Reviewerly/exHarmony huggingface dataset:
Decision Support System Penentuan Jurusan Siswa Baru SMA Menggunakan Metode Simple Additive Weighting

There is a change in the curriculum as well as the increasing number of students from year to year, many criteria are used in determining student majors according to the interests of students talent and desires. With these constraints it will affect the calculations process, so that the results obtained later do not rule out the possibility of non-objective selection. Therefore, the process of majoring in sma negeri 1ungaran which is still done manually need to switch digitization, namely by making a Decision Support System which will later be more effective, efficient and determined according to exixting criteria. This research was designed and developed using the PHP and MYSQL programming languages as the database and using the Simple Additive Weighting (SAW) method to perform weighted summation calculations containing ceveral criteria. From the results of testing using blackbox shows that this program can work according to its function, namely being able to input data and perform calculations for determining student majors. As well as accuracy testing which produces a fairly good level of accuracy of 80% from 100%. From the results of this study, it can be concluded that this system can determine students majors according to their academic interests and talents. And provide effectiveness and efficiency of teachers in helping majors.
KETERAMPILAN MENULIS PUISI DENGAN TEKNIK BRAINWRITING PADA SISWA KELAS X MADRASAH ALIYAH MELALUI PENERAPAN CONTEXTUAL APPROACH

This study aims to describe the poetry writing skills of grade X students. MA Darul Hikmah Temon Sawoo Ponorogo by utilizing brainwriting techniques and contextual approaches. Research data collection was carried out by interviews, observations, and tests. The data analysis method of this study is comparative descriptive analysis. Triangulation of data collection methods is the data validity technique used. Accuracy of theme and meaning, style, diction, and rhyme. All four aspects of poetry writing showed improved results. The grade point average before the study was 68, meeting the minimum completeness requirement of 75. The average value in cycle I is 75. A total of 15 students or 56% have met the Minimum Completeness Criteria (KKM). However, it has not met the target of 75% of students or 19 students scoring above KKM, so the percentage does not meet the achievement indicators. The average score of writing student poetry increased to 84% in Cycle II. Eighty-four percent of the 26 students had achieved minimum mastery scores. It can be concluded that the goal of achieving a minimum completion score of 75% of students can be achieved. As a result, Class X students of MA Darul Hikmah Temon Sawoo Ponorogo can improve their poetry writing skills by utilizing brainwriting strategies and Contextual Approaches.
Reading Patterns, Scanning and the ‘Control F’/Search Icon: How Students Really (Don’t) Read

Purpose: The purpose of this research was to investigate whether digital reading had an impact on reading skills, as well as students’ tendencies to read online or offline in the L2 English speaking classroom. Approach/Methodology/Design: This quantitative-qualitative mixed methods case study involved was comprised of a convenient, purposive sample of 16 participants for semi structured focus group interviews in an English-speaking private university. Findings: The findings seem to suggest that reading digitally prods students not only to adopt skimming patterns, but to use the “Control F” command to bypass reading altogether. Furthermore, students’ reading preferences for online/offline material was also revealed, showing a significant tendency of students to revert to online material “just to understand” or “get an idea” while deep understanding was substantially associated with preference for offline reading. Practical Implications: The findings of this study may serve to guide teachers of English in the L2 classroom how to navigate online texts comprehensively, what skills to promote while reading online and which to forgo. Originality/value: This study sheds light on the possible misuse of “Control F” during online reading exercises and underscores the necessity of using this tool over and above reading strategies and skills, and not as a stand-alone tool in itself,
Solar Radiation Forecasting Using Artificial Intelligence Techniques for Energy Management System

The purpose of this paper is to highlight solar radiation forecasts with high accuracy and reliability over a 24-h horizon in order to deduct a prediction of the energy production of a photovoltaic (PV) system integrated in a positive energy building. This prediction will feed algorithms for optimal energy management within the building. Our study will focus attention on the most widely used and effective techniques for solar radiation prediction, namely: MLP-based neural networks and the popular ensemble learning technique under the name “STACKING”. The aim is to predict hourly solar radiation for time horizons ranging from h + 1 to h + 24 using current and past values of daily solar radiation, temperature and humidity. At this point, different natures of these input parameters have been tested. In addition, the most appropriate forecasting technique that gives the best results was selected. In our simulations, 3 years of available data (2019–2022) were used for the city of Rabat. Python was used to develop 24-h GHI prediction models. To evaluate the performance of our prediction models, two evaluation scales were selected, namely: nRMSE (normalized root mean square error), nMAE (normalized mean absolute error). The good results and the best evidence are the results of the scales; Also obtained nRMSE = 2.384% and nMAE = 1.556%. The outputs obtained show that in order to acquire good results, it is necessary to take into account the input parameters that have an effect on the accuracy, the optimal selection of the number of past values as well as the optimal selection of the prediction technique.
Penggunaan Media Gambar sebagai Upaya Meningkatkan Hasil Belajar Mata Pelajaran Ilmu Pengetahuan Sosial (IPS) Siswa Kelas VII.H SMP Negeri 1 Mataram

