Reviewerly/exHarmony huggingface dataset:
Sharp Second-Order Pointwise Asymptotics for Lossless Compression with Side Information

The problem of determining the best achievable performance of arbitrary lossless compression algorithms is examined, when correlated side information is available at both the encoder and decoder. For arbitrary source-side information pairs, the conditional information density is shown to provide a sharp asymptotic lower bound for the description lengths achieved by an arbitrary sequence of compressors. This implies that for ergodic source-side information pairs, the conditional entropy rate is the best achievable asymptotic lower bound to the rate, not just in expectation but with probability one. Under appropriate mixing conditions, a central limit theorem and a law of the iterated logarithm are proved, describing the inevitable fluctuations of the second-order asymptotically best possible rate. An idealised version of Lempel-Ziv coding with side information is shown to be universally first- and second-order asymptotically optimal, under the same conditions. These results are in part based on a new almost-sure invariance principle for the conditional information density, which may be of independent interest.
Security and Privacy in Information Management in a Distributed Environment for Public Organizations

Security and privacy problems in information management are evident in public organizations. The objective of this research is the analisys risks that these organizations run, since computer attacks have increased along with both internal and external threats. Causing information and database thefts, there are risk analysis methodologies which are oriented to the objective for the preservation of guaranteeing the security and privacy of the information. Were used the deductive method and exploratory research to analyze the articles in the references and in the information available online and MAGERIT methodology what protects the information in its integrity, confidentiality and availability guaranteeing the security of the system and processes of public organizations. It turned out a Control of Security and Privacy factors, Threat Probability, Risk Assessment Formula, Prototype of Risk Management for Public Organizations and Privacy and security factor formula. It was concluded that MAGERIT is an alternative what allow mitigate the vulnerabilitys, threat and risks its processes in public organizations for protecting their information.
Energy-aware Coverage Path Planning for Unmanned Aerial Vehicles

Coverage Path Planning (CPP) problem is a motion planning subtopic in robotics, where it is necessary to build a path for a robot to explore every location in a given scenario. Unmanned Aerial Vehicles (UAV) have been employed in several applications related to the CPP problem. However, one of the significant limitations of UAVs is endurance, especially in multi-rotors. Minimizing energy consumption is pivotal to prolong and guarantee coverage. Thus, this work proposes energy-aware coverage path planning solutions for regular and irregular-shaped areas containing full and partial information. We consider aspects such as distance, time, turning maneuvers, and optimal speed in the UAV’s energy consumption. We propose an energy-aware spiral algorithm called E-Spiral to perform missions over regular-shaped areas. Next, we explore an energy-aware grid-based solution called EG-CPP for mapping missions over irregular-shaped areas containing no-fly zones. Finally, we present an energy-aware pheromone-based solution for patrolling missions called NC-Drone. The three novel approaches successfully address different coverage path planning scenarios, advancing the state-of-the-art in this area.
DEVELOPMENT OF THE MODEL OF NOVOSIBIRSK AKADEMGORODOK SCIENCE AND TECHNOLOGY PARK USING 3DS MAX AND UNITY TOOLS

The aim of tht work is to develop an interactive three-dimensional model of the territory of the Scientific and Technical Park of Novosibirsk Academgorodok. 3D objects were modeled in Autodesc 3ds Max; virtual space was implemented in Unity. In the process, textures and virtual materials for the implementation of photorealistic visualization of a three-dimensional scene were prepared. The model is compiled by Unity and is ready for use on desktop computers or laptops running Windows operating system. The software obtained can be used for virtual tours around the territory of the Scientific and Technical Park of Novosibirsk Academgorodok.
Time-minimum control of quantum purity for 2-level Lindblad equations

We study time-minimum optimal control for a class of quantum two-dimensional dissipative systems whose dynamics are governed by the Lindblad equation and where control inputs acts only in the Hamiltonian. The dynamics of the control system are analyzed as a bi-linear control system on the Bloch ball after a decoupling of such dynamics into intra- and inter-unitary orbits. The (singular) control problem consists of finding a trajectory of the state variables solving a radial equation in the minimum amount of time, starting at the completely mixed state and ending at the state with the maximum achievable purity. The boundary value problem determined by the time-minimum singular optimal control problem is studied numerically. If controls are unbounded, simulations show that multiple local minimal solutions might exist. To find the unique globally minimal solution, we must repeat the algorithm for various initial conditions and find the best solution out of all of the candidates. If controls are bounded, optimal controls are given by bang-bang controls using the Pontryagin minimum principle. Using a switching map we construct optimal solutions consisting of singular arcs. If controls are bounded, the analysis of our model also implies classical analysis done previously for this problem.
Bayesian density regression for discrete outcomes

