Reviewerly/exHarmony huggingface dataset:
Bots Recognition in Social Networks Using the Random Forest Algorithm

Online social networks are of essence, as a tool for communication, for millions of people in their real world. However, online social networks also serve an arena of information war. One tool for infowar is bots, which are thought of as software designed to simulate the real user’s behaviour in online social networks. The paper objective is to develop a model for recognition of bots in online social networks. To develop this model, a machine-learning algorithm “Random Forest” was used. Since implementation of machine-learning algorithms requires the maximum data amount, the Twitter online social network was used to solve the problem of bot recognition. This online social network is regularly used in many studies on the recognition of bots. For learning and testing the Random Forest algorithm, a Twitter account dataset was used, which involved above 3,000 users and over 6,000 bots. While learning and testing the Random Forest algorithm, the optimal hyper-parameters of the algorithm were determined at which the highest value of the F 1 metric was reached. As a programming language that allowed the above actions to be implemented, was chosen Python, which is frequently used in solving problems related to machine learning. To compare the developed model with the other authors’ models, testing was based on the two Twitter account datasets, which involved as many as half of bots and half of real users. As a result of testing on these datasets, F 1 -metrics of 0.973 and 0.923 were obtained. The obtained F 1 -metric values are quite high as compared with the papers of other authors. As a result, in this paper a model of high accuracy rates was obtained that can recognize bots in the Twitter online social network.
The Effort to Improve Communication Skills of "Asking Something" Through T-MOTECH in Multi-Handicapped Children

This study originated from problems in special school, the problem was a multi-handicapped child who had problems communicating especially in asking for something (specifically in asking for food, drinking, dolls and wants to go to the toilet. This study aims to prove communication skills in asking can be improved through T-MOTECH media on multi-handicapped children (X). This study used the experimental research of Single Subject Research (SSR), with A-B design and data analysis techniques using graphical visual analysis. Subjects were multi-handicapped children of Kindergarten level in special school. The results of this study indicate that, the ability of multi-handicapped children (X) in communicating requests for food, drink, dolls and wants to go to the toilet. At the beginning of the baseline condition (A) the observations were carried out 6 times with a mean level of 46.7%, at the intervention condition observations were carried out 8 times with the mean level 86.9%, so it can be concluded that the hypothesis was accepted because of children's communication skills in asking for food, drinking, dolls and toilets in multi-handicapped children can be improved with T-MOTECH media.
Image quality optimization towards lidar registration based on iterative termination

Image quality optimization is a key technique in image processing, whose goal is to improve image quality by image enhancement or image format transform. This paper aims at optimizing image acquisition using Lidar registration, which can cope with disadvantages of conventional algorithms such as low-resolution. Specifically, we propose an iterative termination optimization strategy based on image quality perception features and local mean estimation. First, fuzzy images with different types and degrees of distortion are incorporated to form a representative natural image set, and feature maps of fuzzy images are extracted by the natural scene statistical method in the spatial domain. Noticeably, the proposed algorithm which performs iterative deblurring operation records the optimal iteration point based on recording the quality value FSIM of the restored image, and calibrates the corresponding feature vector in the sample library with the optimal iteration point (step number). Afterwards, we leverage LME method to implement an estimate of the number of iteration steps. Based on these two steps, the estimation of the initial iterative monitoring point is completed, so that the subsequent adaptive iterative termination work is more purposeful to monitor the defuzzification metric. The optimization operation can be completed faster effectively.
Early Detection of Preeclampsia using a Rule-Based System Information System

Abstract Preeclampsia is the highest cause of maternal death. One program to reduce Maternal Mortality Rate (MMR) is by early detection. The incomplete history of maternal history that has occurred has led to the determination of an incorrect diagnosis and has resulted in errors in decision making. The results of the survey in the detection of preeclampsia include midwives not conducting a history of past medical history, physical examination during the first antenatal care visit is incomplete. During this time the detection and diagnosis model is still done manually, so it was not effective and efficient. The inspection data obtained using the information system becomes more complete and faster. Data obtained with information systems can be seen anywhere and anytime. This study produced innovations for the detection of preeclampsia in pregnant women using a rule-based system information system. This study uses the Quasy experimental posttest-only design. The subjects of this study were 66 pregnant women. Data collection was carried out using questionnaires and checklists. The results showed that early detection of preeclampsia using a rule-based system information system could improve the completeness of data on pregnant women up to 35.66%, and speed up the detection time of preeclampsia up to 35.60%. This system also has an effectivenessvalue 84.8%.
Initial Steps Towards Infrastructuring Body-Centric Computing

