Reviewerly/exHarmony huggingface dataset:
Perancangan Sistem Informasi Perpustakaan Berbasis Desktop Pada SMA Negeri 1 Samalantan Kabupaten Bengkayang

The purpose of this study was to find out how to design and build a library information system, determine the feasibility and user response after the desktop-based library information system was implemented at SMA Negeri 1 Samalantan, Bengkayang Regency. The research method used in this research is Research and Development. The research model used is ADDIE. The research was conducted by trial by media experts and practitioners as many as 4 people and 70 respondents, the research was conducted from May to June 2022. The results of this study are desktop applications in the form of splash display, apk initial display, login, registration, dashboard, student data, data teachers, book catalogues, borrowing, returns, reports, and developers. The design of this desktop - based library information system uses Java Netbeand software version 8.2 and Xampp version 8.1.2.
Typological gaps in iambic nonfinality correlate with learning difficulty

This paper discusses gaps in stress typology that are unexpected from the perspective of a foot-based theory and shows that the patterns pose difficulties for a computationally implemented learning algorithm. The unattested patterns result from combining theoretical elements whose effects are generally well-attested, including iambic footing, nonfinality, word edge alignment and a foot binarity requirement. The patterns can be found amongst the 124 target stress systems constructed by Tesar and Smolensky (2000) as a test of their approach to hidden structure learning. A learner with a Maximum Entropy grammar that uses a form of Expectation Maximization to deal with hidden structure was found to often fail on these unattested languages.
Learning Phonotactics in a Differentiable Framework of Subregular Languages

Phonotactic constraints have been argued to beregular, meaning that they can be represented usingfinite-state automata (Heinz, 2018); furthermore, they have been argued to occupy a even more restrictedregion of the regular language class known as the subregular hierarchy (Rogers &amp; Pullum, 2011). Ourcontribution is to present a simple model of phonotactic learning from positive evidence. Our approach isbased on probabilistic finite-state automata (Vidal et al., 2005a,b). We study the model’s ability to induce localand nonlocal phonotactics from wordlist data, both with and without formal constraints on the automaton.In particular, we evaluate the ability of our learner to induce nonlocal phonotactic constraints from data ofNavajo and Quechua. Our work provides a framework in which different formal models of phonotactics canbe compared, and sheds light on the structural nature of phonological acquisition (Dai, 2021; Shibata &amp; Heinz,2019; Heinz &amp; Rogers, 2010, 2013).
On technical debt in mathematical programming: An exploratory study

Abstract The Technical Debt (TD) metaphor describes development shortcuts taken for expediency that cause the degradation of internal software quality. It has served the discourse between engineers and management regarding how to invest resources in maintenance and extend into scientific software (both the tools, the algorithms and the analysis conducted with it). Mathematical programming has been considered ‘special purpose programming’, meant to program and simulate particular problem types (e.g., symbolic mathematics through Matlab). Likewise, more traditional mathematical programming has been considered ‘modelling programming’ to program models by providing programming structures required for mathematical formulations (e.g., GAMS, AMPL, AIMMS). Because of this, other authors have argued the need to consider mathematical programming as closely related to software development. As a result, this paper presents a novel exploration of TD in mathematical programming by assessing self-reported practices through a survey, which gathered 168 complete responses. This study discovered potential debts manifested through smells and attitudinal causes towards them. Results uncovered a trend to refactor and polish the final mathematical model and use version control and detailed comments. Nonetheless, we uncovered traces of negative practices regarding Code Debt and Documentation Debt , alongside hints indicating that most TD is deliberately introduced (i.e., modellers are aware that their practices are not the best). We aim to discuss the idea that TD is also present in mathematical programming and that it may hamper the reproducibility and maintainability of the models created. The overall goal is to outline future areas of work that can lead to changing current modellers’ habits and assist in extending existing mathematical programming (both practice and research) to eventually manage TD in mathematical programming.
Classification of B-Lymphoblasts against Normal Lymphocytes by Deep Convolutional Neural Network

Leukemia is a life-threatening ailment and is a non-tumor type of cancer which results in a large number of abnormal white blood cells known as blasts. Image processing and machine learning techniques can be used to help pathologists to detect and classify these cancer cells. The present work tries to classify the blast cells of acute leukemia (B-Lymphoblasts) against healthy lymphocytes by a custom made Deep Convolutional Neural Network (DCNN) developed by the authors and named as LeukNet using an online dataset C-NMC 2019. The LeukNet is able to classify the cancer cells against the healthy cells with an Accuracy of 86.73% without much complicated segmentation and feature extraction. Even though the accuracy is slightly less compared to the related works on the same dataset, the classification model used in the study is comparatively simple.
A Ring-LWE-based digital signature inspired by Lindner–Peikert scheme

