Reviewerly/exHarmony huggingface dataset:
An Entanglement-Based Wavelength Multiplexed Quantum Communication Network

Quantum networks scale the advantages of quantum communication protocols to more than just two distant users. Here we present a fully connected quantum network architecture in which a single entangled photon source distributes quantum states to a multitude of users [1]. Our network architecture thus minimizes the resources required of each user without sacrificing security or functionality. As no adaptations of the source are required to add users, the network can readily be scaled to a large number of clients, whereby no trust in the provider of the quantum source is required. Unlike previous attempts at multi-user networks, which have been based on active components, and thus limited to some duty cycle, our implementation is fully passive and thus provides the potential for unprecedented quantum communication speeds. We experimentally demonstrate the feasibility of our approach using a single source of bi-partite polarization entanglement which is multiplexed into 12 wavelength channels to distribute 6 states between 4 users in a fully connected graph using only 1 fiber and polarization analysis module per user.
Environmental extreme events detection: A survey

The application of statistics in extreme events detection is quite diverse and leads to diverse formulations, which needs to be designed for the specific problem. Each formula needs to be tailored specifically to work with the available data in the given situation. This diversity is one of the driving forces of this survey towards identifying the most common mixture of components utilized in the analysis of environmental outlier detection. Indeed for some arbitrary applications, it may not always be possible to use off-the-shelf models due to the wide variations in problem formulations. In this paper, we summarize the statistical methods involved in the detection of environmental extremes such as wind ramps, high precipitation and extreme temperatures. Then we organize the discussion along different outlier detection types, present various outlier definitions, and briefly introduce the corresponding techniques. Environmental extreme events detection challenges and possible future work are also discussed.
Reconnecting Citizens to Politics via Blockchain – Starting the Debate

Elections are not the only but arguably one of the most important pillars for the proper functioning of liberal democracies. Recent evidence across the globe shows that it is not straightforward to conduct them in a free and fair manner. One constant concern is the role of money in politics, more specifically, election campaign financing. Frequent scandals are proof of the difficulties encountered with current approaches to tackle the issue. Suggestions on how to overcome the problem exist but seem difficult to implement. With the help of blockchain technology we might be able to make a step forward. A separate crypto currency specifically designed to pay for costs of political campaigning and advertising could be introduced. Admittedly, at this stage, there are many open questions. However, under the assumption that blockchain technology is here to stay, it is an idea that deserves further exploration.
Residents Perception on Child Abuse and Influence on Social Studies Students' Academic Achievements

Social studies is a discipline that deals with social change and ensures that meaningfully interaction of the recipients with their physical and social environments is attained.It engenders sound education of the citizens, as well as inculcates a sense of social consciousness and social responsibility.Social studies a study that can help an individual to understand his environment, find out the problems of his environment as well as solving existing social problems.It is a study that inculcates the value of honesty, cooperation, and the needs of the nation, as well as the desirable skills to solve environmental problem.It is against this background that mass failure of students on the subject has resulted to several reactions by members of the public.Could child abuse be the remote cause to low academic achievement of students in social studies?
Towards Continuous Variables Quantum Computing with Trapped Ions

The usually employed methods of using spin states of atomic-like systems for quantum information encoding [1,2] suffer from scalability issues. For example, it becomes very challenging to control a large number of trapped atomic ions each representing a physical qubit. The alternative approach would be to exploit the large Hilbert space provided by a near-harmonic trapping potential and encode the information in the oscillator states [3,4]. We use sideband-resolved addressing of motional states in a single trapped <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">171</sup> Yb <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> ion to demonstrate a conditional beam splitter gate. The conditional beam splitter (CBS) Hamiltonian |e〉〈e|(a <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">†</sup> b + ab <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">†</sup> ) swaps the quantum states of two motional modes of a trapped ion, conditioned on the ion's internal state. It thus can be viewed as a SWAP gate and we uilize it to demonstrate SWAP tests, implement single shot parity measurements, and generate maximally entangled NOON states of motion (see Figure 1).
Tagging and Tag Recommendation

