Reviewerly/exHarmony huggingface dataset:
On Application of Bayesian Parametric and Non-parametric Methods for User Cohorting in Product Search

In this paper, we study the applicability of Bayesian Parametric and Non-parametric methods for user clustering in an E-commerce search setting.To the best of our knowledge, this is the first work which presents a comparative study of various Bayesian clustering methods in the context of product search.Specifically, we cluster users based on their topical patterns from their respective product search queries.To evaluate the quality of the clusters formed, we perform collaborative query recommendation task.Our findings indicate that simple parametric model like Latent Dirichlet Allocation (LDA) outperforms more sophisticated non-parametric methods like Distance Dependent Chinese Restaurant Process and Dirichlet Process based clustering in both tasks.
Decentralized Organizational Theory: Distributed Trust and Blockchain

Over the past decade, distributed trust technologies such as blockchain have grown in prominence. Yet organizational theory has not adequately addressed the decentralization enabled by such technologies. While a select number of published works have advanced knowledge of theoretical implications of blockchain, as a field, we have just started to scratch the surface of this fundamental shift. Recognizing this potential, this symposium aims to showcase the latest research on blockchain-enabled decentralized organizing that has clear implications for organizational and management research and adds to the repertoire of perspectives and existing theory impacted by distributed trust. In doing so, we seek to contribute to the emerging body of research connecting blockchain to broader organizational and management theories and help inform less technologically focused scholars on the implications of this rising technology on their own theoretical work. Blockchain and Decentralized Autonomous Organizations: Literature Review and Research Agenda Presenter: Carlos Santana; U. of Deusto Presenter: Laura Albareda; LUT U., School of Business and Management Decentralized and Centralized Organizing Collide in the Field of Blockchain and Healthcare Presenter: Chang Lu; U. of Alberta The Rise of Decentralized Organizations and Cyborg Management Presenter: Ying-Ying Hsieh; Imperial College Business School Presenter: Jean-philippe Vergne; Ivey Business School Crowd-Funding: Early-Stage Venture Resource Acquisition from Decentralized Autonomous Organizations Presenter: Alex Michael Murray; U. of Oregon
Malaria Prevalence in Rice Farm Settlements South East Nigeria

Aim: Agrarian settlements in Nigeria possess a lot of factors that predisposes the populace to malaria. The present study focused on the evaluation of three-year prevalence of malaria in rice farm settlements, South-East Nigeria.&#x0D; Study Design: The study was a retrospective descriptive survey designed to evaluate the prevalence of malaria infection between January 2015 and December 2017. A total of 29, 458 records were examined, 21, 559 and 7, 899 from Ebonyi and Enugu States respectively for three years (2015 – 2017). Male comprises 11, 453 of the records while the female was 18, 005.&#x0D; Place and Duration: Southeastern Nigeria \ three years&#x0D; Methodology: Preparation of thick films blood smears for microscopy following standard methods.&#x0D; Results: Overall, 12, 746 (43.3%) were positive for malaria parasites, 7, 651 (35.4%) and 5, 115 (64.8%) from Ebonyi and Enugu respectively. Prevalence of the infection was similar between male and female in Ebonyi State (35.5% vs. 35.3%, χ2 = 0.044, df = 1, p = 0.834); but significantly higher in female compared to male in Enugu State (69.7% vs. 55.7%, χ2 = 154.808, df = 1, p = 0.0001).&#x0D; Conclusion: There was high prevalence of malaria infection in rice farm settlements, south-east Nigeria.
Prediction of Hashtags for Images

Hashtags, usually, are one among the everyday patterns in web-based locale lives.They're often used with pictures or writings through web-based networking social media.They are used to simplify the venture of categorizing any photo that has been uploaded on social media.But, however, manual annotations for images, additionally for figuring out training sets for the machine learning algorithms, wishes introduced effort, work and might also incorporate human judgmental blunders or subjectivity.Hence, alternate methods to routinely generate training sets, like pairs of pictures and also tags are grasped.Choosing or wondering about a realistic hashtag for a photograph is an unwielded procedure.Machine learning helps in making this method easier.In this paper, we have labored on constructing our personal dataset of images that can be used to predict appropriate hashtags for images.The dataset carries pictures under various categories that have been trained and tested in order to be classified.This helps us become aware of and segregate photos and to predict their splendid hashtags.
Longitudinal Count Data Analysis of Factors Affecting Epileptic Seizure of Patients in Case of Gondar Referral Hospital, Northwest Ethiopia

