Reviewerly/exHarmony huggingface dataset:
Formation of an Innovative Educational Environment as a Factor of Students’ Competitiveness

The paper emphasizes that trends in education, the processes of globalization and internationalization of all spheres of human life force countries to carry out transformations in the field of higher education. Russia is also trying to change the system of higher education in order to integrate into the global educational space. Reforms in higher education are reduced to the fact that the University should produce professionally mobile, competitive and in demand in the labor market graduates who are able to activate their potential, show intellectual and creative abilities. The pedagogical community actively implements innovative technologies in educational practice. However, only the variety of pedagogical tools does not provide the necessary level and quality of professional training for the country. It is concluded that the solution to this problem implies the need to implement an appropriate innovation policy in higher education, in which a new type of educational environment will play a decisive role.
Regime Switching Analysis of Cryptocurrencies

In this paper we test for regime changes in the price dynamics of Bitcoin, Ethereum, Litecoin and Monero, as representatives of the cryptocurrencies asset class. Data are observed daily from January, 1, 2016 to October, 15, 2019. Best specifications within Gaussian and Autoregressive Hidden Markov models for price differences are selected through the AIC and BIC information criteria by considering up to four hidden regimes. The empirical results suggest that at most three common states may be considered for the basket of cryptocurrencies under investigation; a fourth state may be relevant as an added factor to the dynamics description of the individual cryptocurrencies rather than to the whole basket. Finally, we test the out-of-sample performance of estimated regime switching models; optimal results, in terms of RMSE and correlation between predicted and real values, are obtained in the case of two common or three individual regimes.
A practicable timing attack against HQC and its countermeasure

&lt;p style='text-indent:20px;'&gt;In this paper, we present a practicable chosen ciphertext timing attack retrieving the secret key of HQC. The attack exploits a correlation between the weight of the error to be decoded and the running time of the decoding algorithm of BCH codes. For the 128-bit security parameters of HQC, the attack runs in less than a minute on a desktop computer using roughly 6000 decoding requests and has a success probability of approximately 93 percent. To prevent this attack, we provide an implementation of a constant time algorithm for the decoding of BCH codes. Our implementation of the countermeasure achieves a constant time execution of the decoding process without a significant performance penalty.&lt;/p&gt;
Policing Hate Crime: Dilemmas in Policy and Practice

In the last decade of the twentieth and the first two decades of twenty-first-century policing hate crime has established itself as an important, high profile area of work undertaken by police services in the UK. In the circumstances, it is to be expected that such a relatively new police initiative would give rise to the dilemmas in policy and practice that are highlighted in this chapter. The first dilemma emerges from the key policy of affording enhanced credibility to individuals reporting allegations of hate crime to police. The second dilemma revolves around the expansion of groups or issues that are included under the rubric of hate crime. The third dilemma is perhaps the most intractable and focuses on those hate crimes that do not involve physical violence or harm. The dilemmas are elucidated to contribute to academic, policy maker and practitioner discussions on the future development of hate crime policing.
Research on Real Radar Pulse Signal Sorting Technology Based on Machine Learning

With the rapid development of radar technology, various new systems and new modes of radar are emerging, and radar signal sorting technology is undoubtedly an important part of studying radar signals. This article introduces the radar pulse characteristic parameters and the basic theory of machine learning. And for the radar pulse signals of known categories, PDW is used as the sorting feature, and the supervised algorithm in the machine learning algorithm is used for sorting. The supervised algorithm selects the algorithm based on the probability-based decision tree and the distance-based KNN algorithm to sort the radar signals, and the sorting accuracy rate is more than 95%, which shows the great sorting effect.
Discriminant-sensitive locality canonical correlation analysis for joint dimension reduction

Canonical correlation analysis (CCA) has been known as a representative joint dimension reduction of multimodal material. However, CCA fails to capture nonlinear discriminant structures hidden in original high-dimensional multi-modal material. To address this issue, we propose a novel unsupervised joint dimension reduction method called discriminant-sensitive locality canonical correlation analysis (DLCCA). The method embeds the locality-based discriminant structures into the between-modal correlation and the within-modal scatters. The low-dimensional nonlinear correlation features characterized as great discrimination can be well extracted by the method in the unsupervised cases. The experiments of face and handwritten recognition has proved the effectiveness and robustness of DLCCA.
Pengembangan Media Comic of Physics Berbasis Literasi Visual Pada Materi Fluida Statis

