Reviewerly/exHarmony huggingface dataset:
OpenCV Using on a Single Board Computer for Incorrect Facemask-Wearing Detection and Capturing

OpenCV (Open Source Computer Vision Library) is a software library intended for real-time dynamic image processing, created by Intel. In this study, the library will be used to detect the face, nose and mouth. Furthermore, the system is equipped with the knowledge that if the mouth and nose or one of them is detected, then the face has not used the mask correctly and the system records the face. The system is supported by an image capture device in the form of a camcorder, a processing device in the form of a single board computer, such as a Raspberry Pi and a display device in the form of a monitor. And the result is that the system is able to make a decision whether the face is wearing a mask correctly or not. By means of labeling around the face in the form of red angular lines, if not properly use the mask. The success rate is 88.4% using detector parameters, namely: scale factor = 1.1 for all face, nose and mouth object libraries.
Students' Difficulties In Speaking English

The objective of this study was to find out students difficulties in speaking English and the causes of the students difficulties in speaking English. The research design was descriptive qualitative method. In collecting the data , observation and interview were used. The subject of the study was the second grade students of MTsN 1 Bungo. The result study showed that that the students of second grade of MTsN 1 Bungo had difficulties in speaking English. It can be classified into two aspects; students’ linguistic problem and students’ personality problem. In students’ linguistic problem there were three difficulties in speaking English that faced by students. They were lack of pronunciation, lack of vocabulary, and lack of grammar. In students’ personality problem there were two difficulties faced by the students in speaking English. They were lack of confidence, shyness and nervous. The cause of the students’ difficulties in speaking English was afraid of making mistakes.
Augmented reality: Caribbean academic libraries of the future

Purpose The purpose of this paper is to provide substantive information for academic libraries in the Caribbean to consider the benefits of implementing augmented reality (AR) for greater access and usage of their services. Design/methodology/approach A qualitative method with the usage of questionnaires as well as the content analysis was used for data collection. Findings The research discovered that there is an impending need for AR in academic libraries, especially in this 21st century filled with ever-evolving technological advancements. Additionally, the research outlined the importance for academic libraries to tap into this are for greater access to its services by students off-campus as well as the physically challenged individuals. Research limitations/implications This paper will only be focusing on the benefits of AR for academic libraries and their furtherance. It will not attest to the cost of implementation as well as it will not consider the maintenance of it for academic libraries in the Caribbean. Practical implications The research disclosed numerous benefits for academic libraries in the Caribbean in developing AR collections. Additionally, with the increasing use and development of technologies in libraries, it is the ideal medium for distance learning as well as greater access to libraries by its students and other users. Originality/value This paper is the penultimate appetizer for the AR conversation and, by extension, implementation. Additionally, it sets the framework for researchers and practitioners that the application service is lacking despite the rise of technological advancements in the Caribbean.
Learnings for Tamil Nadu From Grid-Connected Agricultural Solar Photovoltaic Schemes in India

Our paper studies the design and performance of three schemes Suryashakti Kisan Yojana (SKY) in Gujarat, Surya Raitha Scheme in Karnataka, and the Solar BLDC Pump Pilot in Andhra Pradesh—that predated the launch of KUSUM’s Component C in Tamil Nadu. The objective of this paper is to highlight the learnings from the schemes in these three states, which could prove useful to stakeholders in Tamil Nadu, especially discoms and the state’s Renewable Energy Development Agency in the lead-up to operationalizing Component C.
Financial Innovation and Demand for Money in Nigeria

This study examined the impact of financial innovation on money demand in Nigeria, using quarterly time series for the period 2009-2019. The dependent variable was money demand, represented by broad money, while the independent variable was financial innovation represented by modern payment channels such as volume of Automated Teller Machines (ATMs) transactions, volume of Point of Sales (POS) transactions, volume of Internet banking transactions, and volume of Mobile banking transactions. The study employed the ordinary least squares (OLS) regression technique as the estimation method within the cointegration, granger causality, and error correction modelling. The result obtained showed that financial innovation has mixed impact on money demand in Nigeria during the period of analysis. For instance, financial innovation has positive impact on money demand through volume of ATM transactions in the current period, two periods lagged of volume of mobile banking transactions, current period and one period lagged of volume of internet banking transactions, and current period’s volume of Point of Sales (POS) transactions in Nigeria. On the other hand, financial innovation has negative impact on money demand through one period lagged of volume of point of sales in Nigeria. On the stability of the demand for money function, the result of the stability tests based on the CUSUM test and CUSUM of squares test showed that the demand for money function was stable during the evaluation period. The study recommended that monetary policy strategy of the central bank of Nigeria (CBN) should be fine-tuned to ensure it is well suited to deal with the challenges posed by financial innovation by way of proliferation of sophisticated payment channels.
Testing convexity of functions over finite domains

