Reviewerly/exHarmony huggingface dataset:
PENYULUHAN JUMAMPAH (JURU PEMANTAU SAMPAH) TERHADAP PENGETAHUAN DAN PRAKTIK TENTANG MEMILAH DAN MENABUNG SAMPAH DI SEKOLAH DASAR KECAMATAN MERGANGSAN

Current waste management problems often occur in Yogyakarta. This is due to population growth which results in an increase the amount of waste generated. The occurrence is certainly not comparable with the area and number of Final Disposal Sites that do not increase. One method of waste management that can be applied is a Waste Bank. After conducting a preliminary survey at Anjani Asrih Waste Bank, there are 20-25% of customers were actively saving. Therefore, a method is needed that can increase customer participation. JUMAMPAH (Waste Monitoring Interpreter) is a method created by researchers who is expected to be able to increase children's knowledge about sorting and saving rubbish so that in the long run it can increase the participants of waste bank customers. The research is quasi-experimental research with a Pre-test design Post-test by using Control Group Design. The location of the study was in Surokarsan 2 Elementary School and Kintelan 1 Elementary School. Surokarsan 2 Elementary School as an experimental group received treatment in the form of counseling using media power points while Kintelan 1 Elementary School as a control group did not receive any treatment. The results of the statistical analysis using Mann-Whitney for knowledge of sorting out the waste, waste saving knowledge obtained p-value 0,000 &lt;0,05 and JUMAMPAH practice indicated there was a significant difference. The conclusionof the research show there is significant difference in the value of the knowledge of sorting out waste, saving waste and JUMAMPAH practices on Surokarsan 2 Elementary School students as an experimental group and Kintelan 1 Elementary School students as a control group.
PENGARUH PENGETAHUAN APLIKASI, SOSIALISASI DAN LATIHAN TERHADAP MAKSIMALISASI PEMAKAIAAN APLIKASI MANAJEMEN SURAT (AMS) DI PT PLN (PERSERO) UP3 MADIUN

This research is a quantitative method to form associative causal relationship. The population in this study are all employees of the user management application letter (AMS) of PT PLN (Persero) UP3 Madiun totalling 158 people. The determination of the amount of the sample using the method of census sampling technique or saturated. The sampling technique using a Non Probability Sampling. The research instrument was a questionnaire that has been tested for validity and reliability. Data were analyzed using multiple linear regression analysis. Results of the study prove that: (1) Application knowledge, socialization, and training have an effect partially and simultaneously towards the maximization of management of letter application (AMS)’ usage at PT PLN (Persero) UP3 Madiun. (2) Application knowledge is the variable that dominant influence on the maximization of management of letter application (AMS)’ usage at PT PLN (Persero) UP3 Madiun. Keywords : A pplication knowledge , socialization , training , maximization of application usage
Evaluated Results of Seismic Design Approach Using Inelastic Dynamic Analysis for Equipment

Abstract Securing adequate seismic safety margins has been important in safety reviews regarding the seismic design of equipment and piping systems in nuclear power plants, and there exists an increasing need for a more exact method for evaluating these margins. To this end, it is reasonable to take into account the reduction of seismic responses resulting from inelastic deformation. The authors studied this approach utilizing an elastic allowable limit in existing standard. The applicability of the proposed evaluation method was investigated by comparison with the conventional evaluation method. The proposed method consists of an inelastic dynamic analysis and an elastic-static analysis. The elastic-static analysis uses a load obtained from the inelastic dynamic analysis. For the investigation, the result obtained from the proposed method was compared with that obtained from the conventional elastic analysis to quantify the reduction in responses leading to seismic safety margins. For the comparison, the authors constructed three models that simulate a cantilever-type beam, four-legged tank, and core shroud and applied them to the analysis herein, and the applicability of our method was discussed for these models. In this paper, we present three topics. First, we present a scheme for developing the design approach of using inelastic analysis. Second, we report a sensitivity study of model parameters, such as yielding stress and second stiffness, done by analyzing the cantilever-type beam for the proposed method. Finally, we report the application of the method to the four-legged tank and core shroud.
A Study on Control Algorithm for Robots with Multi-degree of Freedom

