Reviewerly/exHarmony huggingface dataset:
Hardware implementation of Sobel edge detection system for blood cells images-based field programmable gate array

The microscopic-blood image has been used to diagnose various diseases according to the morphological specifications of red and white blood cells. &lt;span lang="EN-US"&gt;However, the manual analysis and procedures are not accurate due to the human error. Therefore, several studies conducted to find new techniques to perform this analysis using computer algorithms. The complexity of these algorithms led to thinking in simpler ways or to the hardware solutions. On the other hand, edge detection is a mathematical procedure that play an essential role in the field of medical image processing. It is considered as one of the foundations' processes for other procedures, such as the segmentation and the classification of the image. The Sobel filter is one of the conventional methods that is used to perform the edge detection process. It is based on finding the local contrast for the level of intensity of the image. This paper presents a proposed and a new method for detecting the edges of cells in the microscopic blood images using Sobel filter and its hardware implementation on the field programmable gate array (FPGA) chip. Three different techniques are proposed: MATLAB, OpenCV standard code, and FPGA customize code which give the best visual results, minimum timing results than the others.&lt;/span&gt;
AFTM - Agent Based Fault Tolerance Manager in Cloud Environment

As the number of cloud users are increasing with times, the probability of failures also increases that takes place in any cloud virtual machine. Failures can occur at any point of time in service delivery. There are numerous techniques for reacting proactively towards these failures. In this framework, a service provider is allocated to the user on the basis of ranking of the service provider. This ranking is done by considering parameters such as trust values (calculated by feedback mechanism), checkpointing overheads, availability and throughput. Checkpoints are beneficial in triggering savepoint so that minimal loss of data takes place if any failure occurs. This paper has also compared the proposed framework with OCI framework which is based on triggering checkpoints on constant rates. Results have proven that AFTM has 33% to 50% better efficiency results as compared to optimal checkpoints interval (OCI) framework. The results shown in paper demonstrates how better the checkpointing overheads, availability and throughput are handled by using AFTM framework. Also, the overheads were reduced to 50% as compared to OCI framework
The Conceptual Framework of a Privacy-Aware Federated Data Collecting and Learning System

The federated learning methods offer a strong background of fusing and publishing simultaneously collected data.One of the most challenging problems in federated learning is to hide the identity of the participants.Privacy-preserving techniques try to perturb the participants' local data to match its distribution to the global data.In this paper, we consider that agents collect local environmental data.Neighboring agents can share some of their raw data to support real-time decisions and reduce deviation from the global data distribution.The agents will fuse their collected data into a global model that supports the long-term decision and plan making.We assume the specific situation where the necessary communication protocol between the agents may lead to sharing too much local raw data uncovering private and sensitive attributes of the data sharers.To handle privacy issues, we introduce a privacy-aware framework.Within this framework, local participants balance the amount of the shared raw data to make it informative enough yet not revealing, effectively bounding the loss of privacy.In this study, we use autonomous vehicle agents as an example to demonstrate the concepts of the proposed framework.
Non-Deterministic Finite Automata to Deterministic Finite Automata Conversion by Subset Construction Method using Python

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
English Multimode Production and Usage by Artificial Intelligence and Online Reading for Sustaining Effectiveness

In order to develop oral English and comprehensive skills, reading and writing English are more important. Artificial Intelligence (AI) plays a vital role in numerous fields of our lives, such as education, healthcare, businesses, and many others. In a short time, AI has become one of the most developed disciplines in educational technology and brings a lot of enhancement to the education system. In particular, AI has aided in the presentation of novel schooling and knowledge approaches that are currently being investigated in several sectors. Different AI-based apps, such as Chatbots, Robotic Assistants, Vidreader, Seeing AI, Classcraft, and 3D holograms, have been developed to help teachers and students take advantage of and enhance the educational system. Intelligent instruction methods and sentimentalized artificial learning assistance are given special study. The education industry’s main goal and desire is to create an intelligent education system, which has been made feasible by the development of teaching assistant robots, smart classrooms based on AI, and English teaching support, among other things. AI tools can now be used in all stages of learning to further improve the educational system. Students and teachers in COVID-19 have carried out their education and teaching in a variety of methods online. Learning, including virtual learning, can be done so that citizens do not lose their knowledge. This study has presented a detailed overview of the approaches and mechanisms used in the literature and then combined the literature evidence in the form of different analyses. This review will help researchers to develop and formulate new solutions in the field.
DePhaseNet: A deep convolutional network using phase differentiated layers and frequency based custom loss for RGBW image sensor demosaicing

