Reviewerly/exHarmony huggingface dataset:
MODEL OF STANDARD FOR INFORMATION RETRIEVAL IN THE COLLECTION OF REGULATORY FRAMEWORK DOCUMENTS

The article proposes a model of the standards’ texts for information retrieval in the collection of documents the regulatory framework. It is proved that the standard means of information retrieval in the collection texts of standards are ineffective due to the compositional features of the texts and the wide use of generalized and abstract vocabulary. Distinctive stylistic features of standards’ texts in normative base are shown in compositional structure, logic of material representation, compactness. It is noted that the standards’ texts have the same structure of material presentation for all texts of this class, and also contain a limited set of structural elements. The description of structural elements of standards is given. It is proved that the compositional structure of the standard’s text has a significant impact on the results of information retrieval in the collection of documents the regulatory framework. The compositional structure of the standard in the Backus-Naur notations is presented. It is developed the model of the standards’ text in the form of a graph, the vertices and edges of which are full-fledged structural elements of the standard, significant both for the content of the text as a whole, and in terms of information retrieval. It is proved that the presentation of the standard’s text in the form of a graph makes it possible in the process of computer analysis the standard’s text to determine the type of structural element, the degree of nesting, by submitting the standard in the form of a finite set of its components.
Identifying Expert Users on Question Answering Sites

Community question answering (CQA) sites are being preferred by an increasingly large community of users for searching their queries related to the academic or non-academic domain. Generally good quality answer or comment is provided by the expert users on the posted questions. Hence, it is the developer’s responsibility to design a system that can to route the question in front of their experts. Recent researches on CQA websites confirmed that many questions remain unanswered. It may happen because the identified experts may not be active. To overcome this issue, in this paper, the user’s activities are investigated to identify active users. The main objective of this research is to identify the right user group that is active and capable of giving quality answers. This will help to improve the site’s reputation, content quality, and user participation on the site.
Advance Security and Challenges with Intelligent IoT Devices

By 2025, more than 25 billion devices are connected over Internet with high cognitive intelligence. Internet of things has major issues and threats regarding security challenges over billions of intelligent devices connecting over Internet. Internet connectivity reaches to 7G in the future. In Japan, it is already on the way and so as complexity regarding connectivity as well as security increases. Internet of things opens the doors for various sectors in advancements but cognitive devices security is become major threat with advancements in IoT. Now, devices are connected with advance and high connectivity Internet and stored over different types of clouds. So, this paper aims purely security to these smart cognitive devices with assistance of deep learning, artificial intelligence with IoT with big data assistance, and discuss the security issues with intelligent devices with different useful applications on day by day basis as well as provide proposed secured methodology for “Smart ATM” with advance applications of deep learning and natural language processing (NLP) with artificial intelligence. In this paper, apply the “Weapon Detection,” “Crime Intention Detection” and using “Edge Computing” with convolutional neural networks irrespective of the use of cloud computing with machine learning for the cognitive intelligence and fast access with high security in smart devices with IoT. This paper also proposes “Smart Laser Fencing” for the Hi-Tech security of smart devices with “Deep-fi Geo-Fencing,” and this eliminates the major security threats in various smart devices and decreases the rate of cyber-crimes majorly in e-money transactions as well as in other various domains.
Annotation and Detection of Arguments in Tweets

Notwithstanding the increasing role Twitter plays in modern political and social discourse, resources built for conducting argument mining on tweets remain limited. In this paper, we present a new corpus of German tweets annotated for argument components. To the best of our knowledge, this is the first corpus containing not only annotated full tweets but also argumentative spans within tweets. We further report first promising results using supervised classification (F1: 0.82) and sequence labeling (F1: 0.72) approaches.
Detection and Adaptive Video Processing of Hyperopia Scene in Sports Video

