Reviewerly/exHarmony huggingface dataset:
Recommending Search Queries in Documents Using Inter N-Gram Similarities

Reading a document can often trigger a need for additional information. For example, a reader of a news article might be interested in information about the persons and events mentioned in the article. Accordingly, there is a line of work on recommending search-engine queries given a document read by a user. Often, the recommended queries are selected from a query log independently of each other, and are presented to the user without any context. We address a novel query recommendation task where the recommended queries must be n-grams (sequences of consecutive terms) in the document. Furthermore, inspired by work on using inter-document similarities for document retrieval, we explore the merits of using inter n-gram similarities for query recommendation. Specifically, we use a supervised approach to learn an inter n-gram similarity measure where the goal is that n-grams that are likely to serve as queries will be deemed more similar to each other than to other n-grams. We use the similarity measure in a wide variety of query recommendation approaches which we devise as adaptations of ad hoc document retrieval techniques. Empirical evaluation performed using data gathered from Yahoo!'s search engine logs attests to the effectiveness of the resultant recommendation methods.
Technology, Education, Life and Non-Life Insurance in Africa

This article examines the relevance of information and communication technology (ICT) in modulating the effect of education on life insurance and non-life insurance consumption in 48 African countries for the period 2004-2014. Education is measured with primary school, secondary school and tertiary school enrollments. ICT is measured with mobile phone, internet and broadband subscriptions. The empirical evidence is based on generalized method of moments. The following main findings are established. First, from the nexuses between education, ICT and life insurance, there are positive conditional effects from the interaction between: (i) broadband subscriptions and primary school enrollment; (ii) broadband subscriptions and secondary school enrollment and (iii) internet penetration and tertiary school enrollment. Second, from the nexuses between education, ICT and non-life insurance: (i) there is a negative net effect from the interactions between mobile phone penetration and primary education while positive net effects are apparent from the interactions between: mobile phone penetration and secondary school enrollment; secondary school enrollment and broadband subscriptions and; tertiary school enrollment and broadband subscriptions.
A Weibull‐distribution‐based hybrid total variation method for speckle reduction in ultrasound images

Speckle reduction is still an intractable task in ultrasound imaging field. Ultrasound speckle is usually described as multiplicative noise with its statistics following a Rayleigh or Gaussian distribution. To employ these two distributions effectively, the authors attempt to describe ultrasound speckle using a Weibull distribution, because it can include the Rayleigh distribution as a special case and also approximate a Gaussian distribution by varying its shape and scale parameters. The authors’ contribution in this paper is to propose a Weibull-distribution-based hybrid total variation (WHTV) method to reduce ultrasound speckle. The WHTV energy functional is convex and consists of a new data fidelity term and a new regularization term. The former is derived from the multiplicative Weibull model of ultrasound speckle based on the maximum likelihood criterion. The latter is a new edge-weighted combination of the first- and second-order total variation, with the advantage of preserving edges while alleviating the staircase effects. The minimization of the WHTV energy functional is implemented by the split Bregman algorithm. Experimental results on synthetic and real ultrasound images have demonstrated not only that the Weibull distribution is a better fitting model for the statistics of ultrasound speckle than other distributions such as Rayleigh, Gaussian, Gamma, and Nakagami, but also that the proposed WHTV method can achieve better despeckling performance than several state-of-the-art variational methods.
Analysis of Modified Shell Sort for Fully Homomorphic Encryption

The Shell sort algorithm is one of the most practically effective in-place sorting algorithms. However, it is difficult to execute this algorithm with its intended running time complexity on data encrypted using fully homomorphic encryption (FHE), because the insertion sort in Shell sort has to be performed by considering the worst-case input data. In this paper, in order for the sorting algorithm to be used on the FHE data, we modify the Shell sort with an additional parameter α, allowing exponentially small sorting failure probability. For a gap sequence of powers of two, the modified Shell sort with input array length n is found to have the trade-off between the running time complexity of O(n3/2√α+loglogn) and the sorting failure probability of 2-α. Its running time complexity is close to the intended running time complexity of O(n3/2) and the sorting failure probability can be made very low with slightly increased running time. Further, the near-optimal window length of the modified Shell sort is also derived via convex optimization. The proposed analysis of the modified Shell sort is numerically confirmed by using randomly generated arrays. For the practical aspect, our modification can be applied to any gap sequence, and we show that Ciura’s gap sequence, which is known to have good practical performance, is also practically effective when our modified Shell sort is applied. We compare our modified Shell sort with other sorting algorithms with the FHE over the torus (TFHE) library, and it is shown that this modified Shell sort has the best performance in running time among in-place sorting algorithms on homomorphic encryption scheme.
Modelling Voting Behaviour During a General Election Campaign Using Dynamic Bayesian Networks

