Reviewerly/exHarmony huggingface dataset:
Two Separate Circles With Same-Radius: Projective Geometric Properties and Applicability in Camera Calibration

In the domain of computer vision, camera calibration is a key step in recovering the two-dimensional Euclidean structure. Circles are considered important image features similar to points, lines, and conics. In this paper, a novel linear calibration method is proposed using two separate same-radius (SSR) circles as the calibration pattern. We show that the distinct pair of dual circles encodes three lines, two of which are parallel to each other and perpendicular to the remaining line. When any two coplanar or parallel circles degenerate to SSR circles, a solution can be found to recover another pair of parallel lines based on the geometric properties of the SSR circles. Using the vanishing points obtained as the key helper for determining the imaged circular points and the orthogonal vanishing points, we deduce the constraints on the image of the absolute conic (IAC) and then employ it for complete camera calibration. Furthermore, a closed-form solution for the extrinsic parameters can be obtained based on the projective invariance of the conic dual to the circular points. Evaluations based on simulated and real data confirmed the effectiveness and feasibility of the proposed algorithms.
The effectiveness of ICT in teaching physics

In the 21st century, the modern employment cannot be imagined without the using of information and communication technology (ICT). Information technology is a set of mathematical and cybernetic techniques that provide the collection, storage, processing and transportation of information on the basis of modern computer technology. Among all educational disciplines, physics is able to easily maintain a computer subject. For a long time the computer has been successfully used here to facilitate the routine work of performing calculations. But information communication technologies can also be used to study theoretical material, training, as a means of modeling and visualization, etc. The choice depends on the goals, objectives and stage of the lesson (explanation, consolidation, repetition of the material, knowledge test, etc.). The most effective way of world informative education space is to fully supply ICT to each branch of education. This paper shows that the use of modern innovative educational methods and technologies is an important component for organizing an effective educational process in physics lessons. The process of informatization in education raises the level of training of teachers, students and the quality of the profession. It allows the student to self-development, self-education and creativity.
Smarty Pants: Exploring Textile Pressure Sensors in Trousers for Posture and Behaviour Classification

In this paper, we introduce a new modality for capturing body postures and social behaviour. Vice versa, we propose a new application area for on-body textile sensors. We have developed &ldquo;smart trousers&rdquo; with embedded textile pressure sensors that allow for classification of a large variety of postural movements as well as interactional states. Random Forest models are used to investigate those. Here, we give an overview of the research conducted and discuss potential use cases of the presented design.
Pemanfaatan Social Network Analysis Untuk Menganalisis Kolaborasi Komunikasi Pada Balai Perikanan Budidaya Laut Ambon

Marine aquaculture center-Ambon (Balai Perikanan Budidaya Laut/BPBL-Ambon) is a technical implementing unit under supervision of Directorate General of Aquaculture, Ministry of Maritime Affairs and Fisheries, Republic of Indonesia. Based on report of government’s internal control system (Sistem Pengendalian Intern Pemerintah/SPIP) specifically at the third quarter, some crucial organizational disadvantages have been identified in BPBL-Ambon, particularly related to the communication and coordination between divisions and technical staffs. To deal with this problem, there is a need to analyze and map the roles of interactions between employees in social networks. This study aims to analyze the patterns of interaction between staffs in the social networks. In this work, social network analysis (SNA) was used, which is based on formal and informal interactions on BPBL-Ambon, enabling to identify the key actors potentially able to act as alternative actors for information access to facilitate communication. The collection of individual data, as well as formal and informal interactions, was carried out using questionnaire and interview, involving entire population (saturation sampling). The collected data were then filetered and tabulated symmetric matrix, as preliminary steps for analysis following exported. SNA approach focuses on determining degree centrality, closeness centrality, and betweenness centrality. As a result, our experimental data suggested that most divisions alreadyshowed an appreciable collaboration in terms of communication; however, the remaining divisions showed 0 value in terms of their relationship with other divisions. Based on these findings, we could solve the problem through acting the alternative actors having the highest indegree value apart from stakeholders from a division capable of acting as alternative sources of information. The involvement of alternative actors could be a meaningful attempt in order to solve communication and coordination problems present in BPBL-Ambon
An Selection System For Automotive Sentiment Classification In Hadoop Using KNN Classifier

