Reviewerly/exHarmony huggingface dataset:
Real time Image Recognition Based on low cost Processing Platform

In this paper, the traffic sign recognition module of a small-scale autonomous car prototype will be presented. The process undergoing the choice of an appropriate algorithm, as well as the factors taken into consideration will be presented in the form of a case study. The current literature presents various ways of achieving the recognition of traffic signs, but most of them are computational expensive, or have difficulty in offering consistent results in conditions that are different from the prerecorded ones. Since the processing on the car is carried on an embedded platform from Nvidia (Jetson TX2), this study is based on the same board, the stream being captured with a low-cost webcam. Classical algorithms like SURF (Speeded Up Robust Features), SIFT (Scale Invariant Feature Transform) or ORB (Oriented fast and Rotated Brief) offer reliable results when the lighting condition between the reference image and the image obtained from the camera are similar. In our setup, the algorithms mentioned above start to behave badly in low light conditions. Therefore, this paper discusses the possibility of using Haar like features alongside a classifier for detecting traffic signs.
Recommendation System for Specialization Selection Using K-Means Density Canopy

The carelessly selection of specialization course leaves some students with difficulty. Therefore, it is needed a recommendation system to solve it. Several approaches could be used to build the system, one of them was K-Means. K-Means required the number of initial centroid at random, so its result was not yet optimal. To determine the optimal initial centroid, Density Canopy (DC) algorithms had been proposed. In this research, DC and K-Means (DCKM) was implemented to build the recommendation system in the problem. The alpha criterion was also proposed to improve the performance of DCKM. The academic quality dataset in the 2018 informatics programs students of ITTP was used. There were three main stages in the system, namely determination of the weight of the course in dataset, implementation of DCKM, and determination of specialization recommendations. The results showed that the system by using DCKM has good quality based on the Silhouette results (at least 0.655). The system also used standar valuation scale in ITTP and silhouette index in the process of system. The results showed that 176 (65.91%) students were recommended in IT specialization, 25 (9.36%) students were recommended in MM specialization and 66 (24.7%) students were recommended in SC specialization.&#x0D;
Graphs based methods for simultaneous smoothing and sharpening

We present two new methods for simultaneous smoothing and sharpening of color images: the GMS3 (Graph Method for Simultaneous Smoothing and Sharpening) and the NGMS3(Normalized Graph-Method for Simultaneous Smoothing and Sharpening). They are based on analyzing the structure of local graphs computed at every pixel using their respective neighbors. On the one hand, we define a kernel-based filter for smoothing each pixel with the pixels associated to nodes in its same connected component. On the other hand, we modify each pixel by increasing their differences with respect to the pixels in the other connected components of those local graphs. Our approach is shown to be competitive with respect to other state-of-the-art methods that simultaneously manage both processes. We provide two methods that carry out the process of smoothing and sharpening simultaneously. The methods are based on the analysis of the structure of a local graph defined from the differences in the RGB space among the pixels in a 3 × 3 window. The parameters of the method are adjusted using both observers opinion and the well-known reference image quality assessment BRISQUE (Blind/Referenceless images spatial quality Evaluator) score.
DEVELOPMENT OF INFORMATIONAL AWARENESS AMONG STUDENTS UNDER THE CONDITIONS OF DIGITAL EDUCATION IMPLEMENTATION IN YAKUTIA

This study was aimed at determining the effectiveness and potential of pedagogical education to develop informational awareness among students under the conditions of educational digitalization in Yakutia, also known as the Republic of Sakha. The problem was investigated primarily through an analysis of the theoretical viewpoints put forward by foreign and Russian researchers on issues regarding updates to education in the informational education space. The loss of moral and value orientations in society and the upbringing amid this backdrop of an entire generation of youth, who are currently becoming educators of a new rising generation, cause societal apprehension and highlight the need to find a solution. Correspondingly, this research probed into the effectiveness with which the following requirement is implemented in the chosen context: capitalizing on the potential of ethnic pedagogy in advancing the evolution of students’ ability to discern and adequately assess the effects of informational environments on youth development. A pedagogical experiment was conducted at the M. K. Ammosov North-Eastern Federal University to examine initial concepts regarding the fulfillment of ethnic pedagogy’s prospects for enhancing the educational value of academic activities in the university. More than 500 students from different colleges and faculties, as well as lecturers who are involved in extracurricular activities, took part in the experiment. The study treated updates in service of the potential of ethnic pedagogy to mold the young generation under the digitalization of the educational process in a regional university as scientific results. The practical significance of the study was established in consideration of the characteristics of media influence, Internet addiction, and psychophysiological stress and in accordance with the requirements of the newly instituted Federal State Educational Standard 3++ (FSES 3++). The obtained methodological and technological data on the development of informational awareness among students can be used in explorations into other areas of education
Local residents and tourists’ opinion on the effects of boating activity towards Kilim waterways

