Reviewerly/exHarmony huggingface dataset:
A survey on the elementary school students’ and parents’ perceptions toward online English classes in the COVID-19 pandemic: a comparison between synchronous and asynchronous classes

It seems indescribable how greatly the COVID-19 has impacted public schools not only in Korea but also all over the world. Since the outbreak in early 2020, elementary schools in Korea have implemented classes based on online platforms in order to practice the social distancing measures. However, there have been growing concerns about whether synchronous or asynchronous classes would better fit the elementary school context. Despite those concerns, there has not been a lot of research on this issue. Therefore, the present study aims to investigate elementary school students’ and parents’ perceptions of the two different online methods. It surveyed a group of students and parents who had participated in both synchronous and asynchronous classes in Seoul. The results show that, first, students and their parents alike preferred synchronous to asynchronous online methods. Second, their perceived achievements in synchronous classes were also better than those in asynchronous classes, although the effects of sex and grade variables were insignificant. Last, the major factor on the preference and perceived achievements of the synchronous method seems to be the interaction between the teacher and students. This study is expected to provide pedagogical implications for designing better online classes in the future.
Some quantitative characteristics of error covariance for Kalman filters

Some quantitative characteristics of error covariance are studied for linear Kalman filters. These quantitative characteristics include the peak value and location in the matrix, the decay rate from peak to bottom, and some algebraic constraints of the elements in the covariance matrix. We mathematically prove a matrix upper bound and its quantitative characteristics for the error covariance of Kalman filters. Computational methods are developed to numerically estimate the elements in a matrix upper bound and its decay rate. The quantitative characteristics and the computational methods are illustrated using three examples, two linear systems and one nonlinear system of shallow water equations.
The formation of students’ telecommunicative competence through the knowledge synthesis in the study of natural sciences

Abstract The article is devoted to the search of the mechanisms of the synthesis of the knowledge that is new for a student in the development of his telecommunicative competence in the study of natural studies at the university. The authors interpret the concept «telecommunication» as targeted transferring, receiving, analyzing, synthesizing and assessing information through the interaction of its real and virtual carriers that can be removed in time and space. The authors define «telecommunicative competence» as the personality trait which determines readiness to specific form of professional activity and life in information society comprising the knowledge of telecommunicative technologies, forms, rules, regulations and traditions of mutual exchange of information; capacities to independent development in the sphere of telecommunicative technologies, assimilation of a great deal of information, result prediction in the solution to the problems in professional activity; the abilities related to transferring, receiving, coding, analyzing, transforming, generating information. During the research the authors identify seven mechanisms of subjective new knowledge synthesis in the following processes: professional inversion of preliminary knowledge; application of gained knowledge in practical work: diversification of professional training content; establishment of interdisciplinary connections between specialty curriculum disciplines; simulation of training and productive situations and processes: functional approach to the development of telecommunicative competence; transference of concepts, principles, regulations, theories set out in a discipline to the subject of another. The studied mechanisms of subjective new knowledge synthesis expand the set of tools that can be used by both a teacher and a student in the formation and the development of telecommunicative competence.
WITHDRAWN: Elevating recruitment process by classifying the enrolled students in the institution using ubiquitous human computing

Employment is a hot topic and in everywhere its requirement of time that to work on recruitment process with proper inputs. Skill employment and providing the jobs accordingly is major challenge in every sector. Where the sector need of actual skill set but lacking the actual resources effect the productivity. Recruitment in Institutions is a regular process again where different students with different interest including entrepreneurship mindset too. Thus understanding their skills and providing the best opportunity suggestion is always required. Thus to deal with such problem which arises in real time to analyze large dataset, an architecture model for analysis is required. Previous system with manual or limited search model is presented which is limited to their capabilities or work in single direction output. In this paper, an approach which works with word processing along with the relation building and mapping them with the possible opportunity is presented. This paper given the algorithm by analysis of skill words, CTM model for relation building and finally mapping to outcome from their generated model. The job prediction performance is computed using RMSE and MASE parameter which shows the efficiency of proposed architecture up to 6.4%. Thus the requirement is real time adoption of such system which can help institutions to map their student according to their subject or knowledge skills.
Single Trace Attack on Key Reconciliation Process for Quantum Key Distribution

In this paper, we propose the first single trace side-channel attack on the key reconciliation process for the quantum key distribution (QKD) system. Through a single power consumption trace measured during the syndrome computation of the key reconciliation process on the sender's side, we were able to fully recover the sifted key and the syndrome. The shared secret key between the two users can be directly obtained from the sifted key and the syndrome. The result of our work shows that the QKD system must consider side-channel attacks on the key reconciliation process for its practical use.
Multimodal Neural Machine Translation for English to Hindi

