Reviewerly/exHarmony huggingface dataset:
Automated Algorithm for Determining the Interplanar Distances of the Crystal Structure of a Substance from Transmission Electron Microscopy Images

A problem of automated image processing of transmission electron microscopy and its application value is considered in this paper. An automated algorithm for estimating the interplanar distances of the crystal structure of a substance from transmission electron microscopy images is proposed. The software implementation of the algorithm was developed and tested on several raster images, and the evaluation results were compared with the results obtained using the specialized software named Gatan Microscopy Suite v.1.8. The high degree of coincidence of the results showed the viability of the proposed approach and the prospects of its further development in the area of transmission electron microscopy images processing.
Empower Fuzzy Cognitive Maps Decision Making abilities with Swarm Intelligence Algorithms

The proposed hybrid algorithm aims at defining an FCM by the information carried by a large dataset about a specific problem, taking into consideration the BCO and FPO principles. The link between them is represented by the application of the DB-Scan clustering technique allowing to identify the right number of cluster without knowing it a priopri. The hybridisation highlights the efficacy of the algorithm in estimating the correlations among the factors involved for a specific problem, with low RMSE and computational time, demonstrated by the case study example.
Context-sensitive app prediction on the suggestion bar of a mobile keyboard

This work augments context-sensitive app prediction feature to the suggestion bar of a mobile virtual keyboard to accommodate fast and easy information acquisition and sharing in textual conversations. The purpose is to eliminate the need for switching between apps while typing. A user study revealed that the proposed method improves performance both in terms of speed and effort for common tasks, such as playing a song or finding and sharing the address of a restaurant. Post-study questionnaire revealed that all participants found the method fast, easy, and likely to facilitate more engaging and meaningful textual conversations. All wanted to keep using it on their mobile devices.
Transactional database design information system web-based tracer study integrated telegram bot

Abstract A database is a software that is used to store data. Data stored is generally in the form of text. Database functions are not only for storing data but also used to speed up access to information systems. Optimal database management can increase access to information systems. One management that can improve performance is the appropriate relational data design. Relational is a relationship between tables. The design of the relational database must pay attention to the selection of data types, data type values, and constraints that will be selected. This study will examine the optimization of the MariaDB database on information systems in tracer studies. Database design will accommodate data from two application sources, namely web-based applications and telegram bots. Using two paths to the database to make it easier for users to register via a telegram bot, users can then access the tracer study questionnaire on web-based applications. The MariaDB database performance test shows that the highest performance average query is 6501 microsecond.
ETAR: An English Teaching Assistant Robot and Its Effects on College Freshmen’s In-Class Learning Motivation

Educational robots have become popular and mature in lots of Robot-Assisted Language Learning (RALL) applications in recent years. This paper reports the development of an English Teaching Assistant Robot (ETAR) and its effects on in-class English learning motivation for the first-year university students in Taiwan. The study uses the social robot NAO, with voice and interactive functions, as a classroom assistant to help teachers to train students’ spoken English in the classroom, including single-word and text reading aloud practice, sing-a-song as well as rollcall in class. After a six-week experiment, the results show that the introduction of the ETAR to the freshman English class improved students’ English learning motivation. Moreover, the teachers’ burden of some fixed mechanical work such as rollcall, word pronunciation, and text reading aloud practice can be reduced, and then teachers can have more energy to take care of more low learning-achievement students.
Cognitive and Physiological Response for Health Monitoring in an Ageing Population: A Multi-modal System

In the last years, a rapid increase of life expectancy has been observed and thus ageing is becoming a hot topic in different research fields. Ensuring the wellbeing of elderly people has a positive impact on healthcare, economy and social equality and thus developing a system that takes into account and integrates physiological parameters, cognitive and physical training, elderly-user experience and perception of the system itself, may represent an innovative multi-modal mean through which ameliorate older people wellbeing.
$DBO$ Trajectory Planning and $HAHP$ Decision-Making for Autonomous Vehicle Driving on Urban Environment