This work aims to develop a Machine Learning framework to predict voting behaviour. Data resulted from longitudinally collected variables during the Portuguese 2019 general election campaign. Naïve Bayes (NB), and Tree Augmented Naïve Bayes (TAN) and three different expert models using Dynamic Bayesian Networks (DBN) predict voting behaviour systematically for each moment in time considered using past information. Even though the differences found in some performance comparisons are not statistically significant, TAN and NB outperformed DBN experts’ models. The learned models outperformed one of the experts’ models when predicting abstention and two when predicting right-wing parties vote. Specifically, for the right-wing parties vote, TAN and NB presented satisfactory accuracy, while the experts’ models were below 50% in the third evaluation moment.
An Educational Guide to Creating Your Own Cryptocurrency

This work presents a comprehensive guide for students and developers who are interested in developing and experimenting with a blockchain technology for cryptocurrencies. Our work allows students to rapidly deploy a new cryptocurrency and provides an accessible way for users to process transactions and mine blocks. A new blockchain can be created using the source code of Litecoin alongside with the Microsoft Azure’s cloud service to deploy the requirements necessary for running the network associated with the blockchain. As Litecoin, we utilize scrypt as the “proof-of-work” algorithm. Built into the codebase for Litecoin, this computationally heavy algorithm is designed to ward off large-scale brute-force attacks, preventing criminals with strong hardware capabilities from bypassing the security measures of the algorithm. This article dives into the intricacies of the steps taken to deploy a new blockchain designed to be used for cryptocurrency.
Improving students’ speaking ability by using role play technique

This study is conducted in order to improve students’ speaking ability at second grade of one junior high school in Wasile, North Halmahera by role play activities. This study is related to the result of a preliminary study showed that the students were still low in English speaking skill; especially in giving interpersonal response. This study used a Classroom Action Research (CAR) which is conducted to solve the students’ problem in English speaking. The Classroom Action Research (CAR) was done based on Kurt Lewin’s design. The writer did two cycles in which each cycle consists of planning, acting, observing, and reflecting. The data were gathered through qualitative and quantitative data. The qualitative data were gained by analyzing the interview and observation result. Then, quantitative data were obtained from the students speaking score of pretest, posttest and questionnaire. Based on the research conducted in the 8th grade of the junior high school, the writer concluded that role-playing activity can improve students’ speaking ability showed by the score they get. Furthermore, from the students’ response Toward the teaching and learning activity during CAR, it is proved that the response of the students toward the teacher professionalism and their interest in learning speaking using role play technique is 75.00% and 78.57% that means it falls into the strong and very strong category. It can be conclude that the students like role play technique. Moreover, the observation checklist showed that the students seemed braver and more confident in speaking. It proven by their participation in the class conversation discussion, perform in the front of the class, pronunciation, fluency and felling confident about speaking.
Exchanging Digital Documents Using Blockchain Technology

Physical documents have many challenges, due to being difficult to exchange and the potential of being lost and sabotaged. Digital documents seem to address some of these challenges, but because of the use of the Internet and reliance on third parties to share and preserve such documents, the privacy and integrity of the digital documents became difficult to maintain. The blockchain technology that Satoshi Nakamoto has proposed for financial transactions can be used to solve the problems of trust in centralized systems and the subversion and alteration thatthese transactions may be exposed to. Blockchain-based systems have shown their effectiveness and ability to solve most security problems related to exchanging systems' transactions. Hence,A decentralized system for exchanging digital documents, using Ethereum blockchain and InterPlanetary File System (IPFS), has been proposed to overcome the problems of using trusted third-parties in exchanging digital documents. The proposed system provides immutable and irreversible logs of the process of sending and receiving documents. In this framework, all registered users can encrypt the documents using the Advanced Encryption Standard (AES) Algorithm, send, and receive documents using the Ethereum Blockchain's smart contract. The framework was simulated locally using Ganache Blockchain and tested on the official Ethereum test network Ropsten, and the experimental results showed the feasibility of the proposed scheme. The smart contract used in the Ethereum blockchain was written in the Solidity programming language, and the website that was needed to interact with the blockchain was built using React and other different javascript libraries like web3. Additionally, Metamask wallet was used to create the Ethereum accounts which are required to connect and communicate with the Smart Contract that is implemented in the Ethereum blockchain. Finally, the model's results indicated that the model may cost an average of 0.0552$ per transaction, and this shows that the system is affordable. Also, the model has satisfied the three main security requirements which are confidentiality, integrity, and availability by using the suitable countermeasure for each one of them.
NOCOL - Nonnegative Orthogonal Constraint Outlier Learning