Summary We develop Bayesian models for density regression with emphasis on discrete outcomes. The problem of density regression is approached by considering methods for multivariate density estimation of mixed scale variables, and obtaining conditional densities from the multivariate ones. The approach to multivariate mixed scale outcome density estimation that we describe represents discrete variables, either responses or covariates, as discretised versions of continuous latent variables. We present and compare several models for obtaining these thresholds in the challenging context of count data analysis where the response may be over‐ and/or under‐dispersed in some of the regions of the covariate space. We utilise a nonparametric mixture of multivariate Gaussians to model the directly observed and the latent continuous variables. The paper presents a Markov chain Monte Carlo algorithm for posterior sampling, sufficient conditions for weak consistency, and illustrations on density, mean and quantile regression utilising simulated and real datasets.
Evaluation by future bachelors directions of training "Physical culture" formation of universal competences

Introduction. The article contains: analysis of scientific works in the field of higher education pedagogy on the formation of universal competencies; an experimental study to assess the formation of universal competencies in future bachelors. The modern professional pedagogical community constantly demonstrates that the presence of universal competencies is decisive for graduates of higher education. At the moment, there is no clear idea of the formation and assessment of universal competencies, in particular for bachelors of physical education. Materials and Methods. We have developed materials for evaluation the future bachelors of universal competencies and conducted an experimental study. For each of the universal competencies, the average value of the assessment, the standard deviation, the confidence interval for the average assessment are determined, the results of the correlation analysis are presented. Results. The data we obtained showed, that the average values of assessments by future bachelors of the direction of training "Physical culture" of universal competencies (the ability to: maintain the proper level of physical fitness to ensure full social and professional activity; search, critical analysis and synthesis of information, apply a systematic approach to solve the tasks; build an individual trajectory of self-development; perceive the intercultural diversity of society; formulate a range of tasks, achieve goals, choosing the best solutions; the ability to conduct business communication in a foreign language) are within 2.97 points - 3.86 points on a 5-point scale. Discussion and Conclusions. Our study shows that the level of formation of the universal competencies of students of the program 49.03.01 “Physical Education” is insufficient. The formation of universal competencies is possible not only in the educational process, but also in scientific research, industrial practices, as part of extracurricular activities. The formation of universal competencies for future bachelors in the educational process of higher education will help to increase the level of knowledge, skills and experience of creative activity in the professional field: search and critical analysis of information; the ability to set goals and determine the range of tasks; communicate effectively in the teamwork, not only with Russian, but also with foreign colleagues; managing time and shaping individual development path; maintain the necessary level of physical activity to achieve professional and social goals. The results of the assessment of the formation of universal competencies of future bachelors are considered by us as an opportunity to improve the modern basic educational program.
Ensemble of Models Trained by Key-based Transformed Images for Adversarially Robust Defense Against Black-box Attacks

We propose a voting ensemble of models trained by using block-wise transformed images with secret keys for an adversarially robust defense. Key-based adversarial defenses were demonstrated to outperform state-of-the-art defenses against gradient-based (white-box) attacks. However, the key-based defenses are not effective enough against gradient-free (black-box) attacks without requiring any secret keys. Accordingly, we aim to enhance robustness against black-box attacks by using a voting ensemble of models. In the proposed ensemble, a number of models are trained by using images transformed with different keys and block sizes, and then a voting ensemble is applied to the models. In image classification experiments, the proposed defense is demonstrated to defend state-of-the-art attacks. The proposed defense achieves a clean accuracy of 95.56 % and an attack success rate of less than 9 % under attacks with a noise distance of 8/255 on the CIFAR-10 dataset.
United Kingdom: New training centre for railway signallers opens after just four weeks – UIC Communications

Network Rail has taken just four weeks to transform an old depot in Newport, south Wales, into a state-of-the-art training centre to ensure it has enough skilled railway signallers across Wales…
An Experimental Study of Semantic Continuity for Deep Learning Models

Deep learning models suffer from the problem of semantic discontinuity: small perturbations in the input space tend to cause semantic-level interference to the model output. We argue that the semantic discontinuity results from these inappropriate training targets and contributes to notorious issues such as adversarial robustness, interpretability, etc. We first conduct data analysis to provide evidence of semantic discontinuity in existing deep learning models, and then design a simple semantic continuity constraint which theoretically enables models to obtain smooth gradients and learn semantic-oriented features. Qualitative and quantitative experiments prove that semantically continuous models successfully reduce the use of non-semantic information, which further contributes to the improvement in adversarial robustness, interpretability, model transfer, and machine bias.
Visual Objects As Context: Exploiting Visual Objects for Lexical Entailment