Abstract In this article, we give a digital signature by using Lindner–Peikert cryptosystem. The security of this digital signature is based on the assumptions about hardness of Ring-LWE and Ring-SIS problems, along with providing public key and signature of compact (1–1.5 kilobytes) size. We prove the security of our signature scheme in the Quantum Random Oracle Model. Our cryptanalysis has been done based on methods of Aggarwal et al. and Chen et al.
Weather Forecasting Analysis using Bayesian Regularization Algorithms

Weather forecasting has become very urgent in various fields of human life, including in big cities. The need for weather forecasting accuracy will be effective and efficient in managing the quality of civilization flexibly. Bayesian regularization is one of the techniques used to obtain accurate results and development of artificial neural networks. The training process achieves the smallest epoch using a general processing unit to solve big data and high resolution. Scenarios performed via dataset partitioning and MSE enhancement. The addition of training data will improve system performance which indicates a significant increasing accuracy. Likewise, the decrease in MSE can increase the system accuracy to achieve a convergence stability point. Weather forecasting can recommend work units within the city and its surroundings, even between provinces or countries.
The Effect Of Communicative Approach On Students' Speaking Ability Indonesian Language In Class Iv Elementary School Barru District

This study aims to describe the learning process of fourth grade students before applying the communicative approach, describe the fourth grade students' speaking skills after applying the communicative approach, and describe the effect of applying the communicative approach to the fourth grade students' speaking skills in Indonesian language learning in Barru District Elementary School.The results showed a very large influence on students' speaking ability in the experimental class using a communicative approach than in the conventional class. From the results of these data shows that using the communicative method the results of students' speaking abilities are greater. The analysis was carried out and it has been statistically tested that tcount is greater than ttable (10.2 2.0399) which causes Ho to be rejected and H1 to be accepted. So that there is an influence between the speaking ability of students who use a communicative approach and those who do not use a communicative approach in the fourth grade of SD Barru Regency.
STUDENTS’ ERRORS IN POSITIONING CONJUNCTION IN WRITING ESSAY

Abstract Conjunction plays an important element in communication which acts as a connector between clauses with other clauses. Some students still make errors in positioning conjunction in writing. This research aimed to identify students’ errors in positioning conjunction in writing essay. This research is conducted to the fifth semester students of English Language Education Study Program, Faculty of Teacher Training Educational Sciences, Pakuan University. There were 20 students who become the participants. The writer uses descriptive method to analyze students’ error in positioning conjunction. The data are collected by using students’ work, questionnaire, and interview note. There are four types of error: omission, addition, misformation, and misordering. From the result of the research, it is found that the highest error is misformation. There are (50,6%) misformation errors. Addition appears as the second error (34,5%). The third error comes from omission (11,5%) and the lowest error is misordering (3,4%).
THE USE OF SCRAMBLED WORD GAME ON STUDENTS’ VOCABULARY

Learning vocabulary is often monotonous because the teacher only asks the students to do the worksheet without giving activities to make them enjoy learning. For that reason, the teacher needs an appropriate medium that can offer a fun learning process and attract the students’ interest. One of the teaching mediums is scrambled word games. The aim of this research is to investigate the use of scrambled word games on students’ vocabulary retention. The population of this research is the first-grade students of SMP Negeri 3 Cikembar. The total number of samples are 10 students of class VII-B. The sample is chosen by simple random sampling technique. In conducting this research, the writer applies a pre-experimental method and uses one group pretest-posttest design. The writer gives pre-test, two treatments, and post-test to the students. Vocabulary test is given to measure the students’ vocabulary in descriptive text as the topic. The data is analyzed by using the t-test formula. Based on the calculation of the data, the mean of the difference is 12.6 and the total of deviation of difference is 764.4. In conclusion, the final result shows that the use of scrambled word games has an effect on students’ vocabulary retention.
Perception Analysis of Economic Education Students on Online Learning in The Covid-19 Pandemic

