Reviewerly/exHarmony huggingface dataset:
STUDY OF SYNDROME OF EMOTIONAL BURNOUT IN STUDENTS OF THE FACULTY OF PHARMACY

According to many studies, the activity of medical students is one of intense emotional types of labor, which affects the state of their mental and physical (somatic) health. The degree of emotional burnout in students of the Faculty of Pharmacy was studied. A high degree of emotional exhaustion and depersonalization was found in 27.6% of students and depersonalization in 23.7% of students, medium level - 22.4% and 26.3% respectively, low level of psychological depletion determined in half of the students in study group. 17.2% of respondents revealed diminished importance of personal achievements and dissatisfaction with learning process to a large extent. Conducted research manifested statistically reliable factors that cause professional burnout, such as: accommodation conditions, occupation in accordance to degree, gender.
Towards an Ontology-Based Approach for Eliciting Possible Solutions to Non-Functional Requirements

Requirements Engineering plays a crucial role in the software development process. Many works have pointed out that Non-Functional Requirements (NFRs) are critical to the quality of software systems. NFRs, also known as quality requirements, can be difficult to elicit due to their subjective diversity nature. In this paper, we introduce the QR Framework which uses an ontology-based approach to support the collection of knowledge on possible solutions to implement NFRs, semi-automatically connecting related NFRs. Preliminary search mechanisms are provided in a tool to facilitate the identification of possible solutions to an NFR and their related consequences to other solutions and/or other NFRs. To evaluate whether our approach aids eliciting NFRs, we conducted a controlled experiment performing a software development scenario. Our results suggest that reusing NFR knowledge can drive software engineers to obtain a closer to complete set of possible solutions to address quality concerns.
Deep Algorithm Unrolling for Blind Image Deblurring

Blind image deblurring remains a topic of enduring interest. Learning based approaches, especially those that employ neural networks have emerged to complement traditional model based methods and in many cases achieve vastly enhanced performance. That said, neural network approaches are generally empirically designed and the underlying structures are difficult to interpret. In recent years, a promising technique called algorithm unrolling has been developed that has helped connect iterative algorithms such as those for sparse coding to neural network architectures. However, such connections have not been made yet for blind image deblurring. In this paper, we propose a neural network architecture based on this idea. We first present an iterative algorithm that may be considered as a generalization of the traditional total-variation regularization method in the gradient domain. We then unroll the algorithm to construct a neural network for image deblurring which we refer to as Deep Unrolling for Blind Deblurring (DUBLID). Key algorithm parameters are learned with the help of training images. Our proposed deep network DUBLID achieves significant practical performance gains while enjoying interpretability at the same time. Extensive experimental results show that DUBLID outperforms many state-of-the-art methods and in addition is computationally faster.
NAO robot fuzzy obstacle avoidance in virtual environment

The fuzzy inference system for obstacle avoidance developed in this paper is designed for NAO humanoid robot. The fuzzy obstacle avoidance (Fuzzy OA) has been tested in Webots virtual environment and the results showed that this method is almost two times faster than the Naoqi framework obstacle avoidance (Naoqi OA) while the robot is much more stable. Because the fuzzy inference system is a method that relies on trial an error and experience, the obstacle avoidance algorithm is subject to improvements. Future developments will take into account these results and will add other fuzzy inference systems for navigation, in order to get more autonomy for Nao robot.
Longitudinal Analysis of Misuse of Bitcoin

We conducted a longitudinal study to analyze the misuse of Bitcoin. We first investigated usage characteristics of Bitcoin by analyzing how many addresses each address transacts with (from January 2009 to May 2018). To obtain a quantitative estimate of the malicious activity that Bitcoin is associated with, we collected over 2.3 million candidate Bitcoin addresses, harvested from the dark web between June 2016 and December 2017. The Bitcoin addresses found on the dark web were labeled with tags that classified the activities associated with the onions that these addresses were collected from. The tags covered a wide range of activities, from suspicious to outright malicious or illegal. Of these addresses, only 47,697 have tags we consider indicative of suspicious or malicious activities. We saw a clear decline in the monthly number of Bitcoin addresses seen on the dark web in the periods coinciding with takedowns of known dark web markets. We also found interesting behavior that distinguishes the Bitcoin addresses collected from the dark web when compared to activity of a random address on the Bitcoin blockchain. For example, we found that Bitcoin addresses used on the dark web are more likely to be involved in mixing transactions. To identify mixing transactions, we developed a new heuristic that extends previously known ones. We found that Bitcoin addresses found on the dark web are significantly more active, they engage in transactions with 20 times the neighbors and 4 times the Bitcoin amounts when compared to random addresses. We also found that just 2,828 Bitcoin addresses are responsible for 99% of the Bitcoin value used on the dark web.
Discriminative structural graph classification

