Reviewerly/exHarmony huggingface dataset:
Short Text Similarity Measurement Based on Coupled Semantic Relation and Strong Classification Features

Measuring the similarity between short texts is made difficult by the fact that two texts that are semantically related may not contain any words in common. In this paper, we propose a novel short text similarity measure which aggregates coupled semantic relation (CSR) and strong classification features (SCF) to provide a richer semantic context. On the one hand, CSR considers both intra-relation (i.e. co-occurrence of terms based on the modified weighting strategy) and inter-relation (i.e. dependency of terms via paths that connect linking terms) between a pair of terms. On the other hand, Based on SCF for similarity measure is established based on the idea that the more similar two texts are, the more features of strong classification they share. Finally, we combine the above two techniques to address the semantic sparseness of short text. We carry out extensive experiments on real world short texts. The results demonstrate that our method significantly outperforms baseline methods on several evaluation metrics.
Ontological Information as Part of Continuous Monitoring Software for Production Fault Detection

The monitoring of manufacturing processes is an important issue in nowadays ERP systems. One of the most important issues is to identify and analyze appropriate data for each of the production units taking part in the process. In the paper authors introduce a new approach towards modelling the relation between production units, signals and factors possible to obtain from the production system. The main idea for the system is based on the ontology of production units. The design of the system using advanced knowledge engineering is elaborated. Since, the implementation of proposed system was one of key assumptions, the relational model is presented that ensures possibility to deploy the system in the future.
Query-Focused Summarization Enhanced with Sentence Attention Mechanism

In today's society where the enormous amount of documents are overflowing, the demand for automatic text summarization has been increasing. In particular, when considering the practical use, it is expected that the query-oriented text summarization, which generates summaries focusing on the given specific queries, will be more important rather than the generic summarization that simply summarizes the entire document. In the automatic summarization methods based on the deep neural model, Long-Short Term Memory (LSTM) which enables longterm information storage, and attention mechanism which allows emphasis on specific time steps from the vectors encoded by the Recurrent Neural Network (RNN), are essential. However, Koehn et al. [1] report that the accuracy is degraded in the encoding of texts longer than 60 tokens entered into the LSTM. Even in the summarization task, it is considered to be a problem that the encoding of a long sentence fails or causes the disappearance of the relation of the sentences, as a factor that greatly deteriorates the quality of the summary result. In this paper, we propose a method to generate a summary by introducing a sentence unit vector in addition to the word unit vector of the original document. We aim to generate summaries considering the importance degree of a sentence unit and the relations between sentences, by learning both the attention mechanism of word unit and the attention mechanism of sentence unit. In the experiment, we examine the summarization model which added the mechanism of sentence unit vector to the latest query-focused summarization model, and clarify the influence on the summary generated by presence or absence of the sentence vector.
The risk assessment and prediction of water inflow into tunnels of Guangzhou Metro Line 21

In order to decrease the negative influence of water inflow on tunnel construction, the risk of water inflow should be evaluated and predicted. Based on the method of experts' evaluation, combined with practical project, an assessment system of water inflow into mountain tunnel was set up, in which 9 evaluation indicators were proposed from the geological factors, hydrologic conditions and engineering three aspects, and every index weight was counted with the method of AHP. With BP neural network as an assessment tool, an integrated prediction model for water inflow into mountain tunnel was established. The assessment and prediction results for water inflow into tunnel of Guangzhou Metro line 21 were compared with results of expert classification. It shows that the error of BP neural network forecasting structure by means of AHP is not more than 3%, and it matches the required precision.
Research on New Computer Software Teaching Method Combining Virtual Display Technology and Multimedia Technology

The application of multimedia teaching technology in college teaching has promoted the development of quality education in colleges and universities and improved the quality of teaching. Contents of the computer software integrated management system more complicated, but also relatively high construction mode, there are some of the more complex situation in the management structure, so there will be many difficulties in the implementation and management, computer software, computer software resources from the investigation, statistical analysis, computer software prediction and modeling, layout optimization, etc. have a large construction scale, so the system is more complicated. This paper combines virtual display technology with multimedia teaching technology, and researches on innovative computer software teaching methods. It lists some feasible and simple integration methods to overcome the teaching shortcomings and achieve better teaching results.
Basic Pedagogical Support in the System of Continuous Education: Optimization of Pedagogical Conditions Modeling