Digital technologies are moving towards the human body. Designing for the body is commonly dubbed as body-centric computing (BCC) and includes two sides: (1) the neuro-science side; (2) the technical side mainly focuses on establishing and enabling the interactions. A conceptual framework enabling the systematic design and development of body-centric applications is introduced. Its novelty and importance are assessed within a set of application scenarios.
An Unsupervised Approach for Keyphrase Extraction Using Within-Collection Resources

It is hard to select and read suitable documents due to the rapidly growing number of scholarly documents. Keyphrases can be considered as the gist of a document so that a researcher can select the documents that they want using keyphrase queries. However, there are also many scholarly documents without any keyphrases tagged by the authors or other researchers. Automatic keyphrase extraction can help researchers to quickly extract keyphrases. This paper proposed an unsupervised approach for keyphrase extraction using graph-based ranking and topic-based clustering under the assumption that we only use the within-collection resources. We use graph-based ranking to describe the relevance between two words and topic-based clustering to embed semantical information into words. In this paper, we assume that each word has its own meaning, and each meaning can be considered as a topic, though we know nothing about these meanings. We use topic-based clustering to assign the “correct meaning” to the “correct word”. In addition, by taking the relevance among phrases into consideration and only using within-collection resources, we can use the graph-based ranking in our approach. The edges in a graph that are built for phrases can describe the hidden relevance between two phrases, and the weights that are set for edges can measure the connection between two phrases. Then, after using the position feature, our approach consists of an enhanced graph-based ranking and a topic-based clustering. The experiments are run on four datasets: KDD, WWW, GSN and ACM. The results indicate that our approach has better performance than the state-of-the-art methods.
A Proposal of Visualization System for Understanding Quantum Algorithms

Quantum algorithms which are performed on quantum computers are hardly developed, because the principle of quantum computers are totally different from classical computers. We would like to discuss the way of visualization to assist understanding quantum algorithms. As the first step, we have developed visualization system of the Bloch sphere, which is the popular way to show the status of one quantum bit. In the system, we can see the Bloch sphere as 3DCG and interact with it. Next, we discuss the problems of visualizing multiple quantum bits and quantum algorithms.
Accelerated Detail-Enhanced Ambient Occlusion

Ambient Occlusion (AO) is a technique to approximate the effect of environment lighting and add realism to a scene by accentuating surface details and adding soft shadows, which is widely used in multimedia applications. Neural Network Ambient Occlusion (NNAO) is a pioneer in introducing deep learning to accurate and real-time AO, but it has two limitations: 1) performance bottleneck under excessive amount of samples ; 2) low contrast and blurred edges leading to unreal effect. To overcome these two limitations, we propose Accelerated Detail-enhanced Ambient Occlusion (ADAO) method based on NNAO by adopting three image processing methods: 1) spiral sampling in screen space; 2) contrast enhancement of AO map; 3) normal-depth edge preserving bilateral filtering. Experimental results show that the proposed method is over 2 times faster than NNAO and produces shadows with more realistic details.
Expressions Recognition Based on Human Face

The apparent actualization acceptance is a beheld assignment that can be performed after animal distress, which is acutely an apparently developing characteristic on the PC attending into field. There are abundant applications and projects utilizes apparent actualization to appraise the animal character, judgment, emotions, and angle. The action of acquainted apparent actualization is a harder assignment because of the few conditions, For example, facial impediments, face shape, light, face hues, and so on. This cardboard presents a PCA arrangement to admit appearances of countenances beneath afflicted altitude and appropriate it. Depends on Eigen faces arrangement utilizing accepted Data abject pictures. In adjustment to defeat the affair of agitation to PCs to admit the highlights and articulations of people.
Hierarchical Discriminant Regression Tree Algorithm Based on BDPCA and its Application in Object Recognition

Aiming at the problem of the slow speed of the clustering and regression for high-dimensional data, the process of the hierarchical discriminant regression tree (HDR) is firstly analyzed. Then, the dimension reduction method of the traditional linear manifold was required to convert the image matrix into the high dimension vector, which greatly increases the processing time of samples. The bi-directional principal component analysis (BDPCA) algorithm can directly reduce the dimensions of the feature matrix and the processing time of samples. In the process of clustering in the output space, HDR tree randomly chooses the center points of input space, which leads to the depth of the HDR tree becoming larger and the balance of the tree becoming also worse. Therefore, we propose a new construction and retrieval algorithm, called 2-dimension HDR (2DHDR), which can reduce the depth of the tree and accelerate the processing speed of samples. Experiments on object block database and ORL face database show that the proposed 2DHDR has a faster construction and retrieval speed than the traditional HDR.
Degraf-Flow: Extending Degraf Features for Accurate and Efficient Sparse-To-Dense Optical Flow Estimation