With the growing popularity of big data analytics in the area of online product review, the biggest issue is voluminous data. Sentiment analysis and opinion mining are useful for solving text and web based issues. For sentiment analysis, this work makes use of the Hadoop framework. The Hadoop is not only reliable but also a fault immune model for processing huge amounts of data. There is a critical role that is played by sentiment analysis in text mining purposes such as in consumer attitude recognition, trade name and product spotting, customer relationship management, and market research. Data is labelled either as subjective or objective based on the subjectivity classification. This subjective classification is further divided as positive, negative or neutral by sentiment classification. The sentiment is classified based on the features which are taken from the data. As feature selection contributes in conserving the classification expense with regard to time and computation load, feature selection has gained a lot of prominence. This work uses the Term Frequency (TF) feature extraction. The objective here is using feature selection based on information Gain (IG) and Particle Swarm Optimization (PSO) for feature selection in sentiment classification. These schemes can decrease the features in the original set as they eliminate redundant features for text sentiment categorization and thus improvise the accuracy of classification. Also, the running time of the learning algorithms is decreased. K-nearest neighbour (KNN) classifier is used for evaluating the suggested scheme. It has been shown by empirical outcomes that compared to the IG based feature selection; the PSO based feature selection scheme attains better and more robust performance.
Microblog Sentiment Classification Method Based on Dual Attention Mechanism and Bidirectional LSTM

In the information age, the network technology continues to develop. As an emerging social media, Sina Weibo has a huge user base. Every day, hundreds of millions of users express their opinions on hot events, or share the joys and worries in life on the Weibo platform. Therefore, the analysis of the user’s emotion has broad application prospects, which could also be used in the fields of public opinion monitoring, opinion guidance, and advertisement placement. This paper proposes a microblog sentiment classification method based on dual attention mechanism and bidirectional LSTM. Firstly, the bidirectional LSTM model is used to semantically encode the microblog text, then the self-attention and sentiment word attention are introduced into the bidirectional LSTM model. Finally, the Softmax classifier is used to classify the sentiment of microblog. In order to verify the validity of the model, several groups of comparative experiments are carried out, which use NLPCC2013 and NLPCC2014 evaluation task datasets as experimental data sets. The results show that the proposed microblog sentiment classification model based on dual attention mechanism and bidirectional LSTM is superior.
Effective Intra Mode Prediction of 3D-HEVC System based on Big Data Clustering and Data Mining

3D-high efficiency video coding (3D-HEVC) structure is a development of HEVC, but some new coding techniques are added on the basis of it to make it more conducive to encoding depth maps and multi-view.In 3D-HEVC, the intra prediction mode decision for depth level contains closed connections with coding unit (CU) partition.This process, quad-tree block splitting, gives the gorgeous coding efficiency to 3D-HEVC, but it incurs unacceptable computational burdens because each possible coding mode is tested to rank the most suitable one.According to previous works, whether the current CU will be divided into smaller sizes is dependent on encoding contexts.In view of that, this paper proposed a novel method to speed up intra coding unit splitting, relying on data clustering and data mining.The experimental results showed that our new approach can reach a satisfied balance between computational burdens and RD cost.
An Enhanced Secure, Robust and Efficient Crypto Scheme for Ensuring Data Privacy in Public Cloud Using Obfuscation &amp; Encryption

Cloud has been emerging, popular and very demanding technology now a day.Cloud has got wide popularity with its sophisticated features.The primary features of cloud include internet access, more storage, easy setup, automatic updates, and low cost and resource provisioning based on "pay as you go" policy.In spite of advantages, security is considered to be more important and drew the attention of many researchers because it is not guaranteed in an open cloud.The data storage is becoming an indispensable measurement in cloud and most of the times cloud does not guarantee that data that has been stored is secured from illegitimate access.Many researchers are working to ensure data security in the cloud but unfortunately they do not provide adequate security to data.This paper is aiming to propose a secure hybrid scheme with obfuscation and cryptography to ensure the privacy of data shared in public cloud.Experimental results show that the proposed scheme yields good results.
“MACHINE-HUMANITY” LIKE A FORM OF URBANISM IN N. BERDYAEV’S CONCEPTION

The problem of the influence of machine and technique on the society and human beings is analyzed in the article. It appears a new form of expansion arrangement which is called “machine humanity” or “automation humanity” by N. Berdyaev. The philosopher is stating that a threat of mechanization and automatization becomes a danger which human race faces. Mechanization of life and society causes changes of humans’ nature. A person is not able to imagine his life without technological devices, well-managed manner of life and space’s organization.Being a part of city boundary, humans start feeling dependence of it; city life’s rhythm changes human beings themselves. Therefore, the crisis of human conscience occurs.Such situation hasn’t only changed the entire life of humans but broke already existed creative harmony. Under the influence of ideas of machine’s and technique’s almightiness, the changes affected such areas as art, literature, architecture, theatre. It becomes changing the consciousness of human being who is not able to realize him out of technical devices and innovations. This influence has a vast meaning for the society and its further development. Humanity started the new age of its evolvement in which growing of the influence of machine and technique on the society was coming.
Practical Model Checking of a Home Area Network System: Case Study

