Reviewerly/exHarmony huggingface dataset:
Oversampling Method To Handling Imbalanced Datasets Problem In Binary Logistic Regression Algorithm

The class imbalance is a condition when one class has a higher percentage than the other then it can affect the accuracy. One method in data mining that can be used to classification is logistic regression method. The method used in this research is RWO-sampling method using random replicate approach for synthetic data generation on descrete attribute. The result of the research can handle the problem of class imbalance, RWO-sampling method with random replicate approach shows better accuracy than RWO-sampling method with roulette and ROS approach. The accuracy value for RWO-Sampling method with roulette and RWO-Sampling approach with random replicate approach has increased to an average of 15.55% of each dataset. As for comparithem with the ROS method has increased an average of 3.7% of each dataset. Furthermore, for testing the underfitting problem in logistic regression, the oversampling method is better than non-oversampling with an increase in accuracy value reaching an average of 2.3% of each dataset.
Pemasangan Lampu Jalan Berbasis Solar Cell untuk Penerangan Jalan di Desa Cilatak Ciomas

The negative impact of fossil-fueled plants and the lack of public understanding of renewable energy lies behind the holding of these PKM activities with the aim of educating and at the same time transferring technology that is directly beneficial to the community in the form of solar powered street lighting. From the surveys that have been conducted where there are no available street lighting facilities so that there are some community activities that are difficult to do at night. From this work, there were two important things, among others, that the community gained an understanding of renewable energy and knowledge to efficiently use energy to reduce the environmental impact of the dominant fossil power plant in Indonesia. The second is that with the help of solar-powered street lights, the community is very helpful in carrying out activities at night and creates more secure conditions in the community.
The Role of Familiarity, Trust And Awareness Towards Online Fraud

The financial loss due to e-commerce fraud in Malaysia has been increasing from the year 2007 to 2012. Online business and consumers are vulnerable towards online fraud due to the increased amount of usage on online transactions. The study aims to investigate the factors that contribute towards online purchases from the experience of online fraud victims. Factors examined include trust, familiarity and awareness towards online products of which a semi structured interview was designed based on these factors. The qualitative study analyses experience of online fraud victims in order to gain insight to individual behaviour and determine the reason why they are induced in making the online purchase. The samples used in this study are the online fraud victims of E-commerce. Through seven phone interviews of online fraud victims, the study conclude that people with higher levels of familiarity, confidence and trust seems to have higher levels of trustworthiness to e-commerce. The awareness of the surroundings on the other hand does not affect people’s inclination to purchase. The main contribution of this study is from an observation on cybercrime when globalisation of the Internet is rapidly moving forward.
Embedding Digital Capability into the Higher Education Curriculum: The Case of Ghana

In this paper, we report on the digital experiences of students at a dual-mode, higher education institution in Ghana.Recognizing the role of digital technologies in an increasingly globalized and competitive economy, Ghanaian authorities have embarked on several initiatives to support the development of the digital skills and capabilities of its students to enhance learning and to improve their employability in the job market.However, evidence exists that despite these initiatives and the massive investment into digital skills development, students in Ghana are still grappling with the acquisition of the skills needed for the workplace of the 21 st century.The question thus arises whether Ghanaian higher education institutions (HEIs) are sufficiently developing the digital capabilities of their students.This study used the 'Digital at course level' dimension of the Joint Information Systems Committee (JISC) -UK Digital Experience Insight Survey to determine the digital experiences of students in their chosen academic discipline to prepare them for their careers.Census sampling targeted all final-year students at a selected higher education institution.The online survey reached 14,286 students and 800 students (5.6%) responded.The results show that students use personal devices to support learning outside the classroom, despite limited attempts to embed the development of digital skills into academic disciplines.Therefore, digital technologies are rarely used to communicate and collaborate, to solve problems or to create digital artefacts in the classroom.The findings also revealed that gender and mode of study did not influence the students' digital experiences.The limitation of the study is that the results for only one institution are reported.
Tightly Secure Two-Pass Authenticated Key Exchange Protocol in the CK Model