Modern optical flow methods make use of salient scene feature points detected and matched within the scene as a basis for sparse-to-dense optical flow estimation. Current feature detectors however either give sparse, non uniform point clouds (resulting in flow inaccuracies) or lack the efficiency for frame-rate real-time applications. In this work we use the novel Dense Gradient Based Features (DeGraF) as the input to a sparse-to-dense optical flow scheme. This consists of three stages: 1) efficient detection of uniformly distributed Dense Gradient Based Features (DeGraF) [1]; 2) feature tracking via robust local optical flow [2]; and 3) edge preserving flow interpolation [3] to recover overall dense optical flow. The tunable density and uniformity of DeGraF features yield superior dense optical flow estimation compared to other popular feature detectors within this three stage pipeline. Furthermore, the comparable speed of feature detection also lends itself well to the aim of real-time optical flow recovery. Evaluation on established real-world benchmark datasets show test performance in an autonomous vehicle setting where DeGraF-Flow shows promising results in terms of accuracy with competitive computational efficiency among non-GPU based methods, including a marked increase in speed over the conceptually similar EpicFlow approach [3].
Image Encryption Algorithm Based on Double Scrambling

In the process of image processing, the scrambling algorithm has an important impact on image security transmission and secure storage. The traditional scrambling algorithm has high time complexity, short transform period, and inconspicuous scrambling effect. This paper proposes a double-encryption scrambling algorithm based on order adjustment. The newly constructed position scrambling method is used to encrypt the image, which greatly increases the amount of keys and can effectively resist illegal attacks. The simulation results show that the algorithm has a good scrambling effect, and the algorithm has the advantages of low similarity and high scrambling, which increases the security of the image.
Ideas of Physical Education and Health in the Pedagogical Heritage of A. Makarenko

The article is devoted to revealing and studying the ideas of physical education and health in the pedagogical heritage of A. Makarenko. The importance of pedagogical skills and authority of the educator in terms of the necessity of using the means of physical education and sports when working with children is demonstrated. The significance of the unity of school, teachers and parents in the organization of physical education and health of children is revealed, as well as its orientation towards the solution of tasks of civic education. The author emphasizes the importance of motor games as a necessary component of the educational process aimed at gradually training a child to perform physical and volitional actions, which are indispensable components of future work and, thus, the main condition of human life in society. The article outlines the views of A. Makarenko on sports activity in the light of the tasks of shaping students’ conscious discipline, a sense of duty and responsibility. It is determined that the means and methods for solving such problems are exercises aimed at volitional overcoming of difficulties, obstacles surmounting, surviving certain adversities implemented not in individualized form, but the ones performed within a team and for a team.
3D Motion and Skeleton Construction from Monocular Video

We describe 3D motion construction as a framework for constructing a 3D motion and skeleton using a monocular video source (2D video). The processes include 3 main phases which involved in generating ground truth 2D annotation using OpenPose, generating the mesh and 3D matrices of person in the sequences of images based on 2D annotation and bounding box and lastly using the matrices to create a HIK 3D skeleton in a standalone Maya application. For the process, we highly relied on the 2D annotation using Convolution Neural Network. We demonstrate our result using a video of Malaysia Traditional Dance called zapin.
Show Me Your Friends, I’ll Tell You Who You Are: Recommending Products Based on Hidden Evidence

One of the goals of a recommender system is to minimize the cognitive load on the user and hence we cannot expect the users to give extensive feedback. The lesser the feedback, the lesser we know about the preferences of the user to make useful recommendations. This work aims to make the most use of user-provided feedback with the help of the hidden evidence in the casebase. The evidence for each product is acquired based on the relation among the products in the domain. The effectiveness of our approach is demonstrated through evaluation on three product domains.
Three-stage Design of a Question-making Activity to Refine Pre-training Preparation in a Blended Training Program