Abstract Robotics, a synthetic discipline developed in recent years, is the advanced hot spot of academic research at present. As the important branch of robots, robots with multi-degree of freedom are similar with human beings in intelligence, behaviors and appearance. A robot with multi-degree of freedom can imitate some simple movements of human beings. This kind of robot has many joints and a complex control circuit, and if it owns some functions human beings do not have, its control circuit will be more complex. In this thesis, the model for dynamics of robots with multi-degree of freedom is built based on lagrangian function, and the control law of robots with multi-degree of freedom is designed. Additionally, in Matlab, the numerical model of robots with six degrees of freedom is built according to the method of Joint-link parametric, which verifies the correctness of parametric of robots and the kinematic formula.
PENGEMBANGAN MEDIA PEMBELAJARAN KOTAK DAN KARTU MISTERIUS UNTUK MENINGKATKAN HASIL BELAJAR SISWA PADA MATA PELAJARAN EKONOMI KELAS X SMA

This research is a type of development research using Box and Mysterious Card learning media (KOKAMI). The type of development used in this study is research and development or Research and Development (R & D) with the Thiagarajan 4-D development model which consists of defining, designing, developing, and disseminating. Data collection was obtained from the results of media and material validation, the results of the pretest and posttest, and the results of student responses to the application of the media. This study shows that the results of material validation in all aspects obtained an average percentage of 85%, while for the results of media feasibility validation in all aspects obtained an average percentage of 90.67%. For the results of the pretest of students on Kokami media an average of 39.33 was obtained while the posttest results of students on Kokami media were obtained an average of 90.67. For the results of student responses in all aspects, the average percentage of results was 92.86%. This shows that the Box and Mysterious Card learning media are very feasible to use.
La contextualización del Análisis Matemático en la Matemática escolar

This paper presents the theoretical and methodological conceptions of designing teaching tasks to promote the contextualization of lessons in Mathematical Analysis in educational Mathematics. It is aimed at conceiving teaching tasks, by adopting a pedagogical professional approach, to promote this contextualization. Theoretical (historical-logical, analytic-synthetic, and inductive-deductive) methods of research were used. Students, graduates, and experienced professors were interviewed in order to collect data on professors’ performance in promoting the contextualization of lessons in Mathematical Analysis in educational Mathematics. Additionally, such documents as syllabuses, regulations, scientific papers, doctoral and master’s theses were analyzed. This paper contributes to the contextualization of lessons in Mathematical Analysis in educational Mathematics through the pedagogical professional approach.
Location Forensics of Media Recordings Utilizing Cascaded SVM and Pole-matching Classifiers

Information regarding the location of power distribution grid can be extracted from the power signature embedded in the multimedia signals (e.g., audio, video data) recorded near electrical activities. This implicit mechanism of identifying the origin-of-recording can be a very promising tool for multimedia forensics and security applications. In this work, we have developed a novel grid-of-origin identification system from media recording that consists of a number of support vector machine (SVM) followed by pole-matching (PM) classifiers. First, we determine the nominal frequency of the grid (50 or 60 Hz) based on the spectral observation. Then an SVM classifier, trained for the detection of a grid with a particular nominal frequency, narrows down the list of possible grids on the basis of different discriminating features extracted from the electric network frequency (ENF) signal. The decision of the SVM classifier is then passed to the PM classifier that detects the final grid based on the minimum distance between the estimated poles of test and training grids. Thus, we start from the problem of classifying grids with different nominal frequencies and simplify the problem of classification in three stages based on nominal frequency, SVM and finally using PM classifier. This cascaded system of classification ensures better accuracy (15.57% higher) compared to traditional ENF-based SVM classifiers described in the literature.
CAREER COUNSELLOR GUIDANCE FOR SECONDARY SCHOOL STUDENTS IN CAREER DECISION-MAKING PROCESS

