Reviewerly/exHarmony huggingface dataset:
Edge detection of aerial images using artificial bee colony algorithm

Edge detection techniques are the one of the best popular and significant implementation areas of the image processing. Moreover, image processing is very widely used in so many fields. Therefore, lots of methods are used in the development and the developed studies provide a variety of solutions to problems of computer vision systems. In many studies, metaheuristic algorithms have been used for obtaining better results. In this paper, aerial images are used for edge information extraction by using Artificial Bee Colony (ABC) Optimization Algorithm. Procedures were performed on gray scale aerial images which are taken from RADIUS/DARPA-IU Fort Hood database. Initially bee colony size was specified according to sizes of images. Then a threshold value was set for each image, which related with images’ standard deviation of gray scale values. After the bees were distributed, fitness values and probability values were computed according to gray scale value. While appropriate pixels were specified, the other ones were being abandoned and labeled as banned pixels therefore bees never located on these pixels again. So the edges were found without the need to examine all pixels in the image. Our improved method’s results are compared with other results found in the literature according to detection error and similarity calculations’. All the experimental results show that ABC can be used for obtaining edge information from images.
Evaluation of Student Learning Motivation Based on Parenting Style: A Qualitative Study

Learning motivation encourages the desire to learn so that students achieve success in school and in the future. The success achieved by students in learning cannot be separated from the role of parents in nurturing. This study was an evaluation of student learning motivation based on the parenting style conducted at SMPN 1 Matangkuli, North Aceh. This research is an observational study with a qualitative approach. The participants in this study were students of SMP Negeri 1 Matangkuli, North Aceh district, and their parents. A total of six students and their parents participated in this study. Data were collected through observation, interviews, and documentation. Interviews were conducted in a semi-structured manner with children and parents about the parenting style used in the family. The results showed that children with democratic parenting have a strong learning motivation. In this study, it was also found that all aspects of learning motivation were owned and experienced by the six participants. In conclusion, students' learning motivation is related to the parenting style they experience in their families. There are three parenting styles applied by parents, namely, authoritarian, democratic, and permissive parenting.
The impact of electronic services technology in minimizing the phenomenon of job burnout in Commercial Bank of Syria

The research aims to test the relationship between electronic service technology and job burnout among employees of the Commercial Bank of Syria, and to what extent modern technology contributes to reducing the negative manifestations of job burnout among workers in addition to the level of technology used in providing various banking services at the Commercial Bank of Syria. Testing the level of job burnout that bank employees are exposed to, and the study found an inverse relationship between electronic service technology and job burnout among workers. Therefore, modern technology contributed to alleviating the causes of this phenomenon by increasing flexibility in banking performance due to the ease of use of this technology and reducing errors committed by employees. It is shown, that the bank does not have the latest technology, which covers all banking services, so that all of them can be provided electronically. As for job burnout, it was noted that there was a low job burnout experienced by workers in the Commercial Bank of Syria, but there was suffering on the part of some workers in facing the problems they are exposed to, and the research concluded with a set of recommendations for the Commercial Bank of Syria in line with the results that were reached through research.
<i>LiteCON</i> : An All-photonic Neuromorphic Accelerator for Energy-efficient Deep Learning

Deep learning is highly pervasive in today's data-intensive era. In particular, convolutional neural networks (CNNs) are being widely adopted in a variety of fields for superior accuracy. However, computing deep CNNs on traditional CPUs and GPUs brings several performance and energy pitfalls. Several novel approaches based on ASIC, FPGA, and resistive-memory devices have been recently demonstrated with promising results. Most of them target only the inference (testing) phase of deep learning. There have been very limited attempts to design a full-fledged deep learning accelerator capable of both training and inference. It is due to the highly compute- and memory-intensive nature of the training phase. In this article, we propose LiteCON , a novel analog photonics CNN accelerator. LiteCON uses silicon microdisk-based convolution, memristor-based memory, and dense-wavelength-division-multiplexing for energy-efficient and ultrafast deep learning. We evaluate LiteCON using a commercial CAD framework (IPKISS) on deep learning benchmark models including LeNet and VGG-Net. Compared to the state of the art, LiteCON improves the CNN throughput, energy efficiency, and computational efficiency by up to 32×, 37×, and 5×, respectively, with trivial accuracy degradation.
Exploring EFL Undergraduate Students’ Attitudes towards Learning Writing over Virtual Learning System: A Case Study of Indonesian College Students