In this paper, we report improvements made and their effect on the design of pre-training preparation in a blended instructional design workshop. Through practical research, the design was refined to include a three-stage question-making activity (QMA) for pre-training preparation. The stages included 1) Submitting a question before the commencement of learning, 2) Working on teaching materials, and 3) Submitting a question based on reflection on pre-training preparation. After verifying the effects, it is suggested that question quality saw an improvement owing to the introduction of the QMA, with some cases becoming more concrete and focused. It was also confirmed that this method can encourage learning motivation by enhancing participants’ confidence, satisfaction, and familiarity with learning content.
Outsourcing Computations Through Smith Normal Form with Access Control

Cloud computing paradigm encounters many practical issues especially data security and access control over the outsourced secret data is a major one. Cloud providing computation as a service must prevent misuse of data by third party cloud service providers. This paper addresses a novel Symmetric Fully Homomorphic Encryption scheme (UGFHE) with access control policies written in XACML to provide controlled access over the ciphers and the homomorphic property is ensured by Smith Normal Form (SNF). The access control policies and ciphers are centrally managed by data owners. The security of the system is analyzed and the performance is also measured.
Exploring example-driven migration

Despite many research efforts to automate API usage migration, it remains often a manual task for developers. We aim to reduce the developer's pain by exploring ways to integrate the migration process into the IDE. Our migration workbench leverages API usage examples and interactive refactorings to migrate code from one API version to the other.
Counting Cars from Aerial Videos Using Deep Learning

Counting vehicles in heavy traffic areas is not an easy task, even for a trained human. Recent advances in real-time object detection using convolutional neural network can jointly detect and identify objects. We propose a straightforward application of using real-time object detection algorithm to count the number of vehicles in high traffic areas. The user defines marks on the road where the proposal counts the number of vehicles crossing the marks. The proposal achieves 87.24% average correct counting in a video sequence with real-time performance.
A Classification Method of Ancient Ceramics Based on Support Vector Machine in Ceramic Cloud Service Platform

To efficiently provide the ancient ceramic composition analysis and testing services, it is necessary to efficiently classify the ancient ceramics in ceramic cloud service platform. In this paper, we get the 8 kinds of major chemical contents of the body and glaze in each sample according to analyze 35 samples. After establishing of the classification model of two samples, the results indicate: as long as choosing SVM algorithm correctly, the classification results of body and glaze samples will be quite ideal, and the support vector machine is a very valuable new method which can process ancient porcelains data.
New Approach for the Aesthetic Improvement of Images Through the Combination of Convolutional Neural Networks and Evolutionary Algorithms

Programs for aesthetic improvements of the images have been one of the applications more widely in the last years so much from the commercial point of view like the private one. The improvement of images has been made through the application of different filters that transform the original image into another whose aesthetics have been improved. In this work a new approach for the automatic improvement of the aesthetics of images is presented. This approach uses a Convolutional Neural Network (CNN) network trained with the AVA photography data set, which contains around 255,000 images that are valued by amateur photographers. Once trained, we will have the ability to assess an image in terms of its aesthetic characteristics. Through an evolutionary differential algorithm, an optimization process will be carried out in order to find the parameters of a set of filters that improve the aesthetics of the original image. As a fitness function the trained CNN will be used. At the end of the experimentation, the viability of this methodology is presented, analyzing the convergence capacity and some visual results.
CAPAS: A Context-Aware System Architecture for Physical Activities Monitoring

Attribute grammars are widely used by compiler-generators since it allows complete specifications of static semantics. They can also be applied to other fields of research, for instance, to human activities recognition. This paper aims to present CAPAS, a Context-aware system Architecture to monitor Physical ActivitieS. One of the components that is present in the architecture is the attribute grammar which is filled after the prediction is made according to the data gathered from the user through the sensors. According to some predefined rules, the physical activity is validated after an analysis on the attribute grammar, if it meets those requirements. Besides that it proposes an attribute grammar itself which should be able to be incorporated in a system in order to validate the performed physical activity.
Detection of Rice Fields in Sleman District using SVM (Support Vector Machine) Method

Remote sensing technology is very well used as a data for making land use maps, because mapping needs are increasingly high especially for detecting land use changes, especially for determining the area, especially rice fields in Sleman district. To obtain information on the area of the rice field from the interpretation of Landsat-8 OLI imagery, a special method is needed, especially for processing remote sensing image data digitally. One method of processing remote sensing images is the Support Vector Machine (SVM) method.This research is useful to implement the SVM method and support research in the geospatial field. This study aims to determine the area of Sleman district rice field with SVM and to determine the accuracy of the SVM method. The Support Vector Machine (SVM) method is a learning machine method that can classify patterns and recognize patterns from input or sample data provided and also includes supervised learning. The SVM method is used to classify segment polygons in which similar pixels.