Reviewerly/exHarmony huggingface dataset:
Brain Storm optimization Algorithm based on Prior Knowledge and Heuristic Crossover Operator for Traveling Salesman Problem

Traveling Salesman Problem (TSP) is a classic combinatorial optimization problem. This paper proposes a Brain Storm optimization (BSO) algorithm based on prior knowledge and heuristic crossover operator (PKHDBSO) to solve TSP. The algorithm firstly uses the convex hull or greedy algorithm to initialize the population, which can improve the initial population quality. Secondly, a heuristic crossover operator is introduced in the process of generating new individuals to improve the search efficiency of the algorithm considering the characteristics of the TSP. The effectiveness and practicability of the algorithm are verified by a set of simulation example. The results demonstrate that the proposed algorithm have better performance in solving large-scale TSP.
Dependence of Unsupervised and Supervised Segment of Three Dimensional Medical Images on Clustering and Deep Learning Formation

The purpose of this paper is to gift a completely unique unattended phase technique for3 Dimensionalmedical pictures. Complex blue brain technology (CNNs) has lead to a great improvement in image dimensions. But, the most recent technology works on administered instructions, requiring huge\1amount of manually interpreted data. Thus, it has become really difficult to decrypt the difficult medical reports. The main proposes paper a unique approach to deep learning and knots for segmentation. Our decision structure consists of 2 coordinates. In the first, we tend to deep study merits represent of coaching spots from a target pictures victim joint knot learning (JULE) that corresponding\1spots representations created by a CNN and updates and measures the CNN framework using knot labels as super ordinate signals. It tend to further extended JULE to three dimensional medical report by using three dimensional convolutions surround the CNN unique style. In\1second section, it tend to use k-means to the deep represents from the fully trained CNN\1project cluster labels to the destination picture so as to get the fully segmental image. We evaluated our ways in which on three footage of malignant neo plastic disease patterns scanned with microcomputed diagrammatical representation (microtechnology).The self-starting carving of medicinal regions in micro-technology would possibly a lot of subsidize to the carving evaluation technique. our tend to aim to mechanically allot every and every pictures into the zone of invasive malignant neo plastic disease, non-invasive malignant neo plastic disease, and traditional trim. Our experiments show improvement of required skills for deep learning of medical images lineup.
Measuring Accuracy Prediction-based Path Planning for UGVs with Visual Measurement Ability

Distributed unmanned ground vehicles (UGVs) with visual measurement cameras provide more flexibility and extendibility to measuring technology in industrial applications. This paper proposes an accuracy prediction-based path planning method for visual measurement system based on UGVs. A task planning framework for the visual measurement system is designed, in which task assignment, measuring position planning, and path planning are integrated. An accuracy prediction model is established using the geometric principle in visual measurement. The optimal measuring positions and paths are obtained for the UGVs based on the accuracy prediction model and virtual potential field method. Simulations and experimental results validate the effectiveness of the proposed method.
Rancang Bangun Sistem Notifikasi Pengunjung Rumah Berbasis Internet of Things

House is a building that is intended to be used as a place to live by humans, because everyone has important activities outside the home such as work, shopping, and so forth. Home visitors sometimes find the house they want to visit uninhabited, so they cannot meet the owner of the house. Therefore, homeowners need an Internet of Things based home visitor notification system, with this technology homeowners can find out who is visiting their home even if they are not at home. This research build a visitor monitoring system that can be controlled with a smartphone, and can record voice messages from visitors. This research uses Response Time method, and Digital Sound Conversion Calculation method. Response time testing on photo file sending systems get an average value of 3.2 seconds with a Standard Error of 3.1%, the remote control system gets an average value of 0.8 seconds with a Standard Error of 6.25%, at audio file sending system gets an average value of 6.7 seconds with a Standard Error of 2.98%. The results of Digital Sound Conversion testing with a duration of 15 seconds at a sampling rate of 11025 hz the size of the data is 323 KB, at a sampling rate of 22050 hz the size of the data is 645 KB, at a sampling rate of 44100 hz the size of the data is 1291 KB.
The 4th Industrial Revolution Era and Emergency Preparedness Policy: Artificial Intelligence, Big Data Analysis, Cloud Computing, Drone Use

