Reviewerly/exHarmony huggingface dataset:
Forecasting News Events Using the Theory of Self-similarity by Analysing the Spectra of Information Processes Derived from the Vector Representation of Text Documents

This paper presents a new methodology developed by the authors to forecast news events based on representing texts as vectors, subtracting information process spectrums from the vectors, and analysing these with the help of self-similarity theory. Spectrum extraction is performed using text processing by applying mathematical linguistics approaches (text markups, normalisation, commenting); the vectors obtained are then clustered according to theme groups and the time of the news appearance. By applying the Hurst self-similarity method to analyse information news process spectrums, their self-similarity features are analysed. The information processes are then classified into two different classes: self-similar and not self-similar. It is proven that if the self-similarity feature is present in the processes investigated, the Hurst self-similarity method (R/S analysis) and almost-periodic functions will enable us to discover the almost periods of repeatability of events, and this will further allow us to forecast their behaviour over time and predict new events.
Forecasting of Multidimensional Time Series Basing on Fuzzy Rule-Based Models

The limitations of existing models and methods for forecasting multidimensional time series are, first of all, the complexity of integration of the indirect influence of interdependent time series under uncertainty, and also reduction of this complex task to forecasting the totality of individual one-dimensional time series. A variety of fuzzy rule-based cognitive models is used to forecast multidimensional time series under uncertain conditions. And the direct and indirect influence of interdependent components of these series is considered. An approach to the structural-parametric adjustment of fuzzy rule-based cognitive models for solving problems of forecasting multidimensional time series is presented. The approach provides: firstly, the parametric adjustment of neuro-fuzzy models of mutual influence weights for all concepts of this model; secondly, the structural adjustment of the model due to changes in the “retrospective depth” for the components of multidimensional time series. The accuracy of forecasting of the multidimensional time series is estimated on the basis of the proposed models. It is compared with the existing approaches based on the forecasting of such series, represented by sets of one-dimensional time series. The greater accuracy of forecasting the multidimensional time series by the developed models is achieved by: first, taking into account the direct and indirect influence of interdependent components of time series under uncertain conditions; secondly, the implementation of flexible structural and parametric adaptation of the model; thirdly, the possibility of setting different “retrospective depths” for components of multidimensional time series.
A Systematic Study on Textual Data Processing in Text Mining

Advancement in digital technology has led to an increase in the text data exponentially. A field called `text mining' turns the massive amount of text data into high quality or actionable knowledge so that it can help in making the optimal decision, reduces the time and human effort to analyze it. We can perform several tasks on text data including part of speech tagging, parsing text, extract the relevant information, classify the text data, clustering, etc. Text representation is a necessary step to do all these tasks and its effect, especially on the end results of text classification or clustering is highly considerable. The aim of this paper is to highlight the prerequisite procedures to represent text data, different text representation methods, the role of dimensionality reduction, different proximity measures and their evaluation methods to assess results of text clustering or classification.
Transparency Is (Full) Disclosure in Corporate Governance

Corporate disclosure and reporting of information has become synonymous with transparency which in discourses idealising its value is part of the rhetoric of good governance. This notion is overtly conveyed in principles and codes of corporate governance practice which have proliferated globally over the last three decades. The possibility for transparency to conceal more than is revealed is considered with regard to corporate communication of information, with the consequence that power and real knowledge of the corporate behavioural agenda remains in corporate hands. Philosophically, the paradoxical and unintended outcome is that corporations are constrained by the norm of transparency in developing authentic moral behaviour, while also exercising power and control since in the information society transparency does not, and indeed cannot, lead to revealing all. Transparency places greater emphasis on judging, or evaluating existing organisational processes than on assessment of (self-)learning processes based on what actually takes place when corporations make decisions.
Classification Based Software Defect Prediction Model for Finance Software System - An Industry Study

Automated software defect prediction is an important and fundamental activity in the domain of software development. Successful software defect prediction can save testing effort thus reduce the time and cost for software development. However, software systems for finance company are inherently large and complex with numerous interfaces with other systems. Thus, identifying and selecting a good model and a set of features is important but challenging problem. In our paper, we first define the problem we want to solve. Then we propose a prediction model based on binary classification and a set of novel features, which is more specific for finance software systems. We collected 15 months real production data and labelled it as our dataset. The experiment shows our model and features can give a better prediction accuracy for finance systems. In addition, we demonstrate how our prediction model helps improve our production quality further. Unlike other research papers, our proposal focuses to solve problem in real finance industry.
HOG feature descriptor based PCA with SVM for efficient &amp; accurate classification of objects in image

