Reviewerly/exHarmony huggingface dataset:
Integrated Probabilistic Data Structure For Accurate and Scalable Sequence Prediction

The inevitable success of predictive analytics lies in the prospect of cutting edge approaches proposed in the domain of data mining and machine learning. Frequent itemsets, sequential pattern mining has been the major sub domains of the data mining since its inception with human work. Sequence prediction is a rather more targeted approach towards the predictive data mining. Sequential pattern mining approaches, while being very efficient in finding associations and interactions of difference itemsets and discovering hidden pattern in the transactional or graph datasets, sequence prediction is better focused on predicting the next item of an existent sequence. Applications of sequence prediction has been in pattern prediction, stock value prediction, websites prefetching, fraud detection, breach prediction, text prediction, product recommendation in extensive manner. Several state-of-the-art algorithms and models have been proposed starting from graphical models, markovian models and tree based structures have been proposed by data mining. Machine learning models like recurrent neural networks with LSTM blocks work pretty well in terms of performance. Our aim is to propose an integrated data structure with better performance measures (space-time efficiency, scalability) and which can be effectively used in online learning setting-stream processing algorithm. The paper also describes the extension prospect of this algorithm which can be used in the time-series forecast.
Methodology for obtaining textual information from images and its analysis

Abstract The paper proposes a solution of the issue of extracting textual information from a document image. There are many interesting solutions of this problem thanks to practical applications, such as text recognition, translation, forensics, etc. The quality of the text information of documents converted into images depends on the type of scene (simple or complex), type of digital camera (low-cost devices, mobile devices), the magnitude and direction of illumination, type of substrate (paper, plastic). These points lead to the fact that the automatic extraction of text from the image is extremely difficult.
Survey on Automatic Liver Segmentation Techniques from Abdominal CT Images

Liver cancer is one of the terminal noncommunicable diseases. These metastatic diseases of the liver are causing each year to increase the number of deaths worldwide. World Health Organization (WHO) states that liver cancer is alone responsible for about 788000 deaths. The automatic segmentation methods have been used to improve the accuracy, a however technique with high accuracy for detection of liver deformity is highly essential. In this survey paper, a comparative analysis is carried out illuminating the benefits and drawbacks of numerous available technologies. The paper concentrates on scrutinizing the applicability of available techniques of liver segmentation of CT images. The prime purpose of this paper is to highlight the condition automated technologies that to develop novel technologies to solve case problems in the medical field.
Various Forms of Finite Memory Structure Filter for Discrete-Time State-Space Model

In this review paper, the finite memory structure(FMS) filter is represented by various forms such as iterative form, matrix form and summation form for discrete-time state-space model in white Gaussian noise environments. These various forms can provide a comprehensive understanding of FMS filtering algorithms. In particular, in case of summation form, the FMS filter is newly extended with consideration of a control input. Simulation results for a sinusoidal signal model show that the FMS filter can be better than the infinite memory structure(IMS) filter for the temporarily uncertain system.
Means to Develop Pedagogical Optimism in the Process of Future Teacher’s Training

The article examines one of the relevant problems of higher pedagogical education: accompanying the development of the personal and creative potential of a future teacher. In this context, the author analyses the notion “pedagogical optimism” as a personally and professionally valuable quality that promotes constructive interaction with subjects of pedagogical process. The paper argues that creating situations of success is a basic way of pedagogical optimism formation when training future teachers. The potential of the case-study technology in solving this problem is revealed.
Natural Spoken Instructions Understanding for Robot with Dependency Parsing

This paper presents a method based on syntactic information, which can be used for intent determination and slot filling tasks in a spoken language understanding system including the spoken instructions understanding module for robot. Some studies in recent years attempt to solve the problem of spoken language understanding via syntactic information. This research is a further extension of these approaches which is based on dependency parsing. In this model, the input for neural network are vectors generated by a dependency parsing tree, which we called window vector. This vector contains dependency features that improves performance of the syntactic-based model. The model has been evaluated on the benchmark ATIS task, and the results show that it outperforms many other syntactic-based approaches, especially in terms of slot filling, it has a performance level on par with some state of the art deep learning algorithms in recent years. Also, the model has been evaluated on FBM3, a dataset of the RoCKIn@Home competition. The overall rate of correctly understanding the instructions for robot is quite good but still not acceptable in practical use, which is caused by the small scale of FBM3.
Agile Software development in distributed team Enhancement Techniques