Machine translation (MT) focuses on the automatic translation of text from one natural language to another natural language. Neural machine translation (NMT) achieves state-of-the-art results in the task of machine translation because of utilizing advanced deep learning techniques and handles issues like long-term dependency, and context-analysis. Nevertheless, NMT still suffers low translation quality for low resource languages. To encounter this challenge, the multi-modal concept comes in. The multi-modal concept combines textual and visual features to improve the translation quality of low resource languages. Moreover, the utilization of monolingual data in the pre-training step can improve the performance of the system for low resource language translations. Workshop on Asian Translation 2020 (WAT2020) organized a translation task for multimodal translation in English to Hindi. We have participated in the same in two-track submission, namely text-only and multi-modal translation with team name CNLP-NITS. The evaluated results are declared at the WAT2020 translation task, which reports that our multi-modal NMT system attained higher scores than our text-only NMT on both challenge and evaluation test set. For the challenge test data, our multi-modal neural machine translation system achieves Bilingual Evaluation Understudy (BLEU) score of 33.57, Rank-based Intuitive Bilingual Evaluation Score (RIBES) 0.754141, Adequacy-Fluency Metrics (AMFM) score 0.787320 and for evaluation test data, BLEU, RIBES, and, AMFM score of 40.51, 0.803208, and 0.820980 for English to Hindi translation respectively.
Unmanned Aerial Vehicle Path Planning for Exploration Mapping

This paper presents a new path planner for the exploration of previously unknown environments. The proposed algorithm uses a Next-Best-View (NBV) fashion to decide the movements ahead. A Rapidly-Exploring Random Tree (RRT) is built and the node with the highest gain is the next configuration to execute, based on the amount of unmapped volume in the field of view (FoV) of the sensor employed. The proposed planner is able to send online a 3D occupancy map of the volume and work with a variety of different depth sensors. The performance is analyzed in various challenging simulations, such as exploration of a city street or a narrow tunnel. Moreover, real experiments indoor and outdoor were conducted to further validate the proposed approach results. In cases with large scale volumes, it can cover a wide range in affordable flight time.
String Indexing for Top-k Close Consecutive Occurrences

The classic string indexing problem is to preprocess a string S into a compact data structure that supports efficient subsequent pattern matching queries, that is, given a pattern string P, report all occurrences of P within S. In this paper, we study a basic and natural extension of string indexing called the string indexing for top-k close consecutive occurrences problem (Sitcco). Here, a consecutive occurrence is a pair (i,j), i < j, such that P occurs at positions i and j in S and there is no occurrence of P between i and j, and their distance is defined as j-i. Given a pattern P and a parameter k, the goal is to report the top-k consecutive occurrences of P in S of minimal distance. The challenge is to compactly represent S while supporting queries in time close to the length of P and k. We give two time-space trade-offs for the problem. Let n be the length of S, m the length of P, and e ∈ (0,1]. Our first result achieves O(nlog n) space and optimal query time of O(m+k), and our second result achieves linear space and query time O(m+k^{1+e}). Along the way, we develop several techniques of independent interest, including a new translation of the problem into a line segment intersection problem and a new recursive clustering technique for trees.
Self-Guided Virtual Tour Using Augmented Reality

The Cape Flats Nature Reserve, situated in the Western Cape, South Africa provides refuge to over 200 plant species, many endemic to the Western Cape. As part of the reserve's recreational activities, scheduled guided tours are offered to the public. The tours focus on the ecological importance and educational aspect of the reserve. A complete tour usually takes more than an hour. Due to the lack of trained tour guides and its strenuous nature, tours are only offered once daily. This has been identified as a challenge by the management of the nature reserve. In this paper a solution to the challenge using an augmented reality mobile application is proposed. The application allows visitors to experience the nature reserve in their own time without a guide. Augmented reality markers are placed at points of interest around the reserve. These in conjunction with the mobile application provide information about plants thereby mimicking actual tour guides. Outlines for the design and development of this self-guided tour application and the results of user acceptance and unit tests are provided in this paper.
Using Taigi Dramas with Mandarin Chinese Subtitles to Improve Taigi Speech Recognition

An obvious problem with automatic speech recognition (ASR) for Taigi is that the amount of training data is far from enough to build a practical ASR system. Collecting speech data with reliable transcripts for training the acoustic model (AM) is feasible but expensive. Moreover, text data used for language model (LM) training is extremely scarce and difficult to collect because Taigi is a spoken language, not a commonly used written language. Interestingly, the subtitles of Taigi drama in Taiwan have long been in Chinese characters for Mandarin. Since a large amount of Taigi drama episodes with Mandarin Chinese subtitles are available on YouTube, we propose a method to augment the training data for AM and LM of Taigi ASR. The idea is to use an initial Taigi ASR system to convert a Mandarin Chinese subtitle into the most likely Taigi word sequence by referring to the speech. Experimental results show that our ASR system can be remarkably improved by such training data augmentation.
A New Method Encryption and Decryption

