill-conditioned, previous methods have required a prior on natural images or multiple images of the same scene. We train a generative adversarial network to learn the probability distribution of clear images conditioned on the haze-affected images using the Wasserstein loss function, using a gradient penalty to enforce the Lipschitz constraint. The method is data-adaptive, end-to-end, and requires no further processing or tuning of parameters. We also incorporate the use of a texturebased loss metric and the L1 loss to improve results, and show that our results are better than the current state-of-the-art.
Understanding How Video Quality Affects Object Detection Algorithms

Video quality is an important practical challenge that is often overlooked in the design of automated video surveillance systems. Commonly, visual intelligent systems are trained and tested on high quality image datasets, yet in practical video surveillance applications the video frames can not be assumed to be of high quality due to video encoding, transmission and decoding. Recently, deep neural networks have obtained state-of-the-art performance on many machine vision tasks. In this paper we provide an evaluation of 4 state-of-the-art deep neural network models for object detection under various levels of video compression. We show that the existing detectors are susceptible to quality distortions stemming from compression artifacts during video acquisition. These results enable future work in developing object detectors that are more robust to video quality.
Adversarial attacks against Fact Extraction and VERification

This paper describes a baseline for the second iteration of the Fact Extraction and VERification shared task (FEVER2.0) which explores the resilience of systems through adversarial evaluation. We present a collection of simple adversarial attacks against systems that participated in the first FEVER shared task. FEVER modeled the assessment of truthfulness of written claims as a joint information retrieval and natural language inference task using evidence from Wikipedia. A large number of participants made use of deep neural networks in their submissions to the shared task. The extent as to whether such models understand language has been the subject of a number of recent investigations and discussion in literature. In this paper, we present a simple method of generating entailment-preserving and entailment-altering perturbations of instances by common patterns within the training data. We find that a number of systems are greatly affected with absolute losses in classification accuracy of up to $29\%$ on the newly perturbed instances. Using these newly generated instances, we construct a sample submission for the FEVER2.0 shared task. Addressing these types of attacks will aid in building more robust fact-checking models, as well as suggest directions to expand the datasets.
Unsupervised joint deconvolution and segmentation method for textured images: a Bayesian approach and an advanced sampling algorithm

The paper tackles the problem of joint deconvolution and segmentation of textured images. The images are composed of regions containing a patch of texture that belongs to a set of K possible classes. Each class is described by a Gaussian random field with parametric power spectral density whose parameters are unknown. The class labels are modelled by a Potts field driven by a granularity coefficient that is also unknown. The method relies on a hierarchical model and a Bayesian strategy to jointly estimate the labels, the K textured images in addition to hyperparameters: the signal and the noise levels as well as the texture parameters and the granularity coefficient. The capability to estimate the latter is an important feature of the paper. The estimates are designed in an optimal manner as a risk minimizer that yields the marginal posterior maximizer for the labels and the posterior mean for the rest of the unknowns. They are computed based on a convergent procedure from samples of the posterior obtained through an advanced MCMC algorithm: Perturbation-Optimization step and Fisher Metropolis-Hastings step within a Gibbs loop. Various numerical evaluations provide encouraging results despite the strong difficulty of the problem.
Superpixel Segmentation Based on Square-Wise Asymmetric Partition and Structural Approximation

Superpixel segmentation aims at grouping discretizing pixels into high-level correlative units and reducing the complexity of subsequent tasks, e.g., saliency detection and object tracking. Existing superpixel segmentation algorithms mainly focus on maintaining the geometrical information, while neglecting the irregular structure of superpixels. In this paper, a superpixel segmentation method is proposed to generate approximately structural superpixels with sharp boundary adherence and comprehensive semantic information. The superpixel segmentation is formulated as a square-wise asymmetric partition problem, where the semantic perceptual superpixels are recorded in a square level to preserve abundant semantic information and save storage simultaneously. Moreover, in order to achieve regular-shape superpixel units to better adhere to image boundaries and contours, a combinatorial optimization strategy is devised to achieve an optimal combination of squares and isolated pixels. Experimental comparisons with some state-of-the-art superpixel segmentation methods on the public benchmarks demonstrate the effectiveness of the proposed method quantitatively and qualitatively. In addition, we have applied the method to brain tissue segmentation to illustrate superior performance.
Optimal Joint Modulation Classification and Symbol Decoding