Identifying anomalous documents in a text corpus is an important problem that has wide applications. Due to the high dimensional and sparse nature of text data, traditional outlier detection methods fail to identify features that distinguish outliers. Inspired by the capability of Nonnegative Matrix Factorization (NMF) for text clustering, we explore it for text outlier detection. In this paper, a novel NMF-based method called Nonnegative Orthogonal Constraint Outlier Learning (NOCOL) is introduced that learns the outliers effectively during the factorization process. Experimental results show the higher accuracy of NOCOL in identifying text outliers in comparison to the state-of-the-art methods.
Simulation and Mathematical Model for Trajectory Path Planning of Robotics Arm Using ROBO DK Solver Package

In general Simulation plays an essential role in engineering studies in various applications like Mechanical , computer science etc. Simulation helps to improve the performance of the System by studying its behaviour with the help of software application . Through simulation engineers can able to understand the mechanism of any machineries / Equipment based on that further inferences can be drawn . In this Paper attempt has been made to simulate the arm of robot using ROBO DK SOLVER Package in order to study the various arm motions within a work cell unit. Using solver package one can bale to simulate by observing the path planning movement of robotic arm with respect to Cartesian coordinate system . Through simulation package the output can be obtained and further analysis carried for better improvement of simplified arm movement within a workstation without any collision between two objects . Finally simulation results are compared to that of mathematical model based on which it proves that mathematical model gives better solution as compared to simulation.
Exploring Neural Turing Machines Applicability in Neural-Symbolic Decision Support Systems

The task of building hybrid decision support systems that combine symbolic and connectionist approaches is actual and challenging. In particular, decision support systems operate with symbolic structures that describe the problem situation, stakeholders, assessment criteria, etc. Integrating connectionist approaches into certain parts of the decision-making process bring robustness, fixed response speed and ability to generalize. This paper examines Neural Turing Machines - a special case of Memory-Augmented Neural Networks - and demonstrates that such an architecture can be integrated into the Decision Support Systems. It was also shown that Neural Turing Machine can solve arithmetic sum task for numbers represented as binary vectors of length 10.
Classification on Participants Renewal Process in Insurance Company: Case Study PT XYZ

Insurance is a form of risk management and one of the fastest-growing businesses. PT XYZ is a company that focuses on health and life insurance. One excellent product owned by PT XYZ is Managed Care (MC) Insurance and it dominates 64.5% of the company's premium income. However, MC has a high claim ratio value. Proven by there were 363 companies that have a claim ratio of more than 76% in 2020. The increase in the total claim ratio is due to the company has not been able to predict the claim ratio when the renewal company applies for an insurance participant. This study focuses on classifying participants on insurance renewal process so that company can be more selective to approve the participants. Participant selection can help a company to reduce the claim ratio. The proposed method is doing classification on insurance participants’ data using 3803 datasets with four attributes and five algorithms and find significant features when generating the model. The models will be validated using k-folds cross-validation with k=10, evaluation results show the accuracy of each algorithm as following, Naïve Bayes 70.00%, Support Vector Machines 67.00%, Decision Tree 95.40%, Logistic Regression 90.20%, and Neural Networks 79.30%. The results of the study recommend the Decision Tree algorithm with an accuracy of 95.40% for the classification of renewal companies that will join as insurance participants because it has a better accuracy value than other algorithms. Decision Tree shows that the most significant features in defining prospective company assessment is the average age.
After Thematic Analysis: Introducing the Fuzzy Thematic Network Analysis in Psychological Research

Thematic Analysis is a qualitative method. The themes that are extracted and conceptualized during the coding process can be interpreted in a thematic network. Global, organizing and basic themes are interpreted as a network using Fuzzy Thematic Network Analysis (FTNA), we introduce this method based on the fuzzy theory for providing the rules of the relationships of the themes by interviewing with psychologists having experience in the field. This method is integrating the precision of quantitative methods into the depth of qualitative methods. This integration can be of interest to all psychologists and mind researchers. This paper introduced this new and interesting method and applied a numerical example for testing the degree of accuracy of them using fuzzy set theory. This method can be a turning point for a new methodology which is called it here Semi-qualitative Methodology.
Predictive Model for Software Vulnerability Management in Telecommunication Infrastructures