Abstract This research aims to obtain the characteristics of Comic of Physics (Coophy) media and to know the feasibility of Comic of Physics (Coophy) media. The research method that used in this study was Research and Development (R&D), and use 4-D development model (Define, Design, Develop, and Dessiminate). The results of the study is show that the Comic of Physics (Coophy) media has 4 main menus, namely Arcimedes Law, Hydrostatic Pressure, Pascal's Law, and Author Info. This Comic of Physics (Coophy) media is packaged in Adobe Flash application. The average score for media expert validation is 91% with very feasible category, the average score for material expert validation is 89% with very feasible category, the average score for education expert validation is 78% with a feasible category and the average of students response result is 90% with very good categories. From these results, we can indicate that the Comic of Physics (Coophy) media can be used as a learning media to support physics learning in static fluid material. Keywords: Adobe Flash, Comic, Development, Learning Media,Visual Literacy.
Face Video Tracking Using Deep Neural Networks

The computational method of the face recognition in a continuous video stream using deep neural networks is offered, that is effective in terms of accuracy and high-speed performance. Adapted architectures of deep neural networks are proposed: the modified MobileNet v2 neural network and the original dual network for identifying a person by face image. The original databases for neural networks are developed. Natural experiments are made, and the characteristics of accuracy and speed of problem solutions were evaluated. To solve the problems video tracking of persons in real time the software modules of the intelligent access control system in Python are developed.
Capacity of a Lossy Photon Channel With Direct Detection

We calculate numerically the capacity of a lossy photon channel assuming photon number resolving detection at the output. We consider scenarios of input Fock and coherent states ensembles and show that the latter always exhibits worse performance than the former. We obtain capacity of a discrete-time Poisson channel as a limiting behavior of the Fock states ensemble capacity. We show also that in the regime of a moderate number of photons and low losses the Fock states ensemble with direct detection is beneficial with respect to capacity limits achievable with quadrature detection.
Radar Tracking With Orthogonal Velocity Measurements for Autonomous Ground Vehicles

In this work we examine the effect of having independent radial and angular velocity radar measurements on 2D tracking performance. With this, we provide design considerations for systems using interferometric angular velocity estimation. We found that on average as the number of clutter detections increases, the reduction in error by adding an additional angular velocity measurement also increases, implying a greater need for orthogonal velocity measurements in high-clutter environments. In particular, in simulation at a clutter rate of 50 detections per measurement, we obtained an error reduction of 14% by adding a radial velocity measurement and a further reduction of 3% from an additional angular velocity measurement. We also tested this method on a publicly available autonomous driving dataset by synthesizing angular velocity measurements and found a similar reduction of 13% and 6% by adding Doppler and angular velocity measurements, respectively.
Use of network educational technologies in the process of formation of information culture of junior schoolchildren

We presented theoretical and practical directions of experimental research conducted in search of mechanisms of effective formation of information culture of junior schoolchildren. We confirmed the assumption that the use of networked educational technologies played an important role in that process. The methodological basis of the theoretical justification was the provisions of didactics on the forms of providing educational information. The generalization of learning methods involves a significant expansion along with increased opportunities to use learning forms and tools. We relied on the provision that in didactics, the method of training includes the choice of a certain method, reception, means and form of training that serve to achieve the given didactic task. The construction of the method should provide the conditions for such interaction between the teacher and the student, which does not only lead the younger student to learn, but also helps him to master the ways of knowledge, contributes to his intellectual development. We presented some methods of networking and proved their effectiveness during the experimental work.
Recent Advances in Video Action Recognition with 3D Convolutions

The performance of video action recognition has improved significantly in recent decades. Current recognition approaches mainly utilize convolutional neural networks to acquire video feature representations. In addition to the spatial information of video frames, temporal information such as motions and changes is important for recognizing videos. Therefore, the use of convolutions in a spatiotemporal three-dimensional (3D) space for representing spatiotemporal features has garnered significant attention. Herein, we introduce recent advances in 3D convolutions for video action recognition.
Semantic Technologies Towards Accountable Artificial Intelligence: A Poultry Chain Management Use Case