Even though the world is fighting epileptic seizure disease in unity and patients are getting treatment, it continued to be a serious health issue for parts of the world and a large number of patients are being registered every year.The main objective of this study was to identify associated risk factors affecting the progression of patients in Gondar Referral Hospital.In this longitudinal count data analysis, data was collected from 337 epileptic seizure patients registered for treatment from January 1, 2016 to April 30, 2018 in the Hospital and Poisson, Poisson-gamma, Poisson-Normal and Poisson-Gamma-Normal models were applied to the data.Poisson-Gamma-Normal model with random intercept was selected as a best model to fit the data based on different model selection criteria.The findings of the study revealed that time, brain injury, treatment, interaction of time with residence and interaction of time with brain injury were significant factors for epileptic seizure of the patients.Minimization of epileptic seizure of patients in response to treatment was observed, which means the patients were at decreased epileptic seizure when enrolled for treatment.Therefore, patients should be encouraged to stay on treatment.
Publisher Correction: A cognitively demanding working-memory intervention enhances extinction

An amendment to this paper has been published and can be accessed via a link at the top of the paper.
Improved El Gamal public key cryptosystem using 3D chaotic maps

Digital information is any type of data that is stored electronically. These data need to protect its assets and unauthorized access to it and therefore need measures to protect digital privacy. This process is done in different ways, one of which is encryption. Encryption provides secure transfer of unauthorized data over insecure channels. In this paper a method is proposed to create the keys to the El Gamal PKC algorithm based on chaos theory. The proposed algorithm uses 3D chaos maps to create keys used in the encryption and decryption process using an El Gamal algorithm. The time spent encoding and decoding in milliseconds was calculated and compared with methods that used 1D and 2D chaotic maps. Also, the results obtained exceeded most of the statistical and NIST tests. The generated results are tested their reactions against many types of attacks. Then, the results showed that the proposed method had an excellent randomness efficiency for creating public and private keys for El Gamal's algorithm.
The Secure I-Voting System Helios++

Voting is the process through which representatives of the country (or an organization) are chosen.However, some voters may not go to the polls to vote for personal or public reasons.One solution to this problem is Internet voting (I-voting) where it can be voted from anywhere and anytime.I-voting has many advantages and certainly, there are disadvantages.Many I-voting systems have been proposed.Helios, an open-source system, is one of the most popular voting systems.This paper presents Helios++, which is an enhanced I-voting system based on Helios and public-key certificates.Improvements to the Helios system have been proposed.A certification authority (CA) has been added and integrated with Helios.This authority creates voter certificates containing public and private keys that are used later in the voting process, where they are used for encryption and generating digital signatures.Signing the votes can be done by either the RSA or DSA algorithms.Indeed, each voter has given one real account and other fake accounts to be used in case the voter is coerced.Finally, the Helios interface has been improved and the Arabic language has been added to the system.
IMPROVING THE TWELFTH GRADE STUDENTSâ€™ VOCABULARY MASTERY THROUGH STORY TELLING TECHNIQUE IN SMA SANTO PETRUS MEDAN

Vocabulary mastery is the basic language aspect that must be mastered before mastering English skill. The more words the students learn, the more they can understand and express their idea in that language. When they have inadequate vocabulary, they can say little things because they do not know how to say it in that language. That is why this study was conducted in order to improve the studentsâ€™ vocabulary mastery. Storytelling technique can help students for remembering or mastering vocabulary. The objectives of this study are to find out whether Storytelling technique can improve the studentsâ€™ English vocabulary mastery and find out the studentsâ€™ responses after being taught by using Storytelling technique. The subjects of this study were the twelfth grade students of SMA Santo Petrus Medan. In conducting this study the writer used Classroom Action Research (CAR) as the method of the study. Tests were used to gather the quantitative data. In addition, field notes and observation sheet were used to gather qualitative data. Based on the finding of this study, it showed in pre-test only 4 students (16%) who passed KKM (Mastery Minimum Criterion) and in post-test 22 students (88%). The result of this study revealed that there was a significant improvement of the studentsâ€™ achievements in the pre-test and in the post-test. It could be seen from the improvement of the studentsâ€™ mean score 59.52 in pre-test to 80.48 in post-test. Furthermore, the students were interested in Storytelling technique. The analysis of the questionnaire showed that most of students agree to learn vocabulary by using Storytelling technique. It could be seen from the table, it showed that total of agree 47.2%. Therefore, Storytelling technique was an effective technique in helping students to improve their vocabulary mastery.
Computer Aided System for Nuclei Localization in Histopathological Images Using CNN