Heritage sites within Kilim Karst Geoforest Park consist of geology, archaeology, ecology, history and culture. Therefore, both tourism and resource conservation should be integrated in order to benefit human and natural resources. This paper presents research that identifies the opinion of tourists and local residents on the effects of boating activities towards natural resource conservation in Kilim Karst Geoforest Park, Langkawi. 320 structured questionnaires were distributed to tourists, local residents in Kilim and staffs who are in charged in the tourism operation of Kilim Karst Geoforest Park including staff of Langkawi Development Authority (LADA), Koperasi Kampung Kilim Langkawi Berhad (KKKLB) and Komuniti Pengurusan Sumber Perikanan (KPSP). The study managed to gather 93% with response rate (n=299) that consist tourists (65), local residents of Kilim (145) and staff (89). Based on the analyses conducted, it was found that boating activities contributed to the pollutions along Kilim waterways in many ways for instance, oil spills and waste disposals. Majority of the respondents agreed that high boats speed increased strong waves, which lead to depletion of mangrove trees and affecting aquatic life. Kilim has become one of the preferred destinations for nature lovers since the area has been given the Geopark and has received countless of tourist arrivals. Nevertheless, this situation had also affected natural resources of the area. Thus, people who involved in tourism activities and the management of Kilim Karst Geoforest Park should concern and possessed adequate knowledge on natural resource conservation so that the area can be experienced by the future generations.
Study on the Training Mode of Accounting Talents under the Background of Artificial Intelligence

&lt;p&gt;Now the speed of social and economic development is faster and faster, the development of science and technology is also fast and the speed of artificial intelligence technology development quickens. All of these promote the rapid development and progress of accounting. This paper tries to analyze and study the education and training mode of accounting talents under the premise of artificial intelligence, put forward the goal of training accounting talents, promote the integrity of accounting teaching system, and explore the optimization and improvement of accounting teaching mode.&lt;/p&gt;
Pengaruh Metode Pembelajaran Bahasa Jawa Melalui Bermain Peran terhadap Perilaku Sopan Santun Anak

THE EFFECT OF JAVANESE LANGUAGE LEARNING METHODS THROUGH ROLE PLAYING ON CHILD POLITE BEHAVIOR. The purpose of this study was to examine how much influence the Javanese language learning method through playing macro roles and playing micro roles on the behavior of courtesy in grade 2 elementary school children in Lamongan Regency. The behavior of courtesy in this study is based on Javanese culture, focused on three aspects namely respecting older people, choosing the language level wisely, and obeying orders. This research was conducted at SDN Made IV and SDN Sukorejo II. This research was conducted in the odd semester of the 2019/2020 school year with a total of 68 research subjects. Data collection techniques using the method of observation. While the data analysis technique uses t-test, which has previously been tested for normality and homogeneity. The results showed that the average value of the experimental class was 58.24, while the control class was 56.5. From the results of the study found that the results of a significant value of 0.018 &lt; 0.05. Then the results are obtained that there are differences in the average behavior of courtesy between children who are taught with the Javanese learning method through playing macro roles and children who are taught with the Javanese learning method through playing micro roles. Thus, the behavior of the children polite given the Javanese language learning method through playing macro roles is higher than the children who were given the Javanese learning method through micro role playing.
KanOCR: Conversion of Printed Kannada Document to Editable form using Convolutional Neural Networks

Optical Character Recognition (OCR) technology in converting an image containing text to an editable text format is of high sense in document image processing.Input to OCR could be a scanned document, or a simple newspaper cut-out.Supervised Learning using Neural Networks yield the output with greater accuracy.Unlike English, Kannada Language has a huge set of characters as it includes kaagunithas, vattaksharas, etc.This makes recognition of the characters much more complex.The paper mainly concentrates on OCR for the Kannada Text which goes through a threshold as a first step converting input image into binary image, making segmentation easier.Characters can be extracted from the documents using various Segmentation methods.The vattaksharas are extracted/differentiated from the words by using base-line technique.When the characters are recognized, they are compared with Unicodes available on the system and then printed.In the above method, CNN plays a pivotal role in reading the character and comparing it with the Unicode look up table values to print the output.This system has been tested with varying fonts.A total number of 37 sample documents are used for experimentation.The system has been developed for only printed Kannada Text.
EasyKG: An End-to-End Knowledge Graph Construction System

