Reviewerly/exHarmony huggingface dataset:
PENERAPAN SISTEM PENGISIAN NILAI MATEMATIKA DAN PENCETAKAN RAPOR PADA KURIKULUM 2013 “E – RAPORˮ SMA NEGERI 2 SEULIMUM

Ahead of the enactment of the new rules, namely referring to the Regulation of the Minister of Education and Culture of the Republic of Indonesia number 53 of 2015 concerning the Assessment of Learning Outcomes by Educators and Education Units in Primary and Secondary Education, related to changes in assessment mechanics in the 2013 curriculum, SMA schools nationally must immediately make changes as soon as possible regarding the process of implementing the assessment, processing grades and how to fill in the report card scores in the 2013 curriculum. This research was conducted with the aim of: (1) Application of the Mathematics Value Filling System and Report Printing in the 2013 Curriculum E - Report Card SMA Negeri 2 Seulimum (2) This is to determine the quality of the application of the Mathematics Score Filling System and Report Printing in the 2013 “e-Report Card” Curriculum of SMA Negeri 2 Seulimum. The research method used is this research method using a qualitative research approach applying different data collection methods in quantitative research. Relevant qualitative research questions are answered by qualitative research methods, so that the data collection method is specific, namely collecting qualitative data. Likewise, if the research question requires quantitative or numerical data. The results of this study are (1) the value data processing information system software in the 2013 E-Report Card curriculum for Senior High Schools (SMA) was created and developed using Codeigniter framework-based technology. Broadly speaking, this system has 3 users, namely Administrators, Teachers (Subject or Practice Teachers, Guardian Teachers, BP Teachers) and Students. (2) this information system has met ISO 25010 quality standards with the results of tests that have been carried out on the functional suitability aspect to get a percentage of 100%, on usability testing it gets a percentage value of 83.41% Very feasible, on performance efficiency testing using the website program get a percentage value of 91% (very high), while the reliability test results obtained 99.9% with the criteria very high.
Character Spotting Using Machine Learning Techniques

This work presents a comparison of machine learning algorithms that are implemented to segment the characters of text presented as an image. The algorithms are designed to work on degraded documents with text that is not aligned in an organized fashion. The paper investigates the use of Support Vector Machines, K-Nearest Neighbor algorithm and an Encoder Network to perform the operation of character spotting. Character Spotting involves extracting potential characters from a stream of text by selecting regions bound by white space.
Language as a bootstrap for compositional visual reasoning

Author(s): Wong, Catherine; Friedman, Yoni; Andreas, Jacob; Tenenbaum, Josh | Abstract: People think and learn abstractly and compositionally. These two key properties of human cognition are shared with natural language: we use a finite, composable vocabulary of nameable concepts to generate and understand a combinatorially large space of new sentence. In this paper, we present a domain of compositional reasoning tasks and an artificial language learning paradigm designed to probe the role language plays in bootstrapping learning. We discuss results from a language-guided program learning model suggesting that language can play an important role in bootstrapping learning by providing an important signal for search on individual problems, and a cue towards named, reusable abstractions across the domain as a whole. We evaluate adults on the same domain, comparing learning performance between those tasked with jointly learning language and solving reasoning tasks, and those who only approach the domain as a collection of inductive reasoning problems. We find that adults provided with abstract language prompts are better equipped to generalize and compose concepts learned across a domain than adults solving the same problems using reasoning alone.
UBC LANGUAGE CHATSIM: AI AND XR IN THE SERVICE OF FOREIGN LANGUAGE LEARNING

Appears in: EDULEARN21 Proceedings Publication year: 2021Pages: 8304-8309ISBN: 978-84-09-31267-2ISSN: 2340-1117doi: 10.21125/edulearn.2021.1673Conference name: 13th International Conference on Education and New Learning TechnologiesDates: 5-6 July, 2021Location: Online Conference
MULTIPLE POPULATION BACTERIAL FORAGING OPTIMIZATION ALGORITHM BASED ON CHAOS THEORY AND RELAY LIFE PREDICTION THROUGH THE INTEGRATION OF DEEP BELIEF NETWORK AND SUPPORT VECTOR REGRESSION

This paper aims to overcome the poor accuracy in relay life prediction. For this purpose, the performance parameters in the life cycle of a typical relay were collected as the input parameters, and subjected to Kalman filtering. Since these parameters form a nonstationary time series, two prediction models were proposed based on support vector regression (SVR) and deep belief network (DBN), respectively. Then, the training error of the SVR was adopted to correct the error of the DBN, and a hybrid prediction model was thus established. After that, a multiple population bacterial foraging optimization (MPBFO) algorithm was developed based on the chaos theory. The multiple population mechanism, together with the migration operator and manual selection operator, was introduced to realize the collaborative search of populations and improve the algorithm's global search ability and prediction accuracy was improved. Meanwhile, the penalty coefficient C and the kernel function width σ were calculated by the MPBFO. Through simulation tests, it is learned that the proposed algorithm achieved fast convergence and high accuracy, and outperformed all the other contrastive algorithms. Suffice it to say that this paper creates a desirable relay life prediction method.
When a crisis strikes: Emotion analysis and detection during COVID-19