Today, the health care industry is extensively using computer aided diagnostic expert system. The expert system for the diagnosis of breast cancer using the histopathological image is the need of time. Analysis of histopathological images is challenging due to its complex architecture with irregularly shaped nuclei. Convolutional neural network (CNN) is a promising technology emerging in recent years. We have designed a computer based expert system to identify nuclei in histopathological images. The system is developed using python programming language. We have used the BreaKHis breast cancer dataset for experimentation and Kaggle dataset for convolution masks generation. Nucleases are localized using custom design Keras and U-Net Hybrid CNN (KUH-CNN) model. The systems can be used by histopathologists for the diagnosis of malignancy in the tissue. The system can also aid the researchers who can implement a machine learning algorithm on the nucleases detected images for further analysis.
How well are the two chains linked together? : A study about the perceived effects of blockchain on transaction costs within supply chains handling physical goods.

The purpose of this study is to produce new knowledge concerning the perceived effects of blockchain technology on transaction costs within supply chains handling physical goods through the created ...
A genetic programming method for classifier construction and cost learning in high-dimensional unbalanced classification

Cost-sensitive learning has been widely used to address the problem of class imbalance. However, cost matrices are often manually designed. In many real-world applications, cost values are often unknown because of the limited domain knowledge. This paper proposes a new genetic programming method to construct cost-sensitive classifiers, which do not require the manually designed cost values. The experimental results show that the proposed method often outperforms existing GP methods.
A Cluster and Process Collaboration-Aware Method to Achieve Service Substitution in Cloud Service Processes

Some cloud services may be invalid since they are located in a dynamically changing network environment. Service substitution is necessary when a cloud service cannot be used. Existing work mainly concerned on service function and quality in service substitution. To select a more suitable substitutive service, process collaboration similarity needs to be considered. This paper proposes a cluster and process collaboration-aware method to achieve service substitution. To compute the process collaboration similarity, we use logic Petri nets to model service processes. All the service processes are transformed into path strings. Service vectors for cloud services are generated by Word2Vec from these path strings. Process collaboration similarity of two cloud services is obtained by computing the cosine value of their service vectors. Meanwhile, similar cloud services are classified as a service cluster. By calculating function similarity and quality matching, a candidate set for services substitution is generated. The service with the highest process collaboration similarity to invalid one in the candidate set is chosen as the substitutive one. Simulation experiments show the proposed method is less time-consuming than traditional methods in finding substitutive service. Meanwhile, the substitutive one has a high cooccurrence rate with neighboring services of the invalid cloud service. Thus, the proposed method is efficient and integrates process collaboration well in service substitution.
About quantum computer software

Quantum computer is the key to controlling complex processes. If its hardware, in general is successfully created on the basis of the physical baggage of the 20th century, the mathematical software is fundamentally lagging behind. Feynman's user interface in the form of quantum gate arrays, cannot be used for the control because it gives the solution of the Schrödinger equation with quadratic slowdown compared to the real process. The software must then imitate the real process using appropriate program primitives written as the programs for classical supercomputer. The decoherence will be reflected by some constant - the number of basic states that can fit into the limited of memory available to software. The real value of this constant can be found in the experimental realization of Grover search algorithm. Rough estimates of this constant are given based on the simplest processes of quantum electrodynamics and nuclear decay.
Multi-label Classification of Short Text Based on Similarity Graph and Restart Random Walk Model

A multi-label classification method of short text based on similarity graph and restart random walk model is proposed. Firstly, the similarity graph is created by using data and labels as the node, and the weights on the edges are calculated through an external knowledge, so the initial matching degree of between the sample and the label set is obtained. After that, we build a label dependency graph with labels as vertices, and using the previous matching degree as the initial prediction value to calculate the relationship between the sample and each node until the probability distribution becomes stable. Finally, the obtained relationship vector is the label probability distribution vector of the sample predicted by the method in this paper. Experimental results show that we provides a more efficient and reliable multi-label short-text classification algorithm.
Link-Based Cluster Ensemble Method for Improved Meta-clustering Algorithm

Ensemble clustering has become a hot research field in intelligent information processing and machine learning. Although significant progress has been made in recent years, there are still two challenging issues in the current ensemble clustering research. First of all, most ensemble clustering algorithms tend to explore similarity at the level of object but lack the ability to explore information at the level of cluster. Secondly, many ensemble clustering algorithms only focus on the direct relationship, while ignoring the indirect relationship between clusters. In order to solve these two problems, a link-based meta-clustering algorithm (L-MCLA) have been proposed in this paper. A series of experiment results demonstrate that the proposed algorithm not only produces better clustering effect but is also less influenced by different ensemble sizes.
Deep statistics