This study aims to improve learning outcomes through the use of media images. This research is a class action research, the research subjects are students of class VII.H SMP Negeri 1 Mataram. This research was conducted in three cycles, each cycle carried out based on four stages, namely: planning, implementing, observing, and reflecting. Of all the activities of implementing action research in class VII.H of SMP Negeri 1 Mataram on the subject matter of Humans, Places, and the Environment carried out in three cycles it can be concluded that the learning outcomes of class VII.H students of SMP Negeri 1 Mataram for the 2022/2023 academic year, using media images can be improved with the final result of the study is the class average before the action 77.87 or 68.42% classical completeness, to 78.79 or 76.32% classical completeness in cycle I, increased to an average of 80 .50 or 84.21% in cycle II and experienced an increase to an average test of 84.18 or 97.37% in cycle III. From the results of student observations it was shown that there was an increase in student activity in cycle I obtained a score of 17 or 60.70% to 20 or 71.43% in cycle II and 23 or 85.71 in cycle III. The increase in student activity in social studies learning is supported by the increase in teacher activity in maintaining and improving the learning atmosphere using media images, this can be seen from the observations of teacher activities where in cycle I a score of 22 or 61.10% was obtained, becoming 26 or 72.23 % in cycle II and increased to 33 or 86.12% in cycle III.
Understanding In-Situ Programming for Smart Home Automation

Programming a smart home is an iterative process in which users configure and test the automation during the in-situ experience with IoT space. However, current end-user programming mechanisms are primarily preset configurations on GUI and fail to leverage in-situ behaviors and context. This paper proposed in-situ programming (ISP) as a novel programming paradigm for AIoT automation that extensively leverages users' natural in-situ interaction with the smart environment. We built a Wizard-of-Oz system and conducted a user-enactment study to explore users' behavior models in this paradigm. We identified a dynamic programming flow in which participants iteratively configure and confirm through query, control, edit, and test. We especially identified a novel method "snapshot" for automation configuration and a novel method "simulation" for automation testing, in which participants leverage ambient responses and in-situ interaction. Based on our findings, we proposed design spaces on dynamic programming flow, coherency and clarity of interface, and state and scene management to build an ideal in-situ programming experience.
Fingerprinting IoT Devices Using Latent Physical Side-Channels

The proliferation of low-end low-power internet-of-things (IoT) devices in "smart" environments necessitates secure identification and authentication of these devices via low-overhead fingerprinting methods. Previous work typically utilizes characteristics of the device's wireless modulation (WiFi, BLE, etc.) in the spectrum, or more recently, electromagnetic emanations from the device's DRAM to perform fingerprinting. The problem is that many devices, especially low-end IoT/embedded systems, may not have transmitter modules, DRAM, or other complex components, therefore making fingerprinting infeasible or challenging. To address this concern, we utilize electromagnetic emanations derived from the processor's clock to fingerprint. We present Digitus, an emanations-based fingerprinting system that can authenticate IoT devices at range. The advantage of Digitus is that we can authenticate low-power IoT devices using features intrinsic to their normal operation without the need for additional transmitters and/or other complex components such as DRAM. Our experiments demonstrate that we achieve ≥ 95% accuracy on average, applicability in a wide range of IoT scenarios (range ≥ 5m, non-line-of-sight, etc.), as well as support for IoT applications such as finding hidden devices. Digitus represents a low-overhead solution for the authentication of low-end IoT devices.
A Hybrid Neural Network Based on Multi-feature Fusion for Paper Recommendation

The purpose of academic paper recommendation is to provide researchers with papers of interest. Existing recommendation methods often takes the title or abstract as the paper's representation and learn based on text matching, ignoring the feature and structural information of the paper, resulting in poor semantic relevance to the recommendation results. A hybrid neural network paper recommendation method based on multi-feature fusion is proposed, which combines attention mechanism to learn deep semantic and structural features of papers to achieve classification and recommendation of papers in different academic fields. Experiments show that the method can better complete the classification and recommendation of papers, and has great accuracy compared with the traditional paper recommendation.
TS-GGNN: Combining Graph and Sequence Features for Vulnerability Detection in Source Code