In Computer vision, object recognition is a very important component and also very challenging. Intention of This paper is to exploit a high confidence object detection framework that boosts up the classification performance with less computational burden and cost efficient. Features are extracted from images by using Histograms of Oriented Gradients (HOG) technique and then for generating principle components as well as reducing dimensions Principal Component Analysis (PCA) has been applied on the extracted features. For classification of objects Support Vector Machine (SVM), Random Forest, Input mapped classifier, M5P classifier and Gaussian process classifier have been employed. A comparative study on performance of those approaches have been conducted. Moreover, for better clarification of the dataset, statistical and automated analysis have been considered. Overall findings demonstrates that, Principle Component Analysis (PCA)based Support Vector Machine (PSO) outperforms other approaches by depicting accuracy of 94.02% and highest F-Score measurement.
Synthesized Speech Detection Based on Spectrogram and Convolutional Neural Networks

The paper presents systems based on convolutional neural networks designed to classify genuine and artificially generated speech signals, which were evaluated on database for logical access designed for 3rd Automatic Speaker Verification Spoofing and Counter-measures Challenge (ASVspoof 2019). Proposed systems achieved remarkable results on the development set, but rather modest on the evaluation set, i.e. equal error rate on development set is 0 % and on evaluation set 9.57 %.
Creating Paraphrase Identification Corpus for Indian Languages

In recent times, paraphrase identification task has got the attention of the research community. The paraphrase is a phrase or sentence that conveys the same information but using different words or syntactic structure. The Microsoft Research Paraphrase Corpus (MSRP) is a well-known openly available paraphrase corpus of the English language. There is no such publicly available paraphrase corpus for any Indian language (as of now). This chapter explains the creation of paraphrase corpus for Hindi, Tamil, Malayalam, and Punjabi languages. This is the first publicly available corpus for any Indian language. It was used in the shared task on detecting paraphrases for Indian languages (DPIL) held in conjunction with Forum for Information Retrieval &amp; Evaluation (FIRE) 2016. The annotation process was performed by a postgraduate student followed by a two-step proofreading by a linguist and a language expert.
Craig Interpolation of Epistemic Logics with Distributed Knowledge

Distributed Knowledge among agents is an important topic in multi-agent systems. While semantic studies of distributed knowledge have been done by several authors in the context of epistemic logic, there are a few proof-theoretic studies. This paper provides cut-free Gentzen-style sequent calculi for epistemic logics with distributed knowledge and establishes Craig Interpolation Theorem for the logics by a constructive method, i.e., Maehara method.
Coderiu: a cloud platform for computer programming e-learning

The need for powerful and flexible platforms for teaching and learning computer programming concepts is increasing due to the growing number of interested attendees. Although implementing a complete platform from scratch would achieve the best flexibility in terms of possible features, it also requires a huge time-consuming effort. This paper proposes Coderiu, a platform built on top of Free and Open Source Software (FOSS) packages suitably integrated to implement the desired learning platform. Such features include platform independence of code editing, which is achieved by leveraging a web-based Integrated Development Environment (IDE). The environment is extended with custom features to enable the automated testing of the solutions and the automatic remote backup of the working directories. Moreover, the architecture was made suitable to be used in classroom exams, which require a controlled environment. A critical aspect of the Coderiu platform, beside the integration of its components and modules, is represented by the scalability. The Coderiu platform is used by hundreds of students in case of courses in relatively small classes, while it is ready to serve a larger user base in the future. For this reason, requirements and performance are studied on a pilot installation to derive insights regarding the resources required for larger deployments.
JSP Digital Asset Trading System

This research presents a novel mechanism of digital asset trading system on blockchain called JSP-DATS. The JSP-DATS includes (1) a novel mechanism of trading, and (2) a novel mechanism of blockchain which will be the infrastructure of the system. The proposed novel mechanism of blockchain uses the "Random-Checker Proof of Stake" consensus model which can decrease transaction time. The blockchain of the JSP-DATS has been designed to multiple layers. This design is easy to develop, and can be used for further research. The internal mechanism of the proposed system including steps of encoding/decoding, key management, and the storage of encrypted digital assets on the blockchain has will be discussed in this paper. In addition, we have implemented the designed model using Microsoft Visual C++ and encryption libraries from the MSDN web-site to create a software prototype. The prototype is used to study and measure the speed of the proposed scheme. The results show that transaction time of the proposed scheme is lower than that in BitCoin and Ethereum blockchain. With the proposed scheme, the seller (digital asset owners) can see transactions of the trading system transparently, and they can receive their percentage share immediately. In addition, we expect that buyers will indirectly benefit from purchasing digital assets at a lower price.
Computing probabilities of integer-valued random variables by recurrence relations