Even though the Artificial Intelligence (AI) has overtaken many decision-making responsibilities in recent years, there still exists legitimate concerns of relying on AI due to their potentially incorrect, unjustified or even unfair results. Consequently, in order to overcome these issues, additional approaches are needed to make AI systems accountable, governable, and in general, trustworthy. In this article, Semantic Technologies are suggested towards making AI systems accountable. The proposed approach enables a fine-grained traceability of the predictions generated by the AI system. Summarising, the presented proposal is expected to make an initial step in the use of Semantic Technologies for addressing different aspects that may contribute to the trustworthiness of AI systems.
Tracker Integrity Performance Assessment

In Air Traffic Management, tracking components are key to optimize traffic, by ensuring spatial separation between airplanes. The aim of the work is to propose the monitoring of the tracking system through a tracker integrity performance assessment. It is based on two components. The first one computes in quasi-real time tracking Key Performance Indicators. The second one aggregates the mandatory and recommended requirements from the Eurocontrol ESASSP standard, and the preferences of the Air Controllers on the priority among these requirements, to construct a single score representing the overall tracking quality of service. We show that these two types of requirements must be handled separately.
Formation of expert competencies for graduates of the specialty “Commodity science”

Abstract The article presents the experience of applying a practice-oriented approach and interaction of educational, expert and supervisory organizations at the training students whose major is “Commodity Science”. The possibility of using the methods of consumer testing of non-food products, the procedure for monitoring the consumer market and digital tools in the educational process is considered.
The AgilECo Pattern Language

Communication is crucial in agile contexts due to the need for collaboration and the number of different people involved, ranging from developers to users and business people. This communication should be effective to ensure everyone is on the same page; otherwise it can lead to undesired results such as misunderstanding and loss of productivity. As such, there is a need to ensure good communication in agile contexts. This paper presents a pattern language to tackle this issue, alongside with the patterns of a section of the language. The pattern language aims to provide a set of practices that help with recurrent problems and increase the effectiveness of communication. The patterns presented in this paper (Information Radiators, Co-Located Team, Open Space, Informal Communication Space and Cubes) address the issues related with the physical environment surrounding an agile team.
Multi-constrained Trajectory Planning for Random Signal Aided Navigation

UAV can't perform combat missions without the trajectory planning system and the navigation system.Traditional UAV trajectory planning systems are often separated from navigation systems. At the same time, UAV active navigation and scene matching assisted navigation have the problems of low safety and poor robustness. In this paper, a new trajectory planning method for random signal aided navigation environment is proposed. First, the random signal navigation environment model and the trajectory planning constraint model are established. Then, the algorithm of selecting matching area and multi constraint trajectory planning between matching areas are proposed. The simulation results show that the matching area selection algorithm can select more matching areas, and the planned route can pass more matching areas. This method combines UAV trajectory planning with a new navigation method (Random Signal Aided Navigation), which improves the diversity and accuracy of navigation and makes the trajectory planning system more robust. It has great research significance and practical value.
Historical Report Assist Medical Report Generation

How to automatically generate diagnostic reports with accurate content, standardized structure and clear semantics, brings great challenges due to the complexity of medical images and the detailed paragraph descriptions for medical images. The structure and the semantic contents of the historical report are very helpful for the current report generation. This paper proposes a text report generation method assisted by historical reports. In the proposed method, both the previous report and the keywords generated from the current images are modeled by using two encoders respectively. The co-attention mechanism is introduced to jointly learn the historical reports and the keywords. The decoder based on the co-attention is used to generate a long description of the image. The progress that learns from the historical report and the current report in the training set helps to generate an accurate report for the new image. Furthermore, the structure in the historical report helps to generate a more natural text report. We conducted experiments on the practical ultrasound data, which is provided by a prestigious hospital in China. The experimental results show that the reports generated by the proposed method are closer to the reports generated by radiologists.
Cryptanalysis of Two Fully Anonymous Attribute-Based Group Signature Schemes with Verifier-Local Revocation from Lattices

Attribute-based group signature (ABGS) is a new variant of group signature, and it allows any group member with certain specific attributes to sign a message on behalf of the whole group anonymously, without revealing the member’s identity or attributes information; once a dispute arises, an opening authority (OA) can effectively reveal the anonymity and track the real identity and attributes of the signer. In order to support for membership revocation and resist against quantum computers, Perera et al. (NSS 2019, WISA 2019) recently proposed two (traceable or not) fully anonymous ABGS schemes with verifier-local revocation (VLR) from lattices. Unfortunately, in this paper we show that their two constructions do not satisfy the full-anonymity and present an improved scheme which obtains the strongest security, full-anonymity, in a relatively simple manner.
Corporate Sector Fraud

