Reviewerly/exHarmony huggingface dataset:
Say What? Learner Reactions to Unexpected Agent Dialogue Moves

For maximally efficient and effective conversation-based intelligent tutoring systems, designers must understand the expectations carried by their intended learners. Strategies programmed into the agents may be interpreted differently by the learners. For example, conversational heuristics in these systems may be biased against false alarms in identifying wrong answers (potentially accepting more incorrect answers), or they may avoid directly answering learner-generated questions in an attempt to encourage more open-ended input. Regardless of pedagogical merit, the learner may view these agents&rsquo; dialogue moves as bugs rather than features and respond by disengaging or distrusting future interactions. We test this effect by orchestrating situations in agent-based instruction of electrical engineering topics (through an intelligent tutoring system called AutoTutor) where the pedagogical agent behaves in ways likely counter to learner expectations. To better understand the learning experience of the user, we then measure learner response via think-aloud protocol, eye-tracking, and direct interview. We find that, with few exceptions, learners do not reason that the actions are meant as instructional or technical strategies, but instead broadly understood as errors. This indicates a need for either alteration of agent dialogue strategies, or else additional (implicit or explicit) introduction of the strategies to productively shape learners&rsquo; interactions with the system.
Statistical and Deep Learning Approaches for Literary Genre Classification

The problem of automatically classifying literary texts into their corresponding genres/subgenres occurs in Natural Language Processing (NLP) and has applications in library classification systems and semantic metadata extraction. Existing literature on this problem attempts to reduce the dimensionality of the datasets such that the models are still able to retain the structure and order of the data, considering the high computational complexity of training neural networks on long text sizes. This paper compares several machine learning and deep learning approaches for literary genre classification using the entire texts for classification. Experimental work is performed in a high peformance computing environment that uses general-purpose computing on graphics processing units (GPGPUs) for optimizing the performance of deep learning-based applications. Results are reported on a large collection of freely available texts sourced from Project Gutenberg and show the effectiveness of the proposed approach.
Study and Design Approach for Generic Medical Equipment Integration with Lis Host

Pathology laboratories are becoming more automated with the help of advance medical equipment and machinery. Interfacing these machines and understanding different test mythology and components extracted from test are the major challenge in information technology or electronics industries. This research work is mainly aimed at study about different laboratories machinery and finding out the multiple symptoms through lab tests and human day to day life activities and predicting the possible chances of different diseases and providing possible solutions to patients with the help of expert practitioners. The study targeted understanding the different medical equipment communication physical media and data exchange standards and the protocols. Here we concluded the study after looking at multiple companies’ equipment and trail on different communication channels.
A Hybrid Agglomerative Method for Colour Image Segmentation

This paper proposes a hybrid method of image segmentation using K-means and agglomerative methods of image segmentation. The K-means method is used to find optimum number of clusters with the help of gap or elbow method and a validity measure. Then this optimum value is used as a limiting value in agglomerative algorithm. The performance of algorithm is measured using a validity index which is measured by two factors. The first factor is intra-cluster distance whose minimum value is desired, and another is inter-cluster distance for which a maximum value is required. Once optimum number of clusters is found then K-means clustering algorithm is again applied to generate large number of clusters, following which pair of clusters with most similar characteristics are merged iteratively until number of clusters are reduced up to optimum number of clusters as decided earlier. The similarity measure is derived from Davies-Bouldin Index. The proposed algorithm is performing better than simple K-means algorithm.
Factors Affecting Job Satisfaction of Police Officers in Charge of Autonomous Police Affairs

자치경찰제 시행 이후 많은 논란이 되었던 문제들 중에 운영 상 문제가 되었던 요인들을 중심으로 실증적인 분석을 실시해 직무만족을 높일 수 있는 결정요인을 확인하는 것이 이 연구의 목적이다. 자치경찰제 운영 상 논란이 되었던 요인은 법령 구축, 인력 구성, 교육 훈련, 예산 지원, 자치단체장의 지원과 관심이었다. 분석결과 자치경찰제 시행 이후 자치경찰사무 담당 경찰관의 직무만족은 매우 낮은 것을 확인할 수 있었으며, 실질적으로 직무만족에 영향을 미치는 요인은 법령 구축, 교육 훈련, 자치단체장의 지원과 관심으로 나타났다. 자치경찰제 시행과 관련해 직무만족에 영향을 미치는 요인에 대해 자치단체장을 중심으로한 법령 구축, 교육 훈련에 대해 정책적 제언(지역별 통합 조례 제정, 경찰 내부 교육훈련 관련 매뉴얼 개발, 자치단체와 통합 운영될 수 있는 매뉴얼 개발, 자치단체장의 실질적인 자치경찰위원회와의 소통, 자치경찰위원회 중심의 의견수렴 등)을 했다The purpose of this study is to identify determinants that can increase job satisfaction by conducting an empirical analysis focusing on factors that have become operational problems among many controversial issues after the implementation of the autonomous police system. The factors that were controversial in the operation of the autonomous police system were the establishment of laws, the composition of manpower, education and training, budget support, and the support and interest of the head of the local government. As a result of the analysis, it was confirmed that the job satisfaction of the police officers in charge of local police affairs after the implementation of the autonomous police system was very low. In relation to the implementation of the autonomous police system, establishment of laws centered on the head of the local government on factors affecting job satisfaction, policy suggestions on education and training (enactment of regional integrated ordinances, development of manuals related to internal police education and training, integrated operation with local governments) developed manuals, communicated with the local autonomous police committee, and collected opinions centered on the autonomous police committee).
A Payload Scrambling Technique for Secure Image Steganography