A novel driving behaviour oriented (DBO) trajectory planner and hierarchical analytic hierarchy process (HAHP) decision maker are presented for intelligent vehicle. Since driving on structural road should satisfy actuator constraints and improve comfortableness as soon as possible, which strictly obeys traffic rules other than making traffic mess, it is rather than purely pursuing the shortest route/time. By analysis traffic rules, the DBO framework is employed to produce trajectories. To make trajectory drivable, cubic B-spline and clothoid curve are modeled to keep continuous curvature, and cubic polynomial curve is to schedule velocity profile satisfying stability and comfort. To pick out the best trajectory, HAHP decision maker is developed to evaluate the candidates. The first layer selects optimal paths considering smoothness and economy, and the second layer selects best trajectory taking smoothness, comfortableness and economy in account. Moreover, DBO rapidly exploring random tree (RRT) replanner is embedded to ensure algorithm completeness. Finally, several typical scenarios are designed to verify the real-time and reliability of the algorithm. The results illustrate that the algorithm has highly real-time and stability evaluated by Statistical Process Control method as the probability for the peak time less than 0.1s is 100% except three obstacles avoidance scenario is 59.31% in 1000 cycles. Since the planned trajectory is smooth enough and satisfy the constraints of the actuator, the mean lateral tracking error is less than 0.2m with 0.5m peak error, and the mean speed error less than 0.5km/h with 1.5km/h peak error for all scenarios.
Virtual eye region: development of a realistic model to convey emotion

The human eyes and their surrounding features are capable of conveying an array of emotional and social information through expressions. Producing virtual human eyes which are able to communicate these complex mental states continues to be a challenging research topic in computer graphics (CG) as subtle inaccuracies can be the difference between realistic and uncanny. With the recent emergence of virtual customer service agents, the demand for expressive virtual eyes is increasing. One essential question that remains to be answered is: Can virtual human eyes effectively transmit emotion? Through a combination of 3D scanning and manual hand modelling techniques, we developed an efficient pipeline to realise a virtual model of the human eye area that displays real-world characteristics. From this model eye expression renders of the six basic emotions, anger, disgust, fear, happiness, sadness and surprise were generated (Ekman et al., 1969). The perceptual quality of the model was evaluated by showing respondents from two age groups the six eye expressions renders and corresponding real-world photos. Respondents then judged which of the six emotions best described each image. Our findings indicate a clear relationship between the recognition levels for both photographic and virtual stimuli plus a significant level of emotional perception was found for the virtual eye expressions of sadness and anger. This research of human cognition and CG is a starting point for investigating the use of artificial human eye expressions as an effective research tool in the perceptual community.
An Empirical Study of the Relationship between Continuous Integration and Test Code Evolution

Continuous Integration (CI) is the practice of automating and improving the frequency of code integration. CI has been widely adopted by software development teams and has brought the attention of researchers to study its benefits. Existing research shows that CI can improve software quality by identifying the errors earlier in the software development life-cycle. One question that remains open, however, is whether CI increases the adoption of testing practices in software projects. The goal of our work is to investigate the evolution of software tests and its relationship with the adoption of Continuous Integration. We set out to compare 82 projects that adopted CI (CI projects) and 82 projects that have never adopted CI (NOCI projects). In total, we studied 3,936 versions of our studied projects to investigate trends on the test code ratio and coverage. We observe that 40.2% of the CI projects have a rising test-code ratio trend while only 17% of the NOCI projects have a rising trend. Additionally, we find evidences that CI projects have improved the overall test coverage while NOCI projects do not experience the same growth. Finally, we build a mixed-effects model to study software development factors than can possibly explain the test ratio. Our models reveal that test ratio is largely explained by the project inherent context rather than code or process factors. In overall, our work demonstrates that Continuous Integration can be empirically associated with a healthier test code evolution.
New Literacy Oriented Ict Guidance Module Era Of Industrial Revolution 4.0 In Improving Humanity Literacy Of Students

Changes in ICT subjects to guide ICT at the junior secondary level demand a change in teaching materials that synergize with the needs of 21st century learning and are integrated with the new literacy capabilities of the industrial revolution era 4.0. The development of ICT guidance teaching modules has produced a module that is designed to teach students independently about ICT material but has more value in strengthening the newliteracy capabilities of the industrial revolution era 4.0 namely big data literacy, technology literacy and humanity literacy. Modules are developed using the Four D‘s development model which states that results through the define phase have found the fact that module development is needed becauseproblems are encountered related to the optimization of ICT learning outcomes and the lack of new literacy skills. Furthermore, in the develope phase displayed the results of module development in Microsoft Excel material, the results of the development were tested through experimental True Experimental Design, the purpose of experimental actions to determine differences in humanity literacy skills between the experimental groups. The results of the development have increased the literacy capabilities of the humanity of students with increasing patterns of critical thinking, creativity,communication and cooperation in carrying out the tasks of giving ICT. The purpose of this study was to analyze the increase in humanity literacy abilities of SMP Negeri 23 Padang students by using ICT guidance modules with literacy orientation, new to the era of industrial revolution 4.0.
Digital Volume Correlation: Progress and Challenges