In all times manual investigation and decryption of enciphered archives is a repetitive and mistake inclined work. Regularly considerably in the wake of investing a lot of energy in a specific figure, no decipherment can be found. Computerizing the unscrambling of different kinds of figures makes it conceivable to filter through the huge number of encoded messages found in libraries and files. We propose in this paper new algorithm has been made to encrypt the information; this algorithm works to shield information from robbery and can't be decrypted in the text. It is taken care of precisely to very accurately to avoid any penetration to arrive at the first text. It tends to be used in companies or some other system; however, it takes a long time to encrypt it. To the first text when encryption to ensure the assurance of information in full and security. Encrypted text contains a unique key, even when stolen. The private key can't be decrypted by the specialist and licensed by the maker of the code in order to protect the data in an excellent manner. While demonstrating in addition much stronger security guarantees with regards to Differential/ direct assaults. Specifically, we are can to provide new Method Encryption and Decryption with strong bounds for all versions.
On weak twins and up-and-down subpermutations

Two permutations (x1, . . . , xw) and (y1, . . . , yw) are weakly similar if xi < xi+1 if and only if yi < yi+1 for all 1 ⩽ i ⩽ w. Let π be a permutation of the set [n] = {1, 2, . . . , n} and let wt(π) denote the largest integer w such that π contains a pair of disjoint weakly similar subpermutations (called weak twins) of length w. Finally, let wt(n) denote the minimum of wt(π) over all permutations π of [n]. Clearly, wt(n) ≤ n/2. In this paper, we show that n/12 ≤ wt(n) ≤ n/2 − Ω(n1/3). We also study a variant of this problem. Let us say that (π(i1), . . . , π(ij)), i1 < ⋅ ⋅ ⋅ < ij, is an alternating (or up-and-down) subpermutation of π if π(i1) > π(i2) < π(i3) > ⋅ ⋅ ⋅ or π(i1) < π(i2) > π(i3) < ⋅ ⋅ ⋅. Let Πn be a random permutation selected uniformly from all n! permutations of [n]. Stanley has shown that the length of a longest alternating permutation in Πn is asymptotically almost surely (a. a. s.) close to 2n/3. We study the maximum length α(n) of a pair of disjoint alternating sub-permutations in Πn and show that there are two constants 1/3 < c1 < c2 < 1/2 such that a. a. s. c1n ≤ α(n) ≤ c2n. In addition, we show that the alternating shape is the most popular among all permutations of a given length.
Development of Methodological Support for Specialized Training in Chemistry in High School

The article discusses the didactic aspects of the formation of functional literacy of students in the process of teaching chemistry. The article reflects the concept of semantic reading in chemistry, the content of which is considered in the context of functional literacy. The article draws attention to the fact that the majority of schoolchildren experience serious difficulties with the literacy of reading chemical texts and the use of what they read in practice and in life. The structural components of task development are revealed: the context, the chemical content of the task and mental activity. Examples of tasks that reveal the quality of understanding the text, as well as methodological approaches to the formation of functional literacy of students in the process of teaching chemistry at a profile level are given.
Ontology-based multi-sensor information integration model for urban gardens and green spaces

Abstract With the continuous development of smart landscape and the application of IOT technology in urban gardens and green spaces, the environmental data and soil data collected by various sensors are conceptually inconsistent with each other. The information derived from the data is mostly used independently, and the relationship between data and information in the database is relatively simple, and it is impossible to explore deeper associations between information. To solve this problem, a multi-sensor information integration model based on ontology is studied. Based on the characteristics of the perception terminal data of smart garden management, this article analyzes the ontological relationship between various resources of the garden and green space, and builds the ontology description model of the urban garden and green space based on the five-tuple structure of the ontology and the seven-step method to realize the information integration of perception data and the description of the relationship between sensors, this model provides a unified information model for the smart garden system to achieve information sharing. This paper describes the construction of local ontology and the related methods of global mapping, illustrate the construction process of ontology, and conducts ontology reasoning and ontology query according to the result of construction.
Uni- and multivariate probability density models for numeric subgroup discovery