We establish new upper and lower bounds on the number of queries required to test convexity of functions over various discrete domains.1.We provide a simplified version of the non-adaptive convexity tester on the line. We re-prove the upper bound in the usual uniform model, and prove an upper bound in the distribution-free setting.2.We show a tight lower bound of queries for testing convexity of functions f: [n] → ℝ on the line. This lower bound applies to both adaptive and non-adaptive algorithms, and matches the upper bound from item 1, showing that adaptivity does not help in this setting.3.Moving to higher dimensions, we consider the case of a stripe [3] × [n]. We construct an adaptive tester for convexity of functions f: [3] × [n] → ℝ with query complexity O(log2 n). We also show that any non-adaptive tester must use queries in this setting. Thus, adaptivity yields an exponential improvement for this problem.4.For functions f: [n]d → ℝ over domains of dimension d ≥ 2, we show a non-adaptive query lower bound .
Learning activities using videos to strengthen intercultural competence in higher education students

The use of properly designed educational videos has proven effective in strengthening intercultural competence in higher education. In this article, we present the results of an investigation whose objective was to determine the characteristics of learning activities that use videos to promote the development of the intercultural competence of students of virtual courses. The study was carried out using a mixed approach in which quantitative and qualitative techniques were used for data collection and analysis. The results showed that the significant changes in students occurred when the activities were based on videos that presented different situational and real contexts, promoting cultural knowledge, critical reflection and experiential analysis. The role of the teacher is paramount in the design of activities and in their role as facilitator and guiding them.
Media culture as an essential bibliospheric component

The author attempts to define the term of “mediaculture” within the context of bibliosphere. The polyscientific nature of the “media culture” concept in its interaction with general personal culture, information and book culture is explored. The concept of information culture as the generic one as relative to media culture is characterized as the product of new stage of social evolution. The media culture genesis in foreign and domestic research, including the recent ones, is discussed. The author identifies the essential elements of the media culture concept and provides her own definition. The dynamics of book status at the current stage of information society is analyzed. The capacities and limitations of the libraries’educating media culture in their users with the focus on information and specifically, media literacy are identified. The author emphasizes the modern user need for personal media culture. The role of modern libraries as curators of digital education in the era of media communications is to promote media culture products among its users with account taken of their demands.
Analyzing factors of Gui simulation as learning media toward students' learning outcomes

The use of simulation tools has been widely used to learn something. Simulation tools have the advantage of imitating a process similar to the actual situation. But there are only a few researches that examine the students' engagement in using simulation tools in the learning process so that it affects the student learning outcomes. This research aims to analyze the factors of GUI simulation as learning media for learning signal coding technique based. PLS-SEM method was used to investigate the impact and the relationship of factors of GUI simulation such as easiness of use, media attractiveness and learning content toward learning outcome. The results showed that the learning content factor gave a large and significant contribution toward learning outcomes, while easiness of use and media attractiveness made a small contribution to the learning outcome.
The Influence of Virtual Space on Contemporary Identity: The Perspective of Philosophy

The issue of identity has been a key philosophical topic among philosophers since the beginning of the 21th century. The Virtual space and the Internet have exerted an enormous influence on people’s life in the age of information. This article studies the influence of virtual space on identity in the Internet age from the philosophical perspective. The authors think that growth of the influence of computers and network technology on human beings has been a progressive and multi-staged process. Synthesising and analysing the achievements of related research both in China and abroad, it is clear that computers’ relationship with human beings has gone through three stages: the first in which the computer was like a tool, the second in which it was like a mirror, and the third in which it is like a gateway or maze, that is, the virtue reality and space. Focusing only on the relations between virtual space and identity ‘on the ground’, the article proposes that the following two guidelines may aid future inquiry. First, discussing and thinking about the issue of identity from the perspective of relations between virtual space and identity can deepen our understanding of the constituent elements of contemporary identity. Second, the discussion of identity in its relation to virtual space can spur more considerations of the ‘self’ qua critical concept.
Unsupervised generative adversarial network for style transfer using multiple discriminators

