Reviewerly/exHarmony huggingface dataset:
Analisis Literasi Teknologi Informasi Guru Matematika SMK Swasta Jakarta Selatan Ditinjau dari Aspek Manajemen Pendidikan

In the era of information technology-based 4.0, all fields use information technology, as well as in the world of education. In learning, the use and use of information technology is very important in supporting the smooth teaching and learning process. So that the ability of teachers in information technology literacy is very important and needs to be developed. This study aims to describe the level of information technology literacy of mathematics teachers in private vocational schools in South Jakarta and identify the factors that influence it. The research method used is descriptive qualitative method. The research sample was 41 mathematics teachers at private vocational schools in South Jakarta, who were taken by purposive sampling technique. Information techniques through questionnaires and interviews. The results showed that the level of information technology literacy for mathematics teachers in private vocational schools in South Jakarta was still low. One of the factors affecting literacy is the lack of training in the use of information.
Tutorial on Software Engineering Education in Co-Located Multi-User Eye-Tracking-Environments

We briefly describe a tutorial on the application of Eye-Tracking technology for Software Engineering Education. We will showcase our setup of a large-scale Eye-Tracking-Classroom and its usage for real-time improvement of traditional learning scenarios in Software Engineering Education. We will focus on the integration of gaze data into modern integrated development environments (IDEs) and demonstrate a complete workflow for its usage in co-located multi-user Eye-Tracking-Environments.
A Novel Abnormal Driving Detection Method via Deep Learning in Wireless Sensor Network

In this study, the abnormal driving detection in the current research hotspot wireless sensor network (WSN) is emphatically discussed, and three improved fusion models based on Densely Connected Convolutional Network (DenseNet), which is named Wide Group Densely Network (WGD), Wide Group Residual Densely Network 1 (WGRD1), and Wide Group Residual Densely Network 2 (WGRD2) respectively, are proposed for the first time.WGD introduces two deep learning network indicators, width and cardinality, into DenseNet.WGRD1 and WGRD2, on the basis of WGD, use two different methods to introduce the important idea of ResNet into DenseNet, which is residual-block output and direct-connected streams are added by elements.These three models use end-to-end learning for training.The experimental analysis based on the abnormal driving image data set shows that the performance of our improved model for abnormal driving detection in the wireless sensor network is better than several excellent deep learning models and traditional deep learning models.
Research on Control System of Tower Concentrated Solar Power Plant

Introduction In order to solve the control problem of Tower Concentrated Solar Power. Method  Firstly based on the astronomical algorithms for calculating the position of the sun. Secondly, this article studied the main control technology of solar field control system, including system architecture, hardware and software design, feedback measurement program based on image processing. At last, it made an overall design and planning for the whole plant control system, and proposed a signal interface between solar field control system and DCS. Result  This article invented a high-precision and easy engineering implementation algorithm for heliostats tracking and controlling, which will reduce engineering cost and shorten the construction period. Conclusion  The research is structural integrity and easy to be realized, it improves accuracy of heliostats tracking and controlling with a lower cost.
Deep-learning Accelerated Fringe Pattern Filtration Using Variational Image Decomposition

Using a deep convolutional neural network we accelerated our unsupervised variational image decomposition algorithm making it not only automatic, robust and accurate but also fast. Reduction of computation time is nearly 90%.
Research and Implementation of Machine Programming Based on Agile Thinking

Abstract With the rapid development of information, communication, network and computer, the field, depth and breadth of computer applications are also rapidly expanding. The traditional production methods, development techniques and development tools of software development are no longer suitable for the system software development with larger and larger scale and more complex structure. Facing the fast changing demand, how to develop software quickly and quickly has become an urgent problem. This paper takes the agile development methodology as the support point and the core idea, puts forward to adopt the agile development thought as the core, take IDE as the tool, take machine programming as the core technology, carries on the research to the agile thinking machine programming technology, carries on the development in Visual Studio, finally, realizes carries on the programming with the machine.
Reversed Image Detection with Convolutional Neural Networks

Automatic image orientation detection is an important image processing problem, although many studies have been done on this subject. In this paper, we used Convolutional Neural Networks(CNN) to detect 0 and 180 oriented images. Each image of the data set we have used contains only one object. Thanks to this, we have demonstrated that the success of the model depends on the image class selected in the training and testing.
Study of the Shape Optimization of the Connection Unit in a Modular Fuel Tank