In this paper, modulation classification and symbol decoding problems are jointly considered and optimal strategies are proposed under various settings. In the considered framework, there exist a number of candidate modulation formats and the aim is to decode a sequence of received signals with an unknown modulation scheme. To that aim, two different formulations are proposed. In the first formulation, the prior probabilities of the modulation schemes are assumed to be known and a formulation is proposed under the Bayesian framework. This formulation takes a constrained approach in which the objective function is related to symbol decoding performance whereas the constraint is related to modulation classification performance. The second formulation, on the other hand, addresses the case in which the prior probabilities of the modulation schemes are unknown, and provides a method under the minimax framework. In this case, a constrained approach is employed as well; however, the introduced performance metrics differ from those in the first formulation due to the absence of the prior probabilities of the modulation schemes. Finally, the performance of the proposed methods is illustrated through simulations. It is demonstrated that the proposed techniques improve the introduced symbol detection performance metrics via relaxing the constraint(s) on the modulation classification performance compared with the conventional techniques in a variety of system configurations.
Probing Prior Knowledge Needed in Challenging Chinese Machine Reading Comprehension.

Machine reading comprehension tasks require a machine reader to answer questions relevant to the given document. In this paper, we present the first free-form multiple-Choice Chinese machine reading Comprehension dataset (C^3), containing 13,369 documents (dialogues or more formally written mixed-genre texts) and their associated 19,577 multiple-choice free-form questions collected from Chinese-as-a-second-language examinations. We present a comprehensive analysis of the prior knowledge (i.e., linguistic, domain-specific, and general world knowledge) needed for these real-world problems. We implement rule-based and popular neural methods and find that there is still a significant performance gap between the best performing model (68.5%) and human readers (96.0%), especially on problems that require prior knowledge. We further study the effects of distractor plausibility and data augmentation based on translated relevant datasets for English on model performance. We expect C^3 to present great challenges to existing systems as answering 86.8% of questions requires both knowledge within and beyond the accompanying document, and we hope that C^3 can serve as a platform to study how to leverage various kinds of prior knowledge to better understand a given written or orally oriented text. C^3 is available at this https URL.
Just Jump: Dynamic Neighborhood Aggregation in Graph Neural Networks

We propose a dynamic neighborhood aggregation (DNA) procedure guided by (multi-head) attention for representation learning on graphs. In contrast to current graph neural networks which follow a simple neighborhood aggregation scheme, our DNA procedure allows for a selective and node-adaptive aggregation of neighboring embeddings of potentially differing locality. In order to avoid overfitting, we propose to control the channel-wise connections between input and output by making use of grouped linear projections. In a number of transductive node-classification experiments, we demonstrate the effectiveness of our approach.
The Potential Impact of Blockchain Technology on Audit Practice

In today’s debate on the potential disruptive effects of blockchain, audit and control professions are rarely in the spotlight although applications such as smart contracts and distributed ledgers could significantly impact them. We conducted a study based on the grounded theory to understand how auditors in Switzerland anticipate the impacts of blockchain on their activities. Based on our findings, three hypotheses have emerged. First, the potential effect of blockchain on the profession is not fully anticipated. Second, the profession will go through a paradigm shift in two ways: become more IT oriented and forward looking. Finally, the profile of the auditors will change.
Creativity in Modern Education

There will be elaborated several facts about creativity in Twenty First-Century educational reforms in six countries(The US, India, Chile, Mexico, China, and Singapore). The crucial source of the content in the paper relies on thebook, “Teaching and Learning for the Twenty-First Century” (Reimeres and Chung, 2016). Nevertheless, myexploration of creativity in modern education in the six countries has a limited range. Creativity depends on threemeaningful factors of education such as equality, quality and implementing modern educational reforms. The papersheds light on similarities and differences of six countries that they experience with the three factors (equality,creativity, and implementing modern educational reforms). All countries strive to apply equality in the whole country,although, none of the countries has employed equality throughout the nation. Some countries made better progress onapplying equality in education, and others are facing many obstacles with equal education throughout the country.Creativity is significant to every educational system, and it is interrelated with equality and applying modern education.The US system of education applies creativity in a few school districts. Other countries state creativity in theircurriculum, but they do not use it in the classrooms at all. Six countries have different economic development, anddifferent economic demands; therefore, they have different approaches to implementing modern educational reforms.Despite the fact all countries have dissimilarities with implementing creativity in educational reforms, they all knowcreativity’s weight in modern education. Creativity as a first step of the innovation that defines the progress ofeducation especially accelerates the growth of the entire economy within a society.
Face and Ecological Validity in Simulations