Tightly secure authenticated key exchange (AKE), whose security is independent from the number of users and sessions (tight security), has been studied by Bader et al. [TCC 2015] and Gjøsteen-Jager [CRYPTO 2018] in the Bellare-Rogaway (BR) model. However, how to achieve tight security in stronger models (e.g., the Canetti-Krawczyk (CK) model and the extended Canetti-Krawczyk (eCK) model) were still left as an open problem by now. In this paper, we investigate this problem in the CK model. We start from a generic construction [ACISP 2008] based on key encapsulated mechanisms (KEMs). We analyze the reason why it cannot achieve tight reduction, by merely assuming the underlying KEMs are secure in the multi-user and multi-challenge setting with corruption as Bader et al. [TCC 2015] and Gjøsteen-Jager [CRYPTO 2018] did. Then we put forward a new generic construction to overcome the potential obstacles. In addition, we introduce a strong type of chosen ciphertext attack in the multi-user and multi-challenge setting with corruption for tag-based key encapsulated mechanism (TB-KEM), where adversaries are not only allowed to adaptively corrupt secret keys of users, generate multi-challenges with different coins, and open some challenges as well. We further prove that the Naor-Yung transform also works in this model, hence our generic construction can be instantiated.
Automated Text Extraction from Images using OCR System

Digital images are getting popular rapidly. Every day, many images have been generated by many groups like students, engineer, doctors, according to their varying needs. They can access images based on its primitive features or associated text. Text present in such images can provide meaningful information. We aim to retrieve the content and summarize the visual information automatically from images. Optical character recognition system that involves several algorithms are required for this purpose. Tesseract is currently the most accurate optical character recognition engine which was developed by HP Labs and is currently owned by Google. In this paper, we extract text from images using text localization, segmentation and binarization techniques. Text extraction can be achieved by applying text detection that identifies image parts containing text, text localization finds the exact position of the text, text segmentation separates the text from its background and binarization process converts the coloured images into binary. On this binary image, character recognition is applied to convert it into ASCII text. Text extraction is used in creating e-books from scanned books, image searching from a collection of visual data etc.
Mapping Land Cover Based on Time Series Synthetic Aperture Radar (SAR) Data in Klaten, Indonesia

&lt;p&gt;&lt;em&gt;Information &lt;/em&gt;&lt;em&gt;on &lt;/em&gt;&lt;em&gt;the existing land cover is important for land management and planning because it can represent the intensity, location, and pattern of human activities. However, mapping land cover in tropical regions is not easy when using optical remote sensing due to the scarcity of cloud-free images. Therefore, the objective of this study is to map the land cover in Klaten Regency using a time-series Sentinel-1 data. Sentinel-1 data is one of remote sensing image&lt;/em&gt;&lt;em&gt;s&lt;/em&gt;&lt;em&gt; with Synthetic Aperture Radar (SAR) system which is well known by its capabilit&lt;/em&gt;&lt;em&gt;y&lt;/em&gt;&lt;em&gt; of cloud penetration and all-weather observation. A time-series Sentinel-1 data of both polarisations, VV and VH were automatically classified using an unsupervised classification technique, ISODATA. The results show that the land cover classifications obtained overall accuracies of 79&lt;/em&gt;&lt;em&gt;.&lt;/em&gt;&lt;em&gt;26% and 73&lt;/em&gt;&lt;em&gt;.&lt;/em&gt;&lt;em&gt;79&lt;/em&gt;&lt;em&gt;% &lt;/em&gt;&lt;em&gt;for VV and VH respectively. It is also found that Klaten is still dominated by the vegetated land (agriculture and non-agricultural land).&lt;/em&gt;&lt;em&gt; T&lt;/em&gt;&lt;em&gt;hese results suggest the opportunity of mapping land cover using SAR multi temporal data. &lt;/em&gt;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;&lt;em&gt; &lt;/em&gt;&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;&lt;em&gt; Keywords&lt;/em&gt;&lt;/strong&gt;&lt;em&gt;: &lt;/em&gt;&lt;em&gt;Land cover; Synthetic Aperture Radar; Time series; Sentinel-1; Klaten&lt;/em&gt;&lt;em&gt;&lt;/em&gt;&lt;/p&gt;
Factors Affecting Techniques of Teaching Theorem Proof

Students' mastering the art of proof is one of the most important educational results of teaching Mathematics in the secondary school.The study shows that there are significant challenges, gaps, and weaknesses in the practice of teaching reasoning and proving.The goal of the research is to determine the way of using traditional teaching practices to present reasoning and proving, the way of organizing the review and mastery of the theorem proofs.In addition, the obvious and hidden factors affecting the students' productivity in these activities were identified.To study the problem, a survey that involved 136 Mathematics teachers across Ukraine was carried out.A standard close-ended questionnaire was developed.Factor analysis was used to analyze the results of the survey.The important factors determining the time efficiency when a teacher organizes teaching and learning of a theorem proof, were discovered.The analysis of the factors and numerical loadings of variables forming them, result in the conclusions presented in this paper.
Leukocytes Classification and Segmentation in Microscopic Blood Smear Image