We propose a new word representation method derived from visual objects in associated images to tackle the lexical entailment task.Although it has been shown that the Distributional Informativeness Hypothesis (DIH) holds on text, in which the DIH assumes that a context surrounding a hyponym is more informative than that of a hypernym, it has never been tested on visual objects.Since our perception is tightly associated with language, it is meaningful to explore whether the DIH holds on visual objects.To this end, we consider visual objects as the context of a word and represent a word as a bag of visual objects found in images associated with the word.This allows us to test the feasibility of the visual DIH.To better distinguish word pairs in a hypernym relation from other relations such as co-hypernyms, we also propose a new measurable function that takes into account both the difference in the generality of meaning and similarity of meaning between words.Our experimental results show that the DIH holds on visual objects and that the proposed method combined with the proposed function outperforms existing unsupervised representation methods.
Secure Hybrid Watermarking Technique In Medical Imaging

Medical imaging organizations currently face a significant problem with stealing of medical data.Watermarking is a powerful tool that can be used to authorize the hidden information in cover image. Theunique features associated with watermarking are authorization and copyright protection. Because of itsunique characteristics, watermarking associated with medical data hiding gives more robustness inmedical imaging. In spite of this adding security to watermarking in medical imaging, gives moreprotection for medical information. This paper explored two main fields that, the encryption of medicalimages using DNA Encoding and Spatiotemporal Chaos Algorithm and the embedding of medical imagesin cover image using hybrid transformation of NSCT, RDWT and SVD. The goal of this study was todevelop a methodology to improve the robustness, imperceptibility and security for medical informationwithout implementing a physical model, thus saving time, money and reducing the risks associated withhacking partners. For this paper, I used patient health document as one watermark and his medical imageas another watermark. The theoretical model has demonstrated that it is possible to use this type oftechnique and apply it to a complex digital image transmission. The correlation observed before and afterencryption and embedding procedures. Experimental results show how robustness and imperceptibilityand security of medical images are improved.
Dynamic Adversarial Patch for Evading Object Detection Models

Recent research shows that neural networks models used for computer vision (e.g., YOLO and Fast R-CNN) are vulnerable to adversarial evasion attacks. Most of the existing real-world adversarial attacks against object detectors use an adversarial patch which is attached to the target object (e.g., a carefully crafted sticker placed on a stop sign). This method may not be robust to changes in the camera's location relative to the target object; in addition, it may not work well when applied to nonplanar objects such as cars. In this study, we present an innovative attack method against object detectors applied in a real-world setup that addresses some of the limitations of existing attacks. Our method uses dynamic adversarial patches which are placed at multiple predetermined locations on a target object. An adversarial learning algorithm is applied in order to generate the patches used. The dynamic attack is implemented by switching between optimized patches dynamically, according to the camera's position (i.e., the object detection system's position). In order to demonstrate our attack in a real-world setup, we implemented the patches by attaching flat screens to the target object; the screens are used to present the patches and switch between them, depending on the current camera location. Thus, the attack is dynamic and adjusts itself to the situation to achieve optimal results. We evaluated our dynamic patch approach by attacking the YOLOv2 object detector with a car as the target object and succeeded in misleading it in up to 90% of the video frames when filming the car from a wide viewing angle range. We improved the attack by generating patches that consider the semantic distance between the target object and its classification. We also examined the attack's transferability among different car models and were able to mislead the detector 71% of the time.
Privug: Quantifying Leakage using Probabilistic Programming for Privacy Risk Analysis.

Disclosure of data analytics results has important scientific and commercial justifications. However, no disclosure should be allowed to happen without due diligence investigation of the risks that it poses for information privacy of the data subjects. Does the data analytics community have the right tools at their disposal to perform such a due diligence? We demonstrate a method, privug, that allows to explore leakage properties, or information privacy risks, involved with disclosing results of a given analytics program. The method uses classical off-the-shelf data science tools, namely Bayesian probabilistic programming libraries, exploiting the fact that they can reinterpret a regular disclosure program in probabilistic terms. This in turn allows information theoretical analysis of the program behavior. These tools and skills are often available for a data scientist pondering the disclosure questions. For privacy researchers, the method provides a fast and lightweight way to experiment with information privacy protection measures and mechanisms. We present privug on three case studies, including an exploration of parameters of a differential privacy mechanism.
IMPROVING ENGLISH PRESENT TENSES COMPREHENSION BY USING ENGLISH-DUBBED ANIME YU-GI-OH ARC-V