Panchromatic Color Filter Arrays with white signal were introduced a while ago, e. g.RGBW Color Filter Array (CFA), assuming to have better resolution in lowlight due to panchromatic signal.However, there is no successful RGBW image sensor in the industry targeting mobile cameras until now.In this work, we introduce a novel Samsung RGBW image sensor and we study its performance in a popular remosaic scenario.We propose a DePhaseNet -a deep fully convolutional network to solve RGBW remosaicing or demosaicing problem.We propose to have 3 layers of phase differentiated inputs and custom frequency-based loss function for each layer.Proposed method successfully suppresses False Colors inherent to RGBW sensor due to heavily undersampled colors.By using this method, we were able not only to increase details preservation, but also increased color reproduction.We found that RGBW sensor is beneficial not only in low light scenarios, but also in widely spread remosaic scenarios.Experiments show improvement in image quality, yielding CPSNR of 42dB for Kodak dataset, reaching the bar of Bayer CFA demosaicing result.Proposed method advances state-of-the-art in RGBW demosaic by 6dB in CPSNR.
PRINCIPAL FACTORS AND LATTICE MINIMA IN CUBIC FIELDS

Let k = ℚ(3√d, ζ3), where d > 1 is a cube-free positive integer, k0 = ℚ(ζ3) be the cyclotomic field containing a primitive cube root of unity ζ3, and G ＝ Gal(k / k0). The possible prime factorizations of d in our main result in previous work (Theorem 1.1 in Aouissi et al, Preprint, arXiv:1808.04678v2) give rise to new phenomena concerning the chain Θ = (θi)i∈ℤ of lattice minima in the underlying pure cubic subfield L = ℚ(3√d) of k. The aims of the present work are to give criteria for the occurrence of generators of primitive ambiguous principal ideals (ν) ∈ PkG / Pk0 among the lattice minima Θ = (θi)i∈ℤ of the underlying pure cubic field L = ℚ(3√d), and to explain the exceptional behavior of the chain Θ for certain radicands d with impact on determining the principal factorization type of L and k by means of Voronoi's algorithm.
Image Data Compression in the Public Reporting System in Lamongan using the Huffman Method and Run Length Encoding

The dry season and the rainy season in the Lamongan area always cause several problems, including water scarcity, flooding, damaged roads, etc.The public report site, which is used to accommodate the aspirations of Lamongan residents who are in trouble, requires image evidence to ensure that there are no false reports.The site has a limit on the size of the file to be uploaded, so if the file size is too large, the upload process cannot be carried out.In this study, an analysis will be carried out to compare what compression method produces photos with the smallest size for further upload on the Lamongan community report site. Huffman compression and Run Length Encoding (RLE)were chosen because the algorithm includes lossless compression where the compressed image will not be damaged even though the size is compressed.From the two methods or algorithms, testing is carried out to find out which algorithm is the best that can produce compressed images with the smallest size.From the experiments conducted, it is known that the RLE method is a better method than Huffman coding.With the RLE method we can compress images up to 93.17%.
Branching Strategy Selection Approach Based on Vivification Ratio

The two most effective branching strategies LRB and VSIDS perform differently on different types of instances.Generally, LRB is more effective on crafted instances, while VSIDS is more effective on application ones.However, distinguishing the types of instances is difficult.To overcome this drawback, we propose a branching strategy selection approach based on the vivification ratio.This approach uses the LRB branching strategy more to solve the instances with a very low vivification ratio.We tested the instances from the main track of SAT competitions in recent years.The results show that the proposed approach is robust and it significantly increases the number of solved instances.It is worth mentioning that, with the help of our approach, the solver Maple_CM can solve additional 16 instances for the benchmark from the 2020 SAT competition.
Virtual education and student perception of teacher performance in the distance learning environment