Blood tests play an important role in the detection of various types of diseases. The problem arises when large quantities of blood samples need to be processed. As the number of samples increases, so does the risk of error. It limits the speed and precision of blood sampling. Blood cells can be divided into three types: Red blood cells (RBC), White blood cells (leukocytes) and platelets. Of these globules, white blood cells play an important role in disease detection because their conventional method of detecting and classifying white blood cells under the microscope is inaccurate and leads to the development of new methods. This paper presents an effective computer method for WBC classification. This method consists of three steps. They are preprocessing, segmentation and classification using an ANN classifier. The experiments were performed on 65 digital blood smear images of 5 different types of WBC cells.
A Multi-robot Control System based on ROS for Exploring Disaster Environment

This control system is implemented to coordinate multiple robots in cooperative exploration tasks, in which a task assignment algorithm is used for calculating the expectation cost to achieve the required tasks and then allocate necessary tasks to specific robots. Robot control systems are used to gather crucial information about a disaster environment, such as a victim's position or changes in an indoor environment before a human is able to enter that environment. There are practical problems in using previous systems because these systems need professionals to operate these multi-robots. The proposed system automatically distributes tasks to each robot based on target positions that are selected by the person who does not possess any prior knowledge about robot operation. Therefore, the operator needs only to determine the target position of interest and not be concerned about controlling the robot. Moreover, the system is implemented using ROS in order to add functions and apply this system to various types of robots. This system has been verified and evaluated by Turtlebot 3 in the Gazebo simulator, as well as in a real setting by conducting the system usability scale (SUS) with 23 firefighters.
About Graph Mappings

Summary In this articles adjacency-preserving mappings from a graph to another are formalized in the Mizar system [7], [2]. The generality of the approach seems to be largely unpreceeded in the literature to the best of the author’s knowledge. However, the most important property defined in the article is that of two graphs being isomorphic, which has been extensively studied. Another graph decorator is introduced as well.
Self-adaptive Decomposition and Incremental Hyperparameter Tuning Across Multiple Problems

The Capacitated Arc Routing Problem (CARP) is a NP-hard combinatorial optimisation problem with numerous real-world applications. Several divide-and-conquer approaches, controlled by one or more hyperparameters, have been proposed to tackle large-scale CARPs. The tuning of hyperparameters can be computationally expensive due to the lack of priori knowledge, the size of the configuration space, and the time required for solving a CARP instance. Motivated by this time consuming task, we propose a scalable approach based on self-adaptive hierarchical decomposition (SASAHiD) to scale up existing methods. We take a state-of-the-art decomposition method for large-scale CARPs called SAHiD as an example to carry out experiments on two sets of real-world CARP instances with hundreds to thousands of tasks. The results demonstrate that SASAHiD outperforms SAHiD significantly with fewer hyperparameters, thus the dimension of associated configuration space is reduced. Moreover, we propose an incremental hyperparameter tuning approach across multiple problem instances to learn the hyperparameters of SASAHiD on a set of instances with different sizes. SASAHiD with optimised hyperparameters achieves better or competitive results with the SASAHiD using default hyperparameters when solving problem instances that it has never seen in the training set.
General framework for binary classification on top samples

Many binary classification problems minimize misclassification above (or below) a threshold. We show that instances of ranking problems, accuracy at the top, or hypothesis testing may be written in this form. We propose a general framework to handle these classes of problems and show which formulations (both known and newly proposed) fall into this framework. We provide a theoretical analysis of this framework and mention selected possible pitfalls the formulations may encounter. We show the convergence of the stochastic gradient descent for selected formulations even though the gradient estimate is inherently biased. We suggest several numerical improvements, including the implicit derivative and stochastic gradient descent. We provide an extensive numerical study.
The Relationship between Cooperative Learning and Skilled Learning with the Satisfaction of Students Studying Foreign Languages (Russian, English, and Arabic)

The Relationship between Cooperative Learning and Skilled Learning with the Satisfaction of Students Studying Foreign Languages (Russian, English, and Arabic)
VARTTA: A Visual Analytics System for Making Sense of Real-Time Twitter Data

