Reviewerly/exHarmony huggingface dataset:
A dynamic cold-start recommendation method based on incremental graph pattern matching

In order to give accurate recommendations for cold-start user who has few records, researchers find similar users for cold-start user according to social network. However these efforts assume that cold-start user's social relationships are static and ignore updating social relationships are time consuming. In social network, cold-start user and other users may change their social relationships as time passes. In order to give accurate and timely recommendations for cold-start user, it is necessary to update similar users for cold-start users according to their latest social relationship continuously. In this paper, an incremental graph pattern matching based dynamic cold-start recommendation method (IGPMDCR) is proposed, which updates similar users for cold-start user based on topology of social network, and gives recommendations according to latest users similar to cold-start user. The experimental results show that IGPMDCR could give accurate and timely recommendations for cold-start user.
Securing IOT Network through Quantum Key Distribution

Current cryptographic techniques broadly specified as conventional cryptography is solely based on the solidity of the mathematical concepts. The advancements in quantum computing can use reversible logic to compute the keys and easily break the existing security in conventional computers. From the analysis of the network structure of Internet of Things (IOT) it is very clear that the entire backbone of the system would collapse if it is attacked or hacked. IOT is a wireless technology that connects “ANYTHING” around to the Internet. IOT is a revolution which should be protected from the attackers as it would lead to several losses which could even be fatal. Hence a strong provision for securing users data in IOT is a real challenge. This paper is attempted to review the fundamentals of Quantum Key Distribution, security aspects for IOT and to address how QKD can be used to secure a IOT system. The challenge encountered is to increase the range and increase the transmission rate of data in QKD systems and to check for a possible solution to adhere these systems with existing information security solutions
Determinants of deposit of commercial banks in Sudan: an empirical investigation (1970-2012)

This present study scrutinises the factors determining the magnitude of deposits of commercial banks operating in Sudan during the period from 1970 to 2012. Factors under scrutiny are inflation rate, total credit as a percentage of GDP, interest rate (profit margin), money supply as the GDP percentage and per capita GDP. This study utilises the approach of autoregressive distributed lag (ARDL) to co-integration and the related error correction model (ECM) for the examination of the factors. The main results obtained suggest that within the long-run, inflation and money supply show negative impacts on total deposits. Conversely, credit, interest rate (profit margin) and real per capita GDP have positive impact on total deposits. Within the short-run, using OLS method, although nearly variables demonstrate that they are statistically significant, they are exhibiting the wrong signs. Nevertheless, the coefficient of the lagged residual (ECt−1) within the ECM model has the correct sign and is highly significant which implies that in the long-run equilibrium, the dependent variable has the inclination for adapting to any deviations.
Controlling video stimuli in sign language and gesture research: The OpenPoseR package for analyzing OpenPose motion tracking data in R

Researchers in the fields of sign language and gesture studies frequently present their participants with video stimuli showing actors performing linguistic signs or co-speech gestures. Up to now, such video stimuli have been mostly controlled only for some of the technical aspects of the video material (e.g., duration of clips, encoding, framerate, etc.), leaving open the possibility that systematic differences in video stimulus materials may be concealed in the actual motion properties of the actor’s movements. Computer vision methods such as OpenPose enable the fitting of body-pose models to the consecutive frames of a video clip and thereby make it possible to recover the movements performed by the actor in a particular video clip without the use of a point-based or markerless motion-tracking system during recording. The OpenPoseR package provides a straightforward and reproducible way of working with these body-pose model data extracted from video clips using OpenPose, allowing researchers in the fields of sign language and gesture studies to quantify the amount of motion (velocity and acceleration) pertaining only to the movements performed by the actor in a video clip. These quantitative measures can be used for controlling differences in the movements of an actor in stimulus video clips or, for example, between different conditions of an experiment. In addition, the package also provides a set of functions for generating plots for data visualization, as well as an easy-to-use way of automatically extracting metadata (e.g., duration, framerate, etc.) from large sets of video files.
iCatcher: A Neural Network Approach for Automated Coding of Young Children’s Eye Movements

Infants’ looking behaviors are often used for measuring attention, real-time processing, and learning – often using low-resolution videos. Despite the ubiquity of gaze-related methods in developmental science, current analysis techniques usually involve laborious post hoc coding, imprecise real-time coding, or expensive eye trackers that may increase data loss and require a calibration phase. As an alternative, we propose using computer-vision methods to perform automatic gaze estimation from low-resolution videos. At the core of our approach is a neural network that classifies gaze directions in real time. We compared our method, called iCatcher, to manually-annotated videos from a prior study in which infants looked at one of two pictures on a screen. We demonstrated that the accuracy of iCatcher approximates that of human annotators and that it replicates the prior study’s results. Our method is publicly available as an open-source repository at https://github.com/yoterel/iCatcher
Cybercrimes Act in Nigeria: Experimenting Compliance With Internationally Recognized Human Rights Provisions

