Reviewerly/exHarmony huggingface dataset:
Automation of regulations output to the website of a higher education institution

Laws of Ukraine on Education and on Higher Education determine the list of documents required for publication on the official website of the institution of higher education. The organization of documents display is carried out by inserting hyperlinks into the corresponding external document files in the specified articles of the site. In order to provide a unified way of displaying information, regulatory and other official documents must be kept in PDF format. The PDF format allows you to save the original document formatting. Mostly, Higher Education Institution websites are organized using content management systems: WordPress, Joomla, Drupal. These systems do not have the means for outputting the list of external files contained in the file structure of the server directory. The connection of documents to the site is done by creating the corresponding text hyperlinks to the files in the content. This way of covering normative documents requires editing of content when changing document files (adding new files, delete old files ) . To ensure the relevance and immediate information sharing with the users, the issues of automating the output of the list of documents and the creation of appropriate hyperlinks to the files of documents stored on the server of the site have been considered. An informational model for forming a list of documents is developed. A template has been proposed for displaying the main, additional file information, and the main document manipulations have been determined. The requirements for the file structure of document storage on the server have been specified. An appropriate PHP script for working with a file directory and creating a list of documents with corresponding hyperlinks to them has been developed. A way to include the results of a PHP script to an article based on AJAX technology has been proposed. Examples of program scripts application for organizing information output using other types of files have been given.
Perceptual stereoscopic images quality assessment method based on visual adaptable characteristics

The quality assessment of stereoscopic images plays an important role in various three-dimensional (3-D) areas and encounters more problems than two-dimensional (2-D) image quality assessment. We propose a perceptual full-reference quality assessment model by considering human adaptable double visual channel. Human binocular combination characteristics and depth perception are both considered in this model, and an adaptable gain-control model is adopted to assign appropriate weights to the information of human visual channels. Experimental results indicate that the proposed algorithm can serve as an efficient predictive image quality feature, which delivers not only highly competitive prediction accuracy but also moderate computational complexity.
ACPJS: An Anti-noise Concept Drift Processing Algorithm Based on JS-divergence

Concept drift involving noise is an important research in the field of data mining. Many concept drift detection models are proposed to promote the research of traditional concept drift detection. In this paper, we propose an anti-noise concept drift processing algorithm based on entropy of information, named ACPJS. In ACPJS, the JS-divergence and Hoeffding Bounds are used to set double threshold for concept drift detection and subsequently a horizontal integrated model will be constructed for anti-noise concept drift processing. In the comparison experiments of multiple data sets, the presented algorithm has shown good performance in concept drift detection, anti-noise performance and classification accuracy.
Graph Based Image Matching Using the Fusion of Several Kinds of Features

We exploit the image saliency, grayscale and RGB information to find feature correspondence based on a graph matching technique. We consider these saliency features, grayscale and average RGB values along with distance information for extracted image features by using a detector-descriptor combination of MSER detector and SIFT Descriptor. Three different affinity matrices are introduced by utilizing single pixel values collected by using the feature vector coordinates from three different forms of the same images. Considerable improvements in image matching performance are obtained while these affinity matrices are combined separately with the distance-based affinity matrix.
Effective Teaching Design Scheme for Network Troubleshooting

With the increasing demand for application-oriented talents in society, the specialized training of network engineering talents has become a growing concern in application-oriented colleges. This paper analyzes the principle of effective teaching, based on the network engineering skill course, deeply studies the network troubleshooting method and network fault classification technology. Through the integration of network troubleshooting courses and effective teaching, practical and effective teaching cases are formed. In the actual teaching, the program is actively applied to the practical teaching to improve the teaching effect, and it also provides a reference for the curriculum construction of other specialties.
A Knowledge Integrated Case-Based Classifier

This paper proposes a case-based classifier using a new approach that integrates rule-based and case-based reasoning approaches for enhanced accuracy. The rule-based reasoning component uses rules generated from a concept lattice of training data, binarized using fuzzy sets. These binarized data are stored as cases in the case-based classification component. The case-based component complements the rule-based component to enhance classification accuracy. Moreover, we designed the case-based component with an embedded similarity measure that uses a vector model for concept approximations. Thus, this design makes it possible to generate high quality rules and classify unseen new cases. In addition, the ability to build a knowledge base in lattice form is important for discovering hierarchical patterns, incrementing or updating the existing knowledge base, and inducing rules with our rule learning algorithm. The novel methodology was implemented and evaluated with benchmark datasets from the UCI repository and historic rubber prices in Thailand, demonstrating improvements in accuracy of classification calls. The results from the fact their several hierarchical datasets are very promising, with improved classification performance over prior reported methods.
Toward a Model of Management Processes to Support or Increase the Competitiveness of a University Professor