The Covid 19 pandemic has changed the way people live, including in the field of education. To avoid overtime, the Minister of Education and Culture has issued a policy regarding the online teaching and learning process. This study aims to determine students' perception of online learning during the Covid19 pandemic. This study uses qualitative method with phenomenological type. The respondents to this study were 9 students from the Economic Education research program at the University of Negeri Malang who participated in e-learning during the Covid19 pandemic. The selection of research subjects by purposeful sampling method. The techniques that will be used to collect data in this study include three techniques, namely: observation technique, interview technique and documentation technique. The data analysis technique in this study includes data analysis steps that include: data reduction, data presentation, and conclusion or verification. Then, to check the validity of the data, the researchers used the triangulation method. The results show that the implementation of e-learning in lectures is less effective, students prefer to study offline in face-to-face classes than e-learning. The Internet, solutions such as the use of fun and soothing learning materials, and the impact of positive and negative impacts perceived by students.
Re-Examining "Media Effects Debate": Students' Off Task Mobile Device Use in Cameroon Higher Education Classrooms

Mobile devices have become an inevitable part of the classroom.But how do we ensure that these mobile devices do not negatively influence learning?This study set out to examine what higher education students do when on their mobile devices while in class, why they do this and recommendations were made at the end on how to ameliorate this situation.The study revealed that students bring several mobile devices to class which are frequently involved in off task activities related to communication and seeking information.Moreover, the study revealed that this involvement differs by different academic majors.However, comfortable language does not have an effect on the students" behaviour.Furthermore, it was reported that the reasons for these off task activities are both teacher and student inclined.Thus, to cultivate a more favourable environment for learning in the presence of mobile devices, both teachers and students have a major role to play.
Resilience Analysis of a Localization Solution for Railway and Tramway Vehicles under Failures in GPS Measurement Data Acquisition

In this paper, a localization solution for railway and tramway vehicles, relying on low-cost on-board sensor technology and on a Kalman-based data fusion scheme, is tested against failures in GPS measurement acquisition. Such failures can be caused by either non-favorable operating conditions (e.g, the presence of tunnels), or intentional/accidental service disruptions. Our analysis, carried out through simulation of a three-dimensional multi-body vehicle, highlights failure conditions under which the localization solution is shown to be resilient with respect to an acceptable performance level, as well as situations that the original framework is not able to cope with. In the latter case, a variant solution, still based on low-cost on-board technology, is shown to be able to recover satisfactory performance.
Theological Review of the Concept of Feng Shui According to Colossians 2:8

Feng shui was a part of the cultural heritage of ancient Chinese society mainly used to design buildings and spaces according to the birth date or the basic elements of the owner or the person who occupied a building to achieve harmony and balance with the environment aimed to bring peace and prosperity. Feng shui practices had become a matter of debate among Christians. The question was whether feng shui practices such as calculations in building construction, determining the direction a tomb should face, or finding a date to start a business did not conflict with God's Word. From a non-theological point of view, feng shui was well accepted because it seemed reasonable. This article discussed feng shui from a theological perspective by referring to Colossians 2:8. In the research of this article, the researchers used a qualitative descriptive method with an exposition approach where data collection was obtained from literature studies and interviews. The results of the discussion on this topic showed that theologians concurred that feng shui did not correspond with God's Word, while the Bible interpreted feng shui as a false philosophy. The conclusion drawn was the rejection of feng shui practices by theologians and the Bible.
Selecting a set of semantic labels to eliminate ambiguity for Vietnamese

The rapid development of automatic control systems in natural language, automatic translation systems based on semantic statistics have been receiving much attention from computer science researchers. However, this method requires a large bilingual corpus and accurate semantic tagging, the construction of which requires a lot of time and effort, because of the ambiguity of the natural language. For Vietnamese, automatic question-and-answer systems are increasingly developing in Vietnam, but the problem of semantic ambiguity has not yet received much attention from domestic studies. In this paper, we build a model to evaluate and select an effective and reasonable set of semantic labels from 3 commonly used sets of semantic labels: LLOCE (Longman Lexicon of Contemporary English), LDOCE (Longman Dictionary) of Contemporary English) and WordNet. And then, select the appropriate set of labels, apply it to automatic semantic labeling systems for Vietnamese, help eliminate semantic ambiguity, and support automatic translation, automatic question-and-answer systems efficiently.
PENYUSUNAN BOOKLET SCAFFOLDING MENGGUNAKAN TEORI POLYA MATERI TRIGONOMETRI

This research aims to compile a scaffolding booklet using Polya's theory of trigonometric subject. The method of this research is a research and development (R&amp;D) method with a 4-D development model. The research subject are the student of class X MIA 1 SMAN 6 Pontianak. Data collection tools used are unstructured interviews and questionnaires. Data analysis is viewed from the results of the questionnaire assessment, how to develop booklets and booklets. From this research, it was found that the clarity of how to solve trigonometric problems in textbooks used in schools is still not clear, because the completeness of the fourth polya step, namely at the stage of re-examining, does not exist and there are still students who do not understand how to solve the problems given. The preparation of this booklet is to improve and clarify how to solve the problems in the book. The completeness of the polya steps that did not exist before came into existence and the stages of solving Polya's problems become complete in the booklet. Students who do not understand the explanations in the textbooks will at least understand enough when given an explanation of how to solve problems in the booklet.
Pairwise Learning via Stagewise Training in Proximal Setting