An important function of the modular fuel tank is rapid combination, as well as convenience of transport for both vehicle equipment and rescue personnel. This modular fuel tank has the function of transportation through the rope, which has certain advantages compared with the traditional 20 L oil barrel. In this paper, we designed a series parallel pump oil system that allowed for quick combination of the modular oil tank. With a DC electric pump, refueling gun, etc., it can form a temporary small oil depot. It can provide emergency fuel supply to disaster areas when energy transportation is blocked by natural disasters, such as earthquakes, floods, and debris flow. The first generation of the product met the basic requirements, but the efficiency of the system was relatively low, as the design and manufacturing cycle is too short. In this context, from the point of view of hydrodynamics, the components can be optimized through the analysis of the system. We used ANSYS Fluent to simulate the flow field, and compared the results with the experiment to determine the correctness of the relevant model. On this basis, the optimization design of the related components was carried out with topology, and flow field simulations were performed in order to get the optimized results. The simulation results showed that the flow rate of the improved system was 91.02 L/min, which is 3.13 L/min higher than the original 87.89 L/min, resulting in a 3.56% increase in efficiency.
The Intelligent Management of Counselors in Higher Vocational Colleges under the Background of Informatization

With the rapid development of social economy and science and technology, under the promotion of information technology, people's life style, behavior concept and values have undergone profound changes. Counselors have played an important role in the current education management process in higher vocational colleges and should strengthen the extensive use of information technology in the process of work. This paper mainly studies the intelligent management of counselors in higher vocational colleges under the background of informatization, so as to provide reference for relevant professionals.
Recommendation algorithm based on user attributes and tag preferences

Abstract Aiming at the problem of sparse data and low recommendation accuracy of recommendation systems in a big data environment, a UT-CF algorithm that combines user attributes and tag preferences is proposed. The algorithm extracts and selects user attribute information, calculates the user attribute similarity matrix according to the user attribute characteristics; obtains the user’s tag score according to each score of the user item score matrix, obtains the user tag preference matrix, and calculates the difference between user tags Similarity, to get the user’s similar neighbors; In order to reduce the search space of similar users, the K-means clustering algorithm based on the principle of maximum distance is introduced. Make the top k recommendations based on the recommendations of the target user’s nearest neighbors. Through the experiment of MovieLens 100K data set, the algorithm improves the accuracy of recommendation.
Modern Challenges in The Application of Personalized Models in Student Education for The Benefit of Business and Society

With each passing day, consumers, particularly students, expect more intelligent and personalized services. The key to providing such services is the concept of a personalized model. Applying appropriate personalized learning models to students is a process that is filled with many challenges. The main purpose of the current report is to explore the challenges that impede the design and implementation of models that are strictly customized to the interests of students. In order to achieve this goal, it is necessary to explore the contemporary factors that are essential in designing personalization models. The factors are mainly related to the requirements of the business towards professionalism among students, as well as the goals set by the state for the education improvement. In order to achieve this goal, it is also necessary to investigate the technologies by which the personalization of models is realized and by which successful implementation of the personalized models is ensured in the students' education.
MENINGKATKAN KEMAMPUAN MENULIS TEKS DESKRIPTIF SISWA KELAS VII SMPN 3 AMUNTAI SELATAN MELALUI MEDIA RUBIC WORD

This research has purposes to improve seventh grade studentsâ€™ ability of SMPN 3 Amuntai Selatan in writing descriptive text using rubic word media and to know the studentsâ€™ and teacherâ€™s activeness in learning process. To reach these purposes, this research using classroom action research where the researcher also become a teacher and she was helped by another teacher as her observer. This research using some phases, they are: planning, implementing, observing, and reflecting. The finding shows that Rubic Word media has improved the studentsâ€™ ability in writing descriptive text. It can be seen from the improvement of studentsâ€™ writing result from the first cycle and second cycle. In first cycle, 20 (60,61%) from 33 students can achieve the target score/score â‰¥ 65. Furthermore, in the second cycle, there are 28 (84,85%) from 33 students can achieve the target score/score â‰¥65. Besides, the studentsâ€™ activeness have been in good level (83,22), and the teacherâ€™s activeness also has been in good level (82,87). So this research can be stopped and reported because it has been successful.
Social deployment of Virtual Light Touch system StA&lt;sup&gt;2&lt;/sup&gt;BLE for standing-function evaluation

