Reviewerly/exHarmony huggingface dataset:
Autoencoders

An autoencoder is a very simple model: a model that predicts its own input. In fact, it may seem deceivingly simple to the point of being worthless. (After all, what use is a model that predicts what we already know?) Autoencoders are extraordinarily valuable and versatile architectures not because of their functionality to reproduce the input, but because of the internal capabilities developed in order to obtain said functionality. Autoencoders can be chopped up into desirable parts and stuck onto other neural networks, like playing with Legos or performing surgery (take your pick of analogy), with incredible success, or can be used to perform other useful tasks, such as denoising.
MLA-Net: Feature Pyramid Network with Multi-Level Local Attention for Object Detection

Feature pyramid networks and attention mechanisms are the mainstream methods to improve the detection performance of many current models. However, when they are learned jointly, there is a lack of information association between multi-level features. Therefore, this paper proposes a feature pyramid of the multi-level local attention method, dubbed as MLA-Net (Feature Pyramid Network with Multi-Level Local Attention for Object Detection), which aims to establish a correlation mechanism for multi-level local information. First, the original multi-level features are deformed and rectified using the local pixel-rectification module, and global semantic enhancement is achieved through the multi-level spatial-attention module. After that, the original features are further fused through the residual connection to achieve the fusion of contextual features to enhance the feature representation. Extensive ablation experiments were conducted on the MS COCO (Microsoft Common Objects in Context) dataset, and the results demonstrate the effectiveness of the proposed method with a 0.5% enhancement. An improvement of 1.2% was obtained on the PASCAL VOC (Pattern Analysis Statistical Modelling and Computational Learning, Visual Object Classes) dataset, reaching 81.8%, thereby, indicating that the proposed method is robust and can compete with other advanced detection models.
A lightweight semantic‐enhanced interactive network for efficient short‐text matching

Abstract Knowledge‐enhanced short‐text matching has been a significant task attracting much attention in recent years. However, the existing approaches cannot effectively balance effect and efficiency. Effective models usually consist of complex network structures leading to slow inference speed and the difficulties of applications in actual practice. In addition, most knowledge‐enhanced models try to link the mentions in the text to the entities of the knowledge graphs—the difficulties of entity linking decrease the generalizability among different datasets. To address these problems, we propose a lightweight Semantic‐Enhanced Interactive Network (SEIN) model for efficient short‐text matching. Unlike most current research, SEIN employs an unsupervised method to select WordNet's most appropriate paraphrase description as the external semantic knowledge. It focuses on integrating semantic information and interactive information of text while simplifying the structure of other modules. We conduct intensive experiments on four real‐world datasets, that is, Quora, Twitter‐URL, SciTail, and SICK‐E. Compared with state‐of‐the‐art methods, SEIN achieves the best performance on most datasets. The experimental results proved that introducing external knowledge could effectively improve the performance of the short‐text matching models. The research sheds light on the role of lightweight models in leveraging external knowledge to improve the effect of short‐text matching.
Petri Net Model of MITM Attack Based on NDP Protocol

Neighbor Discovery Protocol (NDP) is one of the core protocols of IPv6 networks. Since NDP messages are an unauthenticated stateless protocol, they are vulnerable to various types of attacks, and Man-In-The-Middle (MITM) attacks are one of the most well-known attacks in the computer field. During NDP address resolution, attackers change their own IP-MAC mapping relationships by sniffing NA (Neighbor Advertisement) messages, thus spoofing the source host's neighbor cache table and compromising the confidentiality, integrity and availability of the IPv6 network. Therefore, this paper focuses on the MITM attack in the NDP address resolution process, and performs a fine-grained analysis and Petri Net modelling of the attack process.
Escape room na lekcji języka obcego

The idea of implementing an escape room as part of foreign language teaching fits perfectly with the needs of today's learner, where we create a natural learning environment using digital tools. The escape room, whether stationary or virtual, gives space for the use of different types of language tasks. The article first describes the escape room in the light of theoretical considerations. It then presents the results of a survey, conducted among 31 teachers of various foreign languages. The aim of the survey was to gather feedback from the teachers who themselves use escape rooms in their language classes.
A Speech Enhancement Algorithm for Speech Reconstruction Based on Laser Speckle Images