In fields where in situ performance cannot be measured, ecological validity is difficult to estimate. Drawing on theory from social psychology and virtual reality, we argue that face validity can be a useful proxy for ecological validity. We provide illustrative examples of this relationship from work in search-and-rescue HRI, and conclude with some practical guidelines for the construction of immersive simulations in general.
How we Guide, Write, and Cite at CHI

There are many opinions on how to write an influential CHI paper, ranging from writing in an active voice to including colons in the title. However, little is known about how we actually write, and how writing influences impact. We conducted quantitative analyses of the full text of all 6578 CHI papers published since 1982 to investigate. We looked at readability, titles, novelty, and name-dropping and related these measures to the papers' citation count; overall and for different subcommittees. We found that CHI papers are more readable than papers from other fields. Furthermore, readability, title length, and novelty markers all influence citation counts.
Dense-U-Net: densely connected convolutional network for semantic segmentation with a small number of samples

The main contribution of this work is the proposal of a densely connected convolutional network for semantic segmentation, which strengthens utilization of features and improves segmentation results even with limited training samples. To achieve this, we combine the U-Net network and our resulting system is called Dense-U-Net. Compared to traditional convolutional networks such as U-Net, there are additional concatenation layers between each pair of convolutional layers which have the same size of outputs in our Dense-U-Net, each layer can get the feature-maps of all its preceding layers as inputs while its feature-maps can be passed to all subsequent layers, and a higher segmentation quality can be achieved without a need for increasing the volume of datasets finally. We evaluate our proposed architecture by segmentation accuracy, foreground-restricted rand scoring after border thinning V<sup>Rand</sup> and foreground-restricted information theoretic scoring after border thinning V<sup>Info</sup> at the same time, and the results are shown on three different segmentation tasks: ISBI challenge 2012 for segmentation of neuronal structures in electron microscopic stacks, ISBI cell tracking challenge 2014(Glioblastoma-astrocytoma U373 cells) and 2015(HeLa cells), our Dense-U-Net achieves better results than U-Net and several other state-of-the-art networks on all tasks.
Multi-Person Tracking Based on Faster R-CNN and Deep Appearance Features

Mostly computer vision problems related to crowd analytics are highly dependent upon multi-object tracking (MOT) systems.There are two major steps involved in the design of MOT system: object detection and association.In the first step, desired objects are detected in every frame of video stream.Detection quality directly influences the performance of tracking.The second step involves the correspondence of detected objects in current frame with the previous to obtain their trajectories.High accuracy in object detection system results in less number of missing detection and finally produces less fragmented tracks.Better object association increases the affinity between objects in different frames.This paper presents a novel algorithm for improved object detection followed by enhanced object tracking.Object detection accuracy has been increased by employing deep learning-based Faster region convolutional neural network (Faster R-CNN) algorithm.Object association is carried out by using appearance and improved motion features.Evaluation results show that we have enhanced the performance of current state-of-the-art work by reducing identity switches and fragmentation.
Development of a Daily Databank of Solar Radiation Components for Moroccan Territory

The main objective of this work is to create a daily updated database that includes all components of solar radiation, either energetic or spectral radiation. This will lead us to quantify the Moroccan solar potential and to determine the dimensions of all types of solar thermal and photovoltaic systems. Consequently, the obtained database will be the fundamental support for engineers, designers, and all organizations interested in developing solar systems, in different regions throughout Morocco. It will also be a basic tool for researchers in modelling and simulating the new solar systems. Firstly, we used one year’s worth of measurements of the different components of the solar radiation, provided by the National Meteorological Department, to establish the extrapolation equations between the global radiation at the reference site and the global radiation of twenty-eight other sites. As well as with the same measurements, we developed the correlation equations between the global solar radiation and the other solar radiation components. Secondly, from ten years of Fez station’s daily global radiation measurements and through the extrapolation equations, we were able to estimate the global radiation of all Moroccan cities. Then, by using the obtained global radiation data and the correlation equations, we predicted the other components of solar radiation. Subsequently, with a new measurement campaign carried out on several sites, we validated the estimation models by using the usual statistical indicators. In addition, we compared our results with those obtained by other estimation models. The resulting differences for each solar component display the advantage of our model with errors under 6%. To facilitate the use of our results, we compiled them into maps representing the spread of solar radiation across Morocco.