We present an end-to-end system, called EasyKG, throughout the whole lifecycle of knowledge graph (KG) construction. It has a pluggable pipeline architecture containing the components of knowledge modeling, knowledge extraction, knowledge reasoning, knowledge management and so forth. Users can automatically generate such a pipeline so as to obtain a domain-specific KG. Advanced users are allowed to create a pipeline in a drag-and-drop manner with customized components. EasyKG lowers the barriers of KG construction. Moreover, EasyKG allows users to evaluate different components and KGs, and share them across different domains so as to further reduce the cost of construction.
Improved Face and Facial Expression Recognition Based on a Novel Local Gradient Neighborhood

Computing efficiency is a key in biometric identification systems for automatic facial expression recognition. It was integrated within advanced pattern recognition as an excellent paradigm while users shifted towards underlying patterns. Most existing face recognition models suffer from a low recognition rate and significant execution time. To overcome these drawbacks, we propose a new Local Gradient Neighborhood (LGN) descriptor for effective face and facial expression recognition.Firstly, the LGN components obtained by applying LGN for each block of the face image which is represented by 9-size vector.Secondly, the system concatenates features vectors of different blocks to obtain the final feature vector for the face image.Finally, it applies SVM and KNN techniques to classify the input images.Unlike other similar works, the new proposed descriptor is evaluated on two benchmarks, for face recognition and facial expression recognition respectively.The experimental results show an excellent recognition rate and fast execution time.The recognition rate for the ORL face database is 98.50% and the recognition rate for the JAFEE database is 84.28%.
An Agent-Ensemble for Thresholded Multi-Target Classification

We propose an ensemble approach for multi-target binary classification, where the target class breaks down into a disparate set of pre-defined target-types. The system goal is to maximize the probability of alerting on targets from any type while excluding background clutter. The agent-classifiers that make up the ensemble are binary classifiers trained to classify between one of the target-types vs. clutter. The agent ensemble approach offers several benefits for multi-target classification including straightforward in-situ tuning of the ensemble to drift in the target population and the ability to give an indication to a human operator of which target-type causes an alert. We propose a combination strategy that sums weighted likelihood ratios of the individual agent-classifiers, where the likelihood ratio is between the target-type for the agent vs. clutter. We show that this combination strategy is optimal under a conditionally non-discriminative assumption. We compare this combiner to the common strategy of selecting the maximum of the normalized agent-scores as the combiner score. We show experimentally that the proposed combiner gives excellent performance on the multi-target binary classification problems of pin-less verification of human faces and vehicle classification using acoustic signatures.
Cloud Storage Strategy of Blockchain Based on Genetic Prediction Dynamic Files

With the rapid expansion of data volume, traditional data storage methods have been unable to meet the practical application requirements of blockchain cloud storage. Aiming for the cloud storage problem of blockchain, a new storage access method for predicting dynamic file load is proposed. By predicting the load status of cloud storage files in advance, the load of each blockchain data node at the next moment is first estimated. A hierarchical genetic algorithm is used to construct the connection weights between the hidden layer and the output layer, which makes the data network converge faster and more accurate, thereby effectively predicting the node load. In addition, based on the file allocation, an evaluation analysis model is constructed to obtain the time response capability of each file during the allocation process. The node’s periodic load prediction value is used to calculate the corresponding weight of the node and it is continuously updated, retaining the advantages of the static weighted polling algorithm. Combined with the genetic algorithm to help predict the file assignment access strategy of the later load of each node, it can meet the system requirements under complex load conditions and provide a reasonable and effective cloud storage method. The experimental evaluation of the proposed new strategy and new algorithm verifies that the new storage method has a faster response time, more balanced load, and greatly reduced energy consumption.
Robust Extended Recursive Wiener Fixed-Point Smoother and Filter in Discrete-Time Stochastic Systems

As an extension of the linear robust recursive least-squares Wiener fixed-point smoother and filter,this paper originally designs the robust extended recursive Wiener fixed-point smoother and filter forestimating the signal in discrete-time wide-sense stationary stochastic systems. It is a characteristic in this paperthat the signal is modulated with the nonlinear mechanism. As a step to the estimation problem for theobservation mechanism with the nonlinear modulation, the robust signal estimators are proposed for theobservation equation with the linear amplitude modulation of the signal. The observation noise is additionalwhite noise. The system matrix in the state equation contains uncertain parameters. The robust extendedrecursive Wiener estimators are derived from the Wiener-Hopf equation. In the simulation example, it is shownthat the proposed robust extended recursive Wiener fixed-point smoother and filter are superior in estimationaccuracy to the extended recursive Wiener estimators
Reputation System for Fraud Detection in Nigerian Consumer-to-Consumer E-Commerce