Tagging has emerged as one of the best ways of associating metadata with objects (e.g., videos, texts) in Web 2.0 applications. Consisting of freely chosen keywords assigned to objects by users, tags represent a simpler, cheaper, and a more natural way of organizing content than a fixed taxonomy with a controlled vocabulary. Moreover, recent studies have demonstrated that among other textual features such as title, description, and user comments, tags are the most effective to support information retrieval (IR) services such as search, automatic classification, and content recommendation. In this context, tag recommendation services aim at assisting users in the tagging process, allowing users to select some of the recommended tags or to come up with new ones. Besides improving user experience, tag recommendation services potentially improve the quality of the generated tags, benefiting IR services that rely on tags as data sources. Besides the obvious benefit of improving the description of the objects, tag recommendation can be directly applied in IR services such as search and query expansion. In this chapter, we will provide the main concepts related to tagging systems, as well as an overview of tag recommendation techniques, dividing them into two stages of the tag recommendation process: (1) the candidate tag extraction and (2) the candidate tag ranking.
A Selection Process of Graph Databases Based on Business Requirements

Several graph databases provide support to analyze a large amount of highly connected data, and it is not trivial for a company to choose the right one. We propose a new process that allows analysts to select the database that suits best to the business requirements. The proposed selection process makes possible to benchmark several graph databases according to the user needs by considering metrics such as querying capabilities, built-in functions, performance analysis, and user experience. We have selected some of the most popular native graph database engines to test our approach to solve a given problem. Our proposed selection process has been useful to design benchmarks and provides valuable information to decide which graph database to choose. The presented approach can be easily applied to a wide number of applications such as social network, market basket analysis, fraud detection, and others.
INFORMATION AND COMMUNICATION TECHNOLOGY AS MEDIA INNOVATION AND SOURCES OF LEARNING IN SCHOOL

The influence of the development of information and communication technology encourages innovation in learning activities. This study aims to describe the use of ICT as a medium and source of learning in schools. The research was conducted in 3 locations, namely Public Elementary School 2 Gelumbang and Public Elementary School 29 Gelumbang, Gelumbang District, and Senior Scondary School 1 Sungai Rotan, Sungai Rotan District, Muara Enim Regency, South Sumatra Province. This study uses a qualitative-descriptive approach, with observation techniques, and interviews. The results of the study concluded: (1) the teacher invites students to use ICT facilities in schools in learning activities; (2) the use of ICT-based resources and learning media in schools, among others, using computers, laptops, LCD (Liquid Crystal Display), smartphones, internet networks, and Microsoft office applications; (3) to improve teacher competence in utilizing ICT, teachers need to continue to familiarize themselves. Besides that, there should be a solution to the barriers to the use of ICTs, including conducting training through teacher working group activities (TWG), in order to support the improvement of teachers' abilities and knowledge in the ICT field.
Sikap Dengan Motivasi Dalam Memberikan Pertolongan Pertama Kasus Kecelakaan Lalu Lintas Pada Siswa Siswi SMK Negeri 1 Maumere

Traffic accidents are the main killers of young people aged 10-24 years. The students often find victims of traffic accidents but do not provide first aid because of a lack of attitude and motivation in helping. Lack of attitude and motivation in handling traffic accidents can result in the condition of the victim getting worse and leading to death. This study aims to explain the correlation attitudes and motivation in providing first aid traffic accidents to students at SMK Negeri 1 Maumere.  This type of research is correlation analysis. The study design was cross sectional. The sampling used was simple random sampling with a total sample of 75 people. Data were collected using a questionnaire and analyzed using the contingency coefficient test. The results showed that 40 students (53.3%) had negative attitudes and 68 students (90.7%) had high motivation. The results of statistical tests obtained p <α = 0.034 <0.05, then H0 is rejected and Ha is accepted, then there is a correlation attitude and motivation in providing first aid traffic accidents to students at SMK Negeri 1 Maumere. The results of this study are expected so that students can improve attitudes and motivation by increasing the information they have about first aid in traffic accidents. The school to be able to work with health services to hold a first aid simulation on an accident.
EXPERTPEDIA AS A MEANS OF CREATION ONTOLOGICAL INTERNET OF KNOWLEDGE

A methodology for creating semantic wiki systems for knowledge representation using artificial intelligence technologies is proposed. The methodology is based on multi-agent methods for creating knowledge bases and is suitable for developing design and management systems for digital intelligent productions. An Integrated structure of the Internet of Knowledge and the Internet of Things has been developed. The Internet of knowledge is the ontological world of virtual agents that represent object functions that are capable of generating new data based on available information. Schema of the knowledge input into the computer has been analyzed. Semantic wiki system to create knowledge bases, which can be called "Expertpedia", is proposed on the basis of the technology of expert programming. The possibility of replacing the dictionary knowledge base allows us to provide implementation of the conceptual requirements of the wiki system: the use of any languages in the world. The knowledge base has the ability to add modules, select an analog module, translate the knowledge module into one of the programming languages, test the result, as well as determine the module's entry into the knowledge base and remove the selected module. These actions can be performed by any operator, which allows to implement the second conceptual basis of wiki systems: providing the possibility of independent replenishment and correction of the system content. Metaontology of Expertpedia have been developed. The methods of creating multi-agent knowledge bases for design and management in mechanical engineering are considered in detail. The architecture of the CAD agent is given. Examples of application of multi-agent systems for co-building of intelligent systems of semi-automatic design of engineering products are given. The technology of expert programming is described. To further expand the functionality of wiki systems, it is advisable to integrate them with artificial intelligence technologies.
P vs NP Solution – Advances in Computational Complexity, Status and Future Scope

