Reviewerly/exHarmony huggingface dataset:
THE IMPACT OF PICTURE SERIES ON WRITING ABILITY AMONG INDONESIAN JUNIOR HIGH SCHOOL EFL LEARNERS

This study tried to shed light on investigating the impact of picture series on Indonesian Junior High School EFL learners’ abilities in English descriptive writing. To fulfill the objective, quantitative method with quasi experimental design was employed. 50 Indonesian Junior High School EFL learners were selected out of 100 students in a private Junior High School in Banten province. The sample of this research was chosen based on the non-equivalent group design technique. Moreover, the chosen participants were divided into two equal groups: experimental group and control group. In collecting the data, the researchers conducted pre-test and post-test. The data, then, were analyzed by using t-test. The result of t-test revealed that revealed that the experimental group performed better than the control group. Finally, the implications of the findings and recommendations for future research were also discussed.
Equi-Joins Over Encrypted Data for Series of Queries

Encryption provides a method to protect data outsourced to a DBMS provider, e.g., in the cloud. However, performing database operations over encrypted data requires specialized encryption schemes that carefully balance security and performance. In this paper, we present a new encryption scheme that can efficiently perform equi-joins over encrypted data with better security than the state-of-the-art. In particular, our encryption scheme reduces the leakage to equality of rows that match a selection criterion and only reveals the transitive closure of the sum of the leakages of each query in a series of queries. Our encryption scheme is provable secure. We implemented our encryption scheme and evaluated it over a dataset from the TPC-H benchmark.
PENINGKATAN PEMBELAJARAN GRAMATIKA DAN HASIL BELAJAR BAHASA JERMAN TENTANG FAMILIE DENGAN METODE STATIONENLERNEN

The mastery of foreign languages in the era of globalization is very necessary because language will open the horizons of the world of education. One of the foreign languages studied in high school (SMA) and equivalent is German Language. In the learning process, problems will always arise because there are certain levels of difficulty in learning a foreign language. One of the problems that occurred was the problem in class XI IPA 6 of SMA Negeri 1 Magelang. The problem is that students have difficulty in mastering German grammar. The purpose of this study is to improve grammar learning and German learning outcomes in class XI IPA 6 SMA Negeri 1 Magelang. The method used in this study is the Stationenlernen method. The results of this study indicate that there is a change in the learning atmosphere, the previously monotonous learning atmosphere becomes more fun, students are more motivated in the learning process, teachers are more active because they are facilitators, and students are more active because they are involved in group work in a team to work on problems at each station. This study consisted of 2 cycles, namely the cycle applied in meetings 1 to 2. The average results of the repeat scores in the initial conditions were 77.8. In the first cycle the average value was 84.1, while in the second cycle the average value was 88. The conclusion of this study was that there was an increase in students' grammar and learning achievement. This can be seen from the acquisition of values that increase at each meeting.
Analisis Klasifikasi Mobil Pada Gardu Tol Otomatis (GTO) Menggunakan Convolutional Neural Network (CNN)

The concept of a smart city is the most important issue in the development aspect of big cities in the world. Where the city must promise a more comfortable, organized, healthy and efficient life. Smart transportation is part of a smart city that is useful for improving better urban planning. Smart transportation also applies to toll roads, such as automating toll road retribution payments. Automatic Toll Gate (GTO) in Indonesia still uses sensors. However, sensors often misclassify trailers. In addition, the use of sensors also requires additional costs in installation and maintenance. Currently, every toll gate is equipped with cameras for various purposes. By utilizing the camera for vehicle type classification, the cost of the GTO will be reduced. For this reason, utilizing a digital camera with computer vision for vehicle type classification is the solution. Convolutional Neural Networks (CNN) is the most popular technique today in solving computer vision problems. Exploit the existing CNN by replacing the last fully connected output according to the number of vehicle classes. The test results show that mobilenet V2 is better in the classification of vehicle types, the best accuracy is Alexnet 93.81% and Mobilenet 96.19%. Computer vision by utilizing CNN is expected to replace the use of sensors so that implementation costs are cheaper.
Perancangan dan Pemanfaatan Website untuk Mendukung Promosi Desa Wisata Apar Pariaman