In the research of motion video, the existing target detection methods are susceptible to changes in the motion video scene and cannot accurately detect the motion state of the target. Moving target detection technology is an important branch of computer vision technology. Its function is to implement real-time monitoring, real-time video capture, and detection of objects in the target area and store information that users are interested in as an important basis for exercise. This article focuses on how to efficiently perform motion detection on real-time video. By introducing the mathematical model of image processing, the traditional motion detection algorithm is improved and the improved motion detection algorithm is implemented in the system. This article combines the advantages of the widely used frame difference method, target detection algorithm, and background difference method and introduces the moving object detection method combining these two algorithms. When using Gaussian mixture model for modeling, improve the parts with differences, and keep the unmatched Gaussian distribution so that the modeling effect is similar to the actual background; the binary image is obtained through the difference between frames and the threshold, and the motion change domain is extracted through mathematical morphological filtering, and finally, the moving target is detected. The experiment proved the following: when there are more motion states, the recall rate is slightly better than that of the VIBE algorithm. It decreased about 0.05 or so, but the relative accuracy rate increased by about 0.12, and the increase ratio is significantly higher than the decrease ratio. Departments need to adopt effective target extraction methods. In order to improve the accuracy of moving target detection, this paper studies the method of background model establishment and target extraction and proposes its own improvement.
Recognition of Sign Language Letters Using Image Processing and Deep Learning Methods

In order for people to be able to communicate with each other, they must be able to agree mutually.Communication is quite di fficult for individuals with hearing problems.Such individuals make their lives much more difficult by isolating themselves from society.The people living with hearing loss can understand the contact person with often lip-reading method, but it is quite difficult for them to express themselves to the people.Since the use of sign language has not become widespread around the world, the number of people who know sign language is very low, except for individuals with hearing disabilities.In this study, it was achieved t o dynamically recognize the movements of the sign language finger alphabet via image processing using deep learning methods and to translate it into writing.Accordingly, it is aimed to facilitate communication between people who do not know the sign language in daily life and people with hearing loss.The input given to the system is an image of the hand showing any letter from the alphabet.The image of the hand is interpreted by deep learning methods in the system, and it is compared to one of the letters in the alphabet and an output with the similarity ratio to this letter is displayed on the screen.The system has been tested with a total of 1300 images.The overall accuracy rate of the system was calculated as 88% where true positive rate was 87% and false negative rate was 13%.
The Emancipatory Potential of Education: A Critical Tradition in the Philosophy Education

The article attempts to reconstruct the critical tradition in the philosophy of edu­cation on the basis of a review of studies in which education is analyzed from the critical perspective, addressing such issues as deformation of the meanings of education, dehumanization of educational relations, legitimization inequality by means of education. This tradition is described in the article around the issue of the emancipatory potential of education as the conditions for the formation of independent mature thinking, the development of subjectivity and autonomy of the individual. The key directions of the development of critical reflection on education are outlined. Different projects of emancipatory education related to the movement of critical pedagogy are analyzed and compared. A special em­phasis is placed on the crucial for a critical tradition problems: the problem of in­strumentalism in education and the problem of achieving equality and overcom­ing hidden mechanisms of power in the educational relations. The article concludes with demonstrating the relevance of the critical tradition in the analy­sis of education for understanding modern trends in education associated with the dominance of neoliberal logic in assessing its effectiveness.
PhaseWare: Phase map retrieval for fringe projection profilometry and off-axis digital holographic interferometry

Fringe projection profilometry and off-axis digital holographic interferometry are two techniques commonly used for non-contact optical surface measurement and movement tracking; however, they can be difficult to implement due to the complex software involved. To address this, we introduce a MATLAB-based graphical application that performs phase map retrieval for these techniques. Designed with a pipeline-based architecture, PhaseWare assembles the most commonly used algorithms and includes pre-processing (cropping, DC offset removal, filtering), fringe and phase extraction (hologram reconstruction, phase demodulation), and post-processing (denoising, masking, phase unwrapping, background removal, image enhancement).
How Will Blockchain Technology Affect the Future of the Internet?

Today, the Web is indispensable in daily life. However, the Web has become centralized, because of business models. The centralized Web has brought a few major issues that will likely get worse in the near future. In this paper, we discuss problems of the centralized Web and the necessity of decentralized Web, the potential of blockchain for decentralized web architecture. And then, we also discuss the decentralized web architecture that circumvents Internet gatekeepers like Google and Facebook and takes control of our data back from the giant social media companies.
It Started with Templates: The Future of Profiling in Side-Channel Analysis

Abstract Side-channel attacks (SCAs) are powerful attacks based on the information obtained from the implementation of cryptographic devices. Profiling side-channel attacks has received a lot of attention in recent years due to the fact that this type of attack defines the worst-case security assumptions. The SCA community realized that the same approach is actually used in other domains in the form of supervised machine learning. Consequently, some researchers started experimenting with different machine learning techniques and evaluating their effectiveness in the SCA context. More recently, we are witnessing an increase in the use of deep learning techniques in the SCA community with strong first results in side-channel analyses, even in the presence of countermeasures. In this chapter, we consider the evolution of profiling attacks, and subsequently we discuss the impacts they have made in the data preprocessing, feature engineering, and classification phases. We also speculate on the future directions and the best-case consequences for the security of small devices.
Towards Accurate Camouflaged Object Detection with Mixture Convolution and Interactive Fusion.