The paper clarifies the pedagogical conditions for improvement of pedagogical support modeling in the system of continuous education and provides some practical examples. The quality of determinations and models of pedagogical support in continuous education is determined by the adaptive, acme-pedagogic, personified-productive, and functional-labor approaches, which reveal the direction of scientific inquiry and visualize solutions. Social, professional, and educational orientation of the search for optimal pedagogical conditions of optimization reveals various facets and opportunities of the individual and society to improve the quality of education. Traditional and innovative constructs define, describe, and visualize the nuances of the modeling, thus revealing linear and nonlinear models, case conditions and system determinants of the adaptive, acme-pedagogics, personalized, productive, and functional solutions. The systematic character of the scientific research ensures its integrity, uniformity, correctness, accuracy, flexibility, and relevance of the resulting solutions aimed at omproving the modeling the basics of the pedagogical support in the system of continuous education.
Research on Library Personalized Recommendation Method Based on Interest Ontology Modeling

In view of the low stability of user model and unsatisfactory recommendation, which results in the personalized service of library information system at present, the user interest model based on library application domain ontology is established. A dynamic personalized recommendation algorithm based on time attenuation and user-group is proposed. First, the time attenuation function is determined to calculate the time decay law of the user’s interest points, then the influence of different access frequency of interest points on the interest degree is considered, and then the clustering dimensionality reduction decomposition technique is used. The concept of preference variance is introduced to calculate the nearest neighbor of users, and then the ranking of recommended resources is determined. The experimental results show that the algorithm has better comprehensive performance, the recall rate and recall rate of recommended resources are both higher, the universality and effect of the algorithm are better, and the quality of recommendation is improved.
Space Quantum Communication with higher Orbits

Exchanging qubits from MEO orbits allows extending secure communications to critical infrastructures as well as enable the investigation of the interplay of Quantum and Gravitational Physics. The experimental demonstration of feasibility and perspectives are reported.
Using control charts for on-line video summarisation

Many existing methods for video summarisation are not suitable for on-line applications, where computational and memory constraints mean that feature extraction and frame selection must be simple and efficient. Our proposed method uses RGB moments to represent frames, and a control-chart procedure to identify shots from which keyframes are then selected. The new method produces summaries of higher quality than two state-of-the-art on-line video summarisation methods identified as the best among nine such methods in our previous study. The summary quality is measured against an objective ideal for synthetic data sets, and compared to user-generated summaries of real videos.
Acoustic Spirals: Analysis of Bach’s Prelude in C Major

Bach’s preludePrelude in C major of “The Well-Tempered ClavierWell-tempered clavier, Book I” is analyzed with a dynamical approach, by using phase portraitsPhaseportrait and their two and three-dimensional expressionsThree-Dimensional (3D)expression. Different from the traditional analysis based on the theory of harmonyHarmony, our method can elucidate how the music develops in the course of time. The numerical calculation leads us to an image of this musical piece, as if one walks down a staircaseStaircase with several half turns, while spinning around one’s own axis.
Face Recognition Method Based on Convolutional Neural Network

In this paper, a face recognition method based on deep learning is studied and implemented. By adjusting the hierarchical depth and structure of the typical convolutional neural network model ResNet, a new network model structure is designed, which uses the LFW face detection benchmark. The database is used for confirmatory experiments. The experimental results show that the overall accuracy and model size of the system have a good performance.
Question Understanding Based on Sentence Embedding on Dialog Systems for Banking Service

This paper introduce a question understanding system to respond appropriate answers in a dialog system for banking services. The question understanding system provides an automated response service in a specific domain (e.g. banking). This can increase response rate of a customer counseling service, and improve business efficiency and expertise. The question understanding system classify domains, specific categories, and speech acts of questions. Finally, the system analyze meanings and intents of the questions, and searching correct answers even various input sentences. In this paper, we describe methods of keyword tokenizing, pattern recognition, sentence embedding, analyzing dialogue intention, and searching similar FAQs. Through these methods, we have developed the question understanding unit in a real interactive system for financial services for real insurance companies and banks, and analyze the usefulness of the system through practical system implementation examples.
Conditional Random Field Feature Generation of Smart Home Sensor Data using Random Forests

