Reviewerly/exHarmony huggingface dataset:
Aplikasi DonasiKu Berbasis Android

Used goods suitable for use are old goods that have been used once or more than once and are still reasonable to be reused. Most of these used goods are no longer used because they already have other, better substitutes. The current management system for these items is usually collected and stored in the warehouse or many scattered in the corner house until the items become a pile. Rather than being left alone, it is better to reuse these items. For example, they are donating items that are still reasonable to be reused to people who need them more. Donations in the form of used goods are still poorly managed, information on donation activities that are held is less spread out, and if there is, it is not necessarily reliable. So far, the management of the existing donation system is only for donations in the form of money, so the idea of a solution emerged in developing an Android-based donation system application. Causal analysis is used to analyze the problem based on the data to be collected. The method used for software development is extreme programming. This application can solve the problem of managing the used goods donation system so that people can make donations in the form of goods that are faster easier to collect and distribute.
Preparation of Learners’ Autonomous Learning and Their Views on Learning Content Selection in Autonomous Classroom Environment

Self-directed learning is a language learning context where individuals are encouraged to take full responsibility for their own learning and it is also a desirable goal for both parties that is the teacher and the learner. This paper aims to investigate learners' readiness for autonomy and their views on selecting their language learning contents at the university level in English language lessons. The participants were 52 fresh men who are studying at a foundation university in Istanbul, Turkey in 2019-2020 academic year. In this study, the data were gathered through a Likert type Learner Autonomy Questionnaire which was developed by Egel (2003). Descriptive analysis was used to analyze the data. The results showed that the participants have plans to continue learning English independently without their teacher after their formal education is over. It also became clear that learners consciously connect thepreviously learned knowledge to the new ones.Finally, we found out that while the learners prefer to choose their study materials, they seem to hesitate in determining the subject to be learned.
A privacy preserving querying mechanism with high utility for electric vehicles

Electric vehicles (EVs) are gaining popularity due to the growing awareness for a sustainable future. However, since there are disproportionately fewer charging stations than EVs, range anxiety plays a major role in the rise in the number of queries made along the journeys to find an available charging station. On the other hand, the use of personal data in various types of analytics is increasing at an unprecedented rate. Hence, the risks of privacy violation are also surging. Geo-indistinguishability is one of the standards for formalising location privacy as a generalisation of the local differential privacy. However, the noise has to be carefully calibrated considering the implications of potential utility-loss. In this paper, we introduce approximate geo-indistinguishability (AGeoI) which allows the EVs to obfuscate the individual query-locations while ensuring that they remain within their preferred area of interest. It is vital because journeys are often sensitive to a sharp drop in QoS, which has a high cost for the extra distance to be covered. We apply AGeoI and dummy data generation to protect the privacy of EVs during their journeys and preserve the QoS. Analytical insights and experiments are used to demonstrate that a very high percentage of EVs get privacy for free and that the utility-loss caused by the privacy-gain is minuscule. Using the iterative Bayesian update, our method allows for a private and highly accurate prediction of charging station occupancy without disclosing query locations and vehicle trajectories, which is vital in unprecedented traffic congestion scenarios and efficient route-planning.
Fine-tuning Language Models over Slow Networks using Activation Compression with Guarantees

Communication compression is a crucial technique for modern distributed learning systems to alleviate their communication bottlenecks over slower networks. Despite recent intensive studies of gradient compression for data parallel-style training, compressing the activations for models trained with pipeline parallelism is still an open problem. In this paper, we propose AC-SGD, a novel activation compression algorithm for communication-efficient pipeline parallelism training over slow networks. Different from previous efforts in activation compression, instead of compressing activation values directly, AC-SGD compresses the changes of the activations. This allows us to show, to the best of our knowledge for the first time, that one can still achieve $O(1/\sqrt{T})$ convergence rate for non-convex objectives under activation compression, without making assumptions on gradient unbiasedness that do not hold for deep learning models with non-linear activation functions.We then show that AC-SGD can be optimized and implemented efficiently, without additional end-to-end runtime overhead.We evaluated AC-SGD to fine-tune language models with up to 1.5 billion parameters, compressing activations to 2-4 bits.AC-SGD provides up to 4.3X end-to-end speed-up in slower networks, without sacrificing model quality. Moreover, we also show that AC-SGD can be combined with state-of-the-art gradient compression algorithms to enable "end-to-end communication compression: All communications between machines, including model gradients, forward activations, and backward gradients are compressed into lower precision.This provides up to 4.9X end-to-end speed-up, without sacrificing model quality.
O cuidado com o idoso frente a polifarmácia: uma revisão de Literatura / Care for the elderly front polypharmacy: a literature review