Camouflaged object detection (COD), which aims to identify the objects that conceal themselves into the surroundings, has recently drawn increasing research efforts in the field of computer vision. In practice, the success of deep learning based COD is mainly determined by two key factors, including (i) A significantly large receptive field, which provides rich context information, and (ii) An effective fusion strategy, which aggregates the rich multi-level features for accurate COD. Motivated by these observations, in this paper, we propose a novel deep learning based COD approach, which integrates the large receptive field and effective feature fusion into a unified framework. Specifically, we first extract multi-level features from a backbone network. The resulting features are then fed to the proposed dual-branch mixture convolution modules, each of which utilizes multiple asymmetric convolutional layers and two dilated convolutional layers to extract rich context features from a large receptive field. Finally, we fuse the features using specially-designed multi-level interactive fusion modules, each of which employs an attention mechanism along with feature interaction for effective feature fusion. Our method detects camouflaged objects with an effective fusion strategy, which aggregates the rich context information from a large receptive field. All of these designs meet the requirements of COD well, allowing the accurate detection of camouflaged objects. Extensive experiments on widely-used benchmark datasets demonstrate that our method is capable of accurately detecting camouflaged objects and outperforms the state-of-the-art methods.
CT images segmentation method of rectal tumor based on modified U-net

Computer-assisted rectal clinical diagnosis is of great significance for the early detection and treatment of rectal cancer. In data processing, it is quite challenging to achieve automatic segmentation due to the blurred boundary between lesions and healthy rectal tissue. To overcome such difficulties, we propose a rectal tumor segmentation method by using a modified U-net, thereby improving the diagnostic efficiency and accuracy. Firstly, the central coordinates of the rectal part to extract the region of interest are determined. Then, the tumor region is determined in the CT image via the YOLOv3 algorithm. Finally, the residual connection and attention mechanism are introduced to reduce the possibility of misjudgment of healthy rectal tissue as lesions to improve the accuracy of the traditional U - net model, and we use the modified U-net model to segment the rectal tumor region. The experiments show the Dice coefficient of this method can reach 83.45 %, which is about 7% higher than the traditional U-net method, and this shows the validation and merits of the proposed algorithm.
Education of the Future:Reproductive-Productive Transition

Education of the future is a fundamental challenge of the present time that de­fines a horizon of thinking of society and about society. The knowledge society stepping into the role of a horizon brings the substance of the matter to the ability of education to cultivate the creative function of thinking. From the ontological point of view, this article deals with the problem of the education transformation from reproductive to productive forms of working with knowledge (the repro­ductive-productive transition). The prevailing reproductive model of modern ed­ucation is a class-and-lesson (or lecture-seminar) system. Institutionalization of class-and-lesson education in the Reformation years in the XVI c. is ana­lyzed, and key didactic and organizational principles laid down in its basis by Ph. Melanchton are identified. Arguments are presented against qualification of educators in the age of Reformation as humanists. The concept of epistemic dominant is introduced for the purpose of explaining the education transforma­tion process. It is shown that the reproductive-productive transition belongs to the essence of our time. The stability of the class-and-lesson system can be ex­plained by resting on an essential part of educational universals that are timeless in nature. Based on the theory of non-Kuhn’s paradigms, the relationship be­tween the reproductive-productive transition and a shift in the ontological foun­dations of the education phenomenon as to its forms, ways, functions, and gener­alization of its being is shown.
Implementing a Proposal System for Video Streaming Over MANET Using NS3, LXC and VLC