Software vulnerability detection is crucial for maintaining the security and stability of software systems. In this paper, we propose a novel neural network model called TS-GGNN to address the problem of vulnerability detection in source code slices. The TS-GGNN model effectively captures both local and global features of vulnerable code by fusing sequence features with graph features. To achieve this, we utilize graph structure and sequence structure learning approaches to comprehensively extract valuable information from the source code slices. Our experiments are conducted on the SARD dataset, which consists of 61,638 code samples annotated for the presence or absence of vulnerabilities. The results demonstrate that TS-GGNN has the best vulnerability detection performance, with an accuracy of 99.4%, a precision of 98.81%, and an F1 score as high as 99.4% thereby validating the effectiveness of the TS-GGNN model in capturing features relevant to software vulnerabilities.
C2FMI: Corse-to-Fine Black-box Model Inversion Attack

Privacy-preserving machine learning requires that models do not reveal any private information about their training data. However, model inversion attacks (MIAs), which aim to recover the features of training data, pose a huge threat to the security of AI models. Most existing MIAs assume that the target model is white-box, but most models deployed in reality are black-box, and these models can only be accessed like an oracle. There are a few studies for black-box scenarios, but their performance is limited. In this paper, we firstly formulate the MIA problem completely in Bayesian perspective. Second, we propose a novel two-stage MIA approach, the Coarse-to-Fine Model Inversion Attack (C2FMI), which efficiently addresses the MIA problem in the black-box scenario. In stage I of C2FMI, we design a reverse network that constrains the recovered images (also named attacked images) to fall near the manifold space of the training data. In stage II, we design a black-box oriented strategy which further facilitates the attacked images to approach the training data. Empirically, C2FMI achieves a performance that even surpasses existing white-box attack methods. Furthermore, we design the stability analysis method for analyzing the stability of C2FMI along with existing MIAs. Finally, we explore the potential countermeasures which could defend against our attacks.
Perbandingan Minat Belajar Menggunakan Google Meet dan Zoom Meeting di Perguruan Tinggi Keagamaan Islam Negeri

The study aimed to compare learning interests during the Covid-19 pandemic using the Google Meet and Zoom Meeting platforms in the distance learning process for PTKIN Sharia Banking Study Program students in East Java. The population in this study were students of the PTKIN Sharia Banking Study Program in East Java. The sampling method used is purposive sampling. This type of research is quantitative with an event study approach. The results of the comparison of student interest explained that the facility factor, economic factor, and convenience factor significantly differed from student learning interest using the Google Meet and Zoom Meeting platforms for PTKIN Islamic Banking Study Program students in East Java. In addition, the results of a comparison of students' interest in the Google Meet and Zoom Meeting platforms of PTKIN Islamic Banking Study Program students in East Java showed that students' interest in using the Google Meet platform was higher than that of students using the Zoom Meeting platform. That is because Google Meet is easy to access; when experiencing signal interruption, users can rejoin immediately and have no time limit during the learning process because it does not require a premium account.
Evidence of Climate variability by the Spatio-Temporal Analysis of Temperature in the City of Sokoto

Temperature is crucial in observing changes occurring in the environment, such as climate change. Climate change is caused by the emission of excessive greenhouse gas and has been acknowledged as the main cause of various environmental impacts such as an increase in the flood scenario, frequent droughts, increasing wildfires, and heatwaves in various parts of the world. Temperature behaviour which has been studied in the city of Sokoto was used as a yardstick to check for the presence of variance in climate. Temperature data were gotten from the Nigerian Meteorological Agency (NIMET) that spanned thirty-one years (1980–2010) was used for this study. ANOVA and the Mann Kendall trend test were used to analyze the data used. The ANOVA findings revealed that the maximum and minimum temperatures differed significantly between the months. The Mann-Kendall trend test results suggest an increasing trend in maximum temperature and a declining trend in minimum temperature in the annual statistics. The monthly trend was made up of both increasing and decreasing trends. The city of Sokoto being located in the region of the Sahel savannah has very sensitive to minor changes in climate variance, hence this result will help in the planning of agriculture-related activities such as planting time, type of crops to plant, type of animals to be reared, types of animal diseases to provide vaccines for, etc.
A Study on Verification Methods of Vocabulary Grade

In this study, a new method was designed and verified to verify the validity of the 'vocabulary grading list', which selected and graded educational vocabulary to support systematic vocabulary teaching and learning in school education. As a verification method, the following two-way method was devised. First, as a way to expand language life beyond school education, it is a way to verify validity by checking whether the vocabulary grade to be verified is a level that appropriately reflects general language life in the wider world than school education. To implement this plan, a hierarchy of vocabulary extracted from a large-scale corpus, which is a new basic data, was set up and a method of comparative analysis with the grade of this study was designed. Second, it is a convergent method to verify the validity by focusing more on the educational purpose of the vocabulary grade and checking whether it is a level that can explain specific teaching-learning aspects of school education. To implement this plan, a method was designed to reveal the degree of linkage with textbook text readability levels of various subjects through correlation analysis and regression analysis. As a result of the verification, the vocabulary grade of this study was quite consistent with the lexical hierarchy extracted based on the large-scale corpus and the level of readability evaluation of textbook texts, but showed a tendency to show a high degree of concordance, especially in low-level easy vocabulary, and the verification result index was poor. If not, they showed mutual similarity. Through this, it was confirmed that the verification method of this study was partially effective, and it could be said that it was a useful verification result in that it consistently informs the direction of supplementation of the vocabulary ranking list. The vocabulary grade verification method explored in this study is meaningful in that it improved the empirical quality of vocabulary grade setting and grade verification.
Cryptocurrency Price Prediction Using Enhanced PSO with Extreme Gradient Boosting Algorithm