The purpose of this paper is to suggest the development of emergency preparedness policy to implement intelligent government in the 4th Industrial Revolution. For accomplishing the research goal, I examined the significance of emergency preparedness policy in the 4th Industrial Revolution and the emergency preparedness policy of intelligent government in connection with the use of artificial intelligence(AI), big data analysis, cloud computing, and drone. And the development suggestions of emergency preparedness policy in the intelligent government are presented as follows. First, it is necessary to construct a crisis management decision support system using artificial intelligence, big data analysis, and IoT, etc. Second, we have to establish a system and technology for linking and utilizing private and public data. Third, it is desirable to improve the system to utilize big data in the situation of not deleting personal information or invading privacy. Fourth, big data analysis and artificial intelligence should be used to support military operations in the emergency preparedness plan, maintain government functions, and stabilize people s lives. Fifth, we need to combine AI and big data analysis to integrate civil defense resources and emergency preparedness resources, which have been classified as a national crisis and emergency management resource. Sixth, a comprehensive and trans-disciplinary crisisonomy study should be carried out based on the convergence academic system of the government s emergency preparedness policy. Seventh, it is desirable to activate educational opportunities and personnel exchanges with universities for emergency preparedness in intelligent governments to enhance the professionalism and creativity of public officials in charge. Eighth, it is necessary to utilize the expertise of military retirement officials in emergency preparedness in intelligent governments.
Dehazing network based on haze density

Single image dehazing is a challenging ill-posed restoration problem. Most of dehazing algorithms follow the classical atmospheric scattering model and adopt same parameters for different hazy density areas in hazy images. In this paper, we proposed an end-to-end dehazing algorithm, called Dehazing Network based on Haze Density(DNBHD). The proposed network involves a haze density map estimation network and a dehazing network. By the estimated haze density map, hazy image is divided into a mist region and a dense fog region which are respectively feed into dehazing network. Compared with previous dehazing algorithm, DNBHD is independent on the atmospheric scattering model, and considers uniform fog distribution in images. We use different parameters to handle different hazy density regions, avoiding color distortion and inappropriate brightness caused by overall defogging. The experiments show our algorithm achieves significant improvements over the state-of-the-art methods
PENERAPAN MODEL PEMBELAJARAN KOOPERATIF TIPE TAI SEBAGAI UPAYA UNTUK MENINGKATKAN HASIL BELAJAR MATEMATIKA SISWA KELAS IX A SMP NEGERI 2 BUMIJAWA KABUPATEN TEGAL SEMESTER II TAHUN PELAJARAN 2017/2018

The purpose of this research is to improve student mathematics learning outcomes by applying the TAI type of Cooperative learning model. This research is a classroom action research with two cycles. Research subjects class is IX-A, amounting to 28 students. The data collection method of this research are test, observation, and documentation. The results of classroom action research is showed an increase in mathematics learning outcomes, seen from the results of tests between Cycle I and Cycle II, the average value of Cycle I is 73.39, while Cycle II isreached 78.04 resulting in an increase of 4.65. In the mastery aspect of student learning there is a significant increase of 17.86% or 5 students, where is in the first cycle students completed 67.85% or as many as 19 students, while in the second cycle students is completed 85.71% or as many as 24 students from 28 students.
Perceiving Errors of Grammatical Concord Committed By Iraqi undergraduates studying English as A Foreign Language

This paper endeavors to inspect the intralingual errors induced by the undergraduates who learn English as a foreign language. The basic goal of this research is to identify those errors and itemize them into types.Recognizing such errors let the teachers diagnose the vulnerable points that the learners suffer from as it assists the learners in exploring their errors which is an impetus to overcome their errors. Attending a good linguistic magnitude of competence of grammatical concord rules in the learner's mind might not be discharged practically in a real context which occurs due to tenuous practice exerted by the learner. Performing well-formed structures – without errors is bounded up with conceiving the rules of grammatical concord, then practiced appropriately within the context. Otherwise, inefficacious performance stemming from faulty implementation or misuse of rules vitiates the appropriateness of the sentences produced. Rules of concord relating to number and subject-verb types have been almost violated.A parcel of monthly exam answer sheets has been used in the process of analysis in order to advocate the objective of the research.
Image Graph Matching Based on Region Adjacency Graph

Extraction of image features done in traditional pixel-based image analysis is not done effectively. This extraction is caused by extraction, which only represents content. A very promising and challenging approach is extracting graphs from images that represent not only content but also the relation of content. The process of graph extraction from an image begins with the segmentation process. The segmentation method using the Minimum Spanning Tree algorithm is one way to divide the image into homogeneous regions. The regions obtained in the segmentation stage are represented as vertices and relations between neighboring regions are represented as edges. This form of representation is called the Region Adjacency Graph (RAG). By assuming that the RAG obtained is a graph feature of the image, then a graph matching process is performed using it. Image graph matching is performed on the artificial images and batik core images using VF2 algorithm and Graph Edit Distance (GED) algorithm. Based on the experiments, the best evaluation results on artificial image data are obtained through the VF2 algorithm with an f-score value of 90.00%. Both precision and recall values for this algorithm have good values, namely 96.24% and 84.52%. Then in the batik core motif, the highest results are achieved through Graph Edit Distance with the GED ≥ 8 parameter. The f-score value obtained is 59.76%, and precision and recall are 47.46% and 60.00%
The Creation Process of a Secure and Private Mobile Web Browser with no Ads and no Popups