Communication is the basic need of everyone. It may be in the form of text, image or video. No one wants to public his/ her message to everyone. Therefore, such a technique is required that can hide the existence of any secrete communication. Steganography is one of the techniques that can hide the existence of any secret communication. In this paper, a payload scrambling technique is proposed for image steganography. For embedding purpose, we used existing embedding steganography technique. In the proposed technique, the key data is first scrambled then embedded within the cover image using X-OR operation. It is performed over the bits of payload and the cover image for steganography both at the sender and receiver side. The scrambling of the payload has the aim to extend the security and integrity of the payload.
Cost-Effectiveness of Using Tablets for Digital Literacy Training in Tribal Areas of Rajasthan

Digital literacy is lacking in many tribal areas of India. This article investigates the cost-effectiveness of a program that uses tablets for conducting digital literacy trainings in rural Rajasthan. The program is supported by the Ministry of Tribal Affairs, Government Of India. Data from 700 students belonging to tribal communities across Rajasthan are being analyzed. The students received 20 hours of training with tablets, and then they should attempt an exam for basic computer literacy. Costs and effects for conducting the programs for these students in 15 schools are being investigated. The program is found to be cost-effective with a threshold of INR 264 for the willingness to pay.
9. Language Corpora

This chapter presents the main issues related to corpora in the Catalan language. It opens with a discussion of some general matters associated with corpora and empirical perspectives in linguistics, and provides a brief history of Catalan language corpora, taking into account the sociolinguistic conditions under which Catalan corpora have been developed which can explain the present situation and shortcomings in comparison with other languages. The main section provides a detailed inventory of the most important spoken and written corpora of the Catalan language, giving special attention to their structure, annotation and accessibility. Finally, the concluding remarks make some suggestions for how Catalan corpora should proceed in the future in order to maintain, or to regain, their rightful position among the rest of the European languages.
GPStemmer—A Gurmukhi Punjabi Stemmer

Gurmukhi script is a popular script in India to write Punjabi. The other script is Shahmukhi which is popular in Pakistan. Processing Gurmukhi Punjabi text requires the development of suitable natural language processing (NLP) tools, and stemmer is one of them. Some stemmers for Punjabi have been already developed, but this stemmer provides more efficient stemming as in the case of accuracy, coverage of words and fast processing. This stemmer identifies whether the word is inflected before processing it. It uses only a single column list of Punjabi stem words (lexicon) reducing the database access time. It uses a pattern matching technique to get the matched stem word from lexicon in single access. The working of this stemmer is based on the fact that all suffixes have a certain length and they can be removed using a general rule instead of testing for each separate suffix with a different rule. The technique can remove even unidentified suffixes from inflected words. The implementation and testing shows, and this technique is more efficient than existing approaches.
Exploring Current Trends and Challenges in Hesitation Mining

In e-commerce applications, such as Ajio, Flipkart, Myntra, Amazon, Snapdeal, eBay, etc., customers purchase items, during this purchasing customers, buy some items completely, and sometimes they also drop some items. Behind this, so many reasons are available such as quality, quantity, price of the items, discount imposed upon them, etc. to drop items either from the cart or at any initial stage of purchasing. These dropped items, which are hesitated by customers are known as Hesitated Items or almost sold items and mining such items are referred to as Hesitation Mining. Furthermore, in order to discover the relation between purchased or sold items, an approach of data mining known to be association rule mining (ARM) is used. But association rule mining deals only with sold items and does not consider the almost sold items, in simple words this scenario tells that these almost sold items contain a piece of important information which is hesitation information and this traditional association rule mining ignores such type of information, which is also equally essential as frequently purchased items for the growth of an individual organization. Therefore, in order to extract this hesitation information and make selling strategies, an approach known as Vague Association Rule Mining is used. Thus, the main objective of this article is to give an overview of the hesitated items, uncertainty situations and mentions the issues, current trends, challenges, and futuristic vision pertaining to Hesitation Mining. Moreover, the author also mentions the researches done by many researchers in this field of Hesitation Mining.
Testing with AWS Device Farm