The study aimed to find out the EFL students’ attitudes toward learning writing over a virtual learning system. The researcher used a qualitative study. There were 46 undergraduate students participating in the study. The data were collected by using a questionnaire and interview sheet through a google form. The result shows that none of the students has a low attitude toward learning writing through online learning, while 58.69% of them have a moderate attitude toward learning writing through virtual learning and the rest of them have a positive attitude toward learning writing over virtual. The interview also reveals that although the students have a moderate attitude toward learning writing online, they see learning writing through online learning as difficult and ineffective while the students who have a high attitude toward learning writing through virtual mostly view learning writing online as interesting, effective, and easy. The students also faced challenges during learning writing through online learning. They are internet connectivity and distraction. The finding also reveals several students’ efforts to improve their writing skills, among others, are reading a lot to get knowledge and ideas, diary writing, and practice writing.
LRPC codes with multiple syndromes: near ideal-size KEMs without ideals

We introduce a new rank-based key encapsulation mechanism (KEM) with public key and ciphertext sizes around 3.5 Kbytes each, for 128 bits of security, without using ideal structures. Such structures allow to compress objects, but give reductions to specific problems whose security is potentially weaker than for unstructured problems. To the best of our knowledge, our scheme improves in size all the existing unstructured post-quantum lattice or code-based algorithms such as FrodoKEM or Classic McEliece. Our technique, whose efficiency relies on properties of rank metric, is to build upon existing Low Rank Parity Check (LRPC) code-based KEMs and to send multiple syndromes in one ciphertext, allowing to reduce the parameters and still obtain an acceptable decoding failure rate. Our system relies on the hardness of the Rank Support Learning problem, a well-known variant of the Rank Syndrome Decoding problem. The gain on parameters is enough to significantly close the gap between ideal and non-ideal constructions. It enables to choose an error weight close to the rank Gilbert-Varshamov bound, which is a relatively harder zone for algebraic attacks. We also give a version of our KEM that keeps an ideal structure and permits to roughly divide the bandwidth by two compared to previous versions of LRPC KEMs submitted to the NIST with a Decoding Failure Rate (DFR) of $2^{-128}$.
A Line Fitting Algorithm: Linear Fitting on Locally Deflection (LFLD)

The main motivation of the study is to prevent and optimize the deviations in linear connections with complex calculations related to the previous and next steps. This purpose is used for more stable detection and therefore segmentation of object edge/corner regions in Quality Control Systems with Image Processing and Artificial Intelligence algorithms produced by authors within Alpplas Industrial Investments Inc. The dataset used in this area was originally obtained as a result of the edge approaches of the plastic panels manufactured by Alpplas Inc., extracted from the images taken from the AlpVision Quality Control Machine patented with this research. The data consists entirely of the pixel values of the edge points. Dispersed numeric data sets have quite changeable values, create high complexity and require the computation of formidable correlation. In this study, dispersed numeric data optimized by fitting to linearity. The LFLD (Linear Fitting on Locally Deflection) algorithm developed to solve the problem of linear fitting. Dispersed numeric data can be regulated and could be rendered linearly which is curved line smoothing, or line fitting by desired tolerance values. The LFLD algorithm organizes the data by creating a regular linear line (fitting) from the complex data according to the desired tolerance values.
Application of Android-Based Interactive Multimedia and Local Wisdom on non-fiction text material for Fourth Grade students

This research is motivated by the incomplete understanding of students in non-fiction text material because the teacher only uses the lecture method in delivering the material. The purpose of this study was to determine the increase in student learning outcomes in non-fiction text material using interactive media non-fiction texts in Kediri Raya. The method used in this research is Classroom Action Research with 2 cycles of learning with the stages of planning, implementing action, observing, and reflecting. The results of this study have shown an increase in student learning outcomes in the first cycle with an average of 83.18 and the percentage of completeness in the first cycle of 86.36% and this increased in the second cycle obtained an average of 94.54 and the percentage of completeness in the second cycle value is 100%. Thus, it can be concluded that learning non-fiction text stories through interactive media of non-fiction texts in Kediri Raya can improve the learning outcomes of fourth grade students of MI Assalafiyah Pule Kediri.Keywords: Learning outcomes, non-fiction texts, interactive media non-fiction texts of Kediri Raya.
Communication, Information and Education Iron Supplementation and Hemoglobin

nemia in young women can cause decreased concentration in learning, lack of enthusiasm in activities, decreased memory and decreased learning ability at school. National prevalence of anemia in Indonesia reaches 21.7%, with anemia sufferers aged 5-14 years of 26.4% and 18.4% of patients aged 15-24 years with the highest prevalence in women. Iron nutrition anemia in adolescents becomes dangerous if not handled properly, especially in preparation for pregnancy and childbirth when they are adults. The solution related to the problem of anemia in adolescents can be in the form of providing a refresher on knowledge about good nutritional intake for students providing iron supplementation and checking Hb levels in female students. To determine the change in knowledge in adolescent students, a pre and post test was conducted using a knowledge questionnaire about the importance of iron supplementation followed by measuring the status of the risk of anemia. The data shows that the pretest results are 71.43% with sufficient knowledge and the post test shows that 100% of students have good knowledge. From the results of Hb examination, it was found that the average Hb value of female adolescent students was 12.5 gr / dl with the lowest value 11.2 gr / dl and the highest value 14.7 gr / dl. From these data it can be seen that the provision of KIE FE supplementation is useful for students of SMK Kesehatan Panca Atma Jaya Klungkung.
MULTI-FLGANs: Multi-Distributed Adversarial Networks for Non-IID distribution