Style transfer is the rendering of the content image into the style of another image, such that the content image achieves the stylization of the style image. In this paper, we propose the cartoonlization style transfer method by using multiple discriminators in the Generative Adversarial Network (GAN). Cartoon images have specific characteristics which include black outlines, smooth surfaces, blurred effects that separate them from real images. We preprocessed the input images by Felzenszwalb segmentation algorithm and Gaussian kernel to extract the individual features from the input cartoon image dataset. In this work, we have introduced the usage of three discriminators for each such feature to help the generator in learning and generalizing the input data distribution. The generator network used in this work contains only 0.8M trainable parameters, which leads to less inference time. This approach shows that our method with the small size generator network can learn to generate superior results as compared to other methods. Qualitative and quantitative comparison with other methods demonstrated the superiority of our method.
Evaluating the Performance of Lightweight Ciphers in Constrained Environments—The Case of Saturnin

The use of lightweight cryptographic algorithms is essential for addressing security in highly constrained environments such as the Internet of Things. In this paper, the performance of lightweight block ciphers in such highly constrained environments is studied. More precisely, focusing—as a case study—on an important family of lightweight ciphers called “Saturnin”, which has been evaluated as a candidate for standardization in the relative ongoing NIST’s competition, we analyze its efficiency in case that it is implemented in a specific resource-constrained environment. To evaluate the results, a comparative study with the Advanced Encryption Standard (AES) is performed, through an appropriate experimental environment. Our results illustrate that significant gain in performance can be achieved, since Saturnin—whose design is inspired by the design of AES—can be almost two times faster than AES in such restricted environments.
Missing Value Imputation Method for Multiclass Matrix Data Based on Closed Itemset

Handling missing values in matrix data is an important step in data analysis. To date, many methods to estimate missing values based on data pattern similarity have been proposed. Most previously proposed methods perform missing value imputation based on data trends over the entire feature space. However, individual missing values are likely to show similarity to data patterns in local feature space. In addition, most existing methods focus on single class data, while multiclass analysis is frequently required in various fields. Missing value imputation for multiclass data must consider the characteristics of each class. In this paper, we propose two methods based on closed itemsets, CIimpute and ICIimpute, to achieve missing value imputation using local feature space for multiclass matrix data. CIimpute estimates missing values using closed itemsets extracted from each class. ICIimpute is an improved method of CIimpute in which an attribute reduction process is introduced. Experimental results demonstrate that attribute reduction considerably reduces computational time and improves imputation accuracy. Furthermore, it is shown that, compared to existing methods, ICIimpute provides superior imputation accuracy but requires more computational time.
Klasifikasi Penyakit Kanker Kulit Menggunakan Metode Convolutional Neural Network (Studi Kasus: Melanoma)

Skin cancer is one of the most commonly diagnosed cancers worldwide, especially in the white population. One of the most dangerous skin diseases is melanoma cancer. Melanoma is a skin cancer that can develop in melanocytes, the skin pigment cells that produce melanin. Melanin is what absorbs ultraviolet rays and protects the skin from damage. Melanoma is a type of skin cancer that is rare and very dangerous, many laypeople have not been able to distinguish between ordinary moles and melanoma. Therefore, a study on the classification of melanoma skin cancer was carried out using the CNN method, where CNN was able to classify melanoma images. In CNN itself there is an architectural model, while the architecture used in this research is using conv2d layer, max pooling, flatten, dense, dropout, and using ReLu activation. The image size used in this architecture is 128x128, at the 50th epoch, an accuracy rate of 92.64% is obtained. It is hoped that this research can help the community in distinguishing normal moles and melanoma cancer.
Non-Blind Deblurring for Fluorescence: A Deformable Latent Space Approach with Kernel Parameterization