The aim of this work is to create a new style web browser. The other web browsers can have safety issues and have many ads and popups. The other web browsers can fill up cache with the logging of big history of visited web pages. This app is a light-weight web browser which is both secure and private with no ads and no popups, just the plain Internet shown in full screen. The app does not store all user data, so the navigation of webpages is done in incognito mode. The app was made to open any new HTML5 web page in a secure and private mode with big focus on loading speed of the web pages.
Development of a Mobile Movie Recommendation Application

This paper describes the development of a mobile application which is an automated system for movie recommendations based on different categories. For its development the used technologies include - iOS operating system, iOS SDK and Swift programming language. The application is in a close integration with Facebook for which Facebook SDK is used, as well as the networking library AFNetworking which securely sends HTTP requests to the public API of themoviedb and then read the returned responses in JSON format.
The complexity of disjunction in intuitionistic logic

Abstract We study procedures for the derivability problem of fragments of intuitionistic logic. Intuitionistic logic is known to be PSPACE-complete, with implication being one of the main contributors to this complexity. In fact, with just implication alone, we still have a PSPACE-complete logic. We study fragments of intuitionistic logic with restricted implication and develop algorithms for these fragments which are based on the proof rules. We identify a core fragment whose derivability is solvable in linear time. Adding disjunction elimination to this core gives a logic which is solvable in co-NP. These sub-procedures are applicable to a wide variety of logics with rules of a similar flavour. We also show that we cannot do better than co-NP whenever disjunction elimination interacts with other rules.
A Hybrid Image Steganography Using Chaotic Maps in DCT Domain

In this paper, a robust image steganography approach is proposed using chaotic maps and discrete cosine transform (DCT). The energy compaction property of DCT and deterministic pseudorandom characteristics of chaotic map are exploited to develop novel hybrid image steganography in DCT domain. The proposed technique applies DCT on the original image, and two logistic maps (LM) are used to identify the random locations (row and column, respectively) for secret data embedding. Whereas sine map (SM) embeds the secret information in the cover media according to different initial and control parameters. The experimental results demonstrate that the proposed method has high embedding capacity (EC), excellent stego-image quality and capability to withstand against brute attacks. Proposed method outperforms the existing steganography techniques in terms of EC, peak signal to noise ratio (PSNR), and universal image quality index (Q), and bit error rate (BER).
Facial Landmark Localization Using an Ensemble of Regression Trees on PC Game Controlling

Kandimalla, Naren Sai Krishna Mishra, Soumya Ranjan Sanyal, Goutam Sarkar, AnirbanTraditional PC gaming system has remained unmodified over the years. While GPU’s and high graphics machines have taken the gaming industry to the next level, there is a need to physically improve the gamers’ experience. Facial landmarks are the localization of certain points on the face. This paper proposes a novel model to control the computer games. It identifies the face and facial landmarks of the player sitting in front of the PC. To do this an ensemble regression trees are used with cascaded regression model. It performs various computations on the extracted landmarks of the input face to estimate the action to be performed. Then, it gives input to the game accordingly. Likewise, many of the keyboard and mouse controls can be mapped with facial movements. This paper gives an overview of head, eyes, and mouth mapping with the game controls. It eliminates the use of a keyboard or any such hardware device, thus creating a realistic gaming experience. Results show that with a continuous frame by frame input of a single face, the proposed model takes 209 milli-sec in poor lighting conditions and 12 milli-sec in good lighting conditions to identify and perform computations on the facial movements. Moreover, the observed gameplay of the final model is very smooth and robust.
PROFESSIONAL COMPETENCIES OF MUNICIPAL EMPLOYEES: DIAGNOSTICS AND QUALIFICATIONS

The article is devoted to the problems of increasing the professional competence of municipal employees in the information society. The transformation of the traditional knowledge paradigm of education on the basis of the competency-based approach is emphasized, the psychological characteristics of the activities of today's municipal servants are identified, various aspects of the ideas of domestic science on competence and competencies are identified, the specificity of the direction of additional professional education is considered. The presented study consists in the psychological diagnosis of the professional competencies of municipal employees for the further use of the results in the targeted development and implementation of continuing education programs for the mentioned category of students. Particular attention is paid to the attitude of employees towards the formation of a civic identity as an important factor in the consolidation of society. The results of the study are graphically presented in diagrams and comments, indicate the need to adapt a modern employee to changing conditions, the development of a willingness to learn new knowledge and technologies for working with people, to actively participate in the development of civil society and to widely involve additional professional education systems in this process.
Designing a Visual Analytics System for Medication Error Screening and Detection