The problem faced by Miftahul Huda Islamic Unggul Terpadu Elementary School teachers is that they are less competent in marketing schools so they are lacking, they still market door to door or manually so they are less effective and efficient. The problem faced by the teacher prompted the service team to assist in assisting the school's digital marketing training as a means to improve the program to increase new student admissions. From the problems mentioned above, of course, the right solution must be found to overcome them by referring to several theories and related research findings regarding improving teacher competence, especially in increasing digital marketing soft skills. Therefore, this service activity will try to provide solutions faced by partners by holding training and continuing by forming discussion forums
Simulation and Evaluation of a Financial Option Based Model for Cloud Resources Management

Cloud computing, where the use of resources is seen as a service (Resources-as-a-Service (RaaS)) has developed extensively for executing computationally resource-intensive applications. As a result, commercial services of such resources are becoming norm of the day and pricing them have become an important problem in finance. Only a few economic models have been reported for pricing cloud resources. In this paper, a novel application of financial option pricing theory to the management of distributed computing resources especially for pricing is addressed. First, the importance of finance models for the given problem is highlighted following an explain on how option theory fits well to price the distributed computing cloud resources. Various cloud resources such as memory, storage, software, and compute cycles are seen as individual commodities and pricing of the resources is done in isolation and in combination of various resources. Second, `we design and develop pricing model and generate pricing results for usage of such commodities for various resources. In the absence of cloud resource pricing benchmarks/standards, firstly, cloud resources usage is simulated in order to justify the pricing model using CloudSim toolkit. In this part of the work, the integration of a financial option-based pricing model with CloudSim framework is implemented using a cloud simulation tool to price cloud compute resources. Secondly, the model is evaluated using cloud metadata obtained from Amazon Web services. The analysis of cloud resources utilization from simulation and real cloud trace data shows the feasibility of a financial option-based model for pricing cloud resources. With a large number of experiments carried out, a justification of the pricing model is obtained by comparing a simulated system to real cloud trace data based on the spot price for the cloud resources. Keywords: Financial Option, Strike Price, CloudSim, Quality of Service, Compute Commodities
The Relationship of Parenting Patterns with Discipline Class XI Students Learning in MAN 3 Padang City

This research is motivated by the lack of student learning discipline. The purpose of this study is to describe: 1) Student learning discipline. 2) Parenting style. 3) The relationship between parenting styles and students' learning discipline. This type of research is descriptive quantitative research, with a population of 313 students and a sample of 76 students. The sampling technique used is simple random sampling. The research instrument was a questionnaire and analyzed using the percentage technique. The results of this study reveal that 1) The learning discipline of students is in the high category, 2) The parenting style of parents is in the high category, 3) There is a significant relationship between parenting and learning discipline with a correlation coefficient of 0.547 with a strong level of relationship. Based on the results of the study, it can be recommended for parents to further improve learning discipline, especially in asking children which major is better in the future so that they can provide proper treatment to their children.
Attendence Management system using RFID Technology

Attendance management is a problem commonly faced by companies, management using a manual system still causes problems. The problem that is often faced is the difference in employee attendance data reports because they have not been well integrated. The purpose of this study was to design an employee attendance information system at PT. Kartika Utama Semarang. From the above problems, a system is needed to integrate these needs automatically and is managed entirely by a computer using an RFID system in it so that the leadership can monitor attendance and payroll in real time. The application of the author's research and development (R&amp;D) research method in this study. This research resulted in an information system application to facilitate the management of attendance and payroll of PT. Kartika Utama Semarang
Use of innovative educational technologies in fire training classes in conjunction with specific learning conditions.

teaching aids and simulation equipment that synthesize basic legal, tactical, shooting and psychological aspects of firearms and practice scene with the victim.The article outlines the legal and social origins of the powers and conditions of police use of weapons, as well as the responsibilities to eliminate its possible negative legal, physical and organizational consequences.He has been charged with criminal liability for police misconduct.The essence and principles of use of the weapon and significance for performance of the official tasks provided by the legislation are specified.Emphasis is placed on the interdependence of the right of a police officer to use a firearm as the most severe coercive measure with the duty of a police officer to take measures to provide emergency assistance to victims of gunshot wounds, enshrined in the Law of Ukraine "On National Police".The validity and efficiency of the use of innovative technologies in fire training are determined, which allow to improve pedagogical techniques and teaching methods, as well as improve the acquisition of knowledge and promote the formation of stable skills of shooting and safe handling of weapons.It is proposed to develop and implement in the training of police special comprehensive exercises and trainings on scenarios of typical situations of weapons use, in which tasks require the use of a set of knowledge from various fieldsshooting, tactics, home training, crime qualificationthat is a multidisciplinary approach to learning.A prerequisite for such practical classes is the use of interactive multimedia and simulation equipment (shooting ranges, simulators, mannequins, etc.), script development and training,which brings learning closer to reality.
Kesulitan Belajar Peserta Didik Kelas VI SDN 14 Ulakan Tapakis