Crises such as natural disasters, global pandemics, and social unrest continuously threaten our world and emotionally affect millions of people worldwide in distinct ways. Understanding emotions that people express during large-scale crises helps inform policy makers and first responders about the emotional states of the population as well as provide emotional support to those who need such support. We present CovidEmo, ~1K tweets labeled with emotions. We examine how well large pre-trained language models generalize across domains and crises in the task of perceived emotion prediction in the context of COVID-19. Our results show that existing models do not directly transfer from one disaster type to another but using labeled emotional corpora for domain adaptation is beneficial.
A Block Based Forgery Detection System on Biomedical Images

In recent years, with the increase in the quantitative and content of signal processing data in the field of health, the workload of experts working in the field of radiology is accumulating daily. In addition to the complication of determining the correct diagnosis based on the images, there is the issue of probable violations of biomedical images and altering the images. Among these counterfeits on biomedical images, the most common forgery is copy-move. This forgery refers to the copying and transferring a part of the same image to another part. In this study, to the best of our knowledge, for the first time, a block-based copy-move forgery detection method was proposed using Local Similarity Pattern (LSP) texture extraction on a current biomedical data set.
READING CULTURE IN ENGLISH FIRST ADDITIONAL LANGUAGE AT INTERMEDIATE PHASE

Appears in: EDULEARN21 Proceedings Publication year: 2021Pages: 5178-5187ISBN: 978-84-09-31267-2ISSN: 2340-1117doi: 10.21125/edulearn.2021.1067Conference name: 13th International Conference on Education and New Learning TechnologiesDates: 5-6 July, 2021Location: Online Conference
Rancang Bangun Sistem Identifikasi Penggunaan Masker Menggunakan Arduino

After the breakout of pandemic COVID-19, there’s a important requirement for protection methods, the maximum important is a face masks. The task's fundamental aim is to locate the presence of a face masks on human faces in real time. Teachable Machine is the software used for this object detection because it makes creating machine learning models fast and easy with the introduction of this Arduino Uno and p5.js primarily based masks reminder device for humans beings to care more about their health. This assignment consists of a digital camera that detects whether or not a person is sporting a face masks or not the use of facial features, after which adding a serial command to the facemask detection set of policies that instructs the Arduino to turn on or off LEDs primarily based mostly on the country of detection.
Introducing Python Programming

Many textbooks on algorithms will present the algorithms in so-called pseudo-code, something that looks like it is written in a real programming language while it is in fact written in an approximation to such a language but where the abstractions and programming constructs are chosen to make the algorithms look as simple as necessary. Since the goal of these books is to present the essentials of an algorithm and not distract the reader with unnecessary language artifacts, it is a sensible approach. It does, however, occasionally hide too many details from the reader, and since the pseudo-code cannot be run by a computer, it is not possible to experiment with it to test different approaches to how an algorithm could be implemented in practice. In this book, we will not use pseudo-code but present all algorithms in the Python programming language. Python is a very high-level language, and in many ways, Python implementations of common algorithms look very similar to pseudo-code versions of them, but with Python, you get a working implementation.
Internatıonal Law and Blockchaın Governance: Supplements or Competitors

Blockchain's usage is increasing, and the number of possible implications that have been explored and created is mind-boggling.This paper would look at some other potential blockchain implementations in the legal sector, especially in the sense of social regulation.The blockchain-based social regulator, which is neither created nor supported by any government or governmental institution, has the potential to transform the planet or its components into something known as a Decentralized Autonomous Organization (DAO).These assumptions are most likely too broad.The presented research focuses on a key and practical overview of regulations in a blockchain DAO that could be framed in the future and has the potential to affect current legal essentials.The study applies comparative method, using international public law and Lex Mercatoria as primary juxtaposition.
COMMUNICATION SKILLS IMPROVEMENT OF CROATIAN GRADUATE STUDENTS: KRAV MAGA SYSTEM AS TRANSCENDENT TEACHING PLATFORM

COMMUNICATION SKILLS IMPROVEMENT OF CROATIAN GRADUATE STUDENTS: KRAV MAGA SYSTEM AS TRANSCENDENT TEACHING PLATFORM
A new geared linkage mechanism with two length-variable links for path generation

A new geared linkage mechanism with two length-variable links is proposed for planar path generation in this paper. This proposed mechanism consists of a five-bar linkage, a simple gear train, two elementary planetary gear trains and two slider-crank mechanisms. Two combinations, each one including an elementary planetary gear train, a slider-crank mechanism and a link adjacent to fixed link, serve as two length-variable driving links driven by a simple gear train. The proposed mechanism can be regarded as a geared five-bar linkage mechanism with two length-variable driving links. An optimal dimensional synthesis procedure is introduced to determine design variables for generating different type planar prescribed path trajectories. The formation, structure and kinematic analysis of the proposed mechanism are discussed in detail also. Two examples are provided to illustrate the proposed design method.
Dual Processes on Dual Dimensions: Associative and Propositionally-Mediated Discrimination and Peak Shift.