In the optical system for reconstructing speech signals based on laser speckle images, the resonance between the sound source and nearby objects leads to frequency response problem, which seriously affects the accuracy of reconstructed speech. In this paper, we propose a speech enhancement algorithm to reduce the frequency response. The results show that after using the speech enhancement algorithm, the frequency spectrum correlation coefficient between the reconstructed sinusoidal signal and the original sinusoidal signal is improved by up to 82.45%, and the real speech signal is improved by up to 56.40%. This proves that the speech enhancement algorithm is a valuable tool for solving the frequency response problem and improving the accuracy of reconstructed speech.
A Real-Time Open Public Sources Text Analysis System

With the emergence of digital newspapers and social media, one can easily suffer from information overload.The enormous amount of data they provide has created several new challenges for computational and data mining, especially in the natural language processing field.Many pieces of research focusing on the information extraction process, such as named entity recognition, entity linking, and text analysis methodologies, are available.However, there is a lack of development for a system to unify all these advanced techniques.The current state-of-theart systems are either semi-automatic or can only handle shorttext documents.Most of them are not real-time or have a long lag.Some of them are domain restricted.Many of them only focus on a single source: Twitter.In this work, we proposed a system that can automatically collect, extract, and analyze information from public source text documents, like news and tweets.The system can be used in different domains, such as scientific research, marketing, and security-related domains.
Analysis of Content Based Image Retrieval using Deep Feature Extraction and Similarity Matching

Image retrieval using a textual query becomes a major challenge mainly due to human perception subjectivity and the impreciseness of image annotations.These drawbacks can be overcome by focusing on the content of images rather than on the textual descriptions of images.Traditional feature extraction techniques demand for expert knowledge to select the limited feature types and are also sensitive to changing imaging conditions.Deep feature extraction using Convolutional Neural Network (CNN) are a solution to these drawbacks as they can learn the feature representations automatically.This work carries out a detailed performance comparison of various pretrained models of CNN in feature extraction.Features are extracted from men footwear and women clothing datasets using the VGG16, VGG19, InceptionV3, Xception and ResNet50 models.Further, these extracted features are used for classification using SVM, Random Forest and K-Nearest Neighbors classifiers.Results of feature extraction and image retrieval show that VGG19, Inception and Xception features perform well with feature extraction, achieving a good image classification accuracy of 97.5%.These results are further justified by performing a comparison of image retrieval efficiency, with the extracted features and similarity metrics.This work also compares the accuracy obtained by features extracted by the selected pre-trained CNN models with the results obtained using conventional classification techniques on CIFAR 10 dataset.The features extracted using CNN can be used in image-based systems like recommender systems, where images have to be analyzed to generate item profiles.
Augmented reality strategy as a didactic alternative in rural public schools in Colombia

Abstract There are access restrictions to the technologies of the Fourth Industrial Revolution for rural schools in Colombia. These schools, with few students and teachers geographically dispersed, do not benefit from the possibilities of these technologies in the classroom. These educational institutions are governed by the “Escuela Nueva” model that defines several specific methodological moments. The objective of this study was to design a strategy based on augmented reality (AR) for didactic intervention in the classroom, which allows students to interact with educational resources and break down barriers both related to access to technology and related to the role in learning processes. Educational activities were defined, and a mobile application based on AR was developed for schools in Samaná‐Colombia, integrated with the pedagogical moments of the educational model and at the same time strengthening AMID skills (information literacy and digital media) in the teaching work. Instruments were applied seeking to know the benefits in the classroom and the perception of students and teachers. The results show, preliminarily, that the use of AR in learning activities in this rural school makes it possible to bring students and teachers closer to these technologies, generating motivation and improving the effectiveness of the process, additionally is well accepted in the academic community. Experiments show that if they are included harmoniously in the pedagogical model, it does not interfere, nor is it a distraction for other academic activities. The Contribution paper to empirically validate AR in education generate learning in students of armed conflict zones.
Authoring for Story Sifters

Kreminski, Max Wardrip-Fruin, Noah Mateas, MichaelWe discuss the issues of authoring for story sifters: systemsStory sifting (story sifter) that search for compelling emergent narrativeEmergent Narrative (EN) content within the vast chronicles of events generated by interactive emergent narrativeInteractive Emergent Narrative (IEN) simulations. We describe several different approaches to the authoring of sifting patterns that specify how to locate particular kinds of narratively potent situations; address the relationship between sifters and the simulations they operate over from an authoring perspectivePerspective; and sketch several possible approaches to the authoring of sifting heuristics, or high-level encodings of what makes for a compelling story that could be used to guide a sifter’s behaviorBehaviour.
Micro-Image Strain Sensing Method for Displacement and Strain Measurement in One United Sensor