Subgroup Discovery is a supervised, exploratory data mining paradigm that aims to identify subsets of a dataset that show interesting behaviour with respect to some designated target attribute. The way in which such distributional differences are quantified varies with the target attribute type. This work concerns continuous targets, which are important in many practical applications. For such targets, differences are often quantified using z-score and similar measures that compare simple statistics such as the mean and variance of the subset and the data. However, most distributions are not fully determined by their mean and variance alone. As a result, measures of distributional difference solely based on such simple statistics will miss potentially interesting subgroups. This work proposes methods to recognise distributional differences in a much broader sense. To this end, density estimation is performed using histogram and kernel density estimation techniques. In the spirit of Exceptional Model Mining, the proposed methods are extended to deal with multiple continuous target attributes, such that comparisons are not restricted to univariate distributions, but are available for joint distributions of any dimensionality. The methods can be incorporated easily into existing Subgroup Discovery frameworks, so no new frameworks are developed.
Purchasing Models for Streaming Film: Attitudes and Motivation

While library acquisition models are moving steadily away from ownership to access only, film vendors are following suit, but some streaming video purchase models become so expensive over time that one questions the motivation behind this choice. The following study was done to explore the motivations behind this choice, through a survey of academic librarians. The results showed that academic librarians are purchasing or subscribing to something that they perceive to be the preferred format for faculty and students. At the same time, respondents acknowledge the problems with streaming video purchase models, but this choice is being made despite attitudes that streaming video purchasing models are unsustainable.
Naver Labs Europe's Participation in the Robustness, Chat, and Biomedical Tasks at WMT 2020

This paper describes Naver Labs Europe’s participation in the Robustness, Chat, and Biomedical Translation tasks at WMT 2020. We propose a bidirectional German-English model that is multi-domain, robust to noise, and which can translate entire documents (or bilingual dialogues) at once. We use the same ensemble of such models as our primary submission to all three tasks and achieve competitive results. We also experiment with language model pre-training techniques and evaluate their impact on robustness to noise and out-of-domain translation. For German, Spanish, Italian, and French to English translation in the Biomedical Task, we also submit our recently released multilingual Covid19NMT model.
Performance analysis of data hiding in thermal image using alpha blending technique

Hiding data in an image become a very successful technique to communicate with end users, blinding the interpreter’s intention. Various algorithms have been analyzed on the basis of masking the content within the coordinates of the images or in the color of the images used. Few advanced developed complex algorithms to hide the content in the values of the colors to ensure or guarantee robustness of the data hidden. In this research, a simple technique called alpha blending is used to hide the data in an image and its impact is analyzed for different alpha values. The unavoidable impact of noise on the transmitted carrier image was formulated and studied by many researchers using various available algorithms. In this regard: the same is analyzed using alpha blending in thermal images.
Integrating Deep Reinforced Learning and Robotic Process Assessment in Blockchain Digital Transformation for Autonomous Cybersecurity

The term Blockchain Technology (BT) refers to information technology (IT) where data is stored in numerous blocks using multiple distributed servers. Through digital automation the data is refreshed periodically at a frequency set by data architects and network designers. The servers are sometimes on the public internet, but in the aerospace world it is more within private intranet of authorized users to maintain privacy and security. A Blockchain network registers the components within aerospace echo system in a digital ledger accompanied by relevant data. It is virtually immutable. The data residing within BT cannot be changed without the awareness of the collaborative parties. Even if a hacker manages to penetrate one block, the overall integrity of the data would remain intact since the data is distributed among thousands of blocks containing cryptographically protected digital ledgers. Deep reinforced learning (DRL) is a machine learning (ML) process based on reinforced learning (RL) and neural networks (NN). DRL training algorithms adjusts its actions dynamically motivated by a system of reward and punishment. The DRL algorithm learns by interacting with its dynamic environment. The DRL software agents receive rewards for correct actions and penalties for performing incorrectly. These agents learn without human intervention by targeting maximizing the reward and minimizing the penalty. Robotic process automation (RPA) is a business process automation technology based on software agents. It can trigger automated agent-based actions. When RPA and DRL are combined, system operates software agents that can learn in dynamic environment automatically without human interventions. They act as autonomous agents that learns continuously and operates cyber infrastructure autonomously. In this paper we show the implementation process and the benefits of integrating deep reinforced learning and robotic process automation in Blockchain digital transformation for autonomous cybersecurity.
Japanese Abstractive Text Summarization using BERT

In this study, we developed an automatic abstractive text summarization algorithm in Japanese using a neural network. We used a sequence-to-sequence encoder-decoder model for experimentation purposes. The encoder obtained a feature-based input vector of sentences using BERT. A transformer-based decoder returned the summary sentence from the output as generated by the encoder. This experiment was performed using the livedoor news corpus with the above model. However, issues arose as the same texts were repeated in the summary sentence.