The research aims to analyze the improvement of English Present Tense comprehension through visual material such as Japanese Animation Dubbing Series (JADS) or English-dubbed animation. Another purpose of this research is to help teacher on designing creative and fun technique for learning English Grammar. The research was conducted in SMAN 6 Tangsel and 30 students of both control and experimental classes were selected as sample of research. The researcher used purposive sampling technique and conducted both pre-test and post-test to gather the research data. While the control class used conventional technique on learning English Present Tense, the experimental class was using JADS on learning the Tenses. The research lasted for three weeks and researcher used compare the results of pre-test and post-test through SPSS 22. After analyzing the data through statistic method Paired-samples T-test, the t-value of experimental class is 9.180 and the value also becomes positive due to statistic formula. Then, the df is still 29 so t-value of df is 2.462. As the t-value of experimental class is higher than t-value of df (9.180&gt;2.462). Because of the results of these T-values through Paired-samples T-test, researcher concludes that using visual material such as JADS for students in the classroom is effective to improve their comprehension for present tenses.
Efficient texture mapping via a non-iterative global texture alignment

Texture reconstruction techniques generally suffer from the errors in keyframe poses. We present a non-iterative method for seamless texture reconstruction of a given 3D scene. Our method finds the best texture alignment in a single shot using a global optimisation framework. First, we automatically select the best keyframe to texture each face of the mesh. This leads to a decomposition of the mesh into small groups of connected faces associated to a same keyframe. We call such groups fragments. Then, we propose a geometry-aware matching technique between the 3D keypoints extracted around the fragment borders, where the matching zone is controlled by the margin size. These constraints lead to a least squares (LS) model for finding the optimal alignment. Finally, visual seams are further reduced by applying a fast colour correction. In contrast to pixel-wise methods, we find the optimal alignment by solving a sparse system of linear equations, which is very fast and non-iterative. Experimental results demonstrate low computational complexity and outperformance compared to other alignment methods.
Media and information culture of the young adults

The prospects for media environment and its use in the modern youth libraries are exemplified by the services provided by Chelyabinsk Regional Youth Library.
Classification of Pulsars using Extreme Deconvolution

We carry out a classification of the observed pulsar dataset into distinct clusters, based on the $P-\dot{P}$ diagram, using Extreme Deconvolution based Gaussian Mixture Model. We then use the Bayesian Information Criterion to select the optimum number of clusters. We find in accord with previous works, that the pulsar dataset can be optimally classified into six clusters, with two for the millisecond pulsar population, and four for the ordinary pulsar population. Beyond that, however we do not glean any additional insight into the pulsar population based on this classification. Using numerical experiments, we confirm that Extreme Deconvolution-based classification is less sensitive to variations in the dataset compared to ordinary Gaussian Mixture Models. All our analysis codes used for this work have been made publicly available.
The Trajectory of Turkey-educated software practitioners working in different countries

To bridge the skills gap in the software industry, we have already conducted a survey, whose results will benefit both the industry and as well as the academia, by creating the awareness of the expected skillset and the corresponding gaps in the academic curriculum (mostly for Turkish institutions). One of the opportunities the survey data provided as a further study is to analyze the trajectory of 628 software practitioners, who completed their undergraduate degree in Turkey. We believe that such a study will help to characterize and understand the possible educational and career paths of Turkey-educated software practitioners working in different countries. With the help of such initial inputs, we hope that Turkish researchers will study more qualitative approaches to investigate the root causes of these phenomena by increasing more industry-academia collaborations (IACs).
A Poisoning Attack Against the Recognition Model Trained by the Data Augmentation Method

The training model often preprocesses the training set with the data augmentation method. Aiming at this kind of training mode, a poisoning attack scheme is proposed in this paper, which can effectively complete the attack. For the traffic sign recognition system, its decision boundary is changed by the way of data poisoning, so that it would incorrectly recognize the target sample. In this scheme, a “backdoor” belonging to the attacker is added to the toxic sample so that the attacker can manipulate recognition model (i.e., the target sample is classified into expected categories). The attack is difficult to detect, because the victim will take a poison sample as a healthy one. The experimental results show that the scheme can successfully attack the model trained by the data augmentation method, realize the attack function against the selected target, and complete the attack with a high success rate. It is hoped that this work will raise awareness of the important issues of data reliability and data sources.