Reviewerly/exHarmony huggingface dataset:
Holographic Security

This chapter is concerned with the security applications of holographic techniques. Holograms are used in product authentication, product branding and brand protection, and personal and other documents. Serialization to ensure the uniqueness of each hologram for added security is briefly discussed. The theory of optical character recognition and joint transform correlation methods is outlined. Holographic recording using encrypted reference waves enables secure storage of information. Several methods are considered for encrypting optical data as well as holographic recording of encrypted images and their reconstruction and decryption. Millimeter and microwave holographic imaging techniques for the detection of hidden contraband, narcotics, firearms, and explosives are also discussed.
Control Training

The paper devotes to a new conception ”Machine learning control”. The mathematical formulation of this term is presented. It is shown, that a numerical method for solution of the control general synthesis problem belongs to the machine learning control. In common case the machine learning control is a search some control function. There are two approaches to this problem. First approach consists of definition of the function with accuracy to some unknown parameters. A search of these parameters is a machine learning. Artificial neural network technology belongs to the first approach. The second approach consists of search mathematical expression of control function by computer. This is a search of function structure and parameters together. Now so far there is only one way to realize this approach. It is symbolic regression. The paper describes symbolic regression technology in common case and some examples of symbolic regression applications for the control synthesis problem are presented.
Bayesian Learning: Inference and the EM Algorithm

This is the first of two chapters dedicated to Bayesian learning. The main concepts and philosophy behind Bayesian inference are introduced. The evidence function and its relation to Occam's razor rule are presented. The expectation-maximization (EM) algorithm is derived and applied to linear regression and Gaussian mixture modeling. The k-means algorithm for clustering and its affinity to Gaussian mixture modeling are discussed. The exponential family of distributions is introduced. Finally, the concept of probabilistic model mixing is reviewed and the notion of mixture of experts is presented.
Big Data for Ecological Models

The use of data repositories for parameterizing ecological models and storing model runs is becoming more common, yet often these data archives do not contain the appropriate metadata, nor are they maintained for others to use. Data archiving and sharing are additional steps in the scientific process that add value to a researcher׳s work, and more importantly, facilitate transparency and repeatability of a researcher׳s work. Historically, peer-reviewed publications did not allow for the full presentation of underlying datasets, which were only shared through personal contact with a scientist. However, with the expanding use of “supporting online material” (SOM) files that accompany digital publication there is an increased expectation that even large datasets can be made accessible to readers. Thus, researchers are faced with the additional task of becoming their own archivist and depositing data in a repository where it can be used by others. This article introduces basic concepts in data archiving and sharing, including major digital repositories for life science data, commonly used digital file formats, and why metadata is an essential element to successful data sharing when machine-readable data is increasingly used in large-scale studies.
Where the Big Bucks Will Come From: Implementing Product Line Engineering for Railway Rolling Stock

ABSTRACT This paper presents a mid‐term return of experience from Alstom's rolling stock division on its implementation of product line engineering (PLE). It describes the journey we have undertaken to implement PLE, the challenges we have had to face, and the manner in which we are finding our way (most of the time deliberately, sometimes fortuitously) to define a profitable reuse strategy and to structure our approach on PLE. The paper also presents some encouraging intermediate results and concludes with the perspectives and the future challenges in our search for product line systems engineering excellence.
Chinese Event Trigger Word Extraction Using ERNIE-BIGRU-CRF

Event extraction is an important and challenging task in the text data mining of natural language processing. Event extraction consists of event trigger word extraction and event element extraction. Event trigger word extraction is designed to recognize instances of event trigger words of the type specified in the text. Unlike English sentences, Chinese sentences do not have clear word boundaries, and existing Chinese event trigger word extraction methods suffer from cascading errors caused by word separation problems. Aiming at the problems of blurred event entity boundaries and inaccurate extraction results in current Chinese event trigger word extraction, we propose an event trigger word extraction method based on the pretrained ERNIE (enhanced language representation with informative entities) model. Using the knowledge-enhanced semantic representation ERNIE model, the word vector containing context information is calculated, and the ambiguity of the word is adjusted. After ERNIE, a BIGRU(bilateral gated recurrent unit) is added for further semantic coding, and finally, the label sequence with the highest probability is output through the CRF(conditional random field) layer. Compared with the existing best-performance baseline model, experiments on the ACE2005-CN dataset show a significant improvement in the F1 score.
Cryptographic Sensing