Micro-image strain sensing (MISS) is an innovative technology to measure strain within a measurement range of ±8300 microstrains. It has been proved to be effective and satisfy all requirements in the field of structural health monitoring. However, there is still room for improvement and extend the range of measurement. In this paper, an improved method is proposed to increase the measurement range of strain and displacement. Moreover, various tests were conducted to verify the efficiency of the improved method. The results showed that the modified method is efficient and accurate and can be readily used to extend the measurement range of both strain and displacement. This research will likely help stimulate the development of multifunctional sensors to obtain abundant useful information about structures in the field of structural health monitoring. It will allow measuring strain and displacement, which require different levels of accuracy, using one unified sensor.
PEDAGOGICAL STRATEGIES WITH IMMERSIVE TECHNOLOGIES FOR TEACHING AND LEARNING THE JAPANESE LANGUAGE

The article addresses the choice of pedagogical strategies for application of immersive technologies in foreign language teaching and learning, using the Japanese language as an example. The authors defined and substantiated the main pedagogical strategies using immersive technologies: inquiry-based approach, project-based learning, mixed learning, flipped learning, mobile learning, game learning, joint learning, and independent learning. A study was conducted on the aspects of application of immersive technologies by 3rd year students of the Education and Research Institute of Philology of the Taras Shevchenko National University of Kyiv during their educational projects. The students’ attitude to pedagogical strategies using augmented and virtual reality was analyzed. It has been determined that it is difficult for students to apply these technologies, because they do not have enough experience of using them in language learning. Nevertheless, these tools significantly help them in the formation and development of research skills, digital competence, expand their ability to use information and communication technologies in learning Japanese. In our further research, we plan to actively implement augmented and virtual reality in the teaching of the Japanese language to students using basic pedagogical strategies, especially through the inquiry-based approach and project-based learning, combined with other applicable pedagogical strategies - such as blended learning, flipped learning, mobile learning, game-based learning, collaborative learning and self-directed learning.
A probability distribution of dependencies in interlanguage

Abstract The diversity of syntactic units in second language has attracted much scholarly attention. Most existing studies on syntactic diversity have focused on only a small number of syntactic structures, and it is difficult to find studies that consider the full range of syntactic dependencies present in the dataset. Based on a syntactic annotated interlanguage corpus that we constructed, this paper is a quantitative study of dependencies in English-speaking Chinese learners’ interlanguage across proficiency levels. We fit the frequency distributions of dependency type, word class (both as dependent and governor), verb as a governor, and noun as a dependent with a modified right-truncated Zipf-Alekseev distribution and Zipf’s law. Our findings show that: (1) from the mathematical model, interlanguage followed distributional regularities like natural languages in terms of the syntactic structure distribution; (2) most of the determination coefficients’ R 2 were high, indicating that the investigated distributions in interlanguage fit the distributional law finding in natural languages. This also demonstrated that both interlanguages and natural languages consistently conform to the law of linguistic diversity and uniformity; (3) the dependency relation distribution parameters a and b manifest the developmental trend of L2 learners’ proficiency levels, demonstrating that the parameters had universal applicability in reflecting interlanguage proficiency.
Developing Students’ Multimodal Communicative Competence Through Multiliteracies Pedagogy

With the rise of globalization, digitization, and the emergence of the fourth industrial revolution, meaning-making modes are now increasingly multimodal in which written linguistics modes of meaning interface with oral, visual, audio, gestural, tactile, and spatial patterns of meaning. In the EFL context, this situation unavoidably urges changes in the goal of English language teaching to include multimodal communicative competence. In response to such challenges, multiliteracies pedagogy offers plausible solutions. In a multiliteracies classroom, students are supported through a pedagogy that provides them with ample opportunities to develop their knowledge and skills to multimodally examine diverse information sources and content, critically explore the information, and become active meaning-makers and effective communicators. This study aims at investigating the implementation of multiliteracies pedagogy in English language teaching to foster students’ multimodal communicative competence. This is a qualitative case study involving twenty students of the English language department of IAIN Curup. The data of the study are gained through observation, interviews, and students’ artefacts. The data collected from these instruments were examined qualitatively using thematic and categorical coding. The result of this study demonstrates the enactment of multiliteracies pedagogy can develop five competences of multimodal communicative competence: linguistic competence, sociocultural competence, interactional competence, discourse competence, and multimodal competence. Meanwhile the other two competences, strategic competence and formulaic competence, are somewhat hindered due to lack of exposure to authentic English language.
Emotional generative adversarial network for image emotion transfer