We derive a set of recurrence relations for the calculation of the probabilities of a large class of integer-valued random variables. We show that the probability mass function can be recursively computed for random variables with a probability generating function satisfying certain functional form.
Troubleshooting and Traceability Method Based on MapReduce Big Data Platform and Improved Genetic Reduction Algorithm for Smart Substation

Aiming at the management of a large number of state data of smart substation ,fast detection and accurate processing of smart substation fault based on massive data, a fault diagnosis and traceability method based on MapReduce big data programming architecture and improved genetic reduction algorithm is proposed in this paper. The MapReduce big data parallel architecture is used as a processing tool for processing substation state data, which effectively solves the problem of obtaining and managing massive monitoring data. Through the combination of data mining technology and genetic algorithm, it extracts effective information in a large amount of state information, reduces data size, thereby speeding up the processing of fault information and accurately determining equipment fault status, optimizing substation fault diagnosis function, and tracking the location of faults. In order to verify the algorithm, a distributed cluster hardware experiment platform is built. With proposed platform and algorithm, fault information is extracted and reduced from massive data. Fault type and location can be easily get from analysis result.
Field Typing for Improved Recognition on Heterogeneous Handwritten Forms

Offline handwriting recognition has undergone continuous progress over the past decades. However, existing methods are typically benchmarked on free-form text datasets that are biased towards good-quality images and handwriting styles, and homogeneous content. In this paper, we show that state-of-the-art algorithms, employing long short-term memory (LSTM) layers, do not readily generalize to real-world structured documents, such as forms, due to their highly heterogeneous and out-of-vocabulary content, and to the inherent ambiguities of this content. To address this, we propose to leverage the content type within an LSTM-based architecture. Furthermore, we introduce a procedure to generate synthetic data to train this architecture without requiring expensive manual annotations. We demonstrate the effectiveness of our approach at transcribing text on a challenging, real-world dataset of European Accident Statements.
Research and Analysis of Technologies used in Big Data

"Enormous Data" could be a term together with the employment of strategies to catch, process, break down and envision conceivably vast datasets during a wise time span not available to straightforward IT innovations. By augmentation, the stage, instruments and programming used for this reason for existing are by and enormous referred to as "Huge knowledge advancements". In this manuscript, we give the importance, attributes, models, advances, life cycle and diverse totally different components of Big data.
Building and Exploiting Lexical Databases for Morphological Parsing

This paper deals with the use of a new German morphological database for parsing complex German words. While there are ample tools for flat word segmentation, this is the first hybrid approach towards deep-level parsing of German words. We combine the output of the two morphological analyzers for German, Morphy and SMOR, with a morphological tree database. This database was created by exploiting and merging two pre-existing linguistic databases. We describe the state of the art and the essential characteristics of both databases and their revisions. We test our approach on an inflight magazine of Lufthansa and find that the coverage for the lemma types reaches up to 90%. The overall coverage of the lemmas in text reaches 98.8%.
K-Medoids and K-Means Clustering in High School Teacher Distribution

Today, the government still has difficulties in distributing teachers. The problem is not just a shortage teachers but also an excess teachers in several cities. In data mining clustering is useful for obtaining the distribution patterns of datasets used for data analysis processes. The purpose of this research is to compare k-means and k-medoids clustering algorithm to analyze distribution of high school teachers in Indonesia. This research uses four steps, namely dataset selection, preprocessing data, implementation, and evaluation with 4 scenarios with k = 2, 4, 8, and 16. The test results obtained Sum of Squared Error (SSE) of k-medoids is better than k-means for scenario 1 and scenario 2. In contrast to scenario 3 and scenario 4, k-means better than k-medoids. The optimal cluster test results with the Silhouette width obtained at k = 2. It can be concluded that in optimal clustering, k-medoids is better than k-means clustering. This is due to the influence of the outlier on the dataset.
Challenges and Countermeasures of Intellectual Property Rights in Network Environment

With the rise of the network age, the Internet has penetrated into various industries. While facilitating the dissemination of knowledge within and between industries, it has also brought a lot of challenges to the protection of intellectual property rights. In order to better protect intellectual property rights under the network environment, reduce disputes and maintain a healthy network cultural environment, this work started from the challenges faced by intellectual property rights under the network environment and put forward some suggestions, such as building a network protection system, legislative protection and administrative protection, improving the awareness of intellectual property protection and providing technical protection. These measures are conducive to providing reference for relevant network intellectual property protection.
Study on the Play Strategy of Dou Dizhu Poker Based on Convolution Neural Network

