Reviewerly/exHarmony huggingface dataset:
New Technologies in the Instruction of History in Primary Education

Nowadays, the exploitation of augmented reality in education is linked to a multitude of pedagogical subjects, including history, geography, natural sciences and arts. The extensive use of computers in the educational process in recent years, in addition to learners’ familiarity with the use of Internet and applications associated with its operation, have led to a search for creative methods, which will enhance interaction in the pedagogical approach. As a result, a digital learning environment has emerged in which trainees gain access to knowledge in an entertaining and interactive way by making use of innovations and gaining the ability to control their knowledge. The purpose of this paper is to feature the role of new technologies in the educational process by studying the use of augmented reality applications in teaching history. Designing such applications requires direct involvement of the teacher and, as established by the case study, the benefits are extremely important; students acquire a particular interest in the learning object and achieve higher performance, as compared to conventional forms of teaching.
Implementation of hidden communication channel in OSN with histogram analysis

In this report we propose a new steganographic method that allows to be transmitted through social networks stego information. The basis of the method is pre-grouping and classifying a large number of graphical images based on the type of their histograms. By selecting of images with suitable histogram and placing them in the appropriate place in the series of the published album, the formation of the hidden message is achieved. Since no changes are made to the images, the resulting stego channel has a high level of undetectability. The results of the experiments show that the appropriate selection of image classification criteria achieves good resistance of stego channel regardless of the impact of social networks. The proposed method is suitable for transmitting crypto and stego keys.
Photon-induced atom recoil in collectively interacting planar arrays

The recoil of atoms in arrays due to the emission or absorption of photons is studied for sub-wavelength interatomic spacing. The atoms in the array interact with each other through collective dipole-dipole interactions and with the incident laser field in the low intensity limit. Shining uniform light on the array gives rise to patterns of excitation and recoil in the array. These arise due to the interference of different eigenmodes of excitation. The relation between the recoil and the decay dynamics is studied when the array is in its excitation eigenstates. The recoil experienced by a subradiant collective decay is substantially larger than from independent atom decay. A method to calculate the rate of recoil when steady state has been achieved with a constant influx of photons is also described.
Existence-network dimension of information security in modern society

At the present stage, knowledge and information have become the most important resources that allow to achieve the desired goals, minimizing the resources of power, to turn opponents into allies. In addition, the problem of information security is especially relevant, which occupies one of the key places in the system of ensuring the vital interests of all countries without exception. This is primarily due to the urgent need to create a developed information environment of society. But it is through the information environment that threats to national security are most often made in various spheres of activity of the individual, society and the state.Information security is one of the acute socio-cultural problems of modern society, which has a systemic nature and affects the activities of major institutions and subsystems; the context of its influence includes key socio-cultural processes taking place in society. As the development of the information sphere, a person on his way encounters certain difficulties, some of which he has to solve on their own. This leads to the problem of forming in the subject of information culture, the level of which depends on a person's ability to adequately respond to changes in the environment.Network information security practices are determined by their functional significance, not by the location of the entities. Decisions are made online, but are implemented locally and territorially. The Internet is a space of the highest density and intensity of communication. Different types of social structures, determined by the direction, density and intensity of information flows, determine different forms of coordination in society. The ramifications of ways to ensure information security in terms of network communications of the information society are set by such a criterion as the level of communication intensity. Ensuring information security, especially on the Internet, is a practical step to bridge the gaps in information security and information security. Bridging the gaps is carried out in order to orient in the general information space for the control of information.The key risk factor for the information subsystem of society are large-scale socio-communicative and socio-cultural transformations, which carry a number of negative social, especially existential, consequences. In recent years, disorganization and dysfunctional trends directly related to high rates of information change have become clear. Information security is both information protection and protection from information.
Distance learning of students in the modern world