This paper focuses on the discrimination capacity of aggregation functions: these are the permutation invariant functions used by graph neural networks to combine the features of nodes. Realizing that the most powerful aggregation functions suffer from a dimensionality curse, we consider a restricted setting. In particular, we show that the standard sum and a novel histogram-based function have the capacity to discriminate between any fixed number of inputs chosen by an adversary. Based on our insights, we design a graph neural network aiming, not to maximize discrimination capacity, but to learn discriminative graph representations that generalize well. Our empirical evaluation provides evidence that our choices can yield benefits to the problem of structural graph classification.
An Approach to Vulnerabilities, Threats and Risk in Voting Systems for Popular Elections in Latin America

The problems in information security regarding vulnerabilities, threats and risks in voting systems for popular election in Latin America and the world persist; because in most of the countries of the world there is no maturity in democracy and defined policies; the problems of confidentiality, integrity and authenticity in the electoral processes can be evidenced.The objective is to perform the analysis to identify the threats, risks and weaknesses in electoral systems in Latin American countries and determine which of the systems used by different countries may be appropriate, to be considered as an alternative.The deductive method and exploratory research has been used to perform the analysis of the articles and information regarding electoral processes.It resulted in a description in statistical tables of the threats and weaknesses that must be examined to implement a system for electoral voting; considering the culture, technological availability and social conditions of each country.It was concluded that to mitigate the potential risks of the information, it is necessary to identify the weaknesses in the electronic voting system to improve the integrity and security of the electoral process; made in the last three presidential elections in Brazil, Colombia, Ecuador.
Ultrastrong-coupling circuit QED in the radio-frequency regime

We study a circuit QED setup where multiple superconducting qubits are ultrastrongly coupled to a single radio-frequency resonator. In this extreme parameter regime of cavity QED the dynamics of the electromagnetic mode is very slow compared to all other relevant timescales and can be described as an effective particle moving in an adiabatic energy landscape defined by the qubits. The focus of this paper is placed on settings with two or multiple qubits, where different types of symmetry-breaking transitions in the ground- and excited-state potentials can occur. Specifically, we show how the change in the level structure and the wave-packet dynamics associated with these transition points can be probed via conventional excitation spectra and Ramsey measurements performed at GHz frequencies. More generally, this analysis demonstrates that state-of-the-art circuit QED systems can be used to access a whole range of particlelike quantum-mechanical phenomena beyond the usual paradigm of coupled qubits and oscillators.
Region of Attraction for Power Systems using Gaussian Process and Converse Lyapunov Function -- Part I: Theoretical Framework and Off-line Study

This paper introduces a novel framework to construct the region of attraction (ROA) of a power system centered around a stable equilibrium by using stable state trajectories of system dynamics. Most existing works on estimating ROA rely on analytical Lyapunov functions, which are subject to two limitations: the analytic Lyapunov functions may not be always readily available, and the resulting ROA may be overly conservative. This work overcomes these two limitations by leveraging the converse Lyapunov theorem in control theory to eliminate the need of an analytic Lyapunov function and learning the unknown Lyapunov function with the Gaussian Process (GP) approach. In addition, a Gaussian Process Upper Confidence Bound (GP-UCB) based sampling algorithm is designed to reconcile the trade-off between the exploitation for enlarging the ROA and the exploration for reducing the uncertainty of sampling region. Within the constructed ROA, it is guaranteed in probability that the system state will converge to the stable equilibrium with a confidence level. Numerical simulations are also conducted to validate the assessment approach for the ROA of the single machine infinite bus system and the New England $39$-bus system. Numerical results demonstrate that our approach can significantly enlarge the estimated ROA compared to that of the analytic Lyapunov counterpart.
End-to-End Learning of Geometric Deformations of Feature Maps for Virtual Try-On

The 2D virtual try-on task has recently attracted a lot of interest from the research community, for its direct potential applications in online shopping as well as for its inherent and non-addressed scientific challenges. This task requires to fit an in-shop cloth image on the image of a person. It is highly challenging because it requires to warp the cloth on the target person while preserving its patterns and characteristics, and to compose the item with the person in a realistic manner. Current state-of-the-art models generate images with visible artifacts, due either to a pixel-level composition step or to the geometric transformation. In this paper, we propose WUTON: a Warping U-net for a Virtual Try-On system. It is a siamese U-net generator whose skip connections are geometrically transformed by a convolutional geometric matcher. The whole architecture is trained end-to-end with a multi-task loss including an adversarial one. This enables our network to generate and use realistic spatial transformations of the cloth to synthesize images of high visual quality. The proposed architecture can be trained end-to-end and allows us to advance towards a detail-preserving and photo-realistic 2D virtual try-on system. Our method outperforms the current state-of-the-art with visual results as well as with the Learned Perceptual Image Similarity (LPIPS) metric.
MOUNTAINOUS REMOTE SENSING IMAGES REGISTRATION BASED ON IMPROVED OPTICAL FLOW ESTIMATION

