Reviewerly/exHarmony huggingface dataset:
Analisis faktor-faktor Kesulitan Belajar myob accounting dalam Pembelajaran Komputer Akuntansi pada Siswa kelas XI Akuntansi SMK Batik 2 Surakarta Tahun Ajaran 2018/2019

This study aims to find out what are the factors of learning difficulties experienced by students of class XI Accounting at Batik 2 Vacational High School Surakarta in 2018/2019 school year. This type of research is qualitative research with case study design. The research subjects were teachers of myob accounting, officer laboran, and students of class XI Accounting in Batik 2 Vacational High School Surakarta as many as 6 students as respondens. Data collections techniques used include observations, interviews, and documentations. To ensure the validity of the data, triangulations (methods and data sources) is used. Data analysis techniques used data collection, data reduction, data display, and conclusion drawing. The results showed that there were several obstacles faced by students in learning myob accounting, namely lack of understanding in English, lack of understanding in operating computers, lack of understanding of basic accounting, lack of involvement in myob accounting lessons, student perceptions that learning myob accounting must use a laptop, and inadequate school facilities. The conclusion of this study is that some students showed a lack of intellectual property, this was due to a lack of understanding in English, lack of ability to operate computers and lack of basic accounting skills. The school environment also affects the learning of students, during the learning process the students do not want to listen to what is conveyed by the teacher because students feel bored with how to teach the teacher. Inadequate school facilities in computer accounting learning will also affect student learning difficulties. Keyword: Learning Difficulties Myob Accounting.
A Blockchain-Based Decentralized Wi-Fi Sharing Mechanism

Wi-Fi is widely used as an inexpensive way for terminal service access, which brings a trend of Wi-Fi sharing. However, the current Wi-Fi sharing mode has problems such as low reliability and high cost. In order to cope with these problems, this paper puts forward a decentralized Wi-Fi sharing mechanism based on blockchain, including three sub-mechanisms of Wi-Fi authentication, Wi-Fi sharing, and Wi-Fi usage charging. Specifically, Wi-Fi owners first authenticate their Wi-Fi information on the blockchain and only the authenticated Wi-Fi can be connected. Then, the Wi-Fi users pay for the Wi-Fi usage through the smart contracts. Finally, all transactions are registered on the blockchain to ensure the security and reliability of Wi-Fi sharing process. Simulation results show that the proposed mechanism keeps a high Wi-Fi sharing success rate of more than 97 percent at a high Wi-Fi request rate of 200 tps.
Distributed Orchestration in Cloud Data Centers

Orchestration systems in cloud platforms are responsible for creating, managing and assigning the computational and network bandwidth resources to the requesting services. Conventional orchestration approaches in data centers are based on centralized solutions where they are a single point of failure, and a potential performance bottleneck. In this paper, using the notions of Markov approximation method and auction theory, we propose a fully distributed resource management scheme for data centers. The proposed solution takes into account the operational and economic constraints of the services and the servers in the data center and maximizes a global system utility function in a fully distributed manner. Simulation results show the effectiveness of the proposed solution in terms of speed of convergence, accuracy and resource utilization for applicability in next generation cloud systems.
Reporting Progress of Long-Running Operations in HTTP

This document defines a mechanism for following the real-time progressof long-running operations over HTTP.
Towards Award Prediction Based on Big Data Co-author Network

With the field of scholarly big data rising gradually, researchers have shown an increased interest in the study of academic careers and achievements. Although the massive academic data create a lot of opportunities to learn about scholars, it is still difficult to predict academic award winners. To solve this problem, we firstly propose a new indicator set generated by big data co-authorship network to effectively evaluate scholars. On this basis, we then build a Back Propagation Neural Network-based prediction model to predict recipients of the National Science Fund for Distinguished Young Scholars of China in the field of computer science. The experimental results of precision rate, recall rate and F1 prove that this model has good performance in award prediction. In addition to the methodological contribution, our research can not only help scholars get a better understanding of underlying mechanism contained in academic achievements, but also provide them with the support and guidance to manage their academic careers.
Inclusion financière et exportations agricoles des pays de l'UEMOA : rôle de la qualité des institutions

AbstractThe objective of this work is to analyse the effect of financial inclusion on agricultural exports from UEMOA countries through the quality of institutions. The aim is to analyse the role of institutional quality in the coverage of decentralised financial services such as microfinance and banking services. We formulate hypotheses that are empirically tested using econometric estimates with a sample of seven (07) UEMOA countries over the period 2011-2016. The results of the analyses show that financial inclusion through microfinance credit positively affects agricultural exports. It also shows that improving the quality of institutions is the main channel through which financial inclusion indicators affect WAEMU countries' agricultural exports. It is up to governments and decision-makers to : (i) implement institutional quality mechanisms to improve the conditions for the development of rural financial services. (ii) improve land law and governance-based governance for accountability, transparency and corruption in rural areas.
Persepsi Mahasiswa terhadap Teknologi RFID di Perpustakaan: Studi Kasus di Pusat Layanan Perpustakaan Universitas YARSI