The agile method that uses iterative improvement and prototyping is notably utilized in several commercial projects as a mild improvement method capable of gratifying the converting necessities. Quick iterations are required for green product delivery. Traditional software improvement methods aren't very inexperienced for dealing with the fast change of requirements. No matter the benefits of Agile, the complaint of the agile method states that it does no longer pay attention to the problems of architecture and layout Hence by making modifications to the Agile SDLC one can enhance the outcome from it.
Fuzzy Cognitive Model for Analysis of Influence Relations and Industrial Cluster Identification

The analysis of known methods which are used for industrial clusters creation showed the presence of the most important stage—identification and integration potential evaluation of the formed integrations. At the same time, there are no constructive approaches and methods to analyze the state of such sociotechnical clusters, first of all, from the standpoint of their stability and development dynamics due to the significant number and heterogeneity of its constituent entities, a number of their qualitative characteristics, as well as a difficult complex system of interaction between them. At the same time the measure of interaction between the members of the cluster determines the need for its creation. The chapter presents a fuzzy cognitive model designed: first, to analyze the impact of relations between objects and identify sociotechnical clusters; second, to assess the degree of objects compliance to various industrial clusters based on the indicator values from a reasonable set. This model will make it possible to improve the industrial clusters efficiency by optimizing management decisions in the regional scientific and industrial system in terms of the impact on the output and input parameters that determine them. In addition, the inclusion of fuzzy cognitive modeling elements in the analytical and cluster forming decision-making system will allow to identify imbalances in the resulting performance of the innovative sphere of regional scientific and industrial complexes and improve the reliability and efficiency of region stability. As a practical example, confirming the hypothesis of the study, the described model was used to identify energy clusters in the Smolensk region.
Validating the DFA Attack Resistance of AES (Short Paper)

Physical attacks are a serious threat to the Internet of Things devices. Differential power analysis attacks are the most well-known physical attacks that exploit physical information leaked from hardware devices to retrieve secret information. Fault analysis attacks, a type of physical attack, are often considered more powerful than side-channel attacks if an attacker can inject the attacker’s intended faults. In fact, a few times of fault injections have enabled the attacker to retrieve the secret key. In this study, we propose a new model to validate the resistance of block ciphers to Differential Fault Analysis (DFA) attacks by assuming an ideal block cipher in which the differential probability is the same for all input and output differences. We show that Advanced Encryption Standard (AES) is near ideal for DFA attack resistance according to the experimental results.
ПОНЯТТЯ «ІНФОРМАЦІЙНО-ОСВІТНЄ СЕРЕДОВИЩЕ» В АСПЕКТІ ПРОФЕСІЙНОЇ ПІДГОТОВКИ МАЙБУТНІХ ЛОГОПЕДІВ

&#x0D; &#x0D; &#x0D; The article clarifies the concept of "information-educational environment" in terms of professional training of speech therapists. The author’s definition of information-educational environment based on the results of the analysis of the concepts "information environment" and "educational environment". It is a synthetic systematic formation accumulating intellectual, cultural, programmatic, organizational and technical resources and involving the use of a wide range of traditional and new information technologies and their technical means aimed to education and development.&#x0D; &#x0D; &#x0D;
From Linear Systems to Fuzzy Systems to Perception-Based Systems

This is another insight into the genesis of the theory of fuzzy sets and systems in the life of its founder Lotfi A. Zadeh. We present some historical roots of the theory of fuzzy systems and their further development to Computing with Words (CWW) and the Computational Theory of Perceptions (CTP).
A hybrid click model for web search

Annually, web search engine providers spend more and more money on documents ranking in search engines result pages (SERP). Click models provide advantegeous information for ranking documents in SERPs through modeling interactions among users and search engines. Here, a hybrid click model is introduced by combining a PGM-based and a neural network click model. Hybrid click model tries to predict users' clicks behavior on the documets which are represented in SERPs. Indeed, a weighted k-nearest neighbors has been employed to provide final decision based on UBM and LSTM click models scores. The proposed system is evaluated on the Yandex dataset as a standard click log data set. The results demonstrate the superiority of our model over the state-of-the-art click models in terms of perplexity.
THE STUDY ABOUT THE IMPACT OF EMOTIONAL TENSION ON THE STUDENTS’ EXCITEMENT AND MEMORY PROCESSES

&#x0D; &#x0D; &#x0D; &#x0D; Studies have investigated the effect of emotional stress on anxiety and memory processes. The object of the study was first-year students. Psychophysiological research methods have been used. Under the influence of emotional stress, there were changes in memory and an increase in anxiety levels.&#x0D; &#x0D; &#x0D; &#x0D;
A Proposed Method For Recognizing Complex Hand Drawn Graphs Using Digital Geometric Techniques