Abstract. Mountainous remote sensing images registration is more complicated than in other areas as geometric distortion caused by topographic relief, which could not be precisely achieved via constructing local mapping functions in the feature-based framework. Optical flow algorithm estimating motion of consecutive frames in computer vision pixel by pixel is introduced for mountainous remote sensing images registration. However, it is sensitive to land cover changes that are inevitable for remote sensing image, resulting in incorrect displacement. To address this problem, we proposed an improved optical flow estimation concentrated on post-processing, namely displacement modification. First of all, the Laplacian of Gaussian (LoG) algorithm is employed to detect the abnormal value in color map of displacement. Then, the abnormal displacement is recalculated in the interpolation surface constructed by the rest accurate displacements. Following the successful coordinate transformation and resampling, the registration outcome is generated. Experiments demonstrated that the proposed method is insensitive in changeable region of mountainous remote sensing image, generating precise registration, outperforming the other local transformation model estimation methods in both visual judgment and quantitative evaluation.
Large-Scale Analysis of Infrastructure-Leaking DNS Servers

The Domain Name System (DNS) is a fundamental backbone service of the Internet. In practice, this infrastructure often shows flaws, which indicate that measuring the DNS is important to understand potential (security) issues. Several works deal with the DNS and present such problems, mitigations, and attack vectors. A so far overlooked issue is the fact that DNS servers might answer with information about internal network information (e.g., hostnames) to external queries. This behavior results in a capability to perform an active network reconnaissance without the need for individual vulnerabilities or exploits. Analyzing how public DNS services might involuntarily disclose sensitive information ties in with the trust we have on Internet services. To investigate this phenomenon, we conducted a systematic measurement study on this topic. We crawl all public reachable DNS servers in 15 scans over a period of almost six months and analyze up to 574,000 DNS servers per run that are configured in a way that might lead to this kind of information leakage. With this large-scale evaluation, we show that the amount of this possible infrastructure leaking DNS servers is on average almost 4% over all of our scans on every reachable DNS servers on the Internet. Based on our newest scan, the countries with most of these servers are Romania, China, and the US. In these countries, the share of such servers among of all reachable servers is about 15% in Romania, 9% in China, and 2.9% in the US. A detailed analysis of the responses reveals that not all answers provide useful information for an adversary. However, we found that up to 158,000 DNS servers provide potentially exploitable information in the wild. Hence, this measurement study demonstrates that the configuration of a DNS server should be executed carefully; otherwise, it may be possible to disclose too much information.
Developing a Visual Cryptography Tool for Arabic Text

Over the years, cryptography has been used to encrypt secret data. A new version of cryptography, entitled visual cryptography (VC), has been introduced. This approach is a simple and secure way to allow the secret sharing of images without any cryptographic computations or the use of encryption or decryption keys. Although various studies have been undertaken with regard to improving the VC technique, there has not yet been a study attempting to improve the VC technique for Arabic text although there are thousands of websites that provide services in Arabic serving countries in which Arabic is spoken as a first language. This paper, therefore, develops a tool that can encrypt/decrypt images of Arabic text using the VC approach. We experimentally evaluate the usability aspect. The results indicate the general suitability of the developed tool. This tool sheds light in designing practical tools for electronic voting, anti-phishing, Captcha, watermarking, biometric privacy, online payment systems, and digital signature in the Arabic environment.
Message-Falsification Prevention With Small Quantum Mask in Quaternary Y00 Protocol

Confidentiality, integrity, and availability constitute the triad of cybersecurity. Confidentiality is the property of invisibility against unauthorized parties. Integrity is protecting data from being altered or falsified by unauthorized parties, and availability is the accessibility of data to legitimate parties whenever they need it. However, in quantum cryptography, confidentiality is of central interest, while the latter two are often overlooked. In contrast, the Y00 protocol has an affinity for the conventional optical infrastructure. Therefore, its availability is the same as that of the current optical network. Moreover, the previous work showed that the quaternary Y00 protocol could prevent message falsification to satisfy integrity. This paper proposes a new implementation that uses a quantum mask size reduced to 4 levels from 24 levels that was required in the previous work. This smaller mask is sufficient to prevent the attacker's falsified messages in the middle of the communication line at will, even if she knows the full message. Therefore, the attacker fails to falsify the message with a probability of almost 100% per byte.
Query-efficient Meta Attack to Deep Neural Networks