The existing proposal of falling risk evaluation system, a.k.a. StA2BLE, for the national unprecedented ageing society problem, is research domain. So, the keys of industrialization such as producibility, cost and usability, e.g. professional skillset required to operate, are less considered and consequentially difficult to be adapted by the society. Furthermore, since standing function factors can be identified by the system, so personal effective training program, which could bring huge value to the market, can be automatically generated based on the calculated factors. This paper is to discuss and offer social deployment strategy of the StA2BLE in order to get rid of the barriers.
The 2014–2017 George N. Saridis Best Transactions Paper Award

In 2015, the Board of Governors of IEEE Intelligent Transportation Systems Society had approved the proposal to name the Best Paper Award in IEEE Transactions on Intelligent Transportation Systems as the George N. Saridis Best Transactions Paper Award. After nearly five years of preparation and planning, and almost one year of hard and concentrated effort by the Award Committee, we are pleased to announce the 2014–2017 George N. Saridis Best Transactions Paper Award for papers published in the IEEE Transactions on Intelligent Transportation Systems.
The Analyze of Stagnation Phenomenon and Its' Root Causes in Particle Swarm Optimization

Evolutionary algorithms (EA) are part of artificial intelligence technology, and EA always suffers from the problem of particle stagnation. Most of the researches on particle stagnation focused on particle stagnation and searched in incorrect regions. However, by observes the particle moving trajectory, the author found that the particle is not only stagnation in a point but also wandering in a region. Therefore, this study analyzing the particle behaviors and break down the stagnating phenomenon to reinforce the understanding of stagnation problems currently. This study summarizes the phenomenon of particle stagnation and detailed discuss the root causes to reinforce the lack of understanding in particle stagnation currently. Finally, the author proposes corresponding strategies for different cases for the related researches in the future to handling the stagnation problem more comprehensively.
Ensemble of Deep Learning Models for In-Hospital Mortality Prediction

Using machine learning in health care for supporting doctors to diagnose diseases is receiving a lot of research interest. Currently, many hospitals use Electronic Health Records (EHR) to store medical records, which is an extremely valuable data source for machine learning. This paper uses the Medical Information Mart for Intensive Care (MIMIC-III) dataset to solve the problem of predicting mortality in hospitals. We have proposed standardizing numerical attributes in two ways: normalizing using mean and variance on Training set and standardizing using mean and variance according to 48 h of each sample. Neural network architectures based on CNN models have been proposed and tested. Ensemble technique has been applied to each parameter representation type and each model type. Test results show that the ensemble method has improved the performance of the system. Test results on the Test set achieve AUROC is 0.851, AUPRC is 0.452 and min (Se, + P) is 0.459.
DOTA2 Winner Predictor using Machine Learning

DotA2 (Defense of the Ancient 2) is an online multiplayer game. It’s a real time strategy game played between two teams of five players. DotA2 rules are extremely mind-boggling. We have created a model to predict the winner of Dota2 by using Logistic Regression. By obtaining the information of heroes and the average MMR of all the ten players we are predicting the outcome of the match. The win percentage is dependent on hero selection and MMR of players. We also determine the optimum 10th pick by invoking a brute force execution.
E-COMMERCE CLUSTERING ANALYSIS BASED ON LARGEST VISITORS

E-Commerce is an Electric Commerce which is very much and popularly used by all people. The increase in buying and selling online, making the number of e-commerce in Indonesia and the great benefits of e-commerce has made researchers conduct e-commerce cluster analysis so that online shop owners can join the most clusters so that they can increase their business. E-commerce clustering analysis based on the number of visitors visiting the e-commerce site or website. Clustering is used to produce the Most, Most, and Enough E-commerce groups. From the results of the clustering, it was found that the e-commerce names Tokopedia, Bukalapak and Shopee were the clusters with the most visitors. Lazada and Blibli are clusters of Many Visitors and JD Id, Orami, Sociolla, zalora, bhinneka, elevenia, blanja, laku6, jakarta Notebook and Ralali are included in the Quite Many Visitors cluster. Clustering data is based on the number of visitors starting in Quartiles 1-2019, Quartiles 2-2019, Quartiles 3-2019, Quartiles 4-2019 and Quartiles 1-2020. Keywords:  Clastering, E-Commerce, Biggest Visitor
Mechanical Movement 2D for Ground Penetrating Radar