INTRODUCTION: Considering the increase in world life expectancy and, Brazil accompanies it, with elderly people who have at least one chronic pathology, of which 56% require pharmacological treatment polypharmacy is present, since it is frequent the use of five more medications.OBJECTIVE: To identify scientific publications on the care of the elderly in the face of poly pharmacy.METHODOLOGY: Review of literature of the narrative type, with data collection in the Virtual Health Library in the databases Latin American and Caribbean Literature on Health Sciences, Nursing Database and Medline using the Descriptors of Elderly Health Sciences, care, poly pharmacy with the operator boleando "AND".RESULTS AND DISCUSSION: A total of 643 articles were found, and 21 studies were selected, with the final selection of 10 articles.Two thematic categories were constructed: aging as a triggering factor of poly pharmacy and frailty in the communication and monitoring of the elderly in drug treatment.CONCLUSION: It is considered relevant the qualification of the gerontological care team, focusing on the theme of this study, as well as the holistic approach in the care process and, thus, it will be possible to review the drug and non-drug approaches that provide the elderly with the experience of old age with quality of life.
Analyzing Tree Architectures in Ensembles via Neural Tangent Kernel

A soft tree is an actively studied variant of a decision tree that updates splitting rules using the gradient method. Although soft trees can take various architectures, their impact is not theoretically well known. In this paper, we formulate and analyze the Neural Tangent Kernel (NTK) induced by soft tree ensembles for arbitrary tree architectures. This kernel leads to the remarkable finding that only the number of leaves at each depth is relevant for the tree architecture in ensemble learning with an infinite number of trees. In other words, if the number of leaves at each depth is fixed, the training behavior in function space and the generalization performance are exactly the same across different tree architectures, even if they are not isomorphic. We also show that the NTK of asymmetric trees like decision lists does not degenerate when they get infinitely deep. This is in contrast to the perfect binary trees, whose NTK is known to degenerate and leads to worse generalization performance for deeper trees.
Crosstalk Correction for Color Filter Array Image Sensors Based on Lp-Regularized Multi-Channel Deconvolution

In this paper, we propose a crosstalk correction method for color filter array (CFA) image sensors based on Lp-regularized multi-channel deconvolution. Most imaging systems with CFA exhibit a crosstalk phenomenon caused by the physical limitations of the image sensor. In general, this phenomenon produces both color degradation and spatial degradation, which are respectively called desaturation and blurring. To improve the color fidelity and the spatial resolution in crosstalk correction, the feasible solution of the ill-posed problem is regularized by image priors. First, the crosstalk problem with complex spatial and spectral degradation is formulated as a multi-channel degradation model. An objective function with a hyper-Laplacian prior is then designed for crosstalk correction. This approach enables the simultaneous improvement of the color fidelity and the sharpness restoration of the details without noise amplification. Furthermore, an efficient solver minimizes the objective function for crosstalk correction consisting of Lp regularization terms. The proposed method was verified on synthetic datasets according to various crosstalk and noise levels. Experimental results demonstrated that the proposed method outperforms the conventional methods in terms of the color peak signal-to-noise ratio and structural similarity index measure.
Convolutional Neural Processes for Inpainting Satellite Images

The widespread availability of satellite images has allowed researchers to model complex systems such as disease dynamics. However, many satellite images have missing values due to measurement defects, which render them unusable without data imputation. For example, the scanline corrector for the LANDSAT 7 satellite broke down in 2003, resulting in a loss of around 20\% of its data. Inpainting involves predicting what is missing based on the known pixels and is an old problem in image processing, classically based on PDEs or interpolation methods, but recent deep learning approaches have shown promise. However, many of these methods do not explicitly take into account the inherent spatiotemporal structure of satellite images. In this work, we cast satellite image inpainting as a natural meta-learning problem, and propose using convolutional neural processes (ConvNPs) where we frame each satellite image as its own task or 2D regression problem. We show ConvNPs can outperform classical methods and state-of-the-art deep learning inpainting models on a scanline inpainting problem for LANDSAT 7 satellite images, assessed on a variety of in and out-of-distribution images.
DAMPAK PEMBELAJARAN DARING TERHADAP HASIL BELAJAR SISWA SEKOLAH DASAR