In order to process a hand drawn input graph and digitize it, various digital geometric techniques have been used. These techniques utilize the inherent combinatorial properties of the relative arrangement of the object and grid lines. Aberrations like jaggedness, waviness, etc are overcome using geometrical techniques. This proposed algorithm uses properties of isothetic cover and digital-geometric straightness combined with the idea of Farey sequence to identify nodes, annotations and edges. We convert the input 2D image into pgm format and take this as input to our algorithm. Additionally, we use outer isothetic cover to recognize the nodes and annotations in the graph. The edges are analyzed with the idea of digital straightness combined with Farey sequence and geometric refinement on the seed vectors. Moreover, results of the two phases are finally compiled to associate each annotation with its associated node or edge using nearest distance measure.
A Survey of College Students’ Personal Information Security Awareness in the Era of Big Data

With the advent of the era of big data, college students enjoy the convenience of digital life, but also make personal information security to a certain extent exposed in the network. This survey analyzes the personal information security awareness of college students, trying to explore how to effectively improve personal information security awareness.
Gaussian Processes Incremental Inference for Mobile Robots Dynamic Planning

Trajectory optimization methods for motion planning attempt to generate trajectories that minimize a suitable objective function. While such methods efficiently find solutions in static environments, they need to be ran from scratch multiple times in the presence of moving obstacles, which incurs unnecessary computation and slows down execution. In this paper, we propose a trajectory optimization algorithm that anticipates the movement of obstacles and solves the planning problem in an iterative manner. We employ continuous-time Gaussian processes as trajectory representations both for the mobile robot and moving obstacles for which future locations are predicted according to a given model. We formulate the simultaneous moving obstacles tracking and mobile robot motion planning problem as probabilistic inference on a factor graph. Since trajectories of moving obstacles are optimized concurrently to motion planning, the proposed approach works in a predictive manner. After computing the initial solution, we use incremental inference for online replanning after an estimate of the moving obstacle position is provided. Our experimental evaluation demonstrates that the proposed approach supports online motion generation in the presence of moving obstacles.
Deep Converged Network for Attention

In natural language processing, feature extraction plays a significant role in different models and most of previous models based on Transformer or BERT have obtained the substantial performance, however, most of these feature extractors with attention mechanism don't fully play their effects. In this study, the Feature Fusion Residual Network (FFRN) is proposed for enterprises sentiment analysis, which can calculate the sentimental tendency toward texts by learning semantics of sentences. To make well use of the ability of Transformer, the paper introduces the feature fusion extractor and deep converged architecture, which use attentional encoder and residual network to learn and resolve the structure of sentences deeply that finally can improve the accuracy of prediction in enterprises sentiment analysis. Experiments on different basic datasets show that proposed model obtains the new state-of-the-art compared to Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) with attention.
Fault Attack of SMS4 Based on Internal Collisions

Abstract A fault attack of SMS4 based on internal collision is proposed. The method assumes that the attacker can attack under the condition of selecting plaintext, and adopts the byte-oriented random fault model to successfully recover the original key by injecting the fault in the first few rounds of the encryption algorithm of the SMS4. Theoretical analysis and experimental results show that the 60 fault injections can successfully recover the original key of the SMS4, with a probability of over 99%. If the attacker can control the location of fault injection, then only 16 fault injections are needed to successfully recover the original key of the SMS4. In general, 30 to 40 fault injections can successfully restore the original key.
Double-Head Attention-Based Convolutional Neural Networks for Text Classification

Because self-attention captures long-distance features without generating sequence dependence, it has been widely used in machine translation and machine reading comprehension. However, there is a lack of research and exploration on self-attention in text classification research methods. This paper proposes a classification model based on Self-Attention, which applies the self-attention mechanism to text classification tasks reasonably. In addition, based on the self-attention model, we further propose a convolutional neural networks(CNN) based on the double-head attention mechanism. The experimental results show that the double-head attention-based convolutional neural networks (DHACNN) improves the classification accuracy and optimizes the test speed.
University Strategic System Engineering based on BAN PT Accreditation Criteria One using SysML and Semantic Approach

Abstract An organizations business processes need to be precisely defined so that the organization does what it should do. Some quality standards such as BAN-PT and ISO 9001 Accreditation require organizations to carry out some process. Sometimes organizations find a difficulty to understand what processes need to be applied based on the standards. This study proposes a SysML and semantic method for analysing standard sentences and providing guidance on what needs to be applied to organizations. The trial was conducted on the Study Program Accreditation standard specifically criterion 1 on strategic management..
A fuzzy rule based effective feature selection approach for augmented reality