The influence of emotions on viewers is important for that it can judge the value of multimedia materials. However, most of the existing image transfer algorithms do not take the affect information embedded in the image into consideration. An emotion is not only depended on colors or a single image. However, previous works in emotion transfer were limited to colors or a single image and shows no more benefits from more images. Besides, some models proposed for image emotion transfer are deterministic, since they can only generate unimodal output for a given input image once the models are trained. This paper proposes a new emotion-based image transfer algorithm named as Emotional Generative Adversarial Network (EGAN) to deal with these issues. To take benefits from more images, this paper utilizes adversarial training to generalize more emotional features. These generalized features are more objective and comprehensive compared with previous works. To achieve diverse outputs of emotional transfer, a new disentangled strategy is proposed, which assumes that the emotional images from different emotion domains can be embedded into a shared latent neutral high-level concept (NHC) space and a shared latent emotional low-level feature (ELF) space. By combining the NHC representation embedded from the input image with the ELF representation embedded from the target emotional reference image, the model can reconstruct a transferred target-emotion image. Therefore, diverse outputs can be obtained by selecting different reference images. Two quantitative measures are presented to evaluate the effect of image emotion transfer. Experimental results show that the proposed method performs better than other state-of-the-art baselines.
A new multi-objective evolutionary algorithm for citation-based summarization: Comprehensive analysis of the generated summaries

The number of scientific publications in different knowledge fields has considerably grown in recent times. This makes difficult for researchers to synthesize all the scientific-technical advances, so automatic summarization methods of scientific papers would be helpful. These methods generate a summary from a reference paper with its most relevant contributions. More specifically, citation-based summarization considers the citation contexts to the reference paper in subsequent publications. For the first time, this problem has been formulated as a multi-objective optimization problem, optimizing the content coverage and the redundancy reduction in a simultaneous way. A Decomposition-based Multi-Objective optimization algorithm for Citation-based Summarization (DMOCS) has been designed, developed, and applied for solving this problem. The results obtained by the proposed approach have improved the existing ones in the scientific literature between 17.47% and 133.50%, increasing the ROUGE percentage improvements when the N-gram is larger. Besides, an exhaustive analysis of the different parts of a scientific paper has been performed, showing that the citations from the citing papers with their corresponding spans in the reference paper impact in the quality of a citation-based summary.
Software Development Estimation Cost Using ANN

There are several inter-related aspects that have an impact on the effort and productivity of software development. Since the majority of these connections are not well understood and are impossible to predict accurately, software development time and effort have always been a challenging undertaking. In use or suggested in the literature, regression-based estimating models predominate. The study looks into the potential of software artificial intelligence methods for creating the following software development effort estimation models: case-based reasoning with artificial neural networks. When there are intricate relationships between variables, artificial neural networks are capable of providing accurate estimation. Numerous interconnected aspects that are involved in software development have an impact on both the development- effort and its productivity. Because more of the relationships were not healthy. The research examines the potential of these 2 artificial intelligence approaches, that is, artificial neural networks (ANNs) and case-based reasoning (CBR) to creating development effort of estimation model.
Using the MOODLE distance learning system for teaching the discipline of Chemistry in a technical educational institution

Problem. The war in the country forced all educators to adapt and adjust their work to remote mode. Distance learning as an educational technology requires a systematic approach, when it is necessary to develop full-fledged distance courses for all academic disciplines at universities. Long-term distance learning should take place using a special online platform, which will be the unified for the entire university.&#x0D; Goal. The goal is consideration of the use of the "MOODLE" system and the prospects of its application in the study of the "Chemistry" discipline by students of the 1st year of the Automobile faculty of Kharkiv National Automobile and Highway University.&#x0D; Methodology. The presented Chemistry distance learning course is developed with the help of a specialized information and educational environment MOODLE. Results. The thematic content of the Chemistry course is implemented on the basis of the educational and methodological complex of the discipline developed in accordance with the requirements of the KhNAHU. All educational material in the MOODLE environment is created in the form of pdf or html files as well as video. The training course is divided into two sections, which are formed according to the topics of the discipline curriculum. Each topic contains a certain set of educational components: theoretical material of lectures, guidelines for performing laboratory works and corresponding presentations; test tasks and examples of their implementation. Broadcasting of lectures and laboratory work with video and audio sequences is carried out using the BigBlueButton Video Conference component. Originality. The distance learning course of Chemistry, developed in the MOODLE system, allows you to organize students' training at a new level. Practical value. Electronic resources of the MOODLE platform contribute to increasing the level of students' knowledge, developing independent work skills, and provide an opportunity for self-assessment of their knowledge, which is a necessary condition for the effectiveness of the learning process.