Reviewerly/exHarmony huggingface dataset:
N-grams of grammatical functions and their significant order in the Japanese clause

The present study investigates the statistically significant order of grammatical functions in Japanese clauses by employing n-gram frequency data of grammatical functions. There are broad rules for the order of grammatical functions, though Japanese is an agglutinative SOV language and complements can be elliptic. I conclude that the time and the place appear between the subject and object with statistical significance. The occasion takes a position before the subject, between the subject and object, or after the object. Therefore, the occasion shows that Japanese is a free word order language. The subject and object play the role of ‘anchors’ in the clause. By using the ‘two-sample test for equality of proportions without continuity correction data’, the study introduces a descriptive verification method of implicit speaker-hearer knowledge.
Implementation of Videos Captured by Static Telecytological Applications for Obtaining Expert Opinions

Published studies compare static and dynamic telecytology systems. Still, no study has focused on implementing short videos captured by static telecytology stations for telemedical applications. The chapter aims to evaluate the diagnostic reproducibility of telecytology in thyroid fine-needle aspiration specimens prepared using liquid-based cytology among four cytopathologists using representative short duration videos captured by a static telecytology station. It also examines the agreement between the contributor's and the reviewer's diagnoses.
An evaluation of indoor thermal environment in fisherman housing in West Sumatera

Abstract Pesisir Selatan is one of the districts in West Sumatra with a humid tropical climate with temperatures reaching 32°C during the day. The majority of coastal communities work as fishermen and build houses on the beach, such as in fishermen’s housing in South Painan. In this area there are two models of fisherman housing, namely government-assisted fisherman housing and fisherman’s original housing. Housing on the beachfront will be faced with quite extreme climatic conditions such as air velocity, air temperature, and air humidity which are relatively high on average. Therefore, it is necessary to evaluate the level of thermal comfort in the space in both models of fisherman housing to determine the performance of each fisherman’s housing in dealing with the climate. This study uses a quantitative approach by measuring the parameters of thermal comfort, namely air temperature, humidity and air velocity using a thermohygrometer and anemometer. The data collection method uses descriptive quantitative. Comfort level is evaluated based on the Indonesian national standard (SNI 03-6572-2001) and adaptive thermal comfort. Based on the results of the study, the level of thermal comfort in both models of fisherman housing is classified as uncomfortable according to SNI 03-6572-2001 and adaptive thermal comfort.
Pixel-Wise Information in Fake Image Detection

In recent years, generative adversarial networks have generated high-quality images that are difficult to differentiate by human eyes. Aside from the positives, improper use of this technology might have severe consequences for society. As a result, digital picture forensic techniques are critical to preventing these damages in our lives. In this paper, we describe a technique for detecting fraudulent face images generated by StyleGAN. Using a U-net-based classifier, we can integrate both global and local information of an image to determine if it is real or fake. A huge testing set of 20,000 pictures is used to confirm the model’s efficacy. For comparison, we do tests on different CNN-based models. The results demonstrate that among the models on the same dataset, the U-net-based classifier has the greatest accuracy (98.43\(\%\)). Finally, prediction maps are shown to demonstrate the relevance of pixel-level information in detecting real/fake images.
Competency Models of the Professional Institute of Education in the Conditions of Digitalization of Public Life

The purpose of the article is to study the current areas of professional development of subjects of learning, educational and administrative processes of the transforming institute of education in the context of the requirements of digital society. Education, based on an institutional approach, is considered as a professional institution. Classical scientific approaches to the sociology of professions are given in the research. A comparative analysis of state educational and professional standards, standard job descriptions, as well as curricula for the training of the professional corps of the Institute of Education made it possible to draw conclusions about the inconsistency of the competencies formulated in them with the request of the employer, society, state and the requirements of the new social reality. The data of the All-Russian study of the competencies and qualifications of teachers, current amendments to the legislation on education that integrate new subjects into its social structure are given and interpreted. The work offers specialist models supplemented with current competencies, demanded by modern realities. Resources and scope of application of online training for formation of actual competencies of specialists of professional institute of education are considered. The project of the online simulator “Electronic Teacher Mentor” is presented.
Topic Recommendation to Expand Knowledge and Interest in Question-and-Answer Agents