This article proposes the revision of the information required to establish the management processes to support or increase the competitiveness of a University professor, where complex processes intervene that must be attended by means of a model based on agents; it also presents the need to identify the characteristics that can and should drive a substantial improvement in the teacher’s performance, intelligent agents based on the perception of information from the environment, to produce a result that can be communicated to another or others through a model based on intelligent agents, which allow to strengthen the competitiveness of a teacher.
Checking the Data Complexity of Ontology-Mediated Queries: A Case Study with Non-uniform CSPs and Polyanna

It has recently been shown that first-order- and datalog-rewritability of ontology-mediated queries (OMQs) with expressive ontologies can be checked in NExpTime using a reduction to CSPs. In this paper, we present a case study for OMQs with Boolean conjunctive queries and a fixed ontology consisting of a single covering axiom $$A \sqsubseteq F \sqcup T$$ , possibly supplemented with a disjointness axiom for T and F. The ultimate aim is to classify such OMQs according to their data complexity: $$\textsc {AC}^0$$ , L, NL, P or coNP. We report on our experience with trying to distinguish between OMQs in P and coNP using the reduction to CSPs and the Polyanna software for finding polymorphisms.
A Technique to Predict Software Change Propagation

Software change can occur frequently and it is inevitable in the lifecycle of software development. It can cause tremendous impact on software engineering including cycle, cost, workload, etc. This work proposes an advanced technique to assess the software changeability on the class level. From the perspective of member variable and function, mutual relations between classes are introduced to record the change likelihood and impact. Change propagation model between classes is developed to transform the change impact and the total impact is calculated to analyse the class changeability. Based on the analysis results, class changes with high risk should be avoided and those with low change risk should be given priority in the software change process. Finally, a simplified software system is applied for the initial evaluation of the method.
A Comparative Study of The Corpus for Story Creation System

In recent years, there are lots of researches to generate creations such as comics and novels by computing methods. Especially, the orders of sentences are very important for the quality of stories. Thus, the aim of the researches is to construct story creation system in order to consider the flow of the story based on the sentence similarity. We suggest the possibility to create a new story by selecting sentences from exiting novels based on similarity. We indicated that the length of sentences and numbers of proper nouns tend to affect the naturalness of the created story.
Projectional Learning Laws for Differential Neural Networks Based on Double-Averaged Sub-Gradient Descent Technique

A new method to design learning laws for neural networks with continuous dynamics is proposed in this study. The learning method is based on the so-called double-averaged descendant technique (DASGDT), which is a variant of the gradient-descendant method. The learning law implements a double averaged algorithm which filters the effect of uncertainties of the states, which are continuously measurable. The learning law overcomes the classical assumption on the strict convexity of the functional with respect to the weights. The photocatalytic ozonation process of a single contaminant is estimated using the learning law design proposed in this study.
Chinese Information Retrieval System Based on Vector Space Model

This paper designs and implements a Chinese information retrieval system based on vector space model. The system will be able to quickly retrieve 50 thousand news documents to help users find the information they want in a short period of time, so as to help their own production and life. The beginning of this system is data capture, that is, data acquisition module, and then the preprocessing of the document, including Chinese word segmentation, discontinuation of words, document feature words extraction, and document vector representation and so on. The system first carries out the data capture, forms the document, then carries out Chinese word segmentation to the document, and handles the discontinuation of words in the Chinese word segmentation. After that, the document word item is extracted, and then vectors are represented and stored on disk. At the same time, the document word items are inverted indexed and stored on disk. When the user inquires, the user query statement is regarded as a document, the document is preprocessed, then the index is read, the related documents read to the vector similarity are calculated. Finally, the comprehensive results are sorted according to the calculated scores. In the experimental result and test part, several examples are given to analyze the recall rate and precision rate, and one of the practical problems is solved by interpolation method.
Using Particle Filter and Machine Learning for Accuracy Estimation of Robot Localization

