Reviewerly/exHarmony huggingface dataset:
Automatic generation of semantic network for question answering

Semantic network model for representing data and knowledge was analysed. Selection of this model for working with text information was justified. The objective of automatic semantic network generation based on an arbitrary Russian-language text was formulated. Initial data, conditions and constraints necessary for network generation algorithm are listed. As a result of the part-of-speech analysis for each word and word order in a sentence, semantic relations between words are determined. The Lexeme dictionary was created to determine the part of speech of words in sentences. A set of question types used in the semantic network was selected. The number of relations in the network is regulated due to the possibility to use only necessary relation types when resolving a specific task. With that, the relations in semantic network can have very different types, which makes it a universal model for representing data and knowledge. The algorithm was developed which allows one to get answers for the questions asked. The semantic network model was generated automatically for the sentences considered. In the proposed algorithm the semantic network is interpreted as unoriented graph on which breadth-first search algorithm is used to find an answer. The proposed algorithms were implemented in a software tool which automatically generates the semantic network for an arbitrary text. The created software tool allows asking questions and getting answers to them based on the information which is stored in the semantic network. The experiments have shown that the generated semantic network gives correct answers to the questions posed. The network is modified by adding and removing information in it. There is a possibility to choose complexity of network structure depending on a specific task being resolved. The proposed approach for building and working with the semantic network allows one to process texts in various languages, to use it in information systems with natural-language interface, and to resolve such tasks as text classification and text search.
An AI-Based Planning Framework for HAPS in a Time-Varying Environment

A High-Altitude Pseudo-Satellite (HAPS) is a fixed-wing, solar-powered Unmanned Aerial Vehicle (UAV) developed to become a flexible alternative to satellites with fixed-orbits for monitoring ground activities over long periods of time. However, given its lightweight build and weak electro-motors, the platform is rather sensitive to weather and cannot fly around hazardous weather zones swiftly. In this work, we formulate the problem of planning missions for multiple HAPS as a hybrid planning problem expressed in PDDL+. The formulation also considers the problem of modeling the platform dynamics, the time-varying environment, and the heterogeneous tasks that need to be carried out. Additionally, we propose a framework that combines a PDDL+ automated planner with an Adaptive Large Neighborhood Search (ALNS) approach, developed to couple the automated planner with a meta-heuristic that is specific for the problem. The task and motion planning are done in an intertwined way within the framework, preserving hence a common decision/search space. We validate our approach with a third-party realistic simulator for HAPS, as well as with a set of benchmark tests, showing that our integrated approach produces executable mission plans.
Analysis of Parliamentary Debate Transcripts Using Community-Based Graphical Approaches (Student Abstract)

Gauging political sentiments and analyzing stances of elected representatives pose an important challenge today, and one with wide-ranging ramifications. Community-based analysis of parliamentary debate sentiments could pave a way for better insights into the political happenings of a nation and help in keeping the voters informed. Such analysis could be given another dimension by studying the underlying connections and networks in such data. We present a sentiment classification method for UK Parliament debate transcripts, which is a combination of a graphical method based on DeepWalk embeddings and text-based analytical methods. We also present proof for our hypothesis that parliamentarians with similar voting patterns tend to deliver similar speeches. We also provide some further avenues and future work towards the end.
Hypergraph Convolutional Network for Multi-Hop Knowledge Base Question Answering (Student Abstract)

Graph convolutional networks (GCN) have been applied in knowledge base question answering (KBQA) task. However, the pairwise connection between nodes of GCN limits the representation capability of high-order data correlation. Furthermore, most previous work does not fully utilize the semantic relation information, which is vital to reasoning. In this paper, we propose a novel multi-hop KBQA model based on hypergraph convolutional network. By constructing a hypergraph, the form of pairwise connection between nodes and nodes is converted to the high-level connection between nodes and edges, which effectively encodes complex related data. To better exploit the semantic information of relations, we apply co-attention method to learn similarity between relation and query, and assign weights to different relations. Experimental results demonstrate the effectivity of the model.
Domain-Liftability of Relational Marginal Polytopes

We study computational aspects of relational marginal polytopes which are statistical relational learning counterparts of marginal polytopes, well-known from probabilistic graphical models. Here, given some first-order logic formula, we can define its relational marginal statistic to be the fraction of groundings that make this formula true in a given possible world. For a list of first-order logic formulas, the relational marginal polytope is the set of all points that correspond to the expected values of the relational marginal statistics that are realizable. In this paper, we study the following two problems: (i) Do domain-liftability results for the partition functions of Markov logic networks (MLNs) carry over to the problem of relational marginal polytope construction? (ii) Is the relational marginal polytope containment problem hard under some plausible complexity-theoretic assumptions? Our positive results have consequences for lifted weight learning of MLNs. In particular, we show that weight learning of MLNs is domain-liftable whenever the computation of the partition function of the respective MLNs is domain-liftable (this result has not been rigorously proven before).
Machine Learning Classification Models with SPD/ED Dataset: Comparative Study of Abstract Versus Full Article Approach