The integrated communication infrastructure is the core of the Smart Grid architecture. Its two-way communication and information flow provides this network with all needed resources in order to control and manage all connected components from the utility to the customer side. This latter, named the Home Area Network or HAN, is a dedicated network connecting smart devices inside the customer home, and using different solutions. In order to avoid problems and anomalies along the process life cycle of developing a new solution for HAN network, the modeling and validation is one of the most powerful tools to achieve this goal. This paper presents a practical case study of such validation. It intends to validate a HAN SDL model, described in a previous work, using model checking techniques. It introduces a method to translate the SDL model to a Promela model using an intermediate format IF. After the generation of the Promela model, verification is performed to ensure that some functional properties are satisfied. The desired properties are defined in Linear Temporal Logic (LTL), and DTSPIN (an extension of SPIN with discrete time) model checker is used to verify the correctness of the model.
Issues in Higher Education: Analysis of 2017 Global Knowledge Index Data and Lessons Learned

Despite considerable efforts to increase the quality of Higher Education (HE) in many countries, the absence of a methodology to guide scholars and policymakers to assess its quality has been a barrier. In 2017, the United Nations Development Program (UNDP) and Mohamad bin Rashid Al Maktoum Knowledge Foundation (MBRF) launched the Global Knowledge Index (GKI), a tool by which data from 131 countries were collected for seven sectors&amp;mdash;one of which was HE. In this paper, an analysis of the HE index data is introduced. Then, three key issues which emerged from data are discussed. The first issue is HE efficiency, which is measured by comparing the indexes of HE inputs and outputs. The second issue is the enabling environment factors that might support or limit the growth of HE. The third issue is the intricate relationship between HE, economy, and Research and Development (R&amp;amp;D). The study found that HE efficiency is declining globally except in a few areas. A strong positive relationship was found between the enabling environment and variables of political stability and government effectiveness and HE&amp;rsquo;s ability of knowledge production. Furthermore, strong relationships were found between HE outputs, economy, and R&amp;amp;D respectively. The study concludes with future directions for increasing the quality of HE.
Fusion of Features and Extreme Learning Machine for Facial Expression Recognition

Human emotion is highly correlated to facial expressions.Due to its growing demand in different sectors, an emotion recognition method is proposed through recognizing facial expressions.The input image is preprocessed and then the resulting image is segmented into four facial expression regions following the newly proposed segmentation method.Histogram of Oriented Gradients (HOG) and Local Binary Patterns (LBP) are fused to extract the necessary features from the four segmented parts.The dimension of the feature vector is reduced using Principal Component Analysis (PCA).To classify the expressions, Extreme Learning Machine (ELM) is used.For evaluating the performance of the proposed method, three widely used and publicly available facial expression datasets (JAFFE, CK+, RaFD) are used.The proposed method achieved 95.3%, 99.84% and 98.65% accuracy while using images from JAFFE, CK+ and RaFD dataset respectively.Performance of the proposed method on these datasets is compared to other facial expression recognition methods on these datasets to indicate that the proposed method achieves state-of-the-art performance.
Cloud-based Framework for Issuing and Verifying Academic Certificates

Verification of certificates is one of the major challenges in every association, school, government, and employers.Most of the employers have been encountering rate of fake certificates.Initially, the traditional based model of verification contributes underway of fake certificates which represent a troublesome in knowing the legitimacy of such scholarly certificates introduced to them because it is highly unlikely they can validate them in a flash.Furthermore, present-day innovation and the ascent of the web are very much advantageous.Document management plays a vital role in securing documents in any organization, such as how the documents are created, reviewed, published and disposed of or retained.Majority if not all universities print graduation certificates and despite the technological advancements digital-based certificates did not yet replace the paper-based.Hence, this research aimed to investigates the techniques that currently being used for document verification.In this research, we proposed a cloud-based framework for issuing and verifying the academic certificates.
Malaria parasite segmentation using U-Net: Comparative study of loss functions