The significance & stature of the P vs NP problem is so imperative that even the failed attempts at proof have furnished unprecedented breakthroughs and valuable insights.While the scientists and researchers do not expect the problem to be solved in foreseeable future, the P vs NP question has been the harbinger of advancement of the theory of computation and complexity theory in particular.Multitude of research papers have been published on number of topics which have begged numerous accolades and awards.This paper presents and highlights a non-technical review of series of complex mathematical research and enlists the notable awards & advances from each subsequent effort.The paper also presents the limitations of existing and proposed techniques and highlights the direction of active future research towards P vs NP solution.
Interpolation-based k-means Clustering Improvement for Sparse, High Dimensional Data

The k-means algorithm is characterized by simple implementation and fast speed, and is the most widely used clustering algorithm. Aiming at the shortcomings of k-means algorithm in noise sensitivity in high-dimensional sparse data sets, the IB k-means (Interpolation-based k-means clustering) algorithm is proposed. Based on the k-means algorithm, the genetic algorithm is used for interpolation, which solves the problem that the sparse data in k-means clustering is easy to merge. The experimental results show that compared with several improved k-means-based clustering methods, the proposed method can achieve better clustering effect and better deal with clustering in high-dimensional sparse data.
고등학교 생명과학 I 세포분열 단원에 대한 교육과정, 교과서, 수업 및 평가의 개념 네트워크 분석

If the concepts used in curriculum, textbook, instruction, and assessment are out of alignment, students may have difficulty in understanding the concepts of science correctly. Therefore, the concepts used the teacher should be in alignment with the scientific concepts presented in the curriculum in science education. The purpose of this study is analyzed the concept networks used in curriculum, textbook, instruction, and assessment related the unit of cell division in life sciencesⅠ and the alignment between these concepts with Semantic Network Analysis. To do this, the concept networks of curriculum, textbook, instruction, and assessment are visualized by 1 - mode network, and the alignment networks between curriculum, textbook, instruction, and assessment are visualized by 2 - mode network. The result of this study is follows. first, the alignment of concepts used in curriculum, textbook, instruction, and assessment in the unit of cell division was very high. Second, the number of concepts used in textbooks and instruction increased. Third, the number of rapidly increasing concepts in textbooks and instruction can be one of the causes of learning deficits.
Extreme learning machine and K-means clustering for the improvement of link prediction in social networks using analytic hierarchy process

The rapid growth of the availability of healthcare related data raises a challenge of extracting useful information. Thus there is an urgent need for the healthcare industry to predict the disease, that reduces the amount of cumbersome tests on patients The aim of this paper is to employ a combination of machine learning algorithms namely extreme learning machine algorithm with k-means clustering and analytic hierarchy process, for the prediction of disease in a patient through the extraction of different patterns from the dataset based on the relationships that exists among the attributes. It would help the physician and the medical scientists to predict the possibility of the disease. In today's era, the percentage of females getting affected by diabetes has increased exponentially. So, the experiments are carried over PIMA diabetes data set that focuses on females are extracted from UCI repository and the results are found to be significant.
Burden Surface Temperature Field Estimation of the Blast Furnace

The blast furnace (BF) gas flow distribution is one of the major influences on BF production situation. While burden surface temperature field of BF is a direct reflection of the BF gas flow distribution. In this paper, a prediction model of burden surface temperature field is established based on the online sequential extreme learning machine algorithm with forgetting factor (WOS-ELM). Firstly, the BF condition data and temperature data of the cross temperature measurement are used to establish a temperature prediction model to predict the temperature of the gas flow of the throat. Secondly, the burden surface temperature is obtained by the predicted temperature and heat transfer principle. Finally, the burden surface temperature field is established using Newton interpolation and angle weight. The large amount of data and comparative experiments show that the prediction model of burden surface temperature field has a high prediction accuracy. It can provide the theoretical guidance for the actual operation of the BF.
The Sequence Labeling Approach for Text Alignment of Plagiarism Detection