When iOS applications are tested on iOS simulators, you can test functionality and behavior of the app, but the simulator doesn’t work exactly like a real hardware device, hence an app may work fine on a simulator but behave differently when installed on a real iOS device. Testing your iOS application on real mobile devices during development improves quality of the application because you can simulate how real users will interact with the application.
Applications of High Dimensional Neural Networks: A Survey

The evolution of artificial neural networks has always been inspired by enormous power of human brain. This survey can be an eye-opener for researchers as its diverse applications of HDNNs in present scenario shows an intelligent way to mimic human brain without creating a complex neuronal architecture having large number of layers. HDNN’s urgency is evident because in science many quantities are measured not by single values for each of them but a group of values defines 1 single unit as: signal has two values: amplitude and phase. The deficiency in present literature on the questions allied with HDNN application looks like sluggish down research focal point and growth in the area. Hence, there exists a call for state-of-the-art addressing high-dimensional problems in neural networks. The study equips readers with a lucid acquaintance of the existing and novel inclination in HDNN replicas. A lot of applications of HDNNs in various disciplines like: healthcare, climate, security, speech recognition, computer vision, music signal processing, production, stock, science, etc. are covered here to confirm advancement in HDNNs. Study divulges that HDNNs is prevalently known as: CVNN, QVNN, 3D VVNN, and OVNN. This paper also reveals that HDNNs have outperformed real valued neural networks in terms of resource utilization, training data set requirement, and accuracy of results. To see a comparative picture of significance and possible implementation of different HDNNs few charts are provided. A motivational message and suggestions for future researches in this area of High-Dimensionality will conclude this paper.
Performing Sentiment Analysis on Twitter Data Using Deep Learning Models: A Comparative Study

Twitter is one of the topmost microblogging social media networks that bridge the gap between users in various communities across the globe. People worldwide have taken to Twitter to express their opinions and general emotions regarding current issues and events in the past. These emotions and opinions have been extensively analyzed in various studies to know the users’ state of interest towards various issues. The sentiment analysis of tweets is complicated due to both the compactness of the form and the frequent use of non-standard spellings or jargon. Although several studies have described sentiment analysis with a high accuracy level, only a few have been tested on Twitter data. This study proposes a comparative analysis of the LSTM networks, Bi-LSTM networks, and the BERT model on two corpora, (1) Sentiment140 dataset and (2) scraped tweets with the hashtag #FarmersProtest. The study also predicts the overall sentiment of the tweets and labels them as positive or negative.
Deep Learning Based Sentiment Aware Ranking for E-commerce Product Search

In the age of information overload, customers are overwhelmed with the number of products available for sale. Search engines try to overcome this issue by filtering relevant items based on the users’ queries. Traditional product search engines rely on the exact match of terms in the query and product meta-data. Recently, deep learning methods have grabbed more attention outperforming traditional methods in many circumstances. In this work, a new approach is presented to extract relevant items in the context of e-commerce product search using similarity measure on the top of query and product embeddings. To refine the search results for the users’ convenience, a deep neural network architecture is proposed to rank products based on the sentiment polarity presented in their associated reviews. The performance of the proposed method is evaluated on the Amazon reviews dataset. The results showed that the proposed method is effective in retrieving relevant and high-quality products.
A Fine-Grained Access Control Scheme in Fog-IoT Based Environment

Fog computing becomes an essential part in IoT-based environment, it offers computing and storage resources at the edge of the network making applications and services more efficient. However, it belongs to a system loaded with security challenges and it is expected to help with its characteristics. On the other side, attribute based encryption is a rising solution that offers a fine-grained access control over encrypted data. Nonetheless, the calculation costs remains heavy and hard to be performed especially in a constrained environment. In this paper, we propose an access control scheme based on cipher-text policy attribute based encryption (CP-ABE) in an IoT- fog based environment by outsourcing the bulk of the calculation to the fog computing layer after a symmetric encryption performed by the devices. Moreover, we propose a verifiability mechanism and an attribute based key management scheme to maintain a fine-grained access control. The tests show that compared to other outsourcing schemes, the execution times on the IoT devices in our work are shorter and tolerable.
Use of N-grams Model and Semantic Similarity to Improve the Results of Search Engine