The purpose of this study is to know the students' learning difficulties by grade VI students. The research approach used is qualitative approach. The subject of this research is the students of grade VI State Elementary School 14 Ulakan Tapakis. Key informant in this research is class VI teacher, while the next informant is grade VI student which is taken based on the suggestion of key informant. Data collection techniques used interviews, and documentation, while to test the validity of data used triangulation technique. Based on the result of the research are students' learning difficulties are caused by internal and external factors. The interent factor is a factor from the students themselves; they are cognitive, effective, psychotic. and external factors, namely those that arise from outside the learner's self, such as the family environment, village/community environment. The efforts that can be use by teachers in overcoming the learning difficulties of class VI students at SDN 14 Ulakan Tapakis are given tasks or instructions in detail, teach the main idea of ??the lesson, provide physical contact/reword
Analisis Faktor – Faktor Kesulitan Belajar Pada Peserta Didik Kelas 8 Di SMP Muhammadiyah Waipare

The purpose of this study is to find out the difficulty learning factors. The framework of this research is students should be able to concentrate and maintain their learning well. To get the maximum learning result, students should not get difficulties in learning process. For that reason, this study desire to analyze the students’ difficulty learning factors. After analyzing the factors, the solution can be taken to solve all problems. This research used descriptive qualitative method. Qualitative data analysis is used to analyze these qualitative data. Qualitative data serve some information related to the difficulties faced by students, understanding level of a subject (cognitive aspect), teaching learning process, students’ attitude (affective), students’ enthusiasm, and students’ motivation. This research is focused on the students’ difficulties learning factors. The students of 8 diSMP Muhammadiyah Waipare is chosen as respondent. Techniques of data collection are interview, observation, and documentation. Data analysis is followed the qualitative research principle. The result of this research shows that the students of 8 diSMP Muhammadiyah Waipare get difficulties in learning process which influenced by internal and external factors
Onboarding

Back when I started my very first job as a software engineer, I had no idea what I was doing.
The Implementation of Presentation Practice Production (PPP) Technique to Improve Students’ Speaking Skill by Using Picture Card as a Media

The objective of this research was to find out the process of implementation of Presentation Practice Production to improve students’ speaking skill. The study was conducted by using two cycles, where each cycle consists of three meetings and each meeting include four steps, namely: Planning, Implementing, Observing, and Reflecting. The qualitative datas were taken from the result of the test that had been given to the students. The test that was given still relevant to the topic that was taught and discussed in the classroom in every cycle. There were two cycles that were considered of four meetings in this research and the test was given in the last of each cycle. The Quantitative data was taken test that gave to the students in the last of each cycle. The result indicated that there was an increasing on the students speaking skill by using presentation, practice, and production (PPP) technique. The mean of the first cycle were 66 it was good enough. The mean of second cycle were 79 it indicated that the scores and the mean in second cycle were better than the first cycle. The percentage of students who got point ? 70 also grew up. In the pre-test, the students who got point ? 70 up were only 2 students (10%). In the post- test of cycle (I) students who got point ? 70 up were 11 students (55%) it means that there were an increasing about 45%. The post-test of cycle (II), students who got point 70 to up there were 20 students (100%) and the increasing were about 45%. For the total increasing of the students’ score from pre-test to post-test of cycle two (II) were100%. In other words, the students’speaking skill improved and became well in the first meeting to the next meeting
Transforming African Education Systems through the Application of IOT

The project sought to provide a paradigm for improving African education systems through the use of the IOT . The created IOT model for Africa will enable African countries, notably Namibia, to exchange educational content and resources with other African countries. The objective behind the IOT paradigm in Africa's education sectors is to provide open access to knowledge and information. The study revealed that there are no recognised platforms in African education systems that are utilised by African governments to interact, communicate, and share educational material directly with African institutions. As a result, the current research developed a model for transforming African education systems using the IOT in the Namibian context, which will serve as a centralised online platform for self-study, new skill acquisition, and self-improvement using materials provided by African institutions of higher learning. Everyone is welcome to use the platform, including students, instructors, and members of the general public
Mediating Role of Organizational Innovation in the Relationship between Human Resources Management Strategies and Organizational Performance Empirical Evidence from Iraq