By providing a high degree of freedom to explore information, QA (question and answer) agents in museums are expected to help visitors gain knowledge on a range of exhibits. Since information exploration with a QA agent often involves a series of interactions, proper guidance is required to support users as they find out what they want to know and broaden their knowledge. In this paper, we validate topic recommendation strategies of system-initiative QA agents that suggest multiple topics in different ways to influence users’ information exploration, and to help users proceed to deeper levels in topics on the same subject, to offer them topics on various subjects, or to provide them with selections at random. To examine how different recommendations influence users’ experience, we have conducted a user study with 50 participants which has shown that providing recommendations on various subjects expands their interest on subjects, supports longer conversations, and increases willingness to use QA agents in the future.
Automated recognition of facial expressions and gender in humans implemented on mobile devices

This paper presents the implementation on mobile devices of an automated system which can recognize 7 basic facial expressions each of them associated to an emotion: happy, sad, angry, disgust, surprise, fear, and neutrality alongside a person’s gender from a facial image using convolutional neural networks and transfer learning. The human faces are extracted from images generated by the camera of the device which runs the application in real time. The server side of the system was built in two versions: desktop which has good performances regarding recognition and processing speed and web which can run on any device which provides a browser and a camera.
Component selection for exponential power mixture models

Exponential Power (EP) family is a much flexible distribution family including Gaussian family as a sub-family. In this article, we study component selection and estimation for EP mixture models and regressions. The assumption on zero component mean in [X. Cao, Q. Zhao, D. Meng, Y. Chen, and Z. Xu, Robust low-rank matrix factorization under general mixture noise distributions, IEEE. Trans. Image. Process. 25 (2016), pp. 4677-4690.] is relaxed. To select components and estimate parameters simultaneously, we propose a penalized likelihood method, which can shrink mixing proportions to zero to achieve components selection. Modified EM algorithms are proposed, and the consistency of estimated component number is obtained. Simulation studies show the advantages of the proposed methods on accuracies of component number selection, parameter estimation, and density estimation. Analysis of value at risk of SHIBOR and a climate change data are given as illustration.
A Study on Handwritten Character Recognition Mechanisms using Neural Networks

Handwritten character recognition grabs the attention of several researchers in recent times. Character Recognition is one of the recent tasks that relates to different forms of handwritten text based image and digits data analysis. The analysis of recognizing the similar patterns for the application requirements encouraged by the feature analytic schemes and classification models.  Neural networks algorithms are widely adopted by the researchers for its attentive and recurrent training network modules. Despite application requirements, the extraction levels of features, so as to train the neural network classifiers depends on several constraints. The usage of high cardinal features will increase the computational complexity. Therefore, it is suggested that an optimized cardinal of features will not affect the boundaries of the recognition process. This paper is an extensive review of effective neural network approaches for handwritten character recognition models. The proposed classifier methods in the literature were explored from handwritten text recognition. The challenges prevail in the feature extraction and word combination slows down the scope of classification tasks. From the conducted review, it can be stated that this research domain has to be explored in a fine -grained way of constructing the classifiers.
Cricket Highlights Generation Using Video Information Retrieval and Feature Selection

In multimedia domain, video processing consumes more time. In this paper, we dealt with improving video feature extraction with optimal resources for cricket highlight generation. These days, both domestic and worldwide broadcasting sports channels are day in and out streaming sports events. To watch full game alongside hectic work hours is tedious but to follow-up the game using highlights daily is something everyone is aligning towards. So, our project focuses on simulating latest technologies to generate cricket highlights. Our model considers both excitement and incident driven features to acknowledge and clip essential events in an exceedingly consistent fashion. Replays, scoreboard, bowler runner up sequence and audio intensity are samples of suggestions accustomed in seizing such events. To test our model, we administered a survey from user reviews to compare highlights of our clips with broadcasted clips. Projecting user approval rate and overlap between both highlight videos adds value of our framework in real time automated highlights generation domain.
Analysis of User Satisfaction on Website Quality of the Ministry of Agriculture, Directorate General of Food Crops

A good website quality is needed to meet user satisfaction. The value of the benefits of the web will be felt by many users if the web has very good quality. The ease of accessing the website is a reflection of the good quality of the website. The positive image of the web owner can be seen from the quality of the website. When doing research on the website of the Ministry of Agriculture, Directorate General of Food Crops, the researcher found several pages that did not meet the website category which were said to be of good quality. Based on these findings, the authors are interested in analyzing user satisfaction with the website to measure the quality of the website of the Ministry of Agriculture, Directorate General of Food Crops using the PIECES method (Performance, Information, Economy, Control/Security, Efficiency, Service). The results of the study indicate that the level of user satisfaction with the website has been indicated as SATISFIED on each indicator, however, in measuring the quality of the website using YSlow (the GTMetrix tools, Pingdom Website Speed Tools), and (Web of Trust) WOT found many deficiencies such as loading the website takes a long time, there are some pages that cannot be found (page not found) and so on. Therefore, the authors provide several recommendations for better website development.
Bengali Functional Sentence Classification through Machine Learning Approach