Robot localization is a fundamental capability of all mobile robots. Because of uncertainties in acting and sensing and environmental factors such as people flocking around robots there is always the risk that a robot loses its localization. Very often behaviors of robots rely on a valid position estimation. Thus, for dependability of robot systems it is of great interest for the system to know the state of its localization component. In this paper we present an approach that allows a robot to asses if the localization is still valid. The approach assumes that the underlying localization approach is based on a particle filter. We use deep learning to identify temporal patterns in the particles in the case of losing/lost localization in combination with weak classifiers from the particle set and perception for boosted learning of a localization monitor. The approach is evaluated in a simulated transport robot environment where a degraded localization is provoked by disturbances cased by dynamic obstacles.
Naturalistic Decision-Making Analogs for the Combat Environment

The combat environment is a challenging space in which to conduct observations relevant to analysis of organizational and cognitive processes, with a low frequency of task repetition, a lack of natural or imposed constraints, and features hostile to external study (e.g., mission secrecy, inaccessible terrain, explosions). An environmental analog assists researchers by providing parallel models and data from a more readily accessible and observable space that can be used for developing concepts and models relevant to the more hostile environment (e.g., space, exoplanets, extreme mountaineering, overwintering in Antarctica). Co-analogs can also be used to understand features and relationships better even in the more accessible space. While there is no commonly accepted process or framework for establishing an environmental analog for naturalistic decision-making, a cognitive task analysis combined with functional abstraction may be used to derive a taxonomic description to demonstrate the appropriateness of establishing an analog relationship.
A Meetei Mayek Basic Characters Recognizer Using Deep Features

This paper presents a Meetei Mayek Isolated Handwritten Basic Character Recognition System (MMIHBCRS) which is a part of an undergoing development of a full-fledged Optical Character Recognition (OCR) system for Meetei Mayek script. In this work, we have developed a standard Meetei Mayek Isolated Handwritten Basic Character Database consisting of 8000 plus characters. The proposed work has used the concept of Transfer Learning where a pretrained Convolutional Neural Network (CNN) has been used for Deep Convolutional Features extraction. The performance of two classifiers K-Nearest Neighbour (K-NN) and Support Vector Machine (SVM) are compared using the extracted Deep Features. Using the Deep Features, the classifiers have achieved very high recognition accuracies for a dataset of 8000 characters where SVM has shown better recognition accuracy compared to K-NN giving an accuracy of 97.55% compared to K-NN’s 96.63%.
Big Data Processing and Big Analytics

Development and wide acceptance of data-driven applications in many aspects of our daily lives is generating waste volume of diverse data, which can be collected and analyzed to support various valuable decisions. Management and processing of this big data is a challenge. The development and extensive use of highly distributed and scalable systems to process big data have been widely considered. New data management architectures (e.g., distributed file systems and NoSQL databases) are used in this context. However, features of big data like their complexity and data analytics demands indicate that these concepts solve big data problems only partially. A development of so called NewSQL databases is highly relevant and even special category of big data management systems is considered. In this chapter, the authors discuss these trends and evaluate some current approaches to big data processing and analytics, identify the current challenges, and suggest possible research directions.
A Cloud-Hosted MapReduce Architecture for Syntactic Parsing

Syntactic parsing is a time-consuming task in natural language processing particularly where a large number of text files are being processed. Parsing algorithms are conventionally designed to operate on a single machine in a sequential fashion and, as a consequence, fail to benefit from high performance and parallel computing resources available on the cloud. We designed and implemented a scalable cloud-based architecture supporting parallel and distributed syntactic parsing for large datasets. The main architecture consists of a syntactic parser (constituency and dependency parsing) and a MapReduce framework running on clusters of machines. The resulting cloud-based MapReduce parsing is able to build a map where syntactic trees of the same input file have the same key and collect into a single file containing sentences along with their corresponding trees. Our experimental evaluation shows that the architecture scales well with regard to number or processing nodes and number of cores per node. In the fastest tested cloud-based setup, the proposed design performs 7 times faster when compared to a local setup. In summary, this study takes an important step toward providing and evaluating a cloud-hosted solution for efficient syntactic parsing of natural language data sets consisting of a large number of files.
Scalable Learning with Privacy Over Graphs

Graphs have well-documented merits for modeling complex systems, including financial, biological, and social networks. Network nodes can also include attributes such as age or gender of users in a social network. However, the size of real-world networks can be massive, and nodal attributes can be unavailable. Moreover, new nodes may emerge over time, and their attributes must be inferred in real time. In this context, the present paper deals with scalable learning of nodal attributes by estimating a nodal function based on noisy observations at a subset of nodes. A multikernel-based approach is developed which is scalable to large-size networks. The novel method is capable of providing real-time evaluation of the function values on newly-joining nodes without resorting to a batch solver. In addition, the novel scheme only relies on an encrypted version of each node's connectivity, which promotes privacy. Experiments on real datasets corroborate the effectiveness of the proposed methods.
Modal Epistemic Logic on Contracts: A Doctrinal Approach