Digital volume correlation consists in registering series of 3D images of experiments to yield 4D displacement fields. These 4D analyses have been conducted for the last two decades. Some achievements and current challenges are reviewed herein.
SGMM-Based Modeling Classifier for Punjabi Automatic Speech Recognition System

A baseline ASR system does not perform better due to improper modeling of training data. Training of system through conventional HMM technique faced the issue of on or near manifold in data space. In this paper, Hybrid SGMM-HMM approach is compared with baseline GMM-HMM technique on Punjabi continuous simple sentences speech corpus. It examined the hybridized HMM technique: SGMM-HMM to overcome the problem of sharing of state parameter information throughout the training and testing of system. The system testing is performed on Kaldi 4.3.11 toolkit using MFCC and GFCC approaches at the front end of the system. The result obtained on SGMM-HMM modeling technique generates an improvement of 3–4% over GMM-HMM approach. The experiments are performed on real environment dataset.
Indian Buffet Neural Networks for Continual Learning

We place an Indian Buffet process (IBP) prior over the structure of a Bayesian Neural Network (BNN), thus allowing the complexity of the BNN to increase and decrease automatically. We further extend this model such that the prior on the structure of each hidden layer is shared globally across all layers, using a Hierarchical-IBP (H-IBP). We apply this model to the problem of resource allocation in Continual Learning (CL) where new tasks occur and the network requires extra resources. Our model uses online variational inference with reparameterisation of the Bernoulli and Beta distributions, which constitute the IBP and H-IBP priors. As we automatically learn the number of weights in each layer of the BNN, overfitting and underfitting problems are largely overcome. We show empirically that our approach offers a competitive edge over existing methods in CL.
The Use of Mathematical Logic in the Automated Decoding of Images

In this paper we consider the current issues of automated interpretation of images of the terrain required in the modeling and visualization of data in geographic information systems. The aim of the work is to develop a method of interpretation of multispectral space images of area objects of the terrain, which takes into account the structural features of the images and with high accuracy makes a decision on the belonging of the object to the class. For this purpose, it is proposed to use the methods of traditional mathematical logic and fuzzy logic. The specific problems, the solution of which is expedient to produce methods of mathematical logic, are considered. However, real phenomena are described by many interrelated attributes. The ways of such interaction are unpredictable, so there is a need to apply the concept of fuzziness to the information obtained in solving the problems of classification of geoinformation objects, in particular, aerospace images.
Identification of Android-based interactive multimedia needs for basic physics content

This study aims to identify the needs of Android-based interactive multimedia in basic physics learning in South Sulawesi. The instrument of this study consisted of test and non-test instruments, the test instruments used were tests of understanding basic physics concepts for prospective physics teacher students, while the non-test instruments took the form of student questionnaires about the multimedia needs of basic physics learning. The number of participants in this study was 39 students who were candidates for physics teachers at one of the private universities in South Sulawesi. The results of the research data analysis showed that the level of understanding of basic physics concepts, namely at a very difficult level of 15.1%, was difficult 46.7%, while at an easy level it was 31.3% and very easy at 6.9%. At the level of the need for the type of learning media, data were obtained that the highest level of needs in a row was interactive multimedia (35.3%), video/simulation (32.5%), mobile learning (21.9%), and broadcast material/power point (10.3%). In conclusion, the need for Android-based interactive multimedia is still very high, this is to balance the technological developments in the field of education, especially basic physics.
A New Heuristic Approach for Treating Missing Value: ABCimp

Missing values in datasets present an important problem for traditional and modern statistical methods. Many statistical methods have been developed to analyze the complete datasets. However, most of the real world datasets contain missing values. Therefore, in recent years, many methods have been developed to overcome the missing value problem. Heuristic methods have become popular in this field due to their superior performance in many other optimization problems. This paper introduces an Artificial Bee Colony algorithm based new approach for missing value imputation in the four real-world discrete datasets. At the proposed Artificial Bee Colony Imputation (ABCimp) method, Bayesian Optimization is integrated into the Artificial Bee Colony algorithm. The performance of the proposed technique is compared with other well-known six methods, which are Mean, Median, k Nearest Neighbor (k-NN), Multivariate Equation by Chained Equation (MICE), Singular Value Decomposition (SVD), and MissForest (MF). The classification error and root mean square error are used as the evaluation criteria of the imputation methods performance and the Naive Bayes algorithm is used as the classifier. The empirical results show that state-of-the-art ABCimp performs better than the other most popular imputation methods at the variable missing rates ranging from 3 % to 15 %.
About Leaks of Confidential Data in the Process of Indexing Sites by Search Crawlers