Federated learning is an emerging concept in the domain of distributed machine learning. This concept has enabled GANs to benefit from the rich distributed training data while preserving privacy. However, in a non-iid setting, current federated GAN architectures are unstable, struggling to learn the distinct features and vulnerable to mode collapse. In this paper, we propose a novel architecture MULTI-FLGAN to solve the problem of low-quality images, mode collapse and instability for non-iid datasets. Our results show that MULTI-FLGAN is four times as stable and performant (i.e. high inception score) on average over 20 clients compared to baseline FLGAN.
Hate Speech in New Media: Investigation of Hate Speech Including Law Numbered 6222 Created by the Fans

The features of the new media, such as digitality, interactivity, hypertextuality, virtuality and user-generated content, are important factors for fans to use new media platforms. These football fans, who are the members of platforms that are open to any interactions, behave with a sense of belonging to the teams they support, at the same time they tag other fan groups as “others”. New media provides an online and free atmosphere. Therefore white supremacy, anger and hate spread uncontrollably against others. Although severe punishments were imposed on the fans who share the detection of violent and abusive content in social media with the entry into force of Law Numbered 6222 on Prevention of Violence and Irregularities in Sports Competitions, the fans use the number itself, 6222, as a code and continue to share hateful contents. Therefore, this study aims to detect hate speeches made using both the number 6222 (Law. No. 6222) as a code and specified keywords on Twitter, which is one of the new media platforms, between the period of June 2019 and December 2019 (7 months). The posts of fans were recorded according to specific keywords by using the "Advanced Search" feature of Twitter and then analyzed by following the content analysis method. Besides, the posts that include the code 6222 enabled us to reach more hate speech content. Therefore, the snowball sampling (chain-referral sampling) technique was applied. To ensure the reliability and validity, all posts were coded in the coding scale according to the categories. As a result of this study, the identities and fan groups are targeted by hate speech, and the categories of hate speech are demonstrated in the graphics.
ANALISIS SISTEM INFORMASI AKUNTANSI PENGGAJIAN PADA DINAS KOMUNIKASI DAN INFORMATIKA PROVINSI SUMATERA UTARA

In compliance with applicable legislation, salary provision must be supported by evidence in the form of paperwork and other records. Several factors must be considered when determining wage to ensure that the pay is commensurate with the work performed by employees, such as attendance lists, so that employees are more encouraged to adhere to attendance policies. In this situation, the payroll accounting information system makes it easier for accountants to complete computations and keep track of everything relevant to payroll transactions. This payroll information system, on the other hand, can lead to data entry errors. If you enter the erroneous account number, especially while distributing salaries, the money could end up in someone else's account, resulting in significant losses. This study was carried out at the North Sumatra Province's Department of Communication and Information (Diskominfo). By performing direct observations and interviews with numerous employees, this study takes a descriptive qualitative method. The findings of this study show that the payroll system is still ineffective since it does not consider employee attendance hours when calculating employee salary. It would be preferable if attendance hours were factored into salary calculations, as this would encourage employees to be more punctual.
Building A Disaster Prepared Village Community To Reduce The Risk Of A Landslide Disaster In Sumurup Village, Bendungan District, Trenggalek Regency

This mentoring research describes the reality of people's lives who have low awareness of natural disaster response. The problems that arise in the community are the lack of public awareness in tackling and dealing with disasters, the absence of a disaster emergency response and response system, and the absence of village government policies that focus on managing landslides. The purpose of this empowerment is to find a strategy in building a resilient community for landslide natural disasters to be more independent in handling and preventing disaster events in Sumurup Village. This study refers to a research approach using the PAR method. Namely making changes in the form of education and training in landslide risk reduction in order to provide a basic understanding of disaster risk reduction by making disaster-prone maps. As well as disaster simulation training accompanied by determining evacuation routes, and installing danger signs. In addition, by advocating village government policies to create funding rules for disaster management. With a series of activities that have been carried out with the community so as to create a strong and independent community attitude in dealing with natural disasters. These activities have resulted in improvements and changes in community capacity, especially for communities directly affected by disasters in reducing the risk of landslides.
Dealing With Multipositive Unlabeled Learning Combining Metric Learning and Deep Clustering