Black-box attack methods aim to infer suitable attack patterns to targeted DNN models by only using output feedback of the models and the corresponding input queries. However, due to lack of prior and inefficiency in leveraging the query and feedback information, existing methods are mostly query-intensive for obtaining effective attack patterns. In this work, we propose a meta attack approach that is capable of attacking a targeted model with much fewer queries. Its high queryefficiency stems from effective utilization of meta learning approaches in learning generalizable prior abstraction from the previously observed attack patterns and exploiting such prior to help infer attack patterns from only a few queries and outputs. Extensive experiments on MNIST, CIFAR10 and tiny-Imagenet demonstrate that our meta-attack method can remarkably reduce the number of model queries without sacrificing the attack performance. Besides, the obtained meta attacker is not restricted to a particular model but can be used easily with a fast adaptive ability to attack a variety of models.The code of our work is available at https://github.com/dydjw9/MetaAttack_ICLR2020/.
Neural Volumes: Learning Dynamic Renderable Volumes from Images

Modeling and rendering of dynamic scenes is challenging, as natural scenes often contain complex phenomena such as thin structures, evolving topology, translucency, scattering, occlusion, and biological motion. Mesh-based reconstruction and tracking often fail in these cases, and other approaches (e.g., light field video) typically rely on constrained viewing conditions, which limit interactivity. We circumvent these difficulties by presenting a learning-based approach to representing dynamic objects inspired by the integral projection model used in tomographic imaging. The approach is supervised directly from 2D images in a multi-view capture setting and does not require explicit reconstruction or tracking of the object. Our method has two primary components: an encoder-decoder network that transforms input images into a 3D volume representation, and a differentiable ray-marching operation that enables end-to-end training. By virtue of its 3D representation, our construction extrapolates better to novel viewpoints compared to screen-space rendering techniques. The encoder-decoder architecture learns a latent representation of a dynamic scene that enables us to produce novel content sequences not seen during training. To overcome memory limitations of voxel-based representations, we learn a dynamic irregular grid structure implemented with a warp field during ray-marching. This structure greatly improves the apparent resolution and reduces grid-like artifacts and jagged motion. Finally, we demonstrate how to incorporate surface-based representations into our volumetric-learning framework for applications where the highest resolution is required, using facial performance capture as a case in point.
Robust and interpretable blind image denoising via bias-free convolutional neural networks

Deep convolutional networks often append additive constant ("bias") terms to their convolution operations, enabling a richer repertoire of functional mappings. Biases are also used to facilitate training, by subtracting mean response over batches of training images (a component of "batch normalization"). Recent state-of-the-art blind denoising methods (e.g., DnCNN) seem to require these terms for their success. Here, however, we show that these networks systematically overfit the noise levels for which they are trained: when deployed at noise levels outside the training range, performance degrades dramatically. In contrast, a bias-free architecture -- obtained by removing the constant terms in every layer of the network, including those used for batch normalization-- generalizes robustly across noise levels, while preserving state-of-the-art performance within the training range. Locally, the bias-free network acts linearly on the noisy image, enabling direct analysis of network behavior via standard linear-algebraic tools. These analyses provide interpretations of network functionality in terms of nonlinear adaptive filtering, and projection onto a union of low-dimensional subspaces, connecting the learning-based method to more traditional denoising methodology.
Multidimensional Algorithmic Thinking Development on Mental Learning Platform

The problem of algorithmic thinking development in computer science, computer technology and information technology courses remains relevant despite a lot of research done in the area. The present article is intended to substantiate the mental learning platform for further use of cognitive methods and training tools; it discloses the essence of multidimensional algorithmic thinking and presents mental and embodied (kinaesthetic) programming training tools. The authors arrive at the conclusion that development of multidimensional algorithmic thinking should be carried out through the use of mental algorithmic maps for different styles of thinking, mental and empirical tasks, as well as kinaesthetic simulators. Such training tools involve all channels of perception and greatly contribute to the understanding and better assimilation of “Algorithmization and programming” subject. The results of the research can be useful for computer science teachers at schools and programming teachers at higher educational institutions
Reversible In-Place Carry-Lookahead Addition with Few Ancillae

We present a reversible, in-place carry-lookahead adder that uses fewer ancillae than previous designs. Specifically, an N-bit adder uses only roughly N ancillae, where previous designs have used roughly 2N ancillae. The cost is 20% higher gate count and 50% higher gate delay.