Author(s): Johnson, Toby D; McLaren, R.P.; Civile, Ciro; McLaren, IPL | Abstract: Dual-process accounts posit that human learning can occur as a consequence of both associative and propositional processes. This can be contrasted with single process accounts that suggest learning is entirely propositional. In this paper, we offer evidence for both associative and propositional processes using a within-subjects two alternative forced choice discrimination paradigm. Stimuli that varied concurrently along two dimensions were created and each participant’s awareness was directed toward one, facilitating rule induction (i.e., propositional processing) on that dimension. Performance on the other dimension was then used to assess associatively-based performance. We report results that are initially inconsistent with both single process and dual-process accounts of discrimination learning. However, we then show how an associative network, that represents stimuli integrally, can predict the performance shown by participants in the experiment, providing evidence for a dual-process account.
Beyond In-Place Corruption: Insertion and Deletion In Denoising Probabilistic Models

Denoising diffusion probabilistic models (DDPMs) have shown impressive results on sequence generation by iteratively corrupting each example and then learning to map corrupted versions back to the original. However, previous work has largely focused on in-place corruption, adding noise to each pixel or token individually while keeping their locations the same. In this work, we consider a broader class of corruption processes and denoising models over sequence data that can insert and delete elements, while still being efficient to train and sample from. We demonstrate that these models outperform standard in-place models on an arithmetic sequence task, and that when trained on the text8 dataset they can be used to fix spelling errors without any fine-tuning.
THE MESSAGES SPECIAL EDUCATION LABELS COMMUNICATE

For more than two decades there has been a concerted effort in the field of Special Education to move beyond a deficit driven model to identify students with disabilities, and the language used to describe these individuals, to communicate a more realistic/strength-based view of these individuals. To this end, initiatives that promote person-first language, identifying the child before the disability, are now the common practice when introducing an individual with a disability. Community and university programs serving individuals with disabilities have changed their titles from Disability Services/Centers to Ability Services/Centers and professional journals in the field of Special Education have changed their titles to eliminate the use of antiquated terms like mental retardation and looking at disabilities rather than disorders. In this study, candidates in a Masters of Special Education Program (N=855), were asked to list characteristics they would use to describe individuals identified with either a learning disability, emotional/behavioral disability, autism spectrum disorder, attention deficit disorder or gifted/talented. Responses were categorized as being either positive/strengths, neutral, or negative/deficits. Results indicate that characteristics used to describe individuals with disabilities were predominately viewed as negative/deficits in all areas except gifted/talented, where the responses were predominately positive/strengths. A qualitative analysis of the candidate responses to a follow-up question related to their responses revealed a number of themes that may serve to inform teacher education in special education.
UniversityCompass: An AHP-Based Ranking and Selection App for University Prospecting in Developing Countries

The university selection problem occurs when university prospects need to make college choices in the face of multiple universities based on preferences. University search web apps are decision support tools used by prospects to ease the search process. A review of such web apps revealed that these platforms were developed for universities in the USA and the UK and do not Nigerian Universities. In this paper, we propose UniversityCompass, an AHP-based ranking and selection app for prospects intending to study in Nigerian Universities. Since its live hosting, UniversityCompass.com had been assessed by over 1,000,000 users in more than 4,000,000 page views, which underscores its usefulness in aiding undergraduates prospecting of Nigerian Universities. We observed the highest spike in page views are the periods that coincides with peak of university search in the country, i.e. January. In addition, more than 85% of the respondents considered UniversityCompass as a suitable platform for university search and rated the app as either “very good” or “excellent”.
Task and Situation Structures for Service Agent Planning

Everyday tasks are characterized by their varieties and variations, and frequently are not clearly specified to service agents. This paper presents a comprehensive approach to enable a service agent to deal with everyday tasks in open, uncontrolled environments. We introduce a generic structure for representing tasks, and another structure for representing situations. Based on the two newly introduced structures, we present a methodology of situation handling that avoids hard-coding domain rules while improving the scalability of real-world task planning systems.
Barcode-based filing information system in hospitals