Plagiarism detection is increasingly exploiting text alignment.Text alignment involves extracting the plagiarism passages in a pair of the suspicious document and its source document.The heuristics have achieved excellent performance in text alignment.However, the further improvements of the heuristic methods mainly depends more on the experiences of experts, which makes the heuristics lack of the abilities for continuous improvements.To address this problem, machine learning maybe a proper way.Considering the position relations and the context of text segments pairs, we formalize the text alignment task as a problem of sequence labeling, improving the current methods at the model level.Especially, this paper proposes to use the probabilistic graphical model to tag the observed sequence of pairs of text segments.Hence we present the sequence labeling approach for text alignment in plagiarism detection based on Conditional Random Fields.The proposed approach is evaluated on the PAN@CLEF 2012 artificial high obfuscation plagiarism corpus and the simulated paraphrase plagiarism corpus, and compared with the methods achieved the best performance in PAN@CLEF 2012, 2013 and 2014.Experimental results demonstrate that the proposed approach significantly outperforms the state of the art methods.
Improving classification performance for an imbalanced educational dataset example using SMOTE

With technology, a lot of data is formed in digital environments.One of the areas with intensive data is educational data sets.By analyzing educational data sets, students' situatiokjgjjööÖns can be predicted by foreseeing.In this way, students can be assisted by anticipating situations such as drop-out due to failure.Educational institutions can take measures to prevent such dropouts and reduce student drop-out.Thus, financial losses of students and educational institutions can be prevented.In this study, the data of five separate associate degree students who were enrolled in Amasya University Distance Education Center in 2016-2017 were used.These are associate degree programs in child development, medical documentation and secretarial, electricity, mechatronics, and internet and network technologies.It was estimated whether the students could graduate or not at the end of the IV.Semester with looking at their I. and II.semester course notes.These data were analyzed by k nearest neighbor (K-NN) and KStar algorithms.Some of the data were obtained from the distance education center as imbalanced data due to the low number of students.In Educational Data Mining, researchers usually overlook the balance of the distribution on a dataset.Unbalanced data can seriously affect the success of classification.Synthetic minority oversampling technique (SMOTE) method was applied to these unbalanced data and how it affected the success of classification was examined.First, the raw data were analyzed with K-nearest neighbors classifier and KStar classifier.In this study, the analysis results of these five chapters are given in tables and comparatively.In this study, it has been seen that SMOTE oversampling method increase the classification success.In areas where unstable data such as educational data mining may exist, higher classification accuracy can be achieved with the help of different oversampling methods.
Data Cleaning Based on Multi-sensor Spatiotemporal Correlation

Sensor-based condition monitoring systems are becoming an important part of modern industry. However, the data collected from sensor nodes are usually unreliable and inaccurate. It is very critical to clean the sensor data before using them to detect actual events occurred in the physical world. Popular data cleaning methods, such as moving average and stacked denoise autoencoder, cannot meet the requirements of accuracy, energy efficiency or computation limitation in many sensor related applications. In this paper, we propose a data cleaning method based on multi-sensor spatiotemporal correlation. Specifically, we find out and repair the abnormal data according to the correlation of sensor data in adjacent time and adjacent space. Real data based simulation shows the effectiveness of our proposed method.
Reduced Data Sets and Entropy-Based Discretization

Results of experiments on numerical data sets discretized using two methods—global versions of Equal Frequency per Interval and Equal Interval Width-are presented. Globalization of both methods is based on entropy. For discretized data sets left and right reducts were computed. For each discretized data set and two data sets, based, respectively, on left and right reducts, we applied ten-fold cross validation using the C4.5 decision tree generation system. Our main objective was to compare the quality of all three types of data sets in terms of an error rate. Additionally, we compared complexity of generated decision trees. We show that reduction of data sets may only increase the error rate and that the decision trees generated from reduced decision sets are not simpler than the decision trees generated from non-reduced data sets.
Hierarchy-of-Visual-Words: a Learning-Based Approach for Trademark Image Retrieval

In this paper, we present the Hierarchy-of-Visual-Words (HoVW), a novel trademark image retrieval (TIR) method that decomposes images into simpler geometric shapes and defines a descriptor for binary trademark image representation by encoding the hierarchical arrangement of component shapes. The proposed hierarchical organization of visual data stores each component shape as a visual word. It is capable of representing the geometry of individual elements and the topology of the trademark image, making the descriptor robust against linear as well as to some level of nonlinear transformation. Experiments show that HoVW outperforms previous TIR methods on the MPEG-7 CE-1 and MPEG-7 CE-2 image databases.