A typical approach to building a feature set for a conditional random field model is to build a large set of conjunctions of atomic tests, all of which adhere to a small number of relatively simple templates. Building more complex features in this way can be difficult, as the more complex templates needed to do this can result in a combinatoric explosion in the number of features. We use the inherent instability of decision trees to produce a small set of more complex conjunctions that are particularly suitable for the problem to be solved, using the same techniques used in generating random forest ensemble classifiers, and build a CRF on these features. We apply this method to an activity recognition problem on a dataset from the CASAS smart home project, in which we predict activities of daily living from sensor activations.
Improving Semantic Relation Extraction System with Compositional Dependency Unit on Enriched Shortest Dependency Path

Experimental performance on the task of relation extraction/classification has generally improved using deep neural network architectures. In which, data representation has been proven to be one of the most influential factors to the model's performance but still has many limitations. In this work, we take advantage of compressed information in the shortest dependency path (SDP) between two corresponding entities to classify the relation between them. We propose (i) a compositional embedding that combines several dominant linguistic as well as architectural features and (ii) dependency tree normalization techniques for generating rich representations for both words and dependency relations in the SDP. We also present a Convolutional Neural Network (CNN) model to process the proposed SDP enriched representation. Experimental results for both general and biomedical data demonstrate the effectiveness of compositional embedding, dependency tree normalization technique as well as the suitability of the CNN model.
Ranking Air-conditioning Systems in a Building based on Sensor Drift Anomalies with Consideration of Contexts

This paper proposes a method to rank air-conditioning systems in a building based on drift anomalies in sensor measurements. The proposed method employs an approach to estimate the drift anomalies from changes in the sensor measurement data of the systems. However, the changes depend on the contexts in which the systems work as well as the drift anomalies. Thus, we propose feature selection and drift anomaly estimation that depend on the contexts. Experimental results imply that the proposed method will be benefical even in a case where the drift anomalies arise over the course of a year.
Analysis of two groups of plane infantry targets as sets of geometric primitives

A comparison of two groups of plane shooting targets (PSTs) characterizing the shooter-silhouette in different observation and rifle-firing positions was the starting point for this study.Selected infantry targets are used for shooting training in the Russian Federation and in the Swiss Confederation.The comparison results of two target groups (five PSTs in each of them) showed a significant similarity of their geometric shapes.To explain this fact, a targets design system (TDS) was developed.The TDS is based on attributing a certain number of simple geometric shapesgeometric primitives (GP).In our case, the number of GPs was equal to ten (five polygons for Russian and five polygons for Swiss targets).The TDS enabled building a human-like target silhouette.If two sides of two adjoining GPs or their parts become common for them, then such GPs can be combined into one common geometric figure whose area is equal to the sum of the two GPs.The TDS was further transformed into two isomorphic graphs.Their adjacency matrix (AM) was obtained.The AM matrices for the Russian PSTs and the Swiss PSTs were the same.To improve the estimation of the area and the coordinates of the target centroid, a matrix modification of Bourke's formulas were proposed.The geometric areas for the Russian and Swiss PSTs and the location of their centroids were refined and compared.GNU Octave, GeoGebra and Mathcad were used as mathematical software for computer calculations and for graphic visualizations.
DSS-Based Ontology Alignment in Solid Reference System Configuration

uebe.Q is a managing software for solid referential information systems, such as ISO 9000 (for quality) and ISO 1400 (for environment). This is a long-term developed software, encompassing extensive and solid business logic with a long and successful record of deployments. A recent business model change imposed that the evolution and configuration of the software, shifts from the company (and especially the development team) to consultants and other business partners, along with the fact that different systems and respective data/information need to be integrated with minimal intervention of the development team. The so far acceptable rigidity, fragility, immobility and opacity of the software became a problem. Especially, the system was prepared to deal with a specific database respecting a specific schema and code-defined semantics. This paper describes the approach taken to overcome the problems derived form the previous architecture, by adopting (i) ontologies for the specification of business concepts and (ii) an information-integration Decision Support System (DSS) for mapping the domain specific ontologies to the database schemas.
Unsupervised Explainable Controversy Detection from Online News