This research aims to build a model that explores the mediating role of organizational innovation in the relationship between human resources management strategies (HRMS) and organizational performance based on empirical data obtained from managers and employees working for the branches of investment boards in northern Iraq. The quantitative research method was used, and the research methodology was aimed to develop the relationship and influence between the model structures. The research hypotheses were examined using partial least squares path modeling based on structural equation modeling (SEM). The findings showed direct and indirect relationships between research constructs. Results showed that human resources management strategies positively and significantly related to organizational innovation and organizational performance. Organizational innovation becomes a necessity, particularly when organizations want to improve their performance and maintain competitiveness. Organizations can focus on all their human elements through innovation in human resources and thus improve organizational performance. In addition, the results presented that organizational innovation is positively and significantly related to enhancing organizational performance. The current research will contribute to enhancing the literature by representing that organizational innovation mediates the relationship between HRMS and organizational performance. Furthermore, results showed that organizational innovation partially mediated the relationship between HRMS and organizational performance.
DNNShield: Dynamic Randomized Model Sparsification, A Defense Against Adversarial Machine Learning

DNNs are known to be vulnerable to so-called adversarial attacks that manipulate inputs to cause incorrect results that can be beneficial to an attacker or damaging to the victim. Recent works have proposed approximate computation as a defense mechanism against machine learning attacks. We show that these approaches, while successful for a range of inputs, are insufficient to address stronger, high-confidence adversarial attacks. To address this, we propose DNNSHIELD, a hardware-accelerated defense that adapts the strength of the response to the confidence of the adversarial input. Our approach relies on dynamic and random sparsification of the DNN model to achieve inference approximation efficiently and with fine-grain control over the approximation error. DNNSHIELD uses the output distribution characteristics of sparsified inference compared to a dense reference to detect adversarial inputs. We show an adversarial detection rate of 86% when applied to VGG16 and 88% when applied to ResNet50, which exceeds the detection rate of the state of the art approaches, with a much lower overhead. We demonstrate a software/hardware-accelerated FPGA prototype, which reduces the performance impact of DNNSHIELD relative to software-only CPU and GPU implementations.
Momentum Transformer: Closing the Performance Gap Between Self-attention and Its Linearization

Transformers have achieved remarkable success in sequence modeling and beyond but suffer from quadratic computational and memory complexities with respect to the length of the input sequence. Leveraging techniques include sparse and linear attention and hashing tricks; efficient transformers have been proposed to reduce the quadratic complexity of transformers but significantly degrade the accuracy. In response, we first interpret the linear attention and residual connections in computing the attention map as gradient descent steps. We then introduce momentum into these components and propose the \emph{momentum transformer}, which utilizes momentum to improve the accuracy of linear transformers while maintaining linear memory and computational complexities. Furthermore, we develop an adaptive strategy to compute the momentum value for our model based on the optimal momentum for quadratic optimization. This adaptive momentum eliminates the need to search for the optimal momentum value and further enhances the performance of the momentum transformer. A range of experiments on both autoregressive and non-autoregressive tasks, including image generation and machine translation, demonstrate that the momentum transformer outperforms popular linear transformers in training efficiency and accuracy.
Skeleton-Parted Graph Scattering Networks for 3D Human Motion Prediction

Graph convolutional network based methods that model the body-joints' relations, have recently shown great promise in 3D skeleton-based human motion prediction. However, these methods have two critical issues: first, deep graph convolutions filter features within only limited graph spectrums, losing sufficient information in the full band; second, using a single graph to model the whole body underestimates the diverse patterns on various body-parts. To address the first issue, we propose adaptive graph scattering, which leverages multiple trainable band-pass graph filters to decompose pose features into richer graph spectrum bands. To address the second issue, body-parts are modeled separately to learn diverse dynamics, which enables finer feature extraction along the spatial dimensions. Integrating the above two designs, we propose a novel skeleton-parted graph scattering network (SPGSN). The cores of the model are cascaded multi-part graph scattering blocks (MPGSBs), building adaptive graph scattering on diverse body-parts, as well as fusing the decomposed features based on the inferred spectrum importance and body-part interactions. Extensive experiments have shown that SPGSN outperforms state-of-the-art methods by remarkable margins of 13.8%, 9.3% and 2.7% in terms of 3D mean per joint position error (MPJPE) on Human3.6M, CMU Mocap and 3DPW datasets, respectively.