Semantic indexing and document similarity is a significant problem of the information retrieval systems (IRS). Recently many works use semantics in information retrieval systems to find the information easily. A search engine is the best example of the application of information retrieval to get the most relevant results. In this work, to facilitate the use of search engines for users for getting the most relevant documents, we propose a new approach for calculating the semantic similarity between user’s query and a list of documents based on the N-grams model (i.e., a sub-sequence of n constructed elements from a given sequence). Results show that our method give us better results than some methods from the literature.
Information-Theoretic and Predictive Statistical Mechanics

We discuss an alternative to Boltzmann’s approach to statistical mechanics, which is simple, elegant and popular, namely the information-theoretic one. That approach is based on probabilities, rather than on microstates. The basic principle that allows us the choose the correct probabilities is the maximization of the Shannon entropy. We define that entropy and explain what makes its maximization natural. We also discuss several applications of the Shannon entropy to the theory of communication, the field in which it was originally introduced.
GEO 2022 National Conference

Nonprofit Business AdvisorVolume 2022, Issue 390 p. 4-4 Conferences & Events GEO 2022 National Conference First published: 09 February 2022 https://doi.org/10.1002/nba.31182Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume2022, Issue390March 2022Pages 4-4 RelatedInformation
DESIGNING INSTRUCTIONAL MATERIALS FOR TEACHING AND LEARNING WRITING RECOUNT TEXT THROUGH EDMODO PLATFORM

This research aims to design instructional materials for teaching and learning writing recount text through the Edmodo platform to tenth-grade students of SMA Kristen Abdi Wacana Pontianak. This instructional material was designed based on the teacher's interview, analysis of the current syllabus, and the English textbook. The researcher applied ADDIE as the research model. In this research, the researcher only implemented three phases which are analyzing, designing, and developing (ADD) without implementing and evaluating phase because of time limitation. The materials and tasks were indicated the teaching and learning focus only on writing recount text. This instructional was designed to include learning through digital learning media. Thus, some of the materials and tasks were provided on the Edmodo platform. The main structures of the instructional are the books' cover, preface, syllabus, basic competence and indicator competence, learning goals, material, and task. The researcher used expert validation to make sure that the material was usable. It revealed that the interpretation result of the product was very good. Besides, these instructional materials which were designed indicate that it is workable as the teaching materials in SMA Kristen Abdi Wacana Pontianak.
Features of Jair Bolsonaro’s Speech Portrait

The paper discusses the characteristic features of the speech portrait of the current President of Brazil, Jair Bolsonaro. A speech portrait in the work is understood as a representation of the peculiarities of a politician's discourse, the originality of which lies in the tactics, lexical ways of implementing certain communication strategies. Based on this definition, the analysis of the most characteristic speech tactics of the president, as well as the lexical and syntactic means of implementing these tactics, will be carried out, special attention will be paid to metaphorical concepts and their role in the president's discourse. The material for the study was the president's public speeches, including his inaugural speech and speeches at the sessions of the UN General Assembly. The theoretical basis was formed by the works of prominent Russian and foreign specialists in cognitive linguistics, cognitive metaphors and political linguistics: E.S. Kubryakova, E.I. Sheigal, A.P. Chudinov, E.V. Budaev, A.N. Baranov, Yu.N. Karaulov, V.I. Karasik, E.V. Osetrova, O.E. Oparina, V.Z. Demyankov, J. Lakoff and M. Johnson and others. The work combines various scientific research methods: the method of continuous sampling, descriptive-analytical and comparative methods. In addition, the toolkit of the theory of conceptual metaphor has become an important methodological basis. An analysis of about 30 speeches by President J. Bolsonaro made it possible to single out the following communicative strategies most characteristic of his discourse: an «analysis-plus» strategy, a strategy for self-presentation, a strategy for discrediting the enemy and self-defense. A detailed study of the implementation of these strategies and the tactics that concretize them allows us to draw a conclusion about the wealth of means used by the president to achieve certain communication tasks. It was revealed that in addition to the widespread use of emotional and evaluative vocabulary, expressive syntactic means, the discourse of politics is organized by conceptual metaphorical and allusive constructions. This work may be of interest to a wide range of specialists: both in the field of linguistics and in the field of political sciences and regional studies.
Моделі соціально-педагогічного інноваційного навчання та виховання учнів закладів загальної середньої освіти

International scientific conference «Innovative projects and programs on psychology, pedagogy and education» : conference proceedings, December 10–11, 2021. Riga, Latvia : “Baltija Publishing”, 2021. 212 pages.