In globalization era career management has been changed. The individual is now responsible for the decisions he or she will make in terms of career. The problem is crucial for secondary school students, as when they graduate secondary school they should know what they going to do next. Some students leave a career decision to the latest moment; however it may lead to serious problems as career uncertainty may impact students’ future career. Career counsellor may guide students, although the final decision about future profession choice should be made by them. The paper provides theoretical framework of career guidance system in Latvia. 76 secondary school students were involved in the study from Rezekne Polish State Gymnasium. The results of the study showed that secondary school students think about their future profession. However, most of the secondary students do not plan to attend career counsellor. Various factors may affect students’ decisions about their future career, though most of them rely on their own opinion.
A General Framework on Enhancing Portfolio Management with Reinforcement Learning

Portfolio management is the art and science in fiance that concerns continuous reallocation of funds and assets across financial instruments to meet the desired returns to risk profile. Deep reinforcement learning (RL) has gained increasing interest in portfolio management, where RL agents are trained base on financial data to optimize the asset reallocation process. Though there are prior efforts in trying to combine RL and portfolio management, previous works did not consider practical aspects such as transaction costs or short selling restrictions, limiting their applicability. To address these limitations, we propose a general RL framework for asset management that enables continuous asset weights, short selling and making decisions with relevant features. We compare the performance of three different RL algorithms: Policy Gradient with Actor-Critic (PGAC), Proximal Policy Optimization (PPO), and Evolution Strategies (ES) and demonstrate their advantages in a simulated environment with transaction costs. Our work aims to provide more options for utilizing RL frameworks in real-life asset management scenarios and can benefit further research in financial applications.
New bounds on the minimum distance of cyclic codes

<p style='text-indent:20px;'>Two bounds on the minimum distance of cyclic codes are proposed. The first one generalizes the Roos bound by embedding the given cyclic code into a cyclic product code. The second bound also uses a second cyclic code, namely the non-zero-locator code, but is not directly related to cyclic product codes and it generalizes a special case of the Roos bound.
Language Aided Speaker Diarization Using Speaker Role Information

Speaker diarization relies on the assumption that acoustic embeddings from speech segments corresponding to a particular speaker share common characteristics. Thus, they are concentrated in a specific region of the speaker space; a region which represents that speaker's identity. Those identities however are not known a priori, so a clustering algorithm is employed, which is typically based solely on audio. In this work we explore conversational scenarios where the speakers play distinct roles and are expected to follow different linguistic patterns. We aim to exploit this distinct linguistic variability and build a language-based segmenter and a role recognizer which can be used to construct the speaker identities. That way, we are able to boost the diarization performance by converting the clustering task to a classification one. The proposed method is applied in real-world dyadic psychotherapy interactions between a provider and a patient and demonstrated to show improved results.
A Fuzzy Spiking Neural Network with State Transition Diagram for Behavior Estimation in Elderly Health Care System

In recent years, the aging of the population has become a critical social issue that plagues developed countries such as Japan. We aim to alleviate the lack of health care for the elderly caused by inadequate caregivers through technical means. This paper develops an elderly health care system based on the information structure space. We chose to use physical sensors to estimate the behavior of older people. We use fuzzy spike neural networks to adapt to differences in different home environments and post-process with state transition diagrams to improve precision. In the experiment, we used the temperature and humidity data to evaluate the behavior of the bathroom. The results show that the system can accurately estimate human behavior with a precision of 79% and a time delay of 1.7 minutes.
Optimization of the cyber security system structure based on accounting of the prevented damage cost