The convolutional neural network is commonly used for classification. However, convolutional networks can also be used for semantic segmentation using the fully convolutional network approach. U-Net is one example of a fully convolutional network architecture capable of producing accurate segmentation on biomedical images. This paper proposes to use U-Net for Plasmodium segmentation on thin blood smear images. The evaluation shows that U-Net can accurately perform Plasmodium segmentation on thin blood smear images, besides this study also compares the three loss functions, namely mean-squared error, binary cross-entropy, and Huber loss. The results show that Huber loss has the best testing metrics: 0.9297, 0.9715, 0.8957, 0.9096 for F1 score, positive predictive value (PPV), sensitivity (SE), and relative segmentation accuracy (RSA), respectively.
Dynamic Slicing for Concurrent Constraint Languages

Concurrent Constraint Programming (CCP) is a declarative model for concurrency where agents interact by telling and asking constraints (pieces of information) in a shared store. Some previous works have developed (approximated) declarative debuggers for CCP languages. However, the task of debugging concurrent programs remains difficult. In this paper we define a dynamic slicer for CCP (and other language variants) and we show it to be a useful companion tool for the existing debugging techniques. We start with a partial computation (a trace) that shows the presence of bugs. Often, the quantity of information in such a trace is overwhelming, and the user gets easily lost, since she cannot focus on the sources of the bugs. Our slicer allows for marking part of the state of the computation and assists the user to eliminate most of the redundant information in order to highlight the errors. We show that this technique can be tailored to several variants of CCP, such as the timed language ntcc, linear CCP (an extension of CCPbased on linear logic where constraints can be consumed) and some extensions of CCP dealing with epistemic and spatial information. We also develop a prototypical implementation freely available for making experiments.
Hot Potatoes Multimedia Applications in Evaluation of Indonesian Learning In SMP Students in Buru District

This study aims to find out wheter there is an influence of the Hot Potatoes multimedia application in evaluating junior high school students' learning outcomes in Indonesian language learning. The study design uses an evaluation model with the multimedia application Hot Potatoes. The method used is quantitative description and qualitative description, which is to show the effect of evaluating student learning outcomes and student and teacher responses to the effectiveness of the use of multimedia Hot Potatoes. The data source of this research is the IX grade junior high school students in Buru Regency. Data collection techniques used in this study are test techniques using the multimedia application Hot Potatoes in the form of a Multiple-Choice Test (multiple-choice). The results showed that the application of the Hot Potatoes multimedia application in evaluating the learning outcomes of junior high school students in Indonesian language learning had a major influence on quality learning. This can be seen from the results of the evaluation of 50 middle school students from cycle I, cycle II, and cycle III. The percentage of students completeness in cycle I was 60%, cycle II was 86%, and cycle III was 94%. Meanwhile, the percentage of students completeness in the first cycle was 40%, second cycle was 14%, and third cycle was 6%.
Civic and citizenship education in the Republic of Croatia: 20 years of implementation

Introduction: General characteristics of Croatia and its schooling system 1 Units of local government are municipalities (hrv."općine") and cities, and units of regional government are counties (hrv."županije").In total there are 576 units of local and regional government of which there are 428 municipalities, 127 cities and 21 counties (note: Zagreb is both capital city and county).
AKADEMIK DATA MINING (ADM) K-MEANS DAN K-MEANS K-NN UNTUK MENGELOMPOKAN KELAS MATA KULIAH KOSENTRASI MAHASISWA SEMESTER AKHIR

University as an educational institution plays an important role in producing graduates. In addition, institutions such as universitas ichsan Gorontalo save the data set. These Data include about student academic data.In the academic field, every semester, increasing the amount of data recorded with data from academic activities. It is like there is a Tsunami of data which indicate that these data are very abundant but do not give any knowledge that is not beneficial to the university, especially the faculty except the knowledge administrative. Universitas ichsan Gorontalo with the number of students reached 9000 people which is accompanied by the number of graduates is still less than ideal any period graduate, it is necessary to apply the pattern determination grade concentration courses effective for the achievement ability of students, academic Data will be used namely the data of the students 2016-2017 who has taken class subjects concentration. The application of K-Means algorithm and K-Means KNN where K=2 result in a cluster for grouping of a Class Focus on the students semester end and each cluster has a predictive value for the second klustering such, the Value of the resulting Accuracy of Algorithms KNN, namely the AUC (Area Under The Curve) =1, the Value of CA=1, the value of F1=1, the value of the precision=1 and recall=1, and the value of accuracy as the best value.