&lt;span&gt;This article aims to carry out a descriptive analysis of the performance of teachers qualified as researchers, in the distance education environment according to the student's perspective. The results will be a frame of reference for university authorities on the path of continuous improvement of virtual education. When carrying out the research, a general qualification of the teaching performance of 14.20 was determined, established the highest grade equal to 20, it can be indicated that there is a good performance of the teacher in the virtual education environment. In addition, the results show that the highest evaluation corresponds to the indicator management of the group and fulfillment of the objectives, which is directly related to the administration of the class, while the lowest rating is for the indicator "Teacher effectiveness so that their students acquire relevant knowledge, skills and attitudes”, which is directly related to the didactic strategies used, that is, to the use of technological tools that today are more than just an option. Finally, it can be noted that of the total of 17 teaching, 23.5% present a very good performance, 35.3% present a good performance and 41.2% present a regular performance.&lt;/span&gt;
Facial Emotion Recognition Through Detection of Facial Action Units and Their Intensity

Facial expression recognition (FER) is a vital process in many applications in computer vision, medical, human-computer interface, video games, AI, E-education, security, distance psychotherapy, and Counselling.In the past, only basic emotions are the focus of research but now, with advancement in technology and need along with basic emotion, reorganization of compound emotions is also getting important.But, recognizing facial expressions with accuracy is challenging.To deal with that, facial action units for detecting appropriate basic and compound facial emotion are useful.Among state-of-the-art methods for FER systems, detection of facial action units (AUs) showed good results.Combining these AUs to detect particular basic and compound facial expressions is key and improves the accuracy of basic and compound facial expressions.We have used standard CNN architecture VGGNet, XceptionNet, and ResNet to conduct experiments in the proposed method to explore CNN architecture that best performs for action unit recognition.We modified them slightly by changing a few final fully connected layers to detect facial action unit intensity.Using this modified architecture for CK+, MMI, DISFA, and DISFA+ database that detects action units with good accuracy, it is intern mapped to find basic and compound facial emotions.Our Proposed method achieves an overall accuracy for Action Unit detection using Xceptionnet network for MMI & DISFA are, giving promising results average F1-score is 72% and 74%, respectively.In contrast, a network for CK+ and DISFA+ has an overall F1-score is 62% for both.
Knowledge-Driven Active Learning

The deployment of Deep Learning (DL) models is still precluded in those contexts where the amount of supervised data is limited. To answer this issue, active learning strategies aim at minimizing the amount of labelled data required to train a DL model. Most active strategies are based on uncertain sample selection, and even often restricted to samples lying close to the decision boundary. These techniques are theoretically sound, but an understanding of the selected samples based on their content is not straightforward, further driving non-experts to consider DL as a black-box. For the first time, here we propose to take into consideration common domain-knowledge and enable non-expert users to train a model with fewer samples. In our Knowledge-driven Active Learning (KAL) framework, rule-based knowledge is converted into logic constraints and their violation is checked as a natural guide for sample selection. We show that even simple relationships among data and output classes offer a way to spot predictions for which the model need supervision. We empirically show that KAL (i) outperforms many active learning strategies, particularly in those contexts where domain knowledge is rich, (ii) it discovers data distribution lying far from the initial training data, (iii) it ensures domain experts that the provided knowledge is acquired by the model, (iv) it is suitable for regression and object recognition tasks unlike uncertainty-based strategies, and (v) its computational demand is low.
Langkah Strategis Implementasi Aplikasi Layanan Interaktif di Perpustakaan

Research on interactive service applications in libraries is still mostly about the use of various applications, application development, and evaluation of service applications in libraries. Utilization of service applications places more emphasis on the technology used, including assessing technical opportunities and obstacles. No research has been found that specifically discusses the preparation and management of interactive applications in libraries. The purpose of this study is to provide an overview of the application of interactive service applications that are mostly carried out in libraries and to open opportunities to obtain other views on the implementation of interactive service applications in libraries. This study uses a qualitative method with a literature review approach. From 20 articles, three major issues are found. They are the use of library service applications, a total of twelve articles; library service application development is found in 5 articles; and evaluation of library service applications in 3 articles. Conclusion from this research is that another view is needed in the implementation of interactive service applications in the library.
Pembelajaran Daring Melalui Game Edukasi Quizizz Terhadap Hasil Belajar Fisika