In the early time, very few studies were accomplished in Bengali functional sentences. However, the study on Bengali has incredibly increased for its structural diversity. Inspired by those studies, Functional sentence classification in Bengali language was completed including machine learning approaches to classify the sentences. Three types of Bengali functional sentences such as Assertive, Interrogative and Exclamatory have been considered for the research. So the leading purpose of the study is to classify the sentence and find out the best algorithm with comparing accuracy rate. Data have been collected, categorized and processed the dataset properly to avoid the conflict. Some popular machine learning algorithms such as Naive Bayes (NB), Decision Tree Classifier (DT), SVM, KNN, Random Forest (RF), and XGB Classifier have been implemented to compare accuracy rates. Parameters such as Precision, Recall, F1-Score, Support and Confusion matrix have been calculated for the comparison. The comparison demonstrated that performance of the Random Forest, SVC, and XGB Classifier is better than Naive Bayes and Decision Tree Classifier. Remarkable issue is that the Random Forest algorithm provided the highest performance value with an accuracy of 75.38% which is average performance for such a dataset.
Investigation of methods for embedding of digital tags in a video stream

When writing this work, an analysis of various types of synchronization algorithms for embedding and extracting of a digital tag was carried out: in the time, spatial and frequency domains. It was found that synchronization algorithms in the frequency domain have the best noise immunity and are preserved during compression, which in its turn makes them the most reliable. The result of the work is an algorithm that allows you to increase the speed of embedding of digital tags into a video stream, and thereby makes it possible to use the technology of embedding of digital tags into video in real time.
An Overdispersed Black-Box Variational Bayesian–Kalman Filter with Inaccurate Noise Second-Order Statistics

Aimed at the problems in which the performance of filters derived from a hypothetical model will decline or the cost of time of the filters derived from a posterior model will increase when prior knowledge and second-order statistics of noise are uncertain, a new filter is proposed. In this paper, a Bayesian robust Kalman filter based on posterior noise statistics (KFPNS) is derived, and the recursive equations of this filter are very similar to that of the classical algorithm. Note that the posterior noise distributions are approximated by overdispersed black-box variational inference (O-BBVI). More precisely, we introduce an overdispersed distribution to push more probability density to the tails of variational distribution and incorporated the idea of importance sampling into two strategies of control variates and Rao-Blackwellization in order to reduce the variance of estimators. As a result, the convergence process will speed up. From the simulations, we can observe that the proposed filter has good performance for the model with uncertain noise. Moreover, we verify the proposed algorithm by using a practical multiple-input multiple-output (MIMO) radar system.
Signed Network Representation by Preserving Multi-order Signed Proximity

Signed network representation is a key problem for signed network data. Previous studies have shown that by preserving multi-order signed proximity (SP), expressive node representations can be learned. However, multi-order SP cannot be perfectly encoded using limited samples extracted from random walks, which reduces effectiveness. To perfectly encode multi-order SP, we have innovatively integrated the informativeness of infinite samples to construct high-level summaries of multi-order SP without explicit sampling. Based on these summaries, we propose a method called SPMF, in which node representations are obtained using low-rank matrix approximation. Furthermore, we theoretically investigate the rationality of SPMF by examining its relationship with a powerful representation learning architecture. In sign inference and link prediction tasks with several real-world datasets, SPMF is empirically competitive compared with state-of-the-art methods. Additionally, two tricks are designed for improving the scalability of SPMF. One trick aims to filter out less informative summaries, and another one is inspired by kernel techniques. Both tricks empirically improve scalability while preserving effective performance. The code for our methods is publicly available.
An Approach for Detecting Frauds in E-Commerce Transactions using Machine Learning Techniques

This paper is primarily focused on E-commerce fraud detection using machine learning techniques. There are many different ways to detect E-commerce fraud using machine learning approach. In this work, comparison study is conducted between various available machine learning algorithms to detect the online frauds. During the comparative study, focus is underlined on comparison of all the algorithms to identify the fraud transactions. When compared to other algorithms, such as support vector machine, Decision Tree, K-nearest neighbour and Random Forest, it has been observed that Logistic regression gives better result among all machine learning algorithms.
Three dimensional path planning of cross-domain robot based on improved A algorithm