The aim of this study is to examine students’ perceptions of the use of RFID (Radio Frequency Identification) technology to support library service. The respondents were 39 students at YARSI University. The data was collected through a questionnaire method. Most of the users (77%) responded positively regarding availability RFID in the libraries. Generally, respondents (100%) had positive attitude and satisfied with the services provided by RFID technology, and that have been cost beneficial to YARSI Library. The study however found out that there were an answer (72%) that RFID technology is not necessary yet.
Including traffic light recognition in general object detection with YOLOv2

With an in vehicle camera many different things can be done that are essential for ADAS or autonomous driving mode in a vehicle. First, it can be used for detection of general objects, for example cars, cyclists or pedestrians. Secondly, the camera can be used for traffic light recognition, which is localization of traffic light position and traffic light state recognition. No method exists at the moment able to perform general object detection and traffic light recognition at the same time, therefore this work proposes methods to combine general object detection and traffic light recognition. The novel method presented is including traffic light recognition in a general object detection framework. The single shot object detector YOLOv2 is used as base detector. As general object class dataset COCO is used and the traffic light dataset is LISA. Two different methods for combined detection are proposed: adaptive combined training and YOLOv2++. For combined training YOLOv2 is trained on both datasets with the YOLOv2 network unchanged and the loss function adapted to optimize training on both datasets. For YOLOv2++ the feature extractor of YOLOv2 pre-trained on COCO is used as feature extractor. On the features LISA traffic light states are trained with a small sub-network. It is concluded the best performing method is adaptive combined training which reaches for IOU 0.5 a AUC of 24.02% for binary and 21.23% for multi-class classification. For IOU of 0.1 this increases to 56.74% for binary and 41.87% for multi-class classification. The performance of the adaptive combined detector is 20% lower than the baseline performance of an detector only detecting LISA traffic light states and 5% lower than the baseline of a detector only detecting COCO classes, however detection of classes from both dataset is almost twice as fast as separate detection with different networks for both datasets.
Public Auditing For Shared Cloud Environment With Efficient User Revocation In The Shared Cloud Environment

The data storage and sharing services in the cloud, users can easily modify and share data as a group. To ensure shared data integrity can be verified publicly, users in the group need to compute signatures on all the blocks in shared data. Different blocks in shared data are generally signed by different users due to data modifications performed by different users. For security reasons, once a user is revoked from the group, the blocks which were previously signed by this revoked user must be re-signed by an existing user. The straight forward method, which allows an existing user to download the corresponding part of shared data and re-sign it during user revocation, is inefficient due to the large size of shared data in the cloud. The public auditing for shared data with efficient user revocation in the shared cloud environment proposed  a novel public auditing mechanism for the integrity of shared data with efficient user revocation in mind. By utilizing the idea of proxy re-signatures, it allow,the cloud to re-sign blocks on behalf of existing users during user revocation.So that, existing users do not need to download and re-sign blocks by themselves. In addition, a public verifier is always able to audit the integrity of shared data without retrieving the entire data from the cloud, even if some part of shared data has been re-signed by the cloud. Moreover,this mechanism is able to support batch auditing by verifying multiple auditing tasks simultaneously. Experimental results show that mechanism can significantly improve the efficiency of user revocation
The Integration of Fuzzy Logic System for Obstacle Avoidance Behavior of Mobile Robot

A mobile robot has a capability of sensing its location under uncertain environment,planning a real-time path as well as controlling its steering angle and speed to reach the target location. A robust controller is embedded in mobile robot whilst analyzing the input and output that help it to navigate without colliding with any obstacles. Meanwhile, Fuzzy Logic Controllers (FLC) is an intelligent technique that proves to be the one of the most reliable controllers that suits well for nonlinear system like robot due to the simple control based on user input without any prior knowledge to the mathematical model. In this paper, the Mamdani and Sugeno FLC are developed for a mobile robot. The smoothness and efficiency that generated from these FLC is analyzed based on simulation of Pioneer P3-DX robot in virtual robotic software for single and multirobot environments under static obstacles environment. Simulation results for the Pioneer P3-DX robot shows the Sugeno FLC able to produce smoother path and reach the goal faster than Mamdani FLC.
Towards Social Artificial Intelligence: Nonverbal Social Signal Prediction in A Triadic Interaction