Alerting users that a web page is controversial has been proposed as one method to support critical thinking about text and discourse. We propose an approach to discover controversial topics in a generic document using unsupervised training. Our approach comprises iterative training of a controversy classifier using a disagreement signal within comments and explaining the controversy of the document by generating a topic phrase describing it. Experiments show the effectiveness of our proposed training method using an EM algorithm. When controversial topic extraction is restricted to quality phrases and incorporates TextRank signals, it outperforms several baseline approaches.
Persuading the Driver: A Framework for Persuasive Interface Design in the Automotive Domain

Designing for persuasive interfaces in cars is a challenging task, particularly considering that new systems should not distract drivers. In order to support designers in their decisions, we present a novel framework for persuasive interface design with a focus on automotive UI. It is based on existing behaviour change models and extends them with categories to support interface design decisions. It serves three purposes: (1) it provides a tool to support designers in decision making when designing persuasive interfaces; (2) it can be used to create alternatives to existing systems; (3) it allows to structure literature reviews in order to identify blind spots and serve as an inspiration to ideate new approaches. we provide an analysis of three examples based on it to illustrate its applicability.
A Simple Neural Approach to Spatial Role Labelling

Spatial Role Labelling involves identification of text segments which emit spatial semantics such as describing an object of interest, a reference point or the object's relative position with the reference. Tasks in SemEval exercises of 2012 and 2013 propose problems and datasets for Spatial Role Labelling. In this paper, we propose a simple two-step neural network based approach to identify static spatial relations along with the three primary roles - Trajector, Landmark and Spatial Indicator. Our approach outperforms the task submission results and other state-of-the-art results on these datasets. We also include a discussion on the explainability of our model.
Nonlinear Rule Based Ensemble Methods for the Prediction of Number of Faults

InNonlinear rule based ensemble methods theLinear rule based ensemble methods previous chapter, we explored the use of linear rule based ensemble methodsEnsemble methods for the number of faultsFaults predictionPrediction . In that work, we used four different ensemble methods, each of them combines the outputs of base learners in a linear form. Results of experimental analysis showed that a stable and accurate fault prediction performancePerformance could be achieved using linear rule based ensemble methodsLinear rule based ensemble methods . However, these ensemble methods capture only the weighted contributions of base learners and combine them in linear way, which may sometimes suffers from the linearity error problem of fitting in a straight line (Fox in Regression diagnostics: an introduction. Sage, 1991 [1]).
Coin Recognition Approach in Social Environments Using Virtual Organizations of Agents

Social systems have gained relevance during the last decade, trying to provide intelligent environments where humans and machines collaborate to resolve a social problem. The main objective of this paper is to obtain an intelligent system specifically designed to help dependent and/or visually disabled people to count money more easily by using a mobile phone camera. The proposed system incorporates an image recognition system for classifying coins by using homography to transform images previously for classification tasks. The main difficulty in the appliance of these techniques relies on the fact that camera position and height are unknown. This process allows changing the perspective of the images in order to calculate different meaningful variables such as diameter and colour employed later to perform classification and counting tasks. The system uses the information of the variables as inputs for classification algorithms that allow us to identify the amount and type of coins. The system has been tested with euro coins. This paper presents the results obtained.
Knowledge Base Completion by Inference from Both Relational and Literal Facts

Knowledge base (KB) completion predicts new facts in a KB by performing inference from the existing facts, which is very important for expanding KBs. Most previous KB completion approaches infer new facts only from the relational facts (facts containing object properties) in KBs. Actually, there are large number of literal facts (facts containing datatype properties) besides the relational ones in most KBs; these literal facts are ignored in the previous approaches. This paper studies how to take the literal facts into account when making inference, aiming to further improve the performance of KB completion. We propose a new approach that consumes both relational and literal facts to predict new facts. Our approach extracts literal features from literal facts, and incorporates them with path-based features extracted from relational facts; a predictive model is then trained on all the features to infer new facts. Experiments on YAGO KB show that our approach outperforms the compared approaches that only take relational facts as input.
Informationized Technological Path for the School-Running Risk Prevention and Supervision of Private Colleges and Universities Under the Background of the Internet

At present, with the decrease of the educational population and the gradual deepening of the higher educational reform, the development of the private colleges and universities is facing various contradictions and challenges. The development concept of “innovation, coordination, green, open and sharing” put forward at the Fifth Plenary Session of the Eighteenth Central Committee of the CPC provides the value guidance and the practical basis for the private colleges and universities to renew their development concepts and solve their development problems.