The type of research used in this research is Pre-Experimental Designs research. The objectives of this research are: 1) To describe the learning outcomes of students in class X MIPA at SMA Negeri 5 Bone before and after the implementation of online learning with the educational game Quizizz in learning; 2) Describe the improvement in learning outcomes of students in class X MIPA at SMA Negeri 5 Bone before and after the implementation of online learning using the Quizizz educational game in learning. The samples in this study were students of class X MIPA 1 SMA Negeri 5 Bone which consisted of 31 students. The data collection technique used in collecting data in this study was by means of a test consisting of a pretest and posttest in the form of multiple choices. The results of the descriptive analysis in this study showed that the scores obtained by students in the posttest were much higher than the scores obtained in the pretest. Where the average score in the pretest is 13.42 with a standard deviation of 4.20, while in the posttest the average score obtained by students is 23.98 with a standard deviation of 4.70. So there is an increase in student learning outcomes before and after being taught by applying the Quizizz educational game, where the increase is in the moderate category.
The Practice of a Method of Self-Study Students Counting in Classrooms Based on Head Detection in Colleges and Universities

In order to solve the problem that it is difficult for students to find self-study classrooms because of the limited classroom resources, combined with the current situation of informatization in colleges and universities, a feasible method of students counting in classrooms based on head detection is proposed.This method first collects the scene images in the classroom at regular intervals based on the existing examination monitoring system, and then uses the offline trained AdaBoost cascade detector to detect the head candidate region in the images.Then, the trained CNN-SVM model is used to further identify the head, and finally the identification results are processed and the number of students in the classrooms is counted.The test and practice show that the query system for the idle situation of self-study classrooms constructed by coordinating the classroom seat capacity, classroom scheduling data and the students counting in the classroom based on the above method can easily query the current crowded degree of the students in the classrooms, which plays a good guiding role for students to find self-study classrooms.The method has strong reference and promotion significance for solving similar problems in other universities.
Developing Students' Willingness to Communicate Using Information Gap Activities

The goal of this study was to increase students’ willingness to communicate by incorporating information gap activities into the teaching and learning process. Three meetings were held to conduct the research. A total of 31 students took part in this study. The instrument used was students’ reflections. The information was gathered in a qualitative manner and triangulation was carried out. The data analysis revealed that (1) implementing information gap activities in classrooms while teaching English increased students’ willingness to communicate, particularly their behavioral intention to communicate; (2) incorporating information gap activities increased students’ motivation and confidence to communicate in English; and (3) implementing information gap activities in the form of pair and group work activities increased students’ motivation and confidence to communicate in English. Furthermore, information gap activities can be used as a teaching strategy in English classes to assist the teacher in effectively managing the class and retaining students’ attention during the learning process.&#x0D; Keywords: information gap activities, students’ willingness to communicate
Teaching Learning Mathematics in Multi-cultural Classroom

In the multicultural classroom, students come from many different back-grounds, experiences, and ethnicities. As a result of poor performance in traditional mathematics assessments, teachers often believe English language learners to be weak students. This study aims to unveil the experiences of mathematics teachers while engaging students in multicultural classrooms and describing their ethnic and cultural influence in primary level mathematics teaching in the context of Nepal. The study focused on finding the reasons of the irregularity of the students, the use of their non - contextualized vocabularies and various other challenges faced by teachers in a multicultural classroom. This study underpins qualitative research design under which I applied phenomenological approach and in-depth interviewed six primary level mathematics teachers in Kirtipur Municipality. The data management was conducted through thematic analysis. The main finding of the study is that the teachers used different strategies that allowed the students to reason and communicate mathematically and develop students' abilities to solve problems. They acted in playing the role of facilitators to accomplish their professional responsibility. The study also revealed that the students, teachers, parents, environment and national policies are all responsible to influence the learning activities of the students. Moreover, teachers’ experiences help in motivating the learners and promoting the teaching learning activities in the multi-cultural classroom.
The Translation Process of Machine Translation for Cultural Terms on Balinese Folktales

The goal of this study is to determine the capability of Google Translate in term of translating cultural terms. This study is conducted to analyze the translation performed by one of the well-known machine translation, Google Translate. The data is collected from the translation of seven traditional Balinese folktales chosen in random via online, namely Manik Angkeran, Kebo Iwa, Lubdaka, Tampaksiring, Origin of Bali, Origin of Singaraja, and Pan Balang Tamak. The data of the study is the translation on cultural terms translated by Google Translate. The cultural terms are classified with Cultural Term Classification Theory by Newmark. The analysis is conducted by comparing the translatied text and the original text to identify the translation method utilized by Google Translate in translating cultural term into English. The Translation Method Theory and Classification by Newmark is used to identify the method of translation utilized by Google Translate. The methods then is used in order to determine the tendency of Google Translate in the translation toward cultural terms.