Computer game is an important research direction in the field of artificial intelligence. Among them, incomplete information poker games are relatively difficult to implement compared to the intelligent algorithm of chess games. Dou Dizhu game is a kind of incomplete information game poker game. This paper uses convolution neural network, through the analysis large number of game data, to obtain a better landlord identity card model. Experimental results show that the model can provide efficient and reasonable results in Dou Dizhu games, similar to poker played by humans.
Multi-Agent System of an Adaptive Learning Hypermedia Based on Incremental Hybrid Case-Based Reasoning

Adaptive learning systems are defined by their ability to adjust the learning process to the needs, characteristics and learning styles of learners. These systems must acquire and process all the information relating to the learning process characterizing the temporal and dynamic evolution of learners’ behavior so that they make appropriate decisions for each change detected during the learning act and ensure deterministic and predictable behaviors, by offering personalized learning paths in real-time. To ensure this customization of the courses, we propose an architecture of a multi-agent adaptive learning system based on the Reasoning from Hybrid Incremental Case which makes it possible to take the right decision in real time in the face of a dynamic learning situation, by performing a classical cycle or a dynamic cycle of reasoning from case. This decision will be made by assigning at each step of the Reasoning from Hybrid Incremental Case at least one agent performing a well-defined task and collaborating with other agents to dynamically support the learner during the learning process by proposing them a personalized learning path in real time.
Les inventions mises en œuvre par ordinateur : actualité et enjeux de l’extension contemporaine de la brevetabilité (Computer-Implemented Inventions: Current Questions and Challenges of the Extension of Patentability)

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EXPANDING THE PROFESSIONAL PEDAGOGICAL PROFILE OF TEACHER FROM PROFESSIONAL (VOCATIONAL) EDUCATION SCHOOL UNDER THE CONDITIONS OF INCLUSION

Relevance: persons with special educational needs require professional qualifications. This leads to theactualization of the issue of expanding the professional and pedagogical profile of the teacher of the P (V) Eschool and the inclusive competence acquisition.Aim: to analyze the legal documents on the provision of inclusive education in the system of vocationaleducation and training and study the needs of teachers of P(V)E school in the formation of inclusive competence.Methods: theoretical (analysis, synthesis, generalization); empirical (interviewing methods, conversation).Results: it has been found out that the problem of expanding the professional and pedagogical profile of theteacher of the professional (vocational) education school in the direction of his inclusive competence development needs to be addressed. It has been established that the creation of the barrier-free educational environment and the architectural accessibility of facilities in educational institutions for education applicants with special educational needs is necessary. The vast majority of teachers require the specially organized training on issues of inclusion introduction. It has been defined that teachers identify such forms of increasing the inclusive competence as training, specialist counseling and full-time targeted courses. The experience of forming the inclusive competence of teachers through the introduction of formal, non-formal and informal education has been presented. It has been stated that it is important to establish cooperation between educational institutions and regional inclusive resource centers. It has been proved that teachers need assistance in developing an individual curriculum for the education applicants with special educational needs and evaluation of its implementation.Conclusions: the introduction of inclusive education in the system of vocational education and training willgive the opportunity to obtain the working
Generalized bent Boolean functions and strongly regular Cayley graphs

In this paper we define the (edge-weighted) Cayley graph associated to a generalized Boolean function, introduce a notion of strong regularity and give several of its properties. We show some connections between this concept and generalized bent functions (gbent), that is, functions with flat generalized Walsh–Hadamard spectrum. In particular, we find a complete characterization of quartic gbent functions in terms of the strong regularity of their associated Cayley graph.
Scalable Architecture for High-Resolution Real-time Optical Flow Processor

This paper describes a scalable architecture for high-resolution real-time optical flow processor and the design for 60-fps real-time Full-HD system. A previously proposed 30-fps WXGA real-time processor is re-designed as an IP (WXGA-IP) for the proposed Full-HD processor, which adopts a scalable multiprocessor structure. In addition, three methods are newly proposed, namely an improved hierarchical method, an improved initial value creation method, which reduces the number of iterative computations, and an adaptive acceleration parameter selection method that uses the built-in CPU. The realtime processing of 60-fps Full-HD video, which requires four times the processing power of WXGA video, is realized using twice the hardware while maintaining flow detection accuracy at a similar operating frequency compared to those of WXGA-IP.