The large number of sites for very different purposes (online stores, ticketing systems, hotel reservations, etc.) collect and store personal information of their users, as well as other confidential data, such as history and results of user interaction with these sites. Some of such data, not intended for open access, nevertheless falls into the search output and may be available to unauthorized persons when specific requests are made. This article describes the reasons for such incidents occurrence and the basic recommendations for technical specialists (developers and administrators) that will help prevent leaks.
Establishing clients’ satisfaction levels with automated library based services

Purpose Library automation is required in academic libraries to meet the dynamic demands of clients. This study aims to investigate the level of clients’ satisfaction of Sam Jonah library automation. Design/methodology/approach The study utilized the descriptive research design by using an exploratory case study research method. A disproportionate stratified random sampling was used to select 322 respondents (undergraduates, postgraduates and lecturers/researchers) who consented to participate in the study. Questionnaire and informal interview were used for the data collection. Findings The study showed that more than half of the clients of Sam Jonah library were moderately satisfied with the library’s automation. Clients mostly access internet/Wi-Fi and reference service. Patronage of the services including photocopying, borrowing of books (circulation), e-resources/academic databases, institutional repository, library website and the OPAC was not encouraging. Increase in user education was recommended to improve their usage. Research limitations/implications The study was limited to only one academic library, namely, Sam Jonah library of the University of Cape Coast, Ghana. Originality/value The study provides insight into a case study of satisfactory level of clients in accessing automated services of Sam Jonah Library to serve as a basis for further research.
An Improved Face Detection Method Based on Face Recognition Application

Face recognition technology has been widely studied and applied for decades, and deep neural networks have greatly improved the indicators of face recognition systems. However, the face recognition application in reality is still subject to interference caused by direction, occlusion, shading and dynamic background, which makes the face recognition system unstable. This paper proposes an improved diagonal detection method, using a K parallel bottleneck connection structure, spacing parameters in each bottleneck connection structure, and using parameter partition sharing to reduce overfitting. The new loss function refines the difference between the detected corner point and the ground truth under different conditions, which can further improve the detection accuracy. Both the standard data set and the experiments in the real environment show that the proposed method has better detection accuracy and robustness.
An Effective Yawn Behavior Detection Method in Classroom

This paper proposes a novel method to detect students’ yawn behavior in real classroom scenes, which is beneficial to analyze learning states of students. The challenges of yawn behavior detection include occlusion, low resolution faces, varied face orientation and interference terms with similar feature like talking. To solve the above mentioned challenges, we first build up a yawn gesture dataset from hundreds of real class videos among primary and middle schools. Then we propose an improved R-FCN and mouth fitting method to efficiently detect yawn behavior. Specifically, we firstly integrate feature pyramid into R-FCN to address small yawn gesture detection. Secondly, we refine detection results according to level of mouth opening by fitting mouth with ellipse. Moreover, to reduce the computational resources of inference, we utilize channel pruning method to reduce the amount of parameters and accelerate the speed of detection by 2 times without hurting performances. Combining these strategies with basic R-FCN, we achieve impressive results in our classroom dataset. The yawn detection reaches 0.90 mAP, which meets the requirements of the real applications.
Attribute-Based Encryption with Publicly Verifiable Outsourced Decryption

Attribute-based encryption (ABE) is a useful cryptographic primitive for access control and fine-grained sharing on encrypted data. However, the main drawback of ABE is that the computational cost grows linearly with the complexity of the access policy. One of the promising solutions for the problem is to outsource computation securely. For example, the decryptor can outsource most of the decryption cost to others, while the underlying plaintext remains confidential. Nonetheless, the existing ABE with outsourced decryption cannot either outsource the decryption of existing ABE ciphertexts or support public verifiability. The first shortcoming demands that we need to design particular outsourced decryption for each ABE scheme. The second one hinders the use of ABE (with outsourced decryption) in the untrusted environment. To solve the above two problems, we propose a generic method to transform any ABE scheme (based on pairings) into an ABE scheme with publicly verifiable outsourced decryption. With the assumptions that the underlying ABE scheme is secure and that the one executing the outsourced decryption is rational, our proposal is secure.
Adjusting Regression Models for Overfitting in Second Language Research

Regression modeling is an increasingly important quantitative tool for second language (L2) research. While superior in many ways to more traditional methods, such as ANOVA, regression modeling, like all procedures, still has limitations, ranging from small sample sizes to a lack of screening for outliers and influential data points (Plonsky and Ghanbar, 2018). Since these limitations are common features in L2 research, this raises concerns that existing studies using regression may overfit the data, perhaps inflating effect size estimates. These issues can be partially alleviated via robust statistics, such as validation. This paper provides L2 researchers with an overview of these issues and an instructive look at one robust validation method: bootstrapping.