Searching the optimum structure of cyber security systems is an important problem which should be solved to realize security mechanisms (means) in computing systems, especially when there are significant resource restrictions (small enterprises, built-in systems, cyber-physical systems, etc.). The complexity of this problem is caused by its uncertain, probabilistic, and nonlinear character. The paper proposes a mathematical model for determination of the cost of the damage prevented by cyber security means and the costs of installation and maintenance of the cyber security system under analysis. The optimization criterion is the minimal cost of the prevented damage. The variables of the problem are the quantities of the security means of various types installed in the security system. The paper considers a method to solve the problem and the features of its implementation. The possibilities of using various methods to increase the accuracy of initial data for the proposed model are analyzed.
Scalability of Kubernetes Running Over AWS - A Performance Study while deploying CPU intensive application containers

Background: Nowadays lot of companies are enjoying the benefits of kubernetes by maintaining their containerized applications over it. AWS is one of the leading cloud computing service providers an ...
Identifikasi Kesesuaian Lahan Lokasi Landing dan Dropping Zone di Kecamatan Asembagus dan Banyuputih Kabupaten Situbondo Jawa Timur Menggunakan Citra Landsat 8 OLI dan SIG

Penentuan lokasi landing dan dropping zone dalam operasi tempur dilakukan dengan menyadap informasi intelijen di wilayah musuh, salah satu cara penyadapan informasi dilakukan dengan menggunakan teknologi penginderaan jauh dan dimodelkan menggunakan Sistem Informasi Geografis (SIG). Tujuan penelitian ini adalah mengkaji kemampuan citra Landsat 8 OLI pan-sharpened untuk menyadap informasi penggunaan lahan di kecamatan Asembagus dan Banyuputih untuk menentukan lokasi landing dan dropping zone. Penyadapan informasi penggunaan lahan dilakukan dengan metode interpretasi visual dan di uji akurasi menggunakan confusion matrix. Identifikasi awal kesesuain lahan lokasi landing dan dropping zone dilakukan dengan metode kuantitatif berjenjang tertimbang berdasarkan standar teknis sesuai dengan (Mahdi, 2008) Hasil penelitian ini diperoleh sepuluh kelas penggunaan lahan di kecamatan Asembagus dan Banyuputih. Dominasi penggunaan lahan di dua kecamatan ini ialah kelas hutan dengan luas 29392 ha atau 47.05% sedangkan kelas tubuh air menjadi yang paling kecil dengan luas 28 ha atau 0.04%. Confusion Matrix uji menghasilkan nilai akurasi 83.10% dengan 71 sampel. Hasil pemodelan menunjukkan bahwa di Kecamatan Asembagus dan Banyuputih memiliki 653 ha atau 1.05% wilayah yang sangat sesuai untuk lokasi landing dan dropping zone. Determination of landing and dropping zones locations in combat operations is carried out by tapping intelligence information in enemy territory, one way of tapping information is done by using remote sensing technology and modeled using Geographic Information Systems (GIS). The purpose of this study was to examine the ability of pan-sharpened Landsat 8 OLI images to tap land use information in Asembagus and Banyuputih sub-districts to determine the location of landing and dropping zones. Tapping information on land use is done by visual interpretation method and tested for accuracy using confusion matrix. Initial identification of land suitability for landing and dropping zone locations is carried out using a weighted tiered quantitative method based on technical standards according to (Mahdi, 2008) The results of this study obtained ten classes of land use in the Asembagus and Banyuputih sub-districts. The dominance of land use in these two sub-districts is the forest class with an area of 29392 ha or 47.05% while the water body class is the smallest with an area of 28 ha or 0.04%. The Confusion Matrix test produces an accuracy value of 83.10% with 71 samples. The modeling results show that in Asembagus and Banyuputih sub-districts have 653 ha or 1.05% of the area that is very suitable for landing and dropping zone locations.
Research Guides: RDG/WRT Classes: Using Newspaper Databases

This guide will help students in RDG and WRT classes find library resources for their assignments.
Towards Efficient Integration of Blockchain for IoT Security: The Case Study of IoT Remote Access