Standard supervised classification methods make the assumption that the training data is fully annotated thus requiring an a-priory labelling process which is both costly and time-consuming. To relax this requirement, many different flavors of weakly supervised learning have been proposed. Among weakly supervised learning strategies, Positive Unlabelled learning (PUL) is gaining attention from the research community due to the wide spectrum of applications it can fit. However, the majority of research studies related to PUL only consider binary classification tasks while real-world applications commonly involve multiple categories. To deal with this limitation, Multi-Positive Unlabelled learning (MPUL) has been recently introduced to learn from examples labelled with multiple positive labels and a single unknown negative label. Up to today, only a limited number of research works were proposed to cope with this more general setting. In this paper, we propose a new MPUL framework based on deep learning strategies. Our framework, named ProtoMPUL (Prototype based Multi-Positive and Unlabelled Learning), combines metric learning and clustering strategies to model the set of positive classes as well as to characterize the unknown negative one. Experimental evaluations on real-world benchmarks considering recent MPUL competitors demonstrates that the proposed framework achieves state-of-the-art performances, thus supporting the validity of the proposed approach.
Analysis of Student Reading Interests During Learning from Home in the COVID-19 Pandemic

Learning activities from home impacts changing students' lifestyles, especially reading activities. This study aimed to describe and explain reading interest among undergraduate students of the Indonesian Language and Literature Education Study Program (S-1 PBSI) of Universitas Sebelas Maret (UNS) while learning from home during the COVID-19 pandemic. This study was qualitative research with a descriptive survey method. The sampling technique was designed for S-1 PBSI UNS who are still active in the Academic Year 2021/2022. The data collection technique in this study employed a questionnaire consisting of five categories:1) attitudes toward reading, 2) reading preferences, 3) reading frequency, 4) reading sources, and 5) types of reading materials during learning from home. The following conclusions are reached as the research results and discussion. First, students still had an enthusiastic attitude towards reading activities while learning from home and reading anything, including books or academic material. Second, students preferred to read online with the fiction reading type in the form of novels rather than literary texts. Third, students' reading frequencies during learning from home were 30 minutes to 1 hour for 3–6 days a week. Students could read for more than one hour for fun, but only 30 minutes to 1 hour to read academic texts. Additionally, they could read 4–6 books in one semester. Fourth, students received online learning resources, mostly websites, and offline learning sources, primarily textbooks. Smartphones were the most commonly used reading media. Fifth, the majority types of student reading were novels.
P‐103: Imaging Quality Optimization of Full Display with Camera Based on Optical Simulation

Full display with camera (FDC),which puts the camera under display, leads the way of smart phone. In this paper, we establish a simulation model with which OLED design can be evaluated and optimized to improve the imaging quality of FDC.
Exponential data encoding for quantum supervised learning

Reliable quantum supervised learning of a multivariate function mapping depends on the expressivity of the corresponding quantum circuit and measurement resources. We introduce exponential-data-encoding strategies that are hardware-efficient and optimal amongst all non-entangling Pauli-encoded schemes, which is sufficient for a quantum circuit to express general functions having very broad Fourier frequency spectra using only exponentially few encoding gates. We show that such an encoding strategy not only reduces the quantum resources, but also exhibits practical resource advantage during training in contrast with known efficient classical strategies when polynomial-depth training circuits are also employed. When computation resources are constrained, we numerically demonstrate that even exponential-data-encoding circuits with single-layer training modules can generally express functions that lie outside the classically-expressible region, thereby supporting the practical benefits of such a resource advantage. Finally, we illustrate the performance of exponential encoding in learning the potential-energy surface of the ethanol molecule and California's housing prices
Special Issue “New Advances in Breast Imaging”

Breast cancer (BC) is the most common cancer in women of all ages, with more than 2 million diagnoses every year and a high economic and psychological impact on both the health care system and the population [...].
Digital Skills of Working Age Population in Croatia

In the focus of the paper are digital skills of working age population in the Republic of Croatia as one of its fundamental pillars of scientific, technological, educational and overall socio-economic development. Moreover, this research perceives a certain level of digital skills development as an unavoidable precondition for Croatia’s involvement in the overall development of the modern world. This research results are based on the comprehensive statistical analysis of the public opinion survey. The methodology involves hypotheses testing afterwards using a two-step random sample. Post stratification has been created on the basis of gender, age, size of residence, education level and working status of respondents. The analysis of the data showed that there is no statistically significant difference in digital skills between men and women and that there is a statistically significant difference in digital skills depending on the age and level of education of the respondents.