Information technology plays a major role in the development of health care systems, one of which is in hospitals. Surakarta City’s Central General Hospital (RSUP) continues to use a traditional filing system. Several barriers remain in the way of implementing these activities, including misfile media record documents and difficulty locating medical record documents. The research method used in this study is descriptive research with data collection methods of observation and interviews. The subjects of this study were medical record officers, and the instruments used in this study were observation guidelines and free guided interview guidelines. Primary and secondary data sources were used in this study. The method of data collection and processing at the Central General Hospital (RSUP) in Surakarta City is collected through the processes of drm data input, borrower data input, borrowing data input, return data input, and evaluation data input, and is then processed quantitatively. The records from the inpatient medical record are then saved to the drm table, borrower table, loan table, return table, and assessment table, and used to produce filing information system production, borrower reports, loan reports, return reports, and appraisal reports using the PHP programming language and the Dream wevare application with a MySQL database.
Face Mask Detection

Manual surveillance of whether people are wearing a mask or not is not only costly but also a risky process. In this present world, which is being hit by the Coronavirus pandemic, the risk of spread of virus is exponentially high when people are not wearing mask. Thus, the automation of the process of detection of people not wearing mask had become a necessity. Our project uses deep learning techniques to determine whether a person is wearing a mask or not. Our proposed system identifies the mask violators automatically which reduces the risk of transmission of virus and also makes it easier for the authority to monitor the mask violators and take action against them without being put at risk of transmitting the virus. We use MobileNetV2 architecture to efficiently achieve best accuracy which is the key to our system. We were able to achieve accuracy of 99.22% after training the model. Detecting and tracking the face mask is the main aim of the project.
CGuider: A Sociable Robotic Platform for Predicting Future Career for Undergraduates

In this research, our main objective was to direct students to choose their future careers based on their experiences, qualifications, and passion. The social robotic platform was created to interact with undergraduate students in the computer science field. The structure was designed as a table-top robot which gives a realistic experience of a typical interview. The robot was equipped with an eye tribe, a microphone, and a speaker which were used to maintain continuous engagement with the student, detect speech and give responses accordingly. Several eye behaviors of the robot was predefined and implemented through an Android mobile application. Eye behaviors would change according to the state of the user’s attention, such as neutral, listening or speaking. DialogFlow API was used to build the conversational flows, which can be used to produce interactive and engaging voice-based conversations with the robot. Knowledge acquired through these interactive conversations such as student’s experiences, knowledge-level, and skills will be then used in the prediction of suitable future career paths. In order to make predictions, we need to observe the specific patterns in individuals which lead to various kinds of career paths. Acquiring these historical data was accomplished through a questionnaire targeted at past students who are currently pursuing career paths both in academics and in the relevant industry. After analysing the collected data, the DialogFlow API conversational flow was constructed and tested. We were able to successfully conclude that CGuider was successful in its attempt to guide undergraduates in proposing and advising to choose a suitable career path to become successful in life.
A Cross Partial Path Weighted Feature Fusion Object Detection Algorithm

In order to solve insufficient data features utilization efficiency and improve the performance of the deep neural network in image recognition, we propose a cross partial path weighted feature fusion (CPP) algorithm based on the Yolo framework. Firstly, CPP deepens the down-sampling depth of the backbone framework; then two weighted sampling partial paths are built in the feature fusion network, and the corresponding FPN layers are weighted and fused to form an output feature maps; finally, the output feature maps of different sizes are used to obtain more accurate bounding box position and category information. The experimental analysis results show that the advantage of our algorithm is to extend the network depth of the model as well as the network width. Our model be able to fuse more feature information so that it improves the recognition accuracy of object detection.
MEASURING LEARNER SATISFACTION IN RELATION TO DIGITAL PROJECT-BASED LEARNING IN HIGHER EDUCATION: DESIGNING THE TOOL

MEASURING LEARNER SATISFACTION IN RELATION TO DIGITAL PROJECT-BASED LEARNING IN HIGHER EDUCATION: DESIGNING THE TOOL
Secure Delegation of Isogeny Computations and Cryptographic Applications.

We address the problem of speeding up isogeny computation for supersingular elliptic curves over finite fields using untrusted computational resources like third party servers or cloud service providers (CSPs). We first propose new, efficient and secure delegation schemes. This especially enables resource-constrained devices (e.g. smart cards, RFID tags, tiny sensor nodes) to effectively deploy post-quantum isogeny-based cryptographic protocols. To the best of our knowledge, these new schemes are the first attempt to generalize the classical secure delegation schemes for group exponentiations and pairing computation to an isogeny-based post-quantum setting. Then, we apply these secure delegation subroutines to improve the performance of supersingular isogeny-based zero-knowledge proofs of identity. Our experimental results show that, at the 128-bit quantum-security level, the proving party only needs about 3% of the original protocol cost, while the verifying party's effort is fully reduced to comparison operations. Lastly, we also apply our delegation schemes to decrease the computational cost of the decryption step for the NIST postquantum standardization candidate SIKE.