We present a new research task and a dataset to understand human interactions via computational methods, to ultimately endow machines with the ability to encode and decode a broad channel of signals humans use. This research direction is essential to make a machine that genuinely communicates with humans, which we call Social Artificial Intelligence. We first formulate the social signal problem as a way to model the dynamics of signals exchanged among interacting individuals in a data-driven way. We then present a new 3D motion capture dataset to explore this problem, where the broad spectrum of signals (3D body, face, and hand motions) are captured in a triadic interaction scenario. Baseline approaches to predict speaking status, formation, and body gestures of interacting individuals are presented in the defined prediction framework.
Pengaruh Penambahan Zat Aditif X terhadap Viskositas, Performa Mesin dan Emisi Gas Buang Sepeda Motor Honda CB150R

&lt;em&gt;Mechanical contact is the process which can not be avoided in machinery system. The way to diminish thrist conditon which caused by the process is to give engine lubricant into that system. The lubricant resistance over the temperature system is influenced by base stock and its additives where contained of. Lubricant oil additives are chemical compounds that will improve or enhance the lubricant performance of base oil. These additives are carefully designed to ensure the functions in machines system. Engine lubricant that used in this research is MPX 1 SAE 10 W-30 and the additives that used is Engine Additive with brand Lupromax. The aim of this research is to analyze the reaction of increasing the additive EA over to viscosity and machine function of Honda All New CB 150R. In this research, it is found that the fusion with EA 5.25% (1200 ml lubricant with 63 ml additives) could: (1) reduce viscosity to 66.56 mm&lt;sup&gt;2&lt;/sup&gt;/s and 9.9 mm&lt;sup&gt;2&lt;/sup&gt;/s at temperatures of 40°C and 100°C, respectively, (2) enhance the torque and energy to 14.5 kW @9000 rpm and 12.23 Nm @7000 rpm with reducing the rate emision of CO to 0.44% vol, HC to 196 ppm vol, CO&lt;sub&gt;2&lt;/sub&gt; to 6.5% vol and enhancing the rate of O&lt;sub&gt;2&lt;/sub&gt; to 10.71%, and (3) reduce fuel consumption to 18.75 ml/km.&lt;/em&gt;
Understanding Adversarial Behavior of DNNs by Disentangling Non-Robust and Robust Components in Performance Metric.

The vulnerability to slight input perturbations is a worrying yet intriguing property of deep neural networks (DNNs). Despite many previous works studying the reason behind such adversarial behavior, the relationship between the generalization performance and adversarial behavior of DNNs is still little understood. In this work, we reveal such relation by introducing a metric characterizing the generalization performance of a DNN. The metric can be disentangled into an information-theoretic non-robust component, responsible for adversarial behavior, and a robust component. Then, we show by experiments that current DNNs rely heavily on optimizing the non-robust component in achieving decent performance. We also demonstrate that current state-of-the-art adversarial training algorithms indeed try to robustify the DNNs by preventing them from using the non-robust component to distinguish samples from different categories. Also, based on our findings, we take a step forward and point out the possible direction for achieving decent standard performance and adversarial robustness simultaneously. We believe that our theory could further inspire the community to make more interesting discoveries about the relationship between standard generalization and adversarial generalization of deep learning models.
HR MANAGER COMPETENCE FORMATION METHODS REGARDING PERSONNEL RISK MANAGEMENT

In a post-crisis society, the most valuable economic and social resources of an organization are human capital. Modern strategies of management employed by an organization need special management approaches that would base on analysis of activities’ mechanisms and processes associated with personnel risks. One of the organizational, methodological and pedagogical solutions to this problem is the educational technology for raising competence level of managers in risk management. The research is aimed at methodological, managerial and organizational recommendations for competence formation of MSc in personnel risks management (major "Human Resource Management"). Research methods: expert survey, questionnaires, interview. The main finding of the research study: respondents were interviewed (Bachelors and Masters of Samara National Research University named after academician SP Korolev, of Samara State Technical University, executives, managers, HR managers, employees of industrial enterprises and of organizations providing services) to assess the relevance of competence in management of personnel-related risks for professional activities of an HR manager; learning and teaching support material has been developed to be used in training and education organized at the university and for management consulting of employers and employees of various organizations; samples of tasks for “Evaluation Tools Fund” have been developed, aimed at identifying the level of formation of the components of professional competence in management of personnel-related risks.
Assisted distillation of Gaussian resources