Through social media platforms, massive amounts of data are being produced. As a microblogging social media platform, Twitter enables its users to post short updates as “tweets” on an unprecedented scale. Once analyzed using machine learning (ML) techniques and in aggregate, Twitter data can be an invaluable resource for gaining insight into different domains of discussion and public opinion. However, when applied to real-time data streams, due to covariate shifts in the data (i.e., changes in the distributions of the inputs of ML algorithms), existing ML approaches result in different types of biases and provide uncertain outputs. In this paper, we describe VARTTA (Visual Analytics for Real-Time Twitter datA), a visual analytics system that combines data visualizations, human-data interaction, and ML algorithms to help users monitor, analyze, and make sense of the streams of tweets in a real-time manner. As a case study, we demonstrate the use of VARTTA in political discussions. VARTTA not only provides users with powerful analytical tools, but also enables them to diagnose and to heuristically suggest fixes for the errors in the outcome, resulting in a more detailed understanding of the tweets. Finally, we outline several issues to be considered while designing other similar visual analytics systems.
Adaptive Tolerance Dehazing Algorithm Based on Dark Channel Prior

The tolerance mechanism based on dark channel prior (DCP) of a single image dehazing algorithm is less effective when there are large areas of the bright region in the hazy image because it cannot obtain the tolerance adaptively according to the characteristics of the image. It will lead to insufficient improvement of the transmission of image, so it is difficult to eliminate the color distortion and block effects in the restored image completely. Moreover, when a dense haze area or a third-party direct light source (sunlight, headlights and reflected glare) is misjudged as sky area, the use of tolerance will cause an inferior dehazing effect such as details lost. Regarding the issue above, this paper proposes an adaptive tolerance estimation algorithm. The tolerance is obtained according to the statistic characteristics of each image to make the estimation of transmission more accurately. The experimental results show that the proposed algorithm not only maintains high operational efficiency but also effectively compensates for the defects of the dark channel prior to some scenes. The proposed algorithm can effectively solve the problem of color distortion recovered by the DCP method in the bright regions of the image.
The activation of contextually predictable words in syntactically illegal positions

We present an eye-tracking study testing a hypothesis emerging from several theories of prediction during language processing, whereby predictable words should be skipped more than unpredictable words even in syntactically illegal positions. Participants read sentences in which a target word became predictable by a certain point (e.g., “bone” is 92% predictable given, “The dog buried his. . .”), with the next word actually being an intensifier (e.g., “really”), which a noun cannot follow. The target noun remained predictable to appear later in the sentence. We used the boundary paradigm to present the predictable noun or an alternative unpredictable noun (e.g., “food”) directly after the intensifier, until participants moved beyond the intensifier, at which point the noun changed to a syntactically legal word. Participants also read sentences in which predictable or unpredictable nouns appeared in syntactically legal positions. A Bayesian linear-mixed model suggested a 5.7% predictability effect on skipping of nouns in syntactically legal positions, and a 3.1% predictability effect on skipping of nouns in illegal positions. We discuss our findings in relation to theories of lexical prediction during reading.
Models to build capacity for African science centres and science communication: needs and assets

Science communication is proliferating in the developing world, however, with respect to science centres, as a whole Africa is being left behind. Here 15 participants in a capacity building program are investigated using traditional needs-based and contemporary asset-based development conceptualisations. These development theories parallel deficit and participatory approaches, respectively, within science communication and demonstrate synergies between the fields. Data showed staffing, funding, governments, host institutions, and audiences are prominent needs and assets, networks are a major asset, and identified other influential factors. Analysis suggests a coordinated model involving individuals, host institutions and governments to facilitate growth of African science centres.
Investigating the Effects of Web-Based Science Material for Guided Inquiry Approach on Information and Communication Skills of Students

In parallel with the development of technology and increase in knowledge, learning-teaching environments have changed, and an understanding has taken into basis that students are active and responsible for their own learning. In accordance with this situation, inquiry-based learning approach and technology integration have come to an important point in science curricula in our country. In the study, it was aimed to investigate the effectiveness of web-based science material developed on the guided inquiry approach on students' information and communication skills. In the current study, embedded design was selected amongst mixed methods. First, web-based material was developed compatible with the guided inquiry approach for the systems in our body unit at sixth grade. Then, the developed material was applied during eight weeks by three teachers in three different classes and its effectiveness was examined. A total of 155 sixth grade students, 71 of whom were in the experimental group and 84 in the control group, participated in the study. In the study; “Information and Communication Skills Perception Scale” was used as a quantitative data collection tool and an observation form was used as the qualitative data collection tool. As a result of the study; although there was an increase in the information and communication skills perception scores of the students in the experimental group in which the web-based science material was applied according to the guided inquiry approach, it was understood that this difference was not significant [t(70)=-3.76, p&amp;gt;.05]. Even though there is an increase in information and communication skills, it is thought that this increase is not significant in terms of the students' perceptions of information and communication skills as these students were born into the technology age, developed high perception towards information and communication skills through their own tablets, computers, phones and other similar medium.