The pairwise objective paradigms are an important and essential aspect of machine learning.Examples of machine learning approaches that use pairwise objective functions include differential network in face recognition, metric learning, bipartite learning, multiple kernel learning, and maximizing of area under the curve (AUC). Compared to pointwise learning, pairwise learning's sample size grows quadratically with the number of samples and thus its complexity.Researchers mostly address this challenge by utilizing an online learning system. Recent research has, however, offered adaptive sample size training for smooth loss functions as a better strategy in terms of convergence and complexity, but without a comprehensive theoretical study. In a distinct line of research, importance sampling has sparked a considerable amount of interest in finite pointwise-sum minimization. This is because of the stochastic gradient variance, which causes the convergence to be slowed considerably. In this paper, we combine adaptive sample size and importance sampling techniques for pairwise learning, with convergence guarantees for nonsmooth convex pairwise loss functions. In particular, the model is trained stochastically using an expanded training set for a predefined number of iterations derived from the stability bounds.In addition, we demonstrate that sampling opposite instances at each iteration reduces the variance of the gradient, hence accelerating convergence. Experiments on a broad variety of datasets in AUC maximization confirm the theoretical results.
Bias Reduction in Representation of Histopathology Images Using Deep Feature Selection

Abstract Appearing traces of bias in deep networks is a serious issue that can play a significant role in ethics and generalization. Recent studies report that the deep features extracted from the histopathology images of The Cancer Genome Atlas (TCGA), the largest publicly available archive of 11,000 patients covering 25 organs and 32 cancer subtypes, are surprisingly able to accurately classify the whole slide images (WSIs) based on their acquisition site. This is clear evidence that the utilized Deep Neural Networks (DNNs) unexpectedly detect the specific patterns of the source site rather than histomorphologic patterns, biased behaviour resulting in degraded generalization. This observation motivated us to propose a method to alleviate the destructive impact of hospital bias through a novel feature selection process. To this effect, we have proposed an evolutionary strategy select a small set of optimal features to not only accurately represent the histological patterns of tissue samples but also to eliminate the features leading to internal bias toward the institution. The performance of the proposed method has been assessed using the features extracted from TCGA images made available by NIH (National Institute of Health). The selected features extracted by a state-of-the-art network trained on TCGA images (i.e., the KimiaNet), considerably decreased the institutional bias. The proposed scheme is not limited to DNNs and/or specific types of biases; it can be employed to reduce various kinds of bias in a much more comprehensive range of data-driven feature extraction models. The conducted experiments, the external validation in specific, clearly demonstrate that the bias cannot be fully controlled just during training a model. Therefore, a feature selection for downstream tasks plays a crucial role in significantly reducing the bias.
SMART WHEEL CHAIR USING IOT

According to one study, there are about 6 million people in the world who are paralyzed and need a wheelchair to move. Wheelchairs had to be moved and supported by in the existing system. However, in normal use, these joystick-controlled wheelchairs are difficult to operate. Especially for paralyzed people, the one-way operation of the hard button and joystick made it difficult to operate the joystick. To overcome these problems, the proposed Smart wheelchair using IOT can be moved with a slight tilt of the hand and is also implemented in voice control and Android applications. This project works perfectly with user-specified voice and gesture commands, and attention is also paid to human stability. This technology is also based on wireless technology, which also saves wiring costs. output from the wheelchair's designed gesture and voice controls and obstacle detection.
IOT Based Health Monitoring System for Covid Patients

The number of COVID patients is rising quickly in the midst of the present pandemic. For the doctors, it is quite demanding and requires a lot of patience to keep track of all the COVID patients. This system is employed to continually keep track of the patient’s health in order to remedy this. The patient’s temperature, heart rate and SPO2 rate will be tracked by this system. As The corona virus and other viruses, as is well known, pose a threat to civilization and are hazardous. Our ability to monitor the patients health problems is made possible by this method. In this framework suggests, a few sensors, GSM and LCD are connected to Raspberry pi, nodeMCU, and Arduino Nano. When an aberrant condition arises, the system afterwards keep track of the patient’s health and notifies the doctor, the patient’s family, and an anybody else who needs to know via an android app.