The purpose of this study is to describe the impact of online learning on the learning outcomes of fourth grade elementary school students.The research method used is qualitative with a descriptive approach. Data was collected by observation, in-depth interviews, and documentation. The research was conducted at SDN 2 Rengging. The focus of the research is the impact of the online learning process on learning outcomes in science and Indonesian subjects. The research informants were 13 consisting of one fourth grade teacher, six guardians and six students consisting of two high-ability students, two medium-ability students and two low-ability students. The data analysis stage in this study used source triangulation.The results of the study found that the implementation of online learning at SDN 02 Rengging went according to the Circular Letter of the Minister of Education and Culture Number 4 of 2020 concerning the Implementation of Education in the Coronavirus Disease (Covid-19) Emergency. Teachers in teaching use the WhatsApp Group, YouTube, and Google Classroom applications. There are positive and negative impacts, but in the learning process at SDN 2 Rengging more highlight the negative impact, namely students do not understand the material. This happens because the teacher only conveys the material without any direct explanation, the child's lack of motivation to learn, the memory is full, and there are too many assignments.
The Effectiveness of Mordiscvein Learning Model to Enhance Student Learning Outcomes in Science Subject of IV Grade in Primary School in Limboto District, Gorontalo Regency

This study aimed to explore the effectiveness of the developed Mordiscvein learning model. This research has been carried out in elementary schools in Limboto District Gorontalo District Gorontalo province. This research was conducted on fourth-grade students of elementary school, including SDN Elementary School 2 Limboto, SDN 11 LImboto, SDN 18 Limboto, and SDN 15 Limboto, with the theme material 6 my ideals within a period of 3 months in the even semester 2021/2022. Data was collected through tests, safety, and questionnaire. Effectiveness analysis data was determined through the results of the test scores of learning outcomes, student activity, and student response to the Mordiscvein learning model. The test data were analyzed using paired T-test and N-Gain analysis. The results found that the Mordiscvein learning model has a high level of effectiveness; it is shown that the Mordiscvein learning model can improve student learning outcomes significantly from the analysis of the initial score and the final score.
Impact of the “DrugSpeak” programme on drug name pronunciation skills and perceptions in a pharmacy student cohort

Introduction: Difficulties in pronouncing many drug names can lead to medication errors in pharmacy practice. A pilot study called DrugSpeak was devised to provide educational scaffolding to enable students to pronounce drug names correctly. Method: Student participants (n = 26) accessed online videos on phonetics and audio files of drug names, and then undertook a workshop that provided them with basic phonetics training to assist them in pronouncing drug names correctly. Surveys and audio recordings of student pronunciations of drug names were conducted before and after intervention with DrugSpeak. Results: Significant increases (p&lt;0.01) in student drug pronunciation and accuracy were observed following DrugSpeak. Students reported reductions in anxiety and enhancements in their confidence levels relevant to drug pronunciation. Conclusion: The DrugSpeak education package yielded promising outcomes in the improvement of student drug pronunciation skills and in providing students with the confidence to tackle drug names unfamiliar to them.
Implementasi Algoritma K-Modes Untuk Mengukur Tingkat Kepuasan Mahasiswa Terhadap Pembelajaran Daring

The outbreak of the corona virus outbreak has made the economic system, education system and other systems very chaotic. That is why Dumai College of Technology has participated in implementing online learning from March 2020 to June 2021 in accordance with government regulations. Brave learning is an alternative in overcoming the lecture system, so students no longer come to campus and only attend lectures from home. Although learning activities are carried out boldly, lectures must still pay attention to the quality and quality of learning, that's why we need a method to analyze the level of student satisfaction in the bold learning process, which can later be used as evaluation material by the campus. The use of the k-modes clustering algorithm is to group the satisfaction results into several clusters including very satisfied, satisfied, dissatisfied and very dissatisfied and the data processed is data in the form of categorical data or data in the form of classification by performing matching equations between training data and centroids for looking for the closest distance and the value of the objective equation, the sample used was 100 respondents who filled out the satisfaction assessment questionnaire, from the results of the questionnaire data processing the cluster was very satisfied with a percentage of 22%, the cluster was satisfied 69%, the cluster was not satisfied 5% and the last in the very dissatisfied cluster as much as 4%
Language Learning Strategies Used among ESL Graduates