Is it possible to measure a physical object in a way that makes the measurement signals unintelligible to an external observer? Alternatively, can one learn a natural concept by using a contrived training set that makes the labeled examples useless without the line of thought that has led to their choice? We initiate a study of "cryptographic sensing" problems of this type, presenting definitions, positive and negative results, and directions for further research.
Toward a Capability Maturity Model for Digital Forensic Readiness

Increasing IT-security breaches and the extensively growing loss due to fraud-related incidents cause the need for being prepared for a digital investigation. A specific capability maturity model can assist organizations to determine their current state accordingly to implement digital forensic readiness measures and get assistance to reach a desired level in having related capabilities implemented. This paper examines how such a model can assist in integrating digital forensic readiness related measures to reach an appropriate maturity level. Through facilitating core elements of the IT-governance framework COBIT 5 and the core characteristics of implementing digital forensic readiness, a proposal for a specific capability maturity model has been conducted. In five maturity levels (initial, managed, defined, quantitatively managed, and optimized), the different stages of implementing digital forensic readiness are represented. It can be shown that with the IT-governance aligned model, the implementation of digital forensic readiness can be assisted.
Multi-objective Grey Wolf Optimizer

Metaheuristics have become very popular in the last two decades. This class of problem solving techniques includes a wide range of algorithms to find reasonably good solutions for problems where deterministic methods are not efficient. Their name come from their mechanism, in which they do not required problem-specific heuristic information. Such methods are stochastic and consider problems as a black box.
Deep Learning

Deep learning has caught a great deal of momentum in the last few years. Research in the field of deep learning is progressing very fast. Deep learning is a rapidly growing area of machine learning. Machine learning (ML) has seen numerous successes, but applying traditional ML algorithms today often means spending a long time hand-engineering the domain-specific input feature representation. This is true for many problems in vision, audio, natural language processing (NLP), robotics, and other areas. To address this, researchers have developed deep learning algorithms that automatically learn a good high-level abstract representation for the input. These algorithms are today enabling many groups to achieve groundbreaking results in vision recognition, speech recognition, language processing, robotics, and other areas.
A secure deduplication scheme based on data popularity with fully random tags

It is difficult to provide semantic security for user data while using deduplication to save storage space in cloud storage. Some studies attempt to provide different levels of security for data according to their popularity for a reasonable trade-off between security and efficiency. However, existing schemes generally need a trusted third party to store deterministic data tags to record data popularity. If the trusted third party is compromised by adversaries, the deterministic tags will expose data information. In this paper, we propose a popularity-based secure deduplication scheme with fully random tags, which does not need to store deterministic tags. Our solution is using the homomorphic encryption to generate comparable random tags to record data popularity and using binary search to reduce time complexity of tag comparison to logarithmic time. Besides, we also design a proof of ownership protocol based on homomorphic encryption to prevent adversaries with only the data tag from tampering with the data popularity, which are not considered in the existing popularity-based schemes. We implement our scheme for system efficiency evaluation. Compared with the scheme of Stanek et al., our scheme has a slight improvement in encryption efficiency.
TOPICAL ACTIVITY ISSUES OF LIBRARIES OF SCIENTIFIC ORGANIZATIONS OF THE SIBERIAN BRANCH OF THE RUSSIAN ACADEMY OF SCIENCES

The reform of science and the restructuring of academic institutions have entailed transformation of library systems. Under the influence of both external and internal factors activities and forms of interaction of information and library units of the Siberian Branch of the Russian Academy of Sciences (SB RAS) have changed.
Origins and Development of 21st Century Security

To understand the security industry of the 21st century, it is necessary to explore the basic development of the concepts that shaped the business. While the business has become a profession, the basic concepts that were developed many years ago remain the backbone of our modern security operations. What has changed are the tools that are now available. The business, in becoming a profession, has developed many organizations offering a variety of services to security managers and related professionals. World events, such as our wars and social/economic crime issues, have had a great influence on the direction and developments in the field. Today, the security field is still growing far faster than its public law enforcement counterpart. Both public law enforcement and private security are working together to make our country and world a better place to live.
Prediction Models

Having/living convenient life through smart devices, people are more interested or more dependent on to predict something for future (i.e., with respect to their health, business, etc.). For that, many prediction models by several researchers are being used in many applications. Due to a vast (rapid) change in lifestyle, people are more prone to a number of life-threatening diseases when it comes to their well-being. Many of these diseases start developing their symptoms in their early stages. But, still many of these diseases like cancer, kidney damages remain unidentified in their developing stages. The earlier a disease is predicted, the easier it becomes to cure it and even prevent it. Predictive modeling provides a huge step forward in medical science in preventing the risk among patients. Prediction modeling is the process of analyzing current conditions to predict future results.
Search Engine Optimization