The article examines the problem of organizing distance learning in the conditions of uncertainty and the forced transition of teachers and students to online interaction. The situation that has arisen is viewed as a natural experiment the education system finds itself in requiring an understanding of its results at a certain stage. A theoretical analysis of the most popular distance learning organization technology implemented in Russian universities under current conditions (the coronavirus pandemic) is conducted and a critical evaluation of the obtained initial results of the readiness of higher education system to transition into the new digital education reality is provided. The discovered problems of distance learning include: the insufficient level of teachers’ digital tolerance and the lack of necessary competencies in them; the psycho-emotional instability of the online interaction participants; low learning motivation and the level of students’ practical skills; the lack of a universal digital platform for the organization of activity of all participants of the learning process. The perspectives for solving the aforementioned problems and priority directions in the improvement of this form of education are established, specifically: the technological direction (the formation of teachers’ ability to create online courses and effectively combine the functions of different digital platforms); the methodical direction (the formation of a set of professional and personal qualities necessary to effectively address professional tasks and expansion of the set of methods and means of organizing students’ activity considering the conditions of online interaction); the psychological dimension (teachers’ awareness of the need for self-improvement in the sphere of digital technologies and the formation of interest in learning and satisfaction with the process and results of distance learning in students). The identified areas are considered as risks the consideration of which in educational activities will make the distance learning process flexible and manageable.
Image Captioning for Video Surveillance System using Neural Networks

Security has always been of paramount importance to humans. In the absence of a sense of security at one’s workplace, home or anywhere else, people feel uneasy and vulnerable. With the improvement of modern technology, along with the lack of time at hand, the need for faster, efficient, accurate as well as low-cost security techniques is more than ever. Image Captioning for Video Surveillance System is proposed to develop visual systems that generate contextual descriptions about objects in images, and then use these descriptions to provide information of the scene that needs to be secured. The proposed system uses a neural network model composed of a Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) to caption the incoming video feed. The main significance of this paper is in integrating the system with Discrete Wavelet Transform (DWT), which is applied on the incoming video feed, so that the compressed LL band frames transferred wirelessly to the model are smaller in comparison, leading to less transfer time and faster processing by the model.
Randomized double and triple Kaczmarz for solving extended normal equations

The randomized Kaczmarz algorithm has received considerable attention recently because of its simplicity, speed, and the ability to approximately solve large-scale linear systems of equations. In this paper we propose randomized double and triple Kaczmarz algorithms to solve extended normal equations of the form $$\mathbf{A}^\top \mathbf{Ax}=\mathbf{A}^\top \mathbf{b}-\mathbf{c}$$ . The proposed algorithms avoid forming $$\mathbf{A}^\top \mathbf{A}$$ explicitly and work for arbitrary $$\mathbf{A}\in \mathbb {R}^{m\times n}$$ (full rank or rank-deficient, $$m\ge n$$ or $$m<n$$ ). Tight upper bounds showing exponential convergence in the mean square sense of the proposed algorithms are presented and numerical experiments are given to illustrate the theoretical results.
Review on Application of Nanotechnology in Animal Health and Production

Nanotechnology is research and technology development at the atomic, molecular and macromolecular levels at the scale of approximately 1 - 100 nanometer range, to provide a fundamental understanding of phenomena and materials at the Nano scale and to create and use structures, devices and systems that have novel properties and functions because of their small and/or intermediate size. Nanotechnology has the potential to solve many more puzzles related to animal health, products and breeding. The applications of nanotechnology become the proving ground for untried and more controversial techniques from Nano capsule vaccines to sex selection in breeding. There are numerous applications of nanotechnology in veterinary medicine including disease diagnosis, treatment, drug delivery, animal breedingand improving and boosting animal origin food product. It provide variety of new nanomaterial and nanoparticle including Nano chips, nanosenser, liopsoms, quantum dot, gold nanoparticle, magnetic nanoparticle etc for vaccination, pathogen detection, disease diagnosis, animal breeding and provide polymeric nanoparticle, carbon nanotube, Nano shell dendrites, etc for delivering antimicrobial nanoparticle and Nano medicine for treatment of disease. It is swiftly changing the diagnosis and treatment patterns at faster and low cost in less time duration.
Progressive Insular Cooperative GP

This work presents a novel genetic programming system for multi-class classification, called progressively insular cooperative genetic programming (PIC GP). Based on the idea that effective multiclass classification can be obtained by appropriately joining classifiers that are highly specialized on the single classes, PIC GP evolves, at the same time, two populations. The first population contains individuals called specialists, and each specialist is optimized on one specific target class. The second population contains higher-level individuals, called teams, that join specialists to obtain the final algorithm prediction. By means of three simple parameters, PIC GP can tune the amount of cooperation between specialists of different classes. The first part of the paper is dedicated to a study of the influence of these parameters on the evolution dynamics. The obtained results indicate that PIC GP achieves the best performance when the evolution begins with a high level of cooperation between specialists of different classes, and then this type of cooperation is progressively decreased, until only specialists of the same class can cooperate between each other. The last part of the work is dedicated to an experimental comparison between PIC GP and a set of state-of-the-art classification algorithms. The presented results indicate that PIC GP outperforms the majority of its competitors on the studied test problems.
Outfit Generation and Recommendation—An Experimental Study