A tele-operated robot stereo vision system is used for stretching out the operator’s eye-hand motion and its distance based co-ordination with experts. The major challenge is the reduction of communication delay by using effective decisions to avoid tele-operation instability. This problem can be h andled effectively by using the principles of Augmented Reality which provides facilities for superimposing virtual objects onto the real video images of the workspace to create a simulation plan in the client system. In this paper, we propose a new feature selection algorithm called Fuzzy Rules and Information Gain Ratio based Feature Selection Algorithm for selecting the optimal number of features from the full set of available features. Also, a new Fuzzy Rule based Neuro-Genetic Classification Algorithm is proposed in this paper for classifying the augmented images more accurately. The main advantages of the proposed model are reduction in classification and communication time and increase in decision accuracy.
Multilingualism online

The book Multilingualism Online presents a deep-seated discussion of multilingualism and multilingual interaction in the digital world. Borrowing its structure from an earlier co-authored monograph...
Reconfigurable Multiplicator Over 2<sup>16</sup>, 2<sup>15</sup> and 2<sup>14</sup> for DVB-S2X Standard

This paper shows that massive streams of data and high data rates requires high-performance digital data-processing systems, development of data transmission optimization methods, optimization of digital filtering and coding/decoding processes. Also forming of reconfigurable parallel multiplicator over Galois field (GF(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">16</sup> ), GF(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">15</sup> ) and GF(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">14</sup> )) for DVB-S2X standard is described, that allow to reduce number of gates for BCH decoding process. Optimization of formed reconfigurable multiplicator is provided. Corresponding outputs are given.
Protection of Scientific and Educational Resources&#x0D; in Information and Library Systems

Introduction. The authors of the article highlight the results of research on the development of methods and&#x0D; tools for ensuring the information security of scientific and educational resources in information library systems and corporate information networks. The analysis of the state of the problem and the existing approaches&#x0D; on this topic are given too.&#x0D; Materials and Methods. The approach justifies the need to protect scientific resources from unauthorized access and the use of fuzzy logic to solve the problem. As a model and solution method, a fuzzy model correspondences is proposed, which makes it possible to comprehensively solve the problem of identifying threats in the&#x0D; event of a situation, as well as provide recommendations on how to eliminate them. For the formation of a&#x0D; knowledge base, experts are involved who determine the functions of belonging to knowlege base “Situation —&#x0D; Threats — Actions to eliminate the threats”.&#x0D; Results. The research results are being implemented as part of the ARMAT++ information library system of the&#x0D; corporate network of electronic libraries of 63 Uzbekistan universities for the protection of scientific and educational information from unauthorized access. Testing of the methods, programs of the subsystem and the expert knowledge base are carried out on the basis of information and resource centers under the project “Virtual&#x0D; electronic library of the Tashkent University of Information Technologies named after Muhammad Al-Khorezmi&#x0D; and its five branches”.&#x0D; Discussion and Conclusions. The use of the apparatus of fuzzy logic for the formation of a knowledge base of&#x0D; the “Situation — Threats — Actions to eliminate the threats” type in information library systems significantly&#x0D; increases the degree of protection of valuable information resources from unauthorized access.
Possibility for Uranium Mineralization of the Trans Urals According to Regional Geochemical Data

Abstract The article discusses the prospects of hydrogenic uranium mineralization in the Trans Urals. Dolmatovskaya geochemical zone is mapped out and minerogenic potential of its anomalous geochemical areas (AGCAs) is estimated from experimental data. A total of twelve ACGAs are ranked by prospectivity using values of minerogenic potential and other geochemical parameters. Minerogenic potential iscalculated from N.A. Bykhover’s formula using analogical models and can be equated to expected reserves. Previously studied Khokhlovsky AGCA is ranked promising and is expected to increase its potential uranium reserves. Previously unknown Garkashinsky and Petrovsky-Medvezhozersky ACGAs are identified as potential areas of medium prospectivity. These areas are geochemically identical to the anomalous geochemical fields of reference uranium ore locations in the zone in question, thus indicating p otential commercial uranium reserves.
Enhancing Industrial Maintenance Through Intelligent Data Analysis

For years, the amount of data generated in many industrial production plants has been said to have great potential for improving maintenance processes. In order to leverage this potential in practice, however, it is necessary to overcome a number of hurdles from automated data exchange, linking separate data sources, evaluating the actual data quality to the automated evaluation of existing data. In the project “Smart Maintenance” together with our industrial partners BMW Motors Steyr and BRP Rotax, we have developed a practical procedure to analyze the operating-, error- and sensor-data in a user-friendly web interface. This enables us to make the data utilizable for improving maintenance processes. The core functionality consists of various filter and aggregation mechanisms. Based on these different forms of visualization allow the intuitive interpretation of events in order to integrate new contexts into the daily routine of maintenance planning.