Problems related to the construction of consensus out of distributed knowledge have become actual again under a new perspective with the diffusion of distributed ledger techniques. In particular, when dealing with contracts, different observers must agree that a contract is in a certain state, even if not all transactions performed under the contract are observable by all of them. In this paper, we revisit previous work on algebraic modelling of labelled non-deterministic concurrent processes, which identified an intuitionistic modal/temporal logic associated with a categorical model. We expand this logic with typical epistemic operators in a categorical framework in order to encompass distributed knowledge to speak about transactions and contracts.
Unsolved Art Thefts

Art theft has a low recovery rate and, consequently, many cases remain unsolved. This chapter outlines five major cases of unsolved art thefts: the Grace Cossington-Smith theft (1977); the Isabella Stewart Gardner Museum robbery (1990); the theft of Frans van Mieris’ A Cavalier (self portrait) (2007); the theft and recovery of Picasso’s Weeping Woman (1986) and the Kunsthal Museum robbery (2012). Each case highlights the challenges in both securing and recovering artworks and in apprehending the thieves. Unfortunately, security flaws are typically exposed after a theft has occurred, while investigations are hampered by the absence of specialised art crime units and art loss registers. The unsolved art thefts profiled in this chapter may have disappeared from the public domain, but they are not forgotten.
Alignment distance of regular tree languages

We consider the tree alignment distance problem between a tree and a regular tree language. The tree alignment distance is an alternative of the tree edit-distance, in which we construct an optimal alignment between two trees and compute its cost instead of directly computing the minimum-cost of tree edits. The alignment distance is crucial for understanding the structural similarity between trees. We, in particular, consider the following problem: given a tree t and a tree automaton recognizing a regular tree language L, find the most similar tree from L with respect to t under the tree alignment metric. Regular tree languages are commonly used in practice such as XML schema or bioinformatics. We propose an O(mn) time algorithm for computing the (ordered) alignment distance between t and L when the maximum degree of t and trees in L is bounded by a constant, and O(mn2) time algorithm when the maximum degree of trees in L is not bounded, where m is the size of t and n is the size of a finite tree automaton for L. We also study the case where a tree is not necessarily ordered, and show that the time complexity remains O(mn) if the maximum degree is bounded and MAX SNP-hard otherwise.
Occlusion Detection of Dense Correspondence Fields Using Motion Statistics and Triangulation

Despite progress made in the accuracy and robustness of the dense matching technique in past years, efficient occlusion detection remains an open problem. In this paper, we present a two-step occlusion detection method to remove false matches in dense matching fields. First, a statistical dense matching method is developed by considering the correspondence between the grids to identify most occlusion regions. Second, to handle the potential misjudgment match in the occlusion boundary, a double-threshold filtering method is first used to reduce the noise in the grid image, which ensures that the gradient operator can accurately extract the boundary grid in the grid image; then, misjudgment matches in the boundary grid region are corrected based on the triangulation with descriptors. The results of the experiments comparing the proposed method and existing occlusion detection methods by, respectively, using the MPI-Sintel and KITTI datasets’ test sequence show that the proposed method has higher accuracy and better robustness.
Mobile App For Speed Reading Test In Bahasa Malaysia

There is a lack of data for speed-reading level in Bahasa Malaysia for young people, which refers to individuals between ages 10 and 24. The information is needed by the National Book Council of Malaysia (NBCM) to assess reading frequencies of young people, correlated using speed-reading tests. Hence, a mobile app for speed-reading test in Bahasa Malaysia called Bacalaju has been developed. Applying Progressive Web Application (PWA) that uses front-end website technology, an Android app is generated by compiling the code with Android SDK (Software Development Kit). After the user took their speed-reading test, the result will be transferred via Asynchronous JavaScript And XML (AJAX) POST request and stored in a database on the cloud. The mobile app is seen to benefit young readers in Malaysia because it enables users to evaluate their reading skills and encourage them to improve the skills, i.e. speed-reading level and comprehension. At the same time, a website was developed to display the overall result, preliminary result indicated that 37.8% of 98 users involved in this study has both good speed-reading level based on the Taylor Grade Equivalent units and able to score more than 50% in their comprehension test.