In this chapter, the authors discuss at length the subject of search engine optimization (SEO), its different characteristics, and why basic SEO knowledge is considered as a useful tool for any web business. The chapter will address both the theoretical and practical issues related to the design and implementation of SEO and also will cover previous research done on this topic. In order to give a better understanding of the importance of SEO in the current state of the internet and in information search, basic knowledge of how search engines operate along with their recent updates will also be provided. The development of World Wide Web (Web 2.0) along with the growth of search engines over the last decade has brought significant changes in web content. In this context, the authors show how SEO has evolved over the years. Nowadays, social media and social signals have increasingly become one of the many factors search engines take seriously into account, which is another issue that this chapter will address.
Multi-rate deep semantic image compression with quantized modulated autoencoder

Recently, deep learning has demonstrated impressive performance in image compression. Methods, that achieve and even outperform conventional codecs performances, are continually emerging. However, most of them need to train and deploy separate networks for rate adaptation. This is impractical and extensive in terms of memory cost and power consumption, especially for broad bitrate ranges. Further, methods that consider the semantic-important structure of the image are extremely sparse. This leads to non-optimized bit allocation for the eye-catching foreground details, that have to be preserved for the almost all computer vision applications. Towards this end, we establish an end-to-end multi-rate deep semantic image compression with quantized conditional autoencoder. It includes two neural networks for the semantic analysis and image compression, respectively. The semantic analysis network extracts the essential semantic regions of the input image, and calculates the Semantic-Important Structural SIMilarity (SI-SSIM) index for each of them. The compression network is then trained to optimize a multi-loss function based on SI-SSIM and conditioned on the activation bitwidths. Performances of our model are evaluated on the JPEG AI dataset for objective and perceptual quality metrics. Obtained results show that our method yields higher performances over JPEG, JPEG 2000 and HEVC intra baselines and competitive performances with VVC intra.
Path Planning of Quadrotors in a Dynamic Environment Using a Multicriteria Multi-Verse Optimizer

Paths planning of Unmanned Aerial Vehicles (UAVs) in a dynamic environment is considered a challenging task in autonomous flight control design.In this work, an efficient method based on a Multi-Objective Multi-Verse Optimization (MOMVO) algorithm is proposed and successfully applied to solve the path planning problem of quadrotors with moving obstacles.Such a path planning task is formulated as a multicriteria optimization problem under operational constraints.The proposed MOMVO-based planning approach aims to lead the drone to traverse the shortest path from the starting point and the target without collision with moving obstacles.The vehicle moves to the next position from its current one such that the line joining minimizes the total path length and allows aligning its direction towards the goal.To choose the best compromise solution among all the non-dominated Pareto ones obtained for compromise objectives, the modified Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is investigated.A set of homologous metaheuristics such as Multiobjective Salp Swarm Algorithm (MSSA), Multi-Objective Grey Wolf Optimizer (MOGWO), Multi-Objective Particle Swarm Optimization (MOPSO), and Non-Dominated Genetic Algorithm II (NSGAII) is used as a basis for the performance comparison.Demonstrative results and statistical analyses show the superiority and effectiveness of the proposed MOMVO-based planning method.The obtained results are satisfactory and encouraging for future practical implementation of the path planning strategy.
Situation Diagnosis Based on the Spatially-Distributed Dynamic Disaster Risk Assessment

A dynamic spatially-distributed model of integral risk assessment is represented in the paper. A multi-risk for a valuable object is formed as a combination of four components such as danger, threat potential, threat level, and potential losses. In order to provide comparing the risks from different disasters and assess their joint influence on the valuable object in the form of multi-risk a quantitative value of each risk component is proposed to represent in the form of qualitative value using the appropriate scales. A diagnostic method for disaster response operations based on the spatially-distributed model of integral risk assessment is developed. A hybrid algorithm of identification of the situation in disaster conditions using the case-based and rule-based reasoning is described. The experiment examining the validity and efficiency of the proposed hybrid diagnosis method is described. It’s concluded that the proposed method provides sufficient performance for the cell size 5 m and above, so it is acceptable for solving the practical forest fire fighting problems in GIS-based DSS.
INTERNET OF THINGS (IOT) AS KEY ENABLER FOR EFFICIENT BUSINESS PROCESSES