In spite of their outstanding experimental relevance, Gaussian operations in continuous-variable quantum systems are subjected to fundamental limitations, as it is known that general resources cannot be distilled within the Gaussian paradigm. We show that these limitations can be overcome by considering a collaborative setting where one party distills resources with the aid of another party, whose operations are assumed to be Gaussian but are otherwise unrestricted; the two parties can only communicate classically. We show that in single-shot scenarios, unlike in the well-known case of entanglement theory, two-way classical communication does not lead to any improvement over one-way classical communication from the aiding party to the aided party. We then provide a concise general expression for the Gaussian resource of assistance, i.e., the distillable resource when the aiding party holds a purification of the aided party's state. To demonstrate the usefulness of this general expression, we apply it to two important kinds of resources, squeezing and entanglement, and find some simple analytic solutions. In the case of entanglement theory, we are able to find general upper bounds on the regularized Gaussian entanglement of assistance, and to establish additivity for tensor powers of thermal states. This allows us to draw a quantitative and enlightening comparison with the performance of assisted entanglement distillation in the non-Gaussian setting. On the technical side, we develop some variational expressions to handle functions of symplectic eigenvalues that may be of independent interest. Our results suggest further potential for Gaussian operations to play a major role in practical quantum information processing protocols.
INCREASING SKILLS OF WRITING DESCRIPTIVE TEXT THROUGH SYNTHETIC ANALYTIC METHOD

Abstract The purpose of this study is to know if the skills of writing descriptive text using synthetic analytic method is better than using the usual method. Writing skills are harder to master. Though it is important for students to master writing skills. In the Indonesian Language curriculum, writing skills are taught especially in junior high school (SMP). Teachers are encouraged to pay more attention to teaching the structure of the descriptive text in developing ideas, setting ideas, and vocabulary. From these problems the writer offers the use of Synthetic Analytic approach to improve students' understanding in learning Indonesian language in junior high.  However, in reality learning writing in schools still face a lot of obstacles and tend to be avoided. This study used quasi-experimental method. The population in this study is one of the students of SMP Negeri in Bandung and the subject of the research sample 2 classes of class VII.  Based on the results of research and discussion obtained Sig value. 0.001 because the value of Sig < 0.05 then H 0 rejected means the improvement of students' text writing skills using synthetic analytic methods is better than those who get the usual method of learning used by teachers. Keywords : Writing skills, Synthetic Analytical Method
PEMBELAJARAN MENULIS TEKS PUISI DENGAN METODE CIRC PADA KELAS VIII SMPN 1 TELUK JAMBE KARAWANG

This class test research was conducted with the aim of increasing the creativity and ability to write poetry in Indonesian language lessons through learning with the Cooperative Integrated Reading and Composition (CIRC) method for class VIII students of BAY JAMBE 1 Junior High School in the first semester of the 2018/2019 academic year. Based on preliminary observations, it is known that the poetry writing skills of class VIII students of TELUK JAMBE KARAWANG SMPN 1 are still low and need to be improved. students still find it difficult to make creative poetry. This makes researchers want to do the research. Based on the above explanation The method used in this study is the experimental method. The results of this experiment prove that learning to write poetry using the CIRC method is effective compared to learning to write poetry without using the CIRC method
JUSTIFICATION OF CRITERIA AND INDICATORS OF FORMATION OF THE DEONTOLOGICAL COMPETENCE OF FUTURE EDUCATION MANAGERS

The article presents the results of theoretical analysis of scientific works on the problems of deontological training of future specialists in higher education institutions. The result of this training is deontological competence as a complex subjective feature that determines the ability for the proper professional behavior and making effective decisions in deontologically determined situations of managerial interaction. The purpose of the research is to theoretically substantiate the criteria and indicators of the formation of the deontological competence of future specialists. To achieve this goal, the following research methods were used: terminological analysis, concretization and generalization of theoretical concepts in the philosophical, psychological and pedagogical works of domestic and foreign scientists. The article describes the results of the research. The criteria and indicators of forming education managers’ deontological competence at university master’s level are proved for the first in pedagogical science. The content of motivation, cognitive, activity-based criteria is revealed. The motivation criterion indicates the motivation for normative behavior, developed values that determine the proper performance of professional duties by education managers, their aims and needs as bearers of professional and moral values; cognitive criterion defines the degree of acquisition of deontological knowledge; technological criterion shows the degree of forming deontological abilities and skills.
Selective DeFRBRizing in Primo

Primo's FRBR groups are generally a big win for usability and discovery. But FRBR results can be problematic for musical materials such as scores and recordings -- and students and faculty in our Conservatory of Music have not been shy about saying so. Hear how we analyzed the problem, arrived at selective deFRBRization as the solution, and then planned, tested, and implemented it in production. (Changes to OTB normalization rules will be covered in detail.) Hear also how the project uncovered a few hidden metadata problems and helped us justify some much-needed database maintenance.
Artificial intelligence and security. Security 4.0

The Artificial Intelligence (AI) has giant possibilities to optimize the fight against crime and strengthen national security. In the conditions of unimaginable accumulation of information and the need for rapid decision-making, only the use of AI can lead to success. Intelligence, counterintelligence, forensic science, counteracting organized crime, rapid processing of available information, drafting of varied decisions, creating plans and multivariate scenarios, performing various analyzes is a time-consuming process. Only its use can significantly shorten this time and thus dramatically increase the possibilities for detection, prevention and curbing crimes.