As vulnerability assessment becomes a key component for any software’s lifecycle, a great importance should substantiate on the methods used in the audit process and on the findings management front. Large-scale technological infrastructures, such as telecommunication networks require particular focus due to their sheer complexity and the criticality aspect of the services they provide. While existing models for vulnerability management can be efficient in fast-paced IT&C environments, there is a need for a predictive model for vulnerability management, including impact and probability of exploitation assessment for complex infrastructures where slow-paced changes in the IT&C environment creates opportunities for adversaries and affects the resilience of telecommunication operators.
Bounding Box Representation of Co-location Instances for $$L_{\infty }$$ Induced Distance Measure

In this paper, we investigate the efficiency of Co-location Pattern Mining (CPM). In popular methods for CPM, the most time-consuming step consists of identifying of pattern instances, which are required to calculate the potential interestingness of the pattern. We tackle this problem and provide an instance identification method that has lower complexity than the state-of-the-art approach: (1) we introduce a new representation of co-location instances based on bounding boxes, (2) we formulate and prove several theorems regarding such a representation that can improve instances identification step, (3) we provide a novel algorithm utilizing the aforementioned theorems and analyze its complexity. Finally, we experimentally demonstrate the efficiency of the proposed solution.
TRAINING OF KAHOOT! AS AN INTERACTIVE GAME-BASED LEARNING EVALUATION PLATFORM FOR STUDENTS

Background: Maintaining student motivation, involvement and consistency in a learning process is a challenge that must be faced by teachers / educators. Especially during the COVID-19 pandemic, this limits learning activities in online form. Teachers / educators need additional skills to be able to manage classes, deliver material and maintain student motivation. Objective: This community service aims to improve the skills of teachers / educators to provide interactive learning and evaluation media based on game learning. Methods: The methods used in this community service activity were training and focus group discussions. Furthermore, the participants prepared the Kahoot quiz material independently according to the subjects they were teaching them. Result: The result of this training activity is an increase in the skills of teachers / educators in mastering Kahoot, and an increase in teacher / educator understanding of the Kahoot material. This can be seen from the occasional increase of 15.97% after the training was completed. The training participants were satisfied with the implementation of the training as evidenced by filling out the feedback questionnaire, with a satisfaction score above 3.5 out of 4. Conclusion: This community service has a positive impact on skills and understanding related to the use of Kahoot for interactive learning and evaluation media.
Optimal selection of sample-size dependent common subsets of covariates for multi-task regression prediction

An analyst is given a training set consisting of regression datasets Dj of different sizes, which are distributed according to some Gj, j=1,…,J, where the distributions Gj are assumed to form a random sample generated by some common source. In particular, the Dj’s have a common set of covariates and they are all labeled. The training set is used by the analyst for selection of subsets of covariates denoted by P∗(n), whose role is described next. The multi-task problem we consider is as follows: given a number of random labeled datasets (which may be in the training set or not) DJk of size nk, k=1,…,K, estimate separately for each dataset the regression coefficients on the subset of covariates P∗(nk) and then predict future dependent variables given their covariates. Naturally, a large sample size nk of DJk allows a larger subset of covariates, and the dependence of the size of the selected covariate subsets on nk is needed in order to achieve good prediction and avoid overfitting. Subset selection is notoriously difficult and computationally demanding, and requires large samples; using all the regression datasets in the training set together amounts to borrowing strength toward better selection under suitable assumptions. Furthermore, using common subsets for all regressions having a given sample size standardizes and simplifies the data collection and avoids having to select and use a different subset for each prediction task. Our approach is efficient when the relevant covariates for prediction are common to the different regressions, while the models’ coefficients may vary between different regressions. Last but not least, we propose a simple and meaningful measure, GENO, that allows comparisons of the predictive value of different subsets of covariates by comparing the sample size they require in order to achieve the same prediction error.
Consistent Maintenance Order Processing with Digital Assistance Systems

Augmented reality-based digital assistance systems have proven their potential to support with instructions considering numerous application scenarios in the product life cycle. For the maintenance of machines and plants, however, their use becomes particularly efficient if the diagnosis at the operator's site can be effectively supported, so that the service provider and his experts can make accurate diagnoses for this maintenance job, plan the execution including the spare parts logistics precisely, and document as well as billing the maintenance case with low effort. For this purpose, a modular digital assistance system is presented that provides support adapted to the process phases.