Most of the failures in business are related to the unavailability of correct business data. Internet of Things (IoT) brings in a huge promise in a number of functional areas related to businesses in terms of enhancement, efficiency, rightness of information, development of new and modern business processes and models and profit gains and loss.The methodology which we propose in this paper constitutes from two key sources: The literature review of the topic and the informal surveys. In this, Interviews of 8-10 business managers of different organisations were conducted to get the correct feedback and business data.This paper aims to show how we can use IoT to make business functions efficient and affordable. We try to provide a critical examination of situations and conditions where IoT can be used and justified in business purposes. The main aim of the paper is to provide a methodology which will be capable of providing wise and successful investment in IoT. The paper points out conditions to maximise the success of IOT adoption in Business applications.
Sequence Labeling Approach to the Task of Sentence Boundary Detection

One of the keys to enable chatbots to communicate with human in a more natural way is the ability to handle long and complex user's utterances. In order to achieve this goal, we propose to integrate the Sentence Boundary Detection (SBD) module into the chatbot architecture, whose role is to take as input a user's utterance from an automatic speech recognition device, in which sentence boundaries are not available, and output the corresponding list of punctuated sentences for downstream modules such as Intent Detection, Topic Classification, Sentiment Analysis, Named Entity Recognition, as well as Coreference Recognition. To address the SBD task, we reformulate it as a sequence labeling task. In this way, both deep neural network models (e.g., Bi-directional Long Short-Term Memory, Convolutional Neural Network) and structured prediction models (e.g., Hidden Markov Model, Maximum Entropy Model, Conditional Random Field) can be leveraged. After reformulating the SBD task, we built a hybrid deep neural network model and achieved good performance on both CornellMovie-Dialog and DailyDialog datasets.
Student’s Perception of Higher Education in Technical Universities and its Connection with Subsequent Professional Development (MSTU, MADI and MIREA - Comparative Analysis) (Continuation)

This article is a continuation of the previous publication devoted to the presentation of the results of a comparative study of students of technical universities: University of Bauman, MADI and MIREA. These results were obtained using a sociological questionnaire with closed-type questions. The questionnaire contained nine main thematic blocks: 1. Professional choice; 2. Profession; 3. Study and education; 4. Educating; 5. Professional and personal competence; 6. Teachers; 7. Communication of students; 8. Humanitarian knowledge in the training of an engineer; 9. Intelligentsia. This article presets a theoretical approach to the problem under study. Some results obtained by the author earlier in the annual studies of MSTU students using the map of psychological and pedagogical monitoring, are presented and analyzed in detail. In particular, the data on relationship between personal characteristics and value orientation of students of technical specializations, (based on the material of the surveyed students of MSTU) with their attitude to study and ideas about their future career are presented.
Usability of an Online Judge for Concurrent Programming Education

Online judges are useful tools for teaching and learning computer programming. Although born in this context, most online judges have been designed to meet the needs of competitive programming rather than programming education. This paper presents a user interface design for an online judge enriched with features of a learning management system (LMS) to satisfy the requirements of teachers and students of concurrent programming identified in a previous study. A wireframe prototype of the proposed interface was built, and its usability was evaluated with students who already had experience using other online judges. The participants managed to complete the eight tasks proposed with the prototype, so it is considered productive. They also reported a high degree of satisfaction when using it and provided suggestions for improvement to enrich the final product according to the user-centered interaction design methodology.
One Approach to the Design of Digital Educational Resources for the Training of Personnel in Power Industry

The relevance of the software product of digital educational resources assessment for the purpose of their further integration into educational process is proved in the article. The basic principles of the procedure of their search are defined. The ranging principle of results of information search is determined. A description of the data model for storing search results is provided. Features of operating in the web applications created by means of a framework of ASP.NET on the basis of the Model-view-controller template and with assistance of technology of cast and authentication of Identify ASP.NET are considered by data. The main screen forms showing work of system of thematic search are given.
Nutriflow: A Diet Recommendation System

In today’s world, people are very busy thinking about their daily diet and their whole life. There has been an increase in body weight related diseases and health problems. Better health, increased energy and longer life span can be the benefits of a proper diet and regular exercise. We created a recommender framework for this project that helps users monitor their calorie targets based on their BMI and provides food suggestions based on the user's background and preferences. The consumer can enter their BMI as well as their dietary habits and allergies, and a daily calorie target will be suggested. We assist users by providing food recommendations based on their personal tastes using Collaborative Filtering and Fuzzy logic for creating a recommender framework. Users can monitor their steps as part of an operation or workout using the Android-based application's Step Counter or Pedometer.