Over the past years, fashion-related challenges have gained a lot of attention in the research community. Outfit generation and recommendation, i.e., the composition of a set of items of different types (e.g., tops, bottom, shoes, accessories) that go well together, are among the most challenging ones. That is because items have to be both compatible amongst each other and also personalized to match the taste of the customer. Recently there has been a plethora of work targeted at tackling these problems by adopting various techniques and algorithms from the machine learning literature. However, to date, there is no extensive comparison of the performance of the different algorithms for outfit generation and recommendation. In this paper, we close this gap by providing a broad evaluation and comparison of various algorithms, including both personalized and non-personalized approaches, using online, real-world user data from one of Europe’s largest fashion stores. We present the adaptations we made to some of those models to make them suitable for personalized outfit generation. Moreover, we provide insights for models that have not yet been evaluated on this task, specifically, GPT, BERT and Seq-to-Seq LSTM.
A New Activation Function Validated on Function Approximation Tasks

In this paper, a bounded, continuous, differentiable and non-monotonically increasing function is proposed (σ05). The efficiency and efficacy of using this function as an activation function for hidden layer nodes of FFANNs is demonstrated on five function approximation problems. The proposed function is compared against four bounded, continuous, differentiable, and non-monotonically increasing function (also called sigmoid functions). The results demonstrate that the usage of the proposed activation function as an activation function leads to creation of networks that generalize better than FFANNs using other (sigmoidal) activation function(s).
Identifying the 'Right' Level of Explanation in a Given Situation

We present a framework for defining the “right” level of explainability based on technical, legal and economic considerations. Our approach involves three logical steps: First, define the main con- textual factors, such as who is the audience of the explanation, the operational context, the level of harm that the system could cause, and the legal/regulatory framework. This step will help characterize the operational and legal needs for explanation, and the corresponding social benefits. Second, examine the technical tools available, including post-hoc approaches (input perturbation, saliency maps...) and hybrid AI approaches. Third, as function of the first two steps, choose the right levels of global and local explanation outputs, taking into the account the costs involved. We identify seven kinds of costs and emphasize that explanations are socially useful only when total social benefits exceed costs.
The Research on the Application of Situational Teaching in College English Teaching

In order to adapt to the development of the times, College English classroom teaching should also keep pace with the times, and teaching methods need to be constantly reformed and innovated. The application of situational teaching method in College English classroom teaching is to create specific and vivid direct or indirect situations according to different themes in class, so that students can learn English easily and happily in interactive situations. With the rational use of situational teaching method, the students’ imagination is enriched, the students’ interest in learning English is stimulated, and the students’ dominant position in College English class is respected. Through which we guide the students to use multiple senses in English language learning, which reflects the trend of quality education in College English class, and enables students to learn and apply English in the process of teaching in fun.
Weibull Tone Mapping for Underwater Imagery

Imagery is a preferred tool for environmental surveys within marine environments, particularly in deeper waters, as it is nondestructive compared to traditional sampling methods. However, underwater illumination effects limit its use by causing extremely varied and inconsistent image quality. Therefore, it is often necessary to pre-process images to improve visibility of image features and textures, and standardize their appearance. Tone mapping is a simple and effective technique to improve contrast and manipulate the brightness distributions of images. Ideally, such tone mapping would be automated, however we found that existing techniques are inferior when compared to custom manipulations by image annotators (biologists). Our own work begins with the observation that these userdefined tonal manipulations are quite variable, though on average, are fairly smooth, gentle waving operations. To predict user-defined tone maps we found it sufficed to approximate the brightness distributions of input and user adjusted images by Weibull distributions and then solve for the tone curve which matched these distributions from input to output. Experiments demonstrate that our Weibull Tone Mapping (WTM) method is strongly preferred over traditional automated tone mappers and weakly preferred over the users' own tonal adjustments.
Overlap junctions for superconducting quantum electronics and amplifiers