Reputation System for Fraud Detection in Nigerian Consumer-to-Consumer E-Commerce Lawal Oluwadamilare Rasheed, AwoyeluIyabo Olukemi Abstract Consumer-to-consumer e-commerce is a way of buying and selling of goods and services that involve consumers selling goods and services to other consumers. Over the years, the number of internet users in Nigeria have rapidly increased, which has in turn led to fast growth in the e-commerce market. The major setback in a Nigerian consumer-to-consumer e-commerce application is the lack of information about the history and the behavior of sellers. Existing reputation systems in literatures relied only on ratings provided by registered users; and this often resulted in cold-start problem. In this paper, a reputation system was formulated based on decision maker ratings, non-registered users’ ratings, and registered users’ ratings. The reputation system can be understood and implemented in a consumer-to-consumer e-commerce platform. Full Text: PDF DOI: 10.15640/jcsit.v7n2a6
Prototype Android Based Payroll Monitoring Application at PT. Anugrah Distributor Indonesia

A Salary is a form of wages or rewards from rights granted by companies to employees. Usually, salaries are given to workers also differ according to position and class level. At PT Anugrah Distributor Indonesia is one of the companies engaged in retail sales. The amount of wages varies as well as determining commissions for each sales process from each unit. Become a complexity factor in determining the amount and salary for employees. In the process of recording and calculating salaries so far using Ms. Excel. Where accountants (finance) as data processors have a central role in processing them experiencing delays in displaying data. The results of the management process and salary reports are in the form of paper and files (softcopy). Delays in displaying data have an impact on the salary that must be reported by the leadership. From these problems, problem-solving is obtained by making an android-based payroll monitoring application prototype, can display salary data and make it easier for leaders to see the salary that has been reported anytime and anywhere. This research was made using observation methods, interview methods, SWOT analysts and literature studies. Using UML as a modeling design.
A Novel Method to Increase Diffusion and Confusion in AES Algorithm

The Advanced Encryption Standard (AES) is the most famous amongst symmetric block cipher algorithms, and the most used one in many different applications, because its encryption strength and its resistance to many attacks.In this paper, a novel approach will be introduced to remove some weakness points in AES and increase its security.Unlike most previous researches, this research will improve the most important and powerful part of AES algorithm which is the MixColumns() transformation.In the original AES, there only one function is used, which it creates a fixed array that is used in MixColumns() transformation, and this fixed array is known by attackers.Alternatively, the expanded secret key will be used to generate a different function at each round of AES.These functions will create a variable dynamic arrays at each round based on expanded secret key.The variable dynamic arrays will increase confusion amongst bits of the encrypted text.After that, the ShiftRow() transformation will be complicated from ShiftRow() with fixed pattern to ShiftRowColumn() with variable dynamic pattern according to expanded secret key.The modified ShiftRowColumn() will increase the diffusion amongst bytes of encrypted text, as will be detailed in experimental results.
Students’ Perception toward Psychological Problems Faced in Public Speaking Class

Students’ Perception toward Psychological Problems Faced in Public Speaking Class of English Department. (A Descriptive Research of Students at English Department Year 2015 at Muhammadiyah University of Makassar). This study aimed to find the students perception toward psychological problems faced in terms of fear and anxiety in public speaking class of English department. The data was collected from Fourth semester students of English Department who learned Public Speaking Subject. This study used descriptive qualitative research design. The instruments used were questionnaire and interview. The questionnaire was distributed to 40 students from different classes and based on the questionnaire scores the researcher took 10 students to be interviewed in order to know their perception. The questionnaire used was PSCAS (Public Speaking Class Anxiety Scale) and the interview used was semi structured interview. Based on findings, it was found that 33 of 40 or 82.5% students were indicated having medium level of fear and anxiety in public speaking class and 7 of 40 or17.5% students were having low level of fear and anxiety in public speaking class. Furthermore, it was found that the students gave negative perception toward psychological problems faced in public speaking class of English department. Moreover, there were several factors of students’ public speaking class fear and anxiety, those were fear of making mistakes, feeling that their friends would laugh at them, fear when looked at the audiences’ eyes, though that other students were better than them, and students did not have good preparation. Students perceived that public speaking class fear and anxiety impact negatively toward their performance in public speaking class such as students forgot their speech preparation, students were confused and stop to speak, made the students uncomfortable to speak English in public speaking class.