In response to the researchers need in the bio-medical domain, we opted for automating the bibliographic research stage. In this context, several classification models of supervised machine learning are used. Namely the SVM, Random Forest, Decision Tree, KNN, and Gradient Boosting. In this paper, we conduct a comparative study between experimental results of full article classification and abstract classification approaches. Furthermore, we evaluate our results by using evaluation metrics such as accuracy, precision, recall and F1-score. We observe that the abstract approach outperforms the full article approach in terms of learning time and efficiency.
Examination and Extension of Strategies for Improving Personalized Language Modeling via Interpolation

In this paper, we detail novel strategies for interpolating personalized language models and methods to handle out-of-vocabulary (OOV) tokens to improve personalized language models.Using publicly available data from Reddit, we demonstrate improvements in offline metrics at the user level by interpolating a global LSTM-based authoring model with a user-personalized n-gram model.By optimizing this approach with a back-off to uniform OOV penalty and the interpolation coefficient, we observe that over 80% of users receive a lift in perplexity, with an average of 5.2% in perplexity lift per user.In doing this research we extend previous work in building NLIs and improve the robustness of metrics for downstream tasks.
Assessing Environmental Education through KBSM Social Sciences Subjects: Three Case Studies

The issue of flood disaster is becoming more prevalent in the present days of Malaysia. Flood is an annual recurring problem and it causes issues of discomfort, economic loss, health and the social well-being of Malaysians. To cope with this hydrometeorological disaster, the authoritative Sendai Framework for Disaster Risk Reduction 2015-2030 (2015) (Hereafter Sendai Framework) identified education as one of the best avenues to cope with flood disaster. This paper focuses on the 4th priority area in the Sendai Framework which aims is enhancing disaster preparedness for effective response and to “Build Back Better” in recovery, rehabilitation and reconstruction. Hence, this paper examines flood education through KBSM social sciences subjects with the idea that the Malaysian secondary school curriculum can help to enhance the preparedness among the students. The social sciences subjects chosen are Geography, English and Moral studies in Malaysian school system. The aim of this paper is to examine the coverage of flood related issues in the KBSM textbooks as well as the ways in which the issues are being discussed and manifested. The methodology used is qualitative content analysis. The findings indicate that in the Malaysian secondary school curriculum, English textbooks have more flood related topics than Moral studies and Geography. The issues discussed in the English textbooks are very current, varied and able to elucidate the synergy of knowledge. Keywords: Flood, disaster, education, KBSM, Malaysia.
Evaluation of Fire Protection Systems in Hotel Building (Case Study: Grand Kanaya Hotel Medan)

Hotel is a public living facility for tourists by providing room services, food and beverage providers, and accommodation with payment terms. Fire is a flame, both small and large, which is usually hard to control and detrimental and can occur in a place that we do not expect. Ensuring the protection of hotel residents is one of the goals of hotel business standards. Grand Kanaya Hotel is a 3-star hotel building in the city of Medan, with 152 units of rooms and several facilities that enable activities involving large numbers of people, such as restaurants and ballrooms. According to the quite high number of occupants, Grand Kanaya Hotel requires a fire protection system that can protect all hotel residents against fire hazards. The study aims to evaluate the fire protection system that has been applied to the building of the Grand Kanaya Hotel and provide references for fire protection systems that are standard in hotel buildings. This research is using the qualitative method that obtains from field observation and uses a type of descriptive research that explains the facts or existing conditions of fire protection systems in the Grand Kanaya Hotel building. The results show that there are several fire protection systems in Grand Kanaya Hotels that not according to the standards, such as the minimum size, the material used is not resistant to fire, and maximum distance lies, which still exceeds the maximum value. The study results are useful as information for other researchers and hotels regarding fire protection systems at the Grand Kanaya Hotel building.
Taming GANs with Lookahead.