Due to their unique properties as lossless, nonlinear circuit elements, Josephson junctions lie at the heart of superconducting quantum information processing. Previously, we demonstrated a two-layer, submicrometer-scale overlap junction fabrication process suitable for qubits with long coherence times. Here, we extend the overlap junction fabrication process to micrometer-scale junctions. This allows us to fabricate other superconducting quantum devices. For example, we demonstrate an overlap junction-based Josephson parametric amplifier that uses only two layers. This efficient fabrication process yields frequency-tunable devices with negligible insertion loss, a gain of ∼30 dB, and quantum limited noise performance. Compared to other processes, the overlap junction allows for fabrication with minimal infrastructure, high yield, and state-of-the-art device performance.
Transportation Recommendation with Fairness Consideration

Recent years have witnessed the widespread use of online map services to recommend transportation routes involving multiple transport modes, such as bus, subway, and taxi. However, existing transportation recommendation services mainly focus on improving the overall user click-through rate that is dominated by mainstream user groups, and thus may result in unsatisfactory recommendations for users with diversified travel needs. In other words, different users may receive unequal services. To this end, in this paper, we first identify two types of unfairness in transportation recommendation, (i) the under-estimate unfairness which reflects lower recommendation accuracy (i.e., the quality), and (ii) the under-recommend unfairness which indicates lower recommendation volume (i.e., the quantity) for users who travel in certain regions and during certain time periods. Then, we propose the Fairness-Aware Spatiotemporal Transportation Recommendation (FASTR) framework to mitigate the transportation recommendation bias. In particular, based on a multi-task wide and deep learning model, we propose the dual-focal mechanism for under-estimate mitigation and tailor-designed spatiotemporal fairness metrics and regularizers for under-recommend mitigation. Finally, extensive experiments on two real-world datasets verify the effectiveness of our approach to handle these two types of unfairness.
Free-running long-distance reference-frame-independent quantum key distribution

Abstract Rapidly and randomly drifted reference frames will shorten the transmission distance and decrease the secure key rate of realistic quantum key distribution (QKD) systems. In this article, we present a free-running reference-frame-independent (RFI) QKD scheme, where measurement events are classified into multiple slices with similar estimated classification parameter. We perform the free-running RFI QKD experiment with a fiber link of 100 km and reference frame misalignment more than 29 periods in 50.7 h. A key rate as high as 742.98 bps is achieved at the total loss of 31.5 dB benefiting from both the new protocol design and the 80 MHz repetition rate system in use. Our system runs 50.7 h freely without any reference frame alignment. In the experiment, the misalignment variation rate tolerance of the experiment is 0.262 rad/s, and could be optimized to 1.309 rad/s. Therefore, our free-running RFI scheme can be efficiently adapted into the satellite-to-ground and drone-based mobile communication scenarios.
Blockchain in Healthcare

The underlying characteristics of blockchain can facilitate data provenance, data integrity, data security, and data management. It has the potential to transform the healthcare sector. Since the introduction of Bitcoin in the fintech industry, the blcockhain technology has been gaining a lot of traction and its purpose is not just limited to finance. This thesis highlights the inner workings of blockchain technology and its application areas with possible existing solutions. Blockchain could lay the path for a new revolution in conventional healthcare systems. We presented how individual sectors within the healthcare industry could use blockchain and what solution persists. Also, we have presented our own concept to improve the existing paper-based prescription management system which is based on Hyperledger framework. The results of this work suggest that healthcare can benefit from blockchain technology bringing in the new ways patients can be treated.
Machine Learning Approaches for IoT-Data Classification

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Deep Learning-Based Service Discovery for Business Process Re-Engineering in the Era of Big Data

In recent years, business process re-engineering has played an important role in the development of large-scale web-based applications. To re-engineer business processes, business services are developed and coordinated by reusing a set of open APIs and services on the internet. Yet, the number of services on the internet has grown drastically, making it difficult for them to be discovered to support the changing business goals. One major challenge is therefore to search for a suitable service that matches a specific business goal from a large number of available services in an efficient and effective manner. To address this challenge, this paper proposes a deep learning approach for massive service discovery. The approach, thus called MassRAFF, employs a combination of the recurrent attention and feature fusion methods. This paper first presents the MassRAFF approach and then reports on an experiment for evaluating this approach. The experimental results show that the MassRAFF approach has performed reasonably well and has potential to be improved further in future work.