The booming Internet of Things (IoT) market has drawn tremendous interest from cyber attackers. The centralized cloud-based IoT service architecture has serious limitations in terms of security, availability, and scalability, and is subject to single points of failure (SPOF). Recently, accommodating IoT services on blockchains has become a trend for better security, privacy, and reliability. However, blockchain's shortcomings of high cost, low throughput, and long latency make it unsuitable for IoT applications. In this paper, we take a retrospection of existing blockchain-based IoT solutions and propose a framework for efficient blockchain and IoT integration. Following the framework, we design a novel blockchain-assisted decentralized IoT remote accessing system, RS-IoT, which has the advantage of defending IoT devices against zero-day attacks without relying on any trusted third-party. By introducing incentives and penalties enforced by smart contracts, our work enables "an economic approach" to thwarting the majority of attackers who aim to achieve monetary gains. Our work presents an example of how blockchain can be used to ensure the fairness of service trading in a decentralized environment and punish misbehaviors objectively. We show the security of RS-IoT via detailed security analyses. Finally, we demonstrate its scalability, efficiency, and usability through a proof-of-concept implementation on the Ethereum testnet blockchain.
Automatic Neural Network Compression by Sparsity-Quantization Joint Learning: A Constrained Optimization-based Approach

Deep Neural Networks (DNNs) are applied in a wide range of usecases. There is an increased demand for deploying DNNs on devices that do not have abundant resources such as memory and computation units. Recently, network compression through a variety of techniques such as pruning and quantization have been proposed to reduce the resource requirement. A key parameter that all existing compression techniques are sensitive to is the compression ratio (e.g., pruning sparsity, quantization bitwidth) of each layer. Traditional solutions treat the compression ratios of each layer as hyper-parameters, and tune them using human heuristic. Recent researchers start using black-box hyper-parameter optimizations, but they will introduce new hyper-parameters and have efficiency issue. In this paper, we propose a framework to jointly prune and quantize the DNNs automatically according to a target model size without using any hyper-parameters to manually set the compression ratio for each layer. In the experiments, we show that our framework can compress the weights data of ResNet-50 to be 836$\times$ smaller without accuracy loss on CIFAR-10, and compress AlexNet to be 205$\times$ smaller without accuracy loss on ImageNet classification.
Binarized Canonical Polyadic Decomposition for Knowledge Graph Completion

Methods based on vector embeddings of knowledge graphs have been actively pursued as a promising approach to knowledge graph completion.However, embedding models generate storage-inefficient representations, particularly when the number of entities and relations, and the dimensionality of the real-valued embedding vectors are large. We present a binarized CANDECOMP/PARAFAC(CP) decomposition algorithm, which we refer to as B-CP, where real-valued parameters are replaced by binary values to reduce model size. Moreover, we show that a fast score computation technique can be developed with bitwise operations. We prove that B-CP is fully expressive by deriving a bound on the size of its embeddings. Experimental results on several benchmark datasets demonstrate that the proposed method successfully reduces model size by more than an order of magnitude while maintaining task performance at the same level as the real-valued CP model.
Image Forgery Detection Based on Trigonometric Transforms

Image forgery detection is the basic key to solve many problems, especially with regard to the social problems such as those in Facebook, and court cases. Copy-move forgery is the type of forgery where a part of the image is copied to other location of the same image to hide important information or duplicate certain objects in the original image which makes the viewer suffer from difficulties to detect the forged region. In this type of image forgery, it is easy to perform forgery, but more difficult to detect it, because the features on the copied parts are similar to those of other parts of the image. This paper presents a comparison study between different trigonometric transforms in 1D and 2D for detecting the forgery parts in the image. This comparison study is based on the completeness rate and the time of processing for the detection. This comparison concludes that the DFT in 1D or 2D implementation is the best choice to detect copy-move forgery compared to other trigonometric transforms. The proposed algorithm can also be used for active forgery detection because of its robustness to detect the manipulation of digital watermarked images or images with signatures.