As technology is advancing the demand on video streaming, it is one of the technologies increasing every day. From the last years, numerous researchers have presented various techniques and several algorithms for correct and dependable video streaming. In this paper, the proposed system methods are introduced based on the described model of the network. The main flowchart is presented for the working procedure of the considered MANET to simulate video streaming between the nodes through VLC media player. In addition, it extracts the features of video and the original quality of experience (QoE) take it from an international dataset. Then, the neural network algorithm is described and applied to train all videos in dataset. Finally testing the video in this neural network and finding new QoE, the experiment begins with entering video and then streaming it by the VLC and sending it through the MANET network, and the video is received by another VLC. The received videos are stored in the VLC, and then, the feature extraction is applied for all the received videos. These features are used as an input in a neural network which gives result as new QoE videos scores. The results of new QoE Score are compared with the original quality of experience score which is found in the dataset. The best qualified video is the one that is close to the original quality of experience video. Two examples are applied in this paper that shows the aim of the system. The suggested system achieves 6% average error and is succeeded in transforming video with high average quality up to 94%.
The possibilities of universal digital technologies for support the professional self-determination of students

The problem and the aim of the study. The problem that the study is aimed at is due to the need to change forms and methods of professional self-determination support for students in the digital society in order to prepare them for obtaining the specialization that is in demand and for the formation of the ability “to be able to learn" throughout the life. The authors purpose for the efficiency of career guidance in a digital school an approach that does not imply the development of a qualitatively new information resource, but that maximizes the spectrum of didactic capabilities of universal information technologies (spreadsheets, databases, etc.). Research methods. The analysis of fundamental scientific works on the problem of personal professionalization and the usage of information technology in planning the trajectory of career development were used to obtain theoretical generalizations. Spreadsheet processing technology is used as a universal digital technology. The study involved 109 students of the Institute of land reclamation, water management and construction named after A. N. Kostyakov and 25 undergraduates in the direction of 44.04.01 Pedagogical education (master's level) of Vyatka state University. To process the results of the pedagogical experiment, we used the analysis of arbitrary contingency tables using the Pearson χ2 (chi-square) criterion. Results. The essence of the concept of "universal digital technology" as a technology implemented with the help of modern technical means and intended for a wide range of users has been clarified. The stages of work in a software environment that supports the appropriate technology and is used in career guidance activities of a digital school are described: application of methods for diagnostics, determination of selection criteria for professions of the future, drawing up a system of tasks, etc. In the experiment the dynamics was assessed by the levels of formation of intellectual qualities, the most significant for professional self-determination of the personality, and statistically significant differences in qualitative changes, were revealed χ2obs. 2 &gt; χ2crit.0.05 (8,9 &gt; 5,99). In conclusion the authors generalized recommendations for educators: orientation in the proposed means; demonstration of capabilities through personal experience and for the professional tasks of specialists; monitoring a range of digital resources for career planning; comparing the needs of society with the opportunities, interests of students, etc.
On Comparison of Multiserver Systems with Two-Component Mixture Distributions

In this paper, we introduce and study the relations between parameters of the two-component Hyperexponential, Exponential-Pareto and two-component Pareto mixture distributions which admit stochastic and failure rate comparisons. Then we apply the failure rate and stochastic ordering techniques to construct the upper and lower bounds for the steady-state performance indexes of a multiserver model with Exponential-Pareto mixture service time distribution.
New exponential dispersion models for count data: the ABM and LM classes

In their fundamental paper on cubic variance functions (VFs), Letac and Mora ( The Annals of Statistics , 1990) presented a systematic, rigorous and comprehensive study of natural exponential families (NEFs) on the real line, their characterization through their VFs and mean value parameterization. They presented a section that for some reason has been left unnoticed. This section deals with the construction of VFs associated with NEFs of counting distributions on the set of nonnegative integers and allows to find the corresponding generating measures. As EDMs are based on NEFs, we introduce in this paper two new classes of EDMs based on their results. For these classes, which are associated with simple VFs, we derive their mean value parameterization and their associated generating measures. We also prove that they have some desirable properties. Both classes are shown to be overdispersed and zero inflated in ascending order, making them as competitive statistical models for those in use in both, statistical and actuarial modeling. To our best knowledge, the classes of counting distributions we present in this paper, have not been introduced or discussed before in the literature. To show that our classes can serve as competitive statistical models for those in use ( e.g. , Poisson, Negative binomial), we include a numerical example of real data. In this example, we compare the performance of our classes with relevant competitive models.