Generative Adversarial Networks are notoriously challenging to train. The underlying minimax optimization is highly susceptible to the variance of the stochastic gradient and the rotational component of the associated game vector field. We empirically demonstrate the effectiveness of the Lookahead meta-optimization method for optimizing games, originally proposed for standard minimization. The backtracking step of Lookahead naturally handles the rotational game dynamics, which in turn enables the gradient ascent descent method to converge on challenging toy games often analyzed in the literature. Moreover, it implicitly handles high variance without using large mini-batches, known to be essential for reaching state of the art performance. Experimental results on MNIST, SVHN, and CIFAR-10, demonstrate a clear advantage of combining Lookahead with Adam or extragradient, in terms of performance, memory footprint, and improved stability. Using 30-fold fewer parameters and 16-fold smaller minibatches we outperform the reported performance of the class-dependent BigGAN on CIFAR-10 by obtaining FID of $13.65$ \emph{without} using the class labels, bringing state-of-the-art GAN training within reach of common computational resources.
Data Acquisition Model for Online Learning Activity in Distance English Teaching based on xAPI

In order to overcome the problems of low accuracy and low recall rate of online learning activity data acquisition, an online learning activity data acquisition model of distance English teaching platform based on xAPI is proposed. According to the visual attribute data of elements, this model divides and judges the online learning activity web page and preliminarily determines the location of noise data. According to the context content rules, the segmentation results of online learning activity web pages are processed in detail, the subblocks are evaluated comprehensively, and the noise data are locked and filtered. The data acquisition model is built by using the modules of user login verification, user mouse track replication, and English online learning activity data acquisition report display. Experimental results show that the model has high recall and precision.
Speech Recognition for Tigrinya language Using Deep Neural Network Approach.

This work presents a speech recognition model for Tigrinya language .The Deep Neural Network is used to make the recognition model. The Long Short-Term Memory Network (LSTM), which is a special kind of Recurrent Neural Network composed of Long Short-Term Memory blocks, is the primary layer of our neural network model. The 40-dimensional features are MFCC-LDA-MLLT-fMLLR with CMN were used. The acoustic models are trained on features that are obtained by projecting down to 40 dimensions using linear discriminant analysis (LDA). Moreover, speaker adaptive training (SAT) is done using a single feature-space maximum likelihood linear regression (FMLLR) transform estimated per speaker. We train and compare LSTM and DNN models at various numbers of parameters and configurations. We show that LSTM models converge quickly and give state of the art speech recognition performance for relatively small sized models. Finally, the accuracy of the model is evaluated based on the recognition rate.
Impediments to mathematical creativity: Fixation and flexibility in proof validation

Mathematical techniques in proof writing can be narrowed down to specific proof styles.Simply put, proofs can be direct or indirect-the latter using the Law of the Excluded Middle from logic as well as the axiom of Choice, to prove existence of mathematical objects.However, the thinking skills involved in writing indirect proofs are prone to errors, especially from novice proof writers such as prospective teachers.Creativity in mathematics entails the use of both direct and indirect approaches to determine the validity of a statement.In this article, I shed some light on this relationship, by reporting on some findings from a study on how students comprehend and validate direct and indirect proofs.Furthermore, I use the constructs of fixation and flexibility from creativity research to examine student approaches to direct and indirect proofs.
Normal voting tensor based points set denoising

<h2>Abstract</h2> Point set denoising is a vital pre-processing step in several computer graphics and medical imaging applications. This article introduces an effective point set denoising software, which is able to remove noise components from the input point set without blurring important sharp features. This software is capable of producing a high fidelity point set, which is desired in several medical applications as it controls the volume shrinkage during the denoising process.
Embedding High-Level Knowledge into DQNs to Learn Faster and More Safely

Deep reinforcement learning has been successfully applied in many decision making scenarios. However, the slow training process and difficulty in explaining limit its application. In this paper, we attempt to address some of these problems by proposing a framework of Rule-interposing Learning (RIL) that embeds knowledge into deep reinforcement learning. In this framework, the rules dynamically effect the training progress, and accelerate the learning. The embedded knowledge in form of rule not only improves learning efficiency, but also prevents unnecessary or disastrous explorations at early stage of training. Moreover, the modularity of the framework makes it straightforward to transfer high-level knowledge among similar tasks.
Method for Creating Motion Comic from Printed Comic

Motion comics, which are used to advertise printed comics, are created by adding video effects and a sound source to panels from the original (printed) comics. We propose a method for creating a motion comic which consists of the following steps: extracting a panel from a printed page, adding video effects, and switching the panel in time with sound effects. Because it is difficult for beginners in video production to perform these steps, we have devised a method that automatically creates a motion comic using the printed comic pages and sound. In our experimental evaluations, the precision of our proposed panel extraction was greater than 0.75, and we successfully implemented our proposed video effects and panel switching method.
A Framework for Political Portmanteau Decomposition