This chapter aims to possess a review of machine learning techniques for detection of corporate fraud in modern era. Detecting company frauds using traditional procedures is time costly as immense volume of information must be analysed. Thus, further analytical procedures should be used. Machine learning techniques are most emerging topic with great importance in field of information learning and prediction. The machine learning (ML) approach to fraud detection has received a lot of promotion in recent years and shifted business interest from rule-based fraud detection systems to ML-based solutions. Machine learning permits for making algorithms that process giant data-sets with several variables and facilitate realize these hidden correlations between user behaviors and also the probability of fallacious actions. Strength of machine learning systems compared to rule-based ones is quicker processing and less manual work. The chapter aims at machine-driven analysis of knowledge reports exploitation machine learning paradigm to spot fraudulent companies.
Identifying Operational Points for Deterministic Execution in Cloud Computing

Cloud based applications are increasingly structured as loosely coupled microservices for various reasons including agility and scalability. Especially in such architectures that feature interacting microservices, performance degradation from a subset of resource-starved microservices could rapidly spread to other microservices creating additional performance hotspots and increasing tail latencies in inter-microservice communication - directly impacting deterministic execution of the overall cloud application. Optimizing resource allocation is one way to address this problem, and it has received some research attention. Specifically, two categories of approaches have been explored: a) correction after detection of performance bottlenecks, and b) predictive identification and correction of upcoming bottlenecks. This paper focuses on CPU support for deterministic cloud computing for either of these approaches, and also on identification of better operational points upfront. It highlights the benefits of using CPU hardware-based approaches through real-world benchmarks and explores an active learning approach to identify operational points enabling static and semi-static pre-configuration of resources. We present measurements from industry benchmark applications to demonstrate benefits of this CPU-based approach and illustrate effectiveness of our proposed preconfiguration method through a simulator featuring configurable number of microservices and inter-connectivity.
Design of Medical Expert Systems Using Machine Learning Techniques

The number of qualified doctors in India is around 7 per 10,000 people, which leads to the various challenges faced by the medical field such as inadequacy of physicians, the slower rate of diagnosis, and unavailability of medical assistance to common people in time. In this scenario, as clinical decision demands the utmost accuracy of diagnosis, it is a tedious and challenging task for physicians. These issues can be addressed by an automated system that helps the clinicians in disease diagnosis. Attaining the maximum accuracy is an open challenge in this scenario. The scope for improving the accuracy of diagnosis has motivated many researchers to propose medical expert systems using various machine learning and optimization techniques. This leads to proposing four different medical expert system designs as follows. As a first step, an expert system based on fuzzy logic and ant colony optimization (ACO) is used. This system uses the ACO algorithm to generate fuzzy classification rules from the training patterns. The stochastic behavior of the ACO algorithm encourages the ants to find more accurate rules. Second, an expert system based on a hybrid genetic algorithm–simulated annealing and support vector 2machine is proposed for the disease diagnosis. The hybrid GASA is used for selecting the most significant feature subset as well as to optimize the kernel parameters of the SVM classifier. As a next step, a decision support system based on Fisher score-extreme learning machine (ELM)-simulated annealing is proposed. The ELM-based learning machine uses a single-hidden layer feedforward neural network. Finally, an expert system based on least square–support vector machine–simulated annealing (LS-SVM-SA) is proposed. FS is used for the selection of the most significant features. To improve the performance of the system, LS-SVM with radial basis function is used for classification and the SA is used for the optimization of the kernel parameters of the LS-SVM.
Writeprint Mining For Authorship Attribution

This chapter presents a novel approach to frequent-pattern based Writeprint creation, and addresses two authorship problems: authorship attribution in the usual way (disregarding stylistic variation), and authorship attribution by focusing on stylistic variations. Stylistic variation is the occasional change in the writing features of an individual, with respect to the type of recipient and the topic of a message. The authorship methods proposed in this chapter and in the following chapters are applicable to different types of online messages; however, for the purposes of experimentation, an e-mail corpus has been used in this chapter, to demonstrate the efficacy of said methods.