In various fields, such as mining, agriculture, and the environment consider the information about the important of soil content. Many methods can be done to determine soil content under the surface. One of the method is Ground Penetrating Radar (GPR). GPR is a radar system that use to detect an object on the ground without having to dig it out. The GPR antenna sends an electromagnetic signal to the ground and then touches the object or target and then generates a wave of reflection that is then sent back by the receiver antenna. At this time to move the GPR worked manually by moving itself, based on my colleagueâ€™s experience is very ineffective most of them get a result that is not maximized and should be re-detection, because the movements inaccurate and unprecise. To solve this problem, an automatic control system is required to make the Mechanical Movement 2D for Ground penetrating Radar. Stepper Motor as the actuator for the GPR antenna movement and Arduino as the brain from the command maker of the tool, it can be made the GPR antenna that moves automatically. In this thesis the mechanical movement system produces an accuracy value of 90.24 % when the tool moves forward, and 88 % when the tool moves backward. The precision movement value is 97% when the tool moves forward, and 91 % when the tool moves backward. The recall value of tool performance is 92 % when the tool moves forward, and 94 % when the tool moves backward. The proposed mechanical movement is expected to provide a contribution in facilitating the detection of GPR and getting more accurate data. Keywords : GPR, Radar System, Stepper Motor, Arduino
Eye Fixation Operational Definition: Effect on Fixation Duration when Using I-DT

Many eye movement metrics such as the eye fixation duration metric depend, directly or indirectly, on eye fixations. However, eye fixations require an operational definition in order to be correctly classified by the eye movement event detection algorithms. This paper examines the effect of 24 different operational definitions of fixation on the results generated by Identification based on Dispersion Threshold (I-DT), the most popular eye movement event detection algorithm; and hence, the fixation duration metric. The 24 operational definitions are a combination of four different values (‎0.5, 1.0, 1.3, and 2.0) of the dispersion threshold and six different value (80ms, 100ms, 150ms, 200ms, 300ms, and 400ms) of the duration threshold of the I-DT algorithm. The preliminary results show that there is no statistically significant difference between the various operational definitions of fixation on the fixation duration metric.
dTASD: A Novel Online Detection Method for Anomalous State of Dry-type Transformer

Due to the advantages of dry-type transformers such as safety, no pollution, and low power consumption, they are widely used in shopping malls, hospitals, data centers and other places. Therefore, anomalous state detection for dry-type transformers is of great significance. However, the traditional detection methods are generally based on the hard threshold judgment method, which is difficult to ensure timeliness and may cause irreversible damage to the device. In this paper, we present dTASD, Dry-type Transformer Anomalous State Detector, a framework that can timely detect the anomalous state of dry-type transformer online. dTASD consists of an offline training model stage and online detecting stage. In offline training model, dTASD adopts the semi-supervised mode, and applies self-organizing map to discretize the three-phase temperature data to solve the challenge of three-phase data fusion. In online detecting, we propose a novel calculation method for anomaly scores to measure the degree of transformer operation deviating from the normal state. The experimental results using Real monitoring data of dry-type transformers installed in a large data center demonstrate dTASD can effectively solve the problem of anomalous state detection for dry-type transformers, and outperforms the existing anomaly detection approaches.
Towards a Formal Model for Quantifying Trust in Distributed Usage Control Systems

Distributed usage control is a form of usage control that spans over multiple domains and computer systems. As a result, usage control components responsible for evaluating policies, gathering information, executing actions and enforcing decisions are operated in the vicinity of different stakeholders with conflicting interests. In order to prevent malicious stakeholders from manipulating these components, remote attestation can be used to verify the integrity of their code base. However, in a distributed case it is not always apparent what sequence of attestations is necessary and which verifier should conduct them. Furthermore, it is unclear what impact a failed attestation has on the trustworthiness of the whole usage control system. To solve these questions, it is necessary to identify which agents need to trust each other in order to securely execute a certain usage control function. Then the sequence of remote attestations that occur across the distributed usage control system can be examined accordingly. In this work we develop a formal model that represents the trust relationships of distributed usage control systems with multiple collaborating actors. Based on the conducted attestations we define simple binary and non-binary trust metrics that quantify the trust level a data owner can expect at a certain point in time. Finally we show how the model can be used to determine the level of trust reached in a real-world scenario.
Invariant Batch Normalization for Multi-source Domain Generalization

We consider the domain generalization problem, where the test domain differs from the training domain. For deep neural networks, we show that the batch normalization layer is a highly unstable component under such domain shifts, and we identify two sources for its instability. Based on this observation, we propose a new learning formulation that can learn robust neural networks so that the corresponding batch normalization layers are invariant under domain shifts. Experimental results on three standard domain generalization benchmarks demonstrate that our method can learn neural network models with significantly more stable batch normalization layers on unseen domains, and the improved stability leads to superior generalization performances.