Abstract Due to the highly volatile tendency of Bitcoin, there is a necessity for a better price prediction model. Only a few researchers have focused on the feasibility to apply various modelling approaches. These approaches may prone to have low convergence issues in outcomes and acquire high computation time. Hence a model is put forward based on machine learning techniques using regression algorithm and Particle Swarm Optimization with XGBoost algorithm, for more precise prediction outcomes of three cryptocurrencies; Bitcoin, Dogecoin, and Ethereum. The approach uses time series that consists of daily price information of cryptocurrencies. In this paper, the XGBoost algorithm is incorporated with an enhanced PSO method to tune the optimal hyper-parameters to yield out better prediction output rate. The comparative assessment delineated that the proposed method shows less root mean squared error, mean absolute error and mean squared error values. In this aspect, the proposed model stands predominant in showing high efficiency of prediction rate.
The rise and fall of Serial Verb Constructions: Finale

This article concludes the special issue of Stellenbosch Papers in Linguistics PLUS dedicated to the diachrony of Serial Verb Constructions. The authors of the ten contributions included in the volume discuss the most important results of their studies and suggest the possible lines for future research.
Flipchart and Booklet As Media to Increase Cadre’s Knowledge About Latent Tuberculosis Prevention in Children

Educating and empowering the community through tuberculosis cadres is crucial in the early detection of latent tuberculosis infection (LTBI) in primary healthcare settings. From the wide variety of educational media that can be used, flipcharts and booklets have a strong impact on engaging the participants through visual media. Therefore, this community service aims to determine the effect of education through flipcharts and booklets on TB cadres to increase LTBI finding in children. A total of 20 TB cadres of Dr. Soetomo Primary Health Care Surabaya participated in this study. Statistical analysis to evaluate the difference between the pre-test and post-test was used with the Wilcoxon test. The result of the pretest showed insufficient knowledge about latent tuberculosis with an average score of 74%. The post-test average score of 91% indicated a 17% increase in score. There was a significant difference in the score of the test before and after giving the material (p=0.001), indicating an increase in LTBI knowledge of the TB cadres after giving health education using flipchart and booklet as evidenced by the increase in average score between pre-test and post-test.
Curriculum-heavy reinforcement learning for multi-domain operations

We present a framework for developing software agents via Machine Learning (ML) entitled Curriculum-Heavy Accelerated Learning in a Competitive Environment (CHALICE). CHALICE is designed to train and deploy intelligent agents capable of executing strategies for air-ground combat as embodied in AFRL’s MIST turn-based wargame system. Such agents can be used to suggest courses of action in real-time to operational planners and to provide an adversarial opponent for evaluation of proposed courses of action. CHALICE uses state-of-the-art Deep Neural Networks (DNNs) to represent the state of the environment and Deep Reinforcement Learning (DRL) to train each agent via repeated feedback from outcomes of the MIST Stratagem game. Unlike recent DRL approaches for strategy games such as Go or StarCraft [1] [2], CHALICE minimizes dependence on existing corpora of human gameplay and trains efficiently with low computational resources and short convergence time (hours to days rather than weeks to months). Over the course of four government-led competitions, CHALICE produced agents that continually improved their performance, resulting in competitive play against human and automated opposing agents at relatively low training cost and time. In this paper, we motivate the operational problem and technical challenges, provide an overview of our technical approach, elaborate on our vision-based and graph-based DNN architecture design and agent training procedure, and present results from the most recent Stratagem competition. We close with a discussion of future research recommendations.
Modified Hausdorff distance and diffusion equation evolution combined fast manifold K means

K-Means is one of the most popular clustering techniques and has been successfully used in many data mining fields. K-Means is computationally expensive for high dimension and high quantity data and various techniques have been developed to reduce the computational cost. These techniques mainly involve improving implementation efficiency. In this paper, we proposed a Hausdorff distance and diffusion equation evolution combined technique to speed up K-Means through reducing number of data points in distance computation. Experiments show that for manifold data, the proposed method not only significantly reduces the computational cost, but also improve the clustering performance.