The performance measures and statistical techniques selected affect the conclusions we can draw on the behavior of the algorithms. For this reason, we propose more robust performance statistics for addressing statistical and practical significance, as well as investigating the exploration and exploitation powers of stochastic optimization algorithms. They are introduced by the Deep Statistical Comparison approach and its variants. Its implementations are available as part of the DSCTool, which provides web services for robust ranking and hypothesis testing, including a proper selection of an omnibus statistical test and post-hoc tests if needed.
Construction of Accurate Teaching Model Based on Intelligent Teaching Tools——Take the Rain Classroom As An Example

The intelligent teaching tools represented by the rain classroom can provide data and intelligent information support for the teaching process. The research status of precision teaching at home and abroad is reviewed, studying the development path of current information teaching, analyzing the problems and influencing factors in the current precision teaching, designing the intelligent teaching tools represented by the rain classroom and put forward the realized method and process of constructing the accurate teaching model by data collection, data analysis, teaching analysis, teaching implementation and teaching intervention, helping to solve the “last-mile problem” of effective use of information technology in the classroom and thus promote the deep integration of information technology and teaching.
UNIVERSITY MANAGEMENT: RECTOR’S POSITION (CONCEPTUAL NOTES)

The key actor in any university management system is the rector; his/her priorities and goals to a large extent determine the prospects of the university. This conceptual article deals with the following issues: what a position in activity is; what a rector’s position is, why it should be the subject of the analysis; what rector’s positions can be; how a rector’s position can be formed. The authors put forward the concept of «rector’s position», based on philosophical ideas about the position as a form of human subjectivity. As a basis for a typological analysis of rectors’ positions, a scheme «Position space» is proposed, wherein three poles are distinguished: «Norm», «Business», «Idea». The «ultimate» rectors’ positions corresponding to these poles are analyzed including the features of the ontological framework, priorities and goals, «operation spaces» and management tools used. It is shown that the designated space contains a whole range of rectors’ positions, as implemented through different management styles. The following rector’s positions are characterized: «Functionary», «Leader», «Investor», «Manager», «Entrepreneur». The formation of the rector’s position as an important part of his professional development is discussed. The proposed typology of positions is based on the experience of the authors as participants in developing programs and flagship projects of universities, on the experience of professional communication with more than 30 rectors of Russian universities. The article is original and new, as it puts forward the concepts of «a rector’s position» and «rector’s positions space». These concepts and the typology of the distinguished positions can be used as a tool for analyzing the rectors’ activity positions, for comprehending the imperatives and limitations inherent in these positions. The position scheme and the «operation space» scheme can serve as guidelines for analysing and assessing the completeness (or partiality) of the university leader’s position. These schemes can be also used in management consulting and as an element of educational programs for university rectors and management teams.
Text Messages: A Computer-Mediated Discourse Analysis

This study explores the discourse of text messages from a microlinguistic perspective by means of concordance analysis.It aims at sorting the dominant phonological, lexical and grammatical features that mark texting as a peculiar asynchronous mode of computer-mediated communication.Also, it investigates how technology reshapes texters' linguistic habits as long as spatio-temporal constraints are imposed.The study goes beyond the description of linguistic features as it takes at its core the explanation of the functions performed by each of these features.Findings showed that most of the phonological, lexical and grammatical features of the discourse of text messages were consciously employed to save space and to speed up communication.Furthermore, the study demonstrated that though the discourse of text messages is space-bound and visually decontextualized, it proved to be cohesive, adaptable and interactive in order to perform common language functions such as greetings, expressing attitudes, congratulations, showing involvement, asking for information and demonstrating social solidarity.Finally, based on textual evidence, findings showed that texters created a set of orthographical surrogates to recompense the absence of verbal and para-verbal cues due to specific technological affordances.
String constraint solving: past, present and future

String constraint solving is an important emerging field, given the ubiquity of strings over different fields such as formal analysis, automated testing, database query processing, and cybersecurity. This paper highlights the current state-of-the-art for string constraint solving, and identifies future challenges in this field.
Design and Implementation of a Embedded Fuzzy Multi-criteria Group Decision Making System for Wellbeing, Health and Security

Fuzzy Multi-criteria Group Decision Making Systems( FMCGDMS) allows complex decision making comprising of more than one domain of knowledge in a specific area. This paper proposes to implementation of an embedded fuzzy system of multi-criteria group decision making architecture considering the criteria wellbeing, health, and security to focus on monitoring the quality of life in older adults, through the variables measurable within criteria.