Drug safety analysts at the U.S. Food & Drug Administration analyze medication error reports submitted to the Adverse Event Reporting System (FAERS) to detect and prevent detrimental errors from happening in the future. Currently this review process is time-consuming, involving manual extraction and sense-making of the key information from each report narrative. There is a need for a visual analytics approach that leverages both computational techniques and interactive visualizations to empower analysts to quickly gain insights from reports. To assist analysts responsible for identifying medication errors in these reports, we design an interactive Medication Error Visual analytics (MEV) system. In this paper, we describe the detailed study of the Pharmacovigilance at the FDA and the iterative design process that lead to the final design of MEV technology. MEV a multi-layer treemap based visualization system, guides analysts towards the most critical medication errors by displaying interactive reports distributions over multiple data attributes such as stages, causes and types of errors. A user study with ten drug safety analysts at the FDA confirms that screening and review tasks performed with MEV are perceived as being more efficient as well as easier than when using their existing tools. Expert subjective interviews highlight opportunities for improving MEV and the utilization of visual analytics techniques in general for analyzing critical FAERS reports at scale.
ESP IN PRACTICE SPEAKING: COMBINING TIME TOKEN TECHNIQUE WITH YOUTUBE VIDEO

Teaching speaking in english for specific purpose, sometimes students are having difficult to engage with the activity, as they face problems like vocalized pause, lost idea, and having low confidence to speak as well as monotous activity that still dominate the teaching and learning process. In serious condition, Imitation and repetition technique used by the lecturer all the time. To overcome the problem, this research offered solution by combining time token technique with youtube video to increase students achievement in speaking. The population of the research were the second semester students of Management in STIE Muhammadiyah Mamuju academic year 2018/2019. The instrument used in the research was pre test and post test to measure students achievement after giving treatment using time token technique with youtube video. The result of the research showed that this technique can affect students’ speaking achievement significantly as shown in test result. It is proved by the difference between pretest and post-test result of the three components of speaking ability and final score of the students speaking ability. This technique can be a good alternative way to varying teaching of speaking.
THE INFLUENCE OF THE ROLE OF SCHOOL, PARENTS AND COMMUNITIES ON THE QUALITY OF EDUCATION IN SMP NEGERI 2 PANGURURAN SAMOSIR REGENCY IN ACADEMIC YEAR 2018/2019

The purpose of the study was to analyze the role of schools, the role of parents in the role of the community and analyze the role of schools, parents and the community on the quality of school education in Pangururan 2 Public Middle School, Samosir Regency. The research method used multiple linear regression analysis. The results of the study (1) the value of tcount for the variable learning planning teacher ability 19.578 is greater than the ttable 0.1918 at the significance of 0.05 and the sig value of 0.00 is smaller than alpha 0.05. Based on the results obtained, Ho is rejected and H1 is accepted. The role of schools has an effect of 79.16% on the quality of education in Pangururan 2 Public Middle School. (2) Value of tcount for parent role variable 11, 258 greater than t table 0.1918 and sig value 0.000 smaller than alpha 0.05. Based on the results obtained, Ho is rejected and H1 is accepted. The role of parents affects 28.2% of the quality of education in Pangururan 2 Public Middle School (3). The value of tcount for the variable community role 6,680 is greater than t table 0,1918 and the value of sig 0,000 is smaller than alpha 0.05. Based on the results obtained, Ho is rejected and H1 is accepted. The role of the community has an effect of 30.6% on the quality of education in Pangururan 2 Public Middle School. (4). The value of Fcount 173.478 is greater than Ftable 2.70 or the value of sig 0.000 is smaller than alpha 0.05. Based on the results obtained, Ho is rejected and H1 is accepted. The role of schools, parents and the community has an effect of 80.5% on the quality of education in Pangururan 2 Public Middle School
An end-to-end distance measuring for mixed data based on deep relevance learning

Distance Measuring between two mixed data objects is the basis of many learning algorithms. The complex relevance between heterogeneous – various types/scales – attributes has a significant influence on the measured results. In this paper, we propose an End-to-End Distance Measuring method for mixe d data based on deep relevance learning, called E2DM. Existing methods confuse the attributes space by mapping the discrete attribute values to new continuous values, or discretize continuous attributes values without considering the relevance. In contrast, E2DM directly manipulates on the original data with data conversion and relevance learning simultaneously to avoid information loss and attribute space confusion. E2DM firstly estimates internal relevance (i.e., relevance within the attribute) influenced distance by considering the categorical attribute value frequency and mapping numerical attribute values into multiple bins. Then it takes a wrapper approach to iteratively optimize relevance influenced distance and bin boundaries using a Frobenius-norm deviation as its objective function. Co-occurrence Mover’s Distance is proposed to explicitly explore relevance between attributes in each iteration. Finally, the distance for numerical attribute values is refined based on the original values and the fallen bin centers. Experimental results on a number of real-world datasets demonstrate that E2DM outperforms the state-of-the-art methods.