The application of Language Learning Strategies (LLS) could provide more positive impacts towards the learning process among learners.However, understanding LLS among Malaysian students of higher education, mainly ESL graduates, has been identified as a gap in several research studies.Therefore, the objectives of the study were to determine the common pattern of LLS among the ESL graduates and to evaluate the differences among the various strategies used in different courses.The aim of this study was to focus on the LLS used by two different groups of graduates which are TESL graduates and non-TESL graduates.A quantitative approach involving a survey design using The Strategy Inventory for Language Learning, known as SILL survey by Oxford (1990) was utilised in this study.The data was presented using descriptive data analysis.Samples were taken from 30 TESL graduates and 30 non-TESL graduates from Malaysian local higher education institutions.The findings showed that there are no significant differences between the percentages of the LLS used among the two groups.The results were used to identify the most and the least used LLS categories by both groups of graduates.According to the findings, there are no significant differences between the most and the least preferred LLS between the two groups.This study is hoped to provide positive contributions to identify LLS used by two different groups of ESL graduates.
An Efficient Video Indexing and Retrieval using Feature Fusion and Naïve Bayes Classifier

Abstract User query towards a cluster/group of video to retrieve videos automatically from available user dataset plays a significant role in developing multimedia technologies. Analyzing video for retrieval with essential features is considered as prior work. Here, input videos are viewed from UCF youtube action data for analysis. Then, foreground segmentation is performed to discover numerous small subset objects as every subset should specify the foreground video class. The segmented region is given for feature extraction. This work considers four different techniques for extracting features, namely chromatic moment, blur, color diversity, and reflection features. The extracted features are fused using Principle Component Analysis to eliminate the high dimensionality features as it may influences classification accuracy. The feature vectors are considered for combining the elements all together. Finally, classification is done with the Naïve Bayes classifier. The training and testing are performed with various combinations of data with 30:70, 40:60, 50:50, 60:40, 70:30, and 80:20, respectively. The efficiency of video retrieval is measured with metrics like accuracy, precision, recall, and F-measure. The proposed model is compared with the in-depth learning approach, where the anticipated model shows better results than that of the existing plan.
UTILITARIANISM WRITES NARRATIVES IN THE CONTENT OF PARAGRAPHS USING AESTHETIC DICTION

This article aims to describe utilitarianism in writing narratives in conveying the hidden message to the contents of paragraphs using aesthetic diction, research conducted in SMP IT Al-Qohhariyyah with the object of research in class IX from the results of the study illustrating that 45% of students were unable to deliver the mandate using aesthetic diction caused by not having an idea of the mandate to be delivered and not knowing the aesthetic diction, 30% of students were able to convey the mandate but did not use the aesthetic diction of this group had the ideas and mandate to be delivered but did not yet know the aesthetic diction and 25% of students were able to convey the mandate the aesthetics of this group have ideas and amants to be conveyed and have known aesthetic diction. The research method used is a quantitative method with a descriptive approach.
Combining System Integrity Verification with Identity and Access Management

Digital transformation and the utilization of Industrial IoT (IIoT) introduces numerous interconnected devices to factories increasing among others the challenge of managing their software versions and giving attackers new possibilities to exploit various software vulnerabilities.&#x0D; Factory networks were earlier isolated from the Internet. However, this separation is no longer valid and there can be connections that allow intruders to penetrate into information systems of factories. Another issue is that although factories typically are physically isolated, it is not necessarily safe to assume that physical security is in good shape as the novel supply networks comprise subcontracted activities and temporary work force. Another threat can also arise from unauthorized monitoring of devices and the unauthorized replacement of existing ones.&#x0D; Based on the previous, it is crucial that IIoT security should be built into factories of the future (FoF) right from the design phase and even low-end devices need to be supported. Trusted computing concept called remote attestation should be used. Remote attestation allows remote parties to verify the integrity of each system component. System components should include trusted hardware components that can be used to measure executable software. The term measurement means calculating the cryptographic hash of the binary component before passing control to it. Trusted hardware components should also have a mechanism to protect the integrity of the measurement list and cryptographic keys that can be used to sign integrity assertions. The verifier part should have a storage of reference integrity metrics identifying the expected values of these measurements.&#x0D; Deploying trusted computing and remote attestation concepts to industrial automation is not straightforward. Even if it is possible to use remote attestation with suitable hardware components, it is not clear how remote attestation should be integrated with various operational technology (OT) industrial automation protocols. Approaches to use remote attestation with existing industrial automation protocols (e.g., OPC UA) is discussed. Advanced identity and access management (e.g., OAuth2, OpenID Connect) can be used to combine integrity measurements with device identity information so that the remote attestation process is triggered by authentication during the first transaction. The focus is on machine-to-machine (M2M) communications with immutable device identities and integrity evidence transfer.