When the robot moves autonomously in the environment, it needs to plan a collision free three-dimensional path from the starting point to the target point. The node search strategy of traditional A* algorithm has the problems of many feasible path turning points, large turning angle, redundant path points and so on, and it is mainly used in two-dimensional path planning. The improved A* algorithm integrates the environment information into the environment modelling by constructing a 2.5-dimensional environment grid map. It regards obstacles and water grids as passable grids and gives different costs. It designs a multi-objective heuristic function based on motion energy consumption and time. In order to avoid the redundant planning path points and the situation that is not conducive to the robot following, it adds part of the space in the backtracking process. At the same time, the redundant path points are reduced. The simulation results show that the improved A* algorithm can realize the three-dimensional path planning of the cross-domain robot. The length of the planned path is shorter and smoother, and the total number of path points is less.
Author Reputation Measurement on Question and Answer Sites by the Classification of Author-Generated Content

In the field of software engineering, practitioners’ share in the constructed knowledge cannot be underestimated and is mostly in the form of grey literature (GL). GL is a valuable resource though it is subjective and lacks an objective quality assurance methodology. In this paper, a quality assessment scheme is proposed for question and answer (Q&amp;A) sites. In particular, we target stack overflow (SO) and stack exchange (SE) sites. We model the problem of author reputation measurement as a classification task on the author-provided answers. The authors’ mean, median, and total answer scores are used as inputs for class labeling. State-of-the-art language models (BERT and DistilBERT) with a softmax layer on top are utilized as classifiers and compared to SVM and random baselines. Our best model achieves [Formula: see text] accuracy in binary classification in SO design patterns tag and [Formula: see text] accuracy in SE software engineering category. Superior performance in SE software engineering can be explained by its larger dataset size. In addition to quantitative evaluation, we provide qualitative evidence, which supports that the system’s predicted reputation labels match the quality of provided answers.
Unidentified Users of Design Documentation

When working with system development and design, one is urged to write design documentation. The design document should be constructed in a specific way and should include all necessary information. However, the users of the design document are not stated and seem to be secondary. This study's aim is therefore to investigate who the users are. A literature review was conducted to answer the question. The literature showed that design documentation is a tool to communicate design and it should include relatable information. The essential role of design documents within software projects were also discussed and hence, they are included in this study. The literature review revealed that the system developer and the individuals which are involved in the production of the software is the users. To investigate this further, a hypothesis was formed and thereafter an observation was done, where videos of system developers were observed. The observation showed that system developers do not verbalize the importance of design documentation. Secondly, the study showed how unidentified the users are and that the discussion of who they are is limited. Furthermore, the design documentation is a valuable tool for communicating amongst other designers but if the documentation is not read, it is argued to be a waste of valuable time. The results presented within this study, therefore indicates that a definition of who the user is of design documentation is required.
3-adic complexity of ternary generalized cyclotomic sequences with period

Abstract In this paper, we study the ternary generalized cyclotomic sequences with a period equal to a power of an odd prime. Ding-Helleseth’s generalized cyclotomic classes of order three are used for the definition of these sequences. We derive the symmetric 3-adic complexity of above mention sequences and obtain the estimate of symmetric 3-adic complexity of sequences. It is shown that 3-adic complexity of these sequences is large enough to resist the attack of the rational approximation algorithm for feedback with carry shift registers.
Multi-UAVs Collaboration Routing Plan Based on Artificial Bee Colony Algorithm with Elite Strategy

In this paper, the multi-UAVs collaboration routing plan problems are defined by route penalty, which is used to perform the multi-UAVs routes. An Artificial Bee Colony with Elite Strategy (ES-ABC), which includes niche technology and elite strategy, is proposed and used to solve the multi-UAVs collaboration routing plan problems. The experimental results show ES-ABC has great real-time and stability in solving multi-UAVs collaboration route planning problem, which can search collaboration routes of satisfying requirements for multi-UAVs quickly.
Study on Droplet Composition Recognition Based on LSTM Network Combined with Convolutional Neural Networks

Abstract The collision and spreading processes of droplets are not the same due to the physical and chemical characteristics of droplets of different composition. A droplet collision experiment system was set up to capture videos of the droplet collision and spreading processes which changed as the droplet composition changed. A cascaded network was applied to extract spatial-temporal features in the videos and realize droplet composition recognition. The proposed method achieved 80% classification accuracy, which was relatively better performance compared to single back propagation network.