Non-blind deblurring (NBD) is a modeling method of the image deblurring problem in computer vision, where the blurring kernel is known or can be externally estimated. In this paper, we attempt to solve a parametric NBD problem, inspired by the simultaneous acquisition of ptychography and fluorescent imaging (FI). Ptychography is an imaging method that favors larger probes, i.e. convolutional kernels, while FI relies on a small probe for high resolution. Also, the kernel can be solved during ptychographic reconstruction. With Ptycho-FI using the same larger kernel, we can perform NBD on the blurred fluorescent images to achieve high-resolution FI, and thus speed up the experiments. To this end, we design a deep latent space deformation network that is directly parameterized by the kernel. The network consists of three components: encoder, deformer, and decoder, where the deformer is specifically meant to rectify the latent space representations of blurred images to a standard latent space, regardless of the kernel. The deformation network is trained with a two-stage training scheme. We conduct extensive experiments to confirm that our parametric model can adapt to drastically different blurring kernels and perform robust deblurring.
The Applicable Law on Digital Fraud

The ongoing COVID-19 pandemic has caused numerous changes to our daily lives. One of these changes was our increased usage of the internet as a medium to purchase products and services or even manage our finances among others. Such overreliance caused numerous challenges to organisations and individuals alike circling around protection of information shared digitally. This is particularly evident since COVID-19 contributed to the increase in cybercrime. To facilitate their cybercrime, cybercriminals designed numerous cyber-attacks to steal sensitive information from organisations and users for financial gains. The advent of the pandemic and remote working brought the issue of jurisdiction in relation to cross-border crime to the forefront. With most of the financial sector working from home using online solutions and several employees not working from the country of employment rather from their country of origin or from other countries, the issue of cybersecurity and cybercrime becomes a matter of prime importance. Due to the pandemic, investigations and international collaborations on cross-border crime became more delicate and, by adding the issue of applicable law and jurisdiction, the picture becomes more problematic especially since pursuing criminal activity turned into a difficult task during the pandemic. With that in mind, the paper probes the issue of cross-border digital crime and the problem of conflict of laws applicable on cybercrime. It scrutinises the exterritoriality of laws through the territoriality principal and probes the role of the principal of jurisdiction over nationals to settle the debate about the governing law over digital financial crimes committed in cyberspace originating in another country. This is done while keeping in mind the need to establish sufficient links between the cybercrime and the applicable law.
Page Layout Analysis for Refining Table Extraction from PDF Documents

Perhaps, PDF is the most popular format to share non-editable documents. PDF documents are often untagged. In particular, this means that positions and the cell structure of tables are not designated explicitly. PDF table detection predicts bounding boxes of tables on document pages. Some of the predictions inevitably happen to be false. This negatively affects the accuracy of table structure recognition. We argue that the page layout analysis in pre- and post-processing can refine the table detection. We suggest pre-processing algorithms for the recognition of headings, running titles, paragraphs, and images in PDF pages. This allows selecting areas of interest inside pages where real tables can be placed. Then we use deep neural networks to predict tables only in these areas. We also propose post-processing algorithms to verify predictions and filter out false table candidates after table detection. Our empirical study shows that the proposed approach reduces errors in the table detection and improve the PDF table extraction overall.
Saturated Terminology Extraction and Analysis in Use

The method and software implementation of saturated terminology extraction has already been used in external industrial and academic projects. This Chapter reports about one industrial and one academic use case of our research contribution. These use cases are the early and ongoing attempts to check if the results of our research could be potentially useful in external industrial and academic settings. Hence, the currently reported outputs of the presented use cases have to be regarded as potential utility and benefits indicators. Consequently, the chapter deliberates about the practical implications, including limitations and benefits, which might be expected by the potential adopters of our research output. Additionally, it presents our prognosis about the potential scope of the technology transfer to the industry of scholarly publishing, as a focal sector.
Automatic content curation of news events

With the rapid development of the internet, a large amount of online news has brought readers a variety of information. Some important events last for some time as the event develops or the topic spreads. When readers want to catch up on the details of a specific news event, most of them use a search engine to collect news and understand the whole story. It usually takes readers a considerable amount of time to sort out the causes and effects of the event. The general method of online news provision aggregates and organizes the content of news articles from a large number of events and presents the content to readers. Most of this type of information is manually organized. To solve these problems, this study proposes an automated method of news curation. First, we extract the topics from the event data set and use word sequences to find the sequence of topic transfer through a hidden Markov model. Second, we calculate the strength of the topic and the variation in the strength to detect important time points during the development of the news event. Finally, a concise summary is generated at each time point. This paper combines two characteristics, chronology and summary, to design a curation method that can effectively help readers quickly grasp the context of a news event. The experimental results show that the method has good performance in each module, such as the detection of the important phases of events and the creation of the news summary.