Portmanteaus are new words formed by combining the sounds and meanings of two words. Given their sticky nature, portmanteaus are often used to create political and personal attacks by combining a target entity with derogatory terms, which can then be spread online for promoting hate speech and defamation. In this paper, we present a framework to decompose political portmanteaus used online into their component words. Using our annotated dataset of political portmanteaus, we train a system that correctly decomposes 76.2% of the political portmanteaus into their component words. Furthermore, for 93.4% of the political portmanteaus, our system finds the correct component words in its top ten results, suggesting that using better ranking methods can lead to stronger results. This work provides a framework for both understanding an intriguing linguistic phenomena and for building hate-speech filters that could catch novel words that would bypass traditional hate speech detection approaches.
Software Crucial Functions Ranking and Detection in Dynamic Execution Sequence Patterns

Because of the sequence and number of calls of functions, software network cannot reflect the real execution of software. Thus, to detect crucial functions (DCF) based on software network is controversial. To address this issue, from the viewpoint of software dynamic execution, a novel approach to DCF is proposed in this paper. It firstly models, the dynamic execution process as an execution sequence by taking functions as nodes and tracing the stack changes occurring. Second, an algorithm for deleting repetitive patterns is designed to simplify execution sequence and construct software sequence pattern sets. Third, the crucial function detection algorithm is presented to identify the distribution law of the numbers of patterns at different levels and rank those functions so as to generate a decision-function-ranking-list (DFRL) by occurrence times. Finally, top-k discriminative functions in DFRL are chosen as crucial functions, and similarity the index of decision function sets is set up. Comparing with the results from Degree Centrality Ranking and Betweenness Centrality Ranking approaches, our approach can increase the node coverage to 80%, which is proven to be an effective and accurate one by combining advantages of the two classic algorithms in the experiments of different test cases on four open source software. The monitoring and protection on crucial functions can help increase the efficiency of software testing, strength software reliability and reduce software costs.
Corporate Social Responsibility as a Mediator of the Effect of Brand Awareness and Corporate Reputation on Customer Loyalty

Factors that promote customer loyalty are of great concern to the banking sector because loyalty predicts business success. The purpose of this study is to examine the mediation role of corporate social responsibility on the effect of brand awareness and corporate reputation on customer loyalty. Data was collected through a cross sectional survey from 405 bank customers. Research hypotheses were tested using the structural equation model. The findings show that corporate social responsibility partially mediates the effect of both brand awareness and corporate reputation on customer loyalty. The results indicate that corporate social responsibility plays a vital role within the banking sector as it mediates the effect of brand awareness and corporate reputation on customer loyalty. If banks engage in successful corporate social responsibility practices, brand awareness and corporate reputation are enhanced and this result in improved customer loyalty. By empirically examining corporate social responsibility as a mediator on the effect of brand awareness and corporate reputation on customer loyalty the study seeks to contribute to the scholarly conversation.
Prevention and Detection of SQL Injection Using Query Tokenization

One of the most serious security vulnerabilities in the current scenario is SQL injection. It stands first in the OWASP top 10 vulnerability attacks. Lack of input validation is one of the main reasons for the cause of these types of attacks. Data can be stolen from the database by the means of SQL injection. Most of the user inputs are going directly to database. An attacker can obtain the data which he does not have access to with the means of SQL injection. The paper aims in developing a method that detects and prevents SQL injection attacks.
Topics of Animal and Human Respiratory System of the Fifth Grade Elementary Schools Developed Through Pop-Up Book Media

The implementation of this research was based on the problems encountered because the material in students’ books was still less extensive in the limitation of examples and explanations on the students’ books in one subject matter on the topic of animal and human respiratory system. This research aimed to develop Pop-Up book media on the topic of animal and human respiratory system. In addition, the results of the interviews showed that the use of media in learning was deficient. This research was carried out by using ADDIE model which was consisted of five phases, namely: (1) analysis, (2) design, (3) development, (4) implementation, and (5) evaluation. However, in this research the implementation and evaluation phases were not carried out due to time limitations, costs, and resources. The subject of this research was the Pop-Up book media. The object of this research was the validity of the Pop-Up book media. The data collection method was a questionnaire. The rating scale instrument was used in the form of validation assessment sheets to measure the validity of Pop-Up book. Data validity was analysed by using the mean formula to find out the average score of media validity. This research produced a Pop-Up book on the topic of animal and human respiratory systems with an average score of 4.56 which was categorized into very good category. It meant that Pop-Up book media was suitable to be used. It was a solution to the problem and could be used by teachers in delivering some materials.