The occurrence of criminal activities has increased owing to the advent of internet or computer technology.Access to internet has posed serious challenges to the existing legal regime and enforcement paradigm.The recent experience concerning rate of technology and online communication has no doubt fashioned-out a dramatic increase in the incidence of criminal activities.It has also resulted in the occurrence of what is considered as a new approach of criminal activities.Although, the emergence of electronic communication advances economic prosperity of the world's commerce, fraudsters have gained access and seen the medium as a fertile ground for pretence.This paper firstly discusses the wide-ranging descriptions that led to the complex concept of cybercrime.The paper then examines the importance of cybercrimes regulations to curb infidelity in the use of computer technology.It also investigates the interface between cybercrimes and human rights and argued that human rights are an onerous matter that should be given proper consideration when dealing with cybercrime offenders.The paper utilizes legal research method by way of examining the Nigerian Cybercrimes Act 2015 and its relationship with several human rights provisions.It concludes by revealing the need to prevent cybercrimes that coincide with the right of an individual to enjoy his right to privacy and at the same time ensuring cyber security.
Object Oriented VS Structured Analysis and Design in System Development Courses

Objectives: The main aim of this study was to investigate the factors that influence students’, academicians’, clients’, as well as developer’s preferences in choosing their preferred approach in system development, namely structured analysis design (SAD) or object-oriented analysis and design (OOAD). Methods: The research design was based on a survey methodology and a case study. For the survey, questionnaires were administered to 30 students and 38 academicians, who were randomly selected from several Malaysian universities. For the case study, the requirements of the information system were modeled and presented to several clients to elicit their feedback. The survey data were analyzed using SPSS Findings: The result shows that students preferred the use of OOAD approach, which clearly outnumbered those who preferred the SAD approach, which stood at 33%. Interestingly, the majority (53%) of academicians preferred the use of a mixture of both approaches. Likewise, the clients shared a similar view with the academicians, whereas the developer preferred the OOAD approach. Application/Improvements: Clearly, the findings suggest that both approaches are essential, but the one that is widely used by developers and preferred by students is OOAD, and thus should be given priority when it comes to structured analysis and design. As such, curriculum designers and institutions of higher learning, particularly those offering system analysis and design and related courses, should make the necessary changes to the existing curriculum such that the academic programs offered will be able to produce highly competent and skilled analysts and designers as required by the industry.
Data Analysis with Pivot Tables and Charts

Once you have the data model created in Power Pivot, you need to create an interface for users to interact with the data model and perform data analysis using the model. Excel is a feature-rich environment for creating dashboards using pivot tables and pivot charts. Furthermore, it is very easy to share Excel files with colleagues or host the Excel workbook on SharePoint for increased performance and security. This chapter covers the basics of building an interface for analyzing the data contained in a Power Pivot model using pivot tables and pivot charts in Excel.
Ugarit: Translation Alignment Visualization

In this paper we introduce Ugarit a web-based tool for manual translation alignment of parallel texts, the aim was to build a user interface to create training data in form of translation pairs to be used later for an automatic translation alignment system at word/phrase level, the tool now is widely used as reading environment for parallel texts and a languages learning tool. The paper gives a short overview of the visualization techniques used to present the alignment results and shows how the translation graph derived from the aligned translation pairs.
Predicting Election Results using NLTK

In today’s world, people are usually using social media networks for trying to communicate with other users and for sharing information across the world. The online social networking sites have become considerable tools and are providing a common medium for a number of users to communicate with each other. Twitter is the most prominent microblogging website and one among the social networking sites that grow on a daily basis. Social media incorporates an extensive amount of data in the form of tweets, forums, status updates, comments, etc. in an attempt to automatically process and analyze these data, applications can rely on analysis approaches such as sentiment analysis. Twitter sentiment analysis is an application of sentiment analysis on data from Twitter (tweets), to obtain user's opinions and sentiments. Natural Language Toolkit (NLTK) is a library based on machine learning methods in python &amp; sentiment analysis tool. Which provides the base for text processing and classification? The research work proposed a machine learning-based classifier to extract the tweets on elections and analyze the opinion of the tweeples (people who use twitter). The tweets can be categorized as positive, negative and neutral towards a particular politician. We classify these processed tweets using a supervised machine learning classification approach. The classifier used to classify the tweets as positive, negative or neutral is Naive Bayes Classifier. The classifier is trained with tweets bearing a distinctive polarity. The percentage of positive and negative tweets is then measured and graphically represented.
Analysing Geochemical Data

The statistical analysis of geochemical data employs the main statistical techniques of averaging, probability distributions, correlation, regression, multivariate analysis and discriminant analysis. A particular problem with major element geochemical data is that it is constrained; that is, the compositions sum to 100% and the data are ‘closed’. A related problem arises when ternary plots are used to display geochemical data. Techniques are described to accommodate the problems associated with compositional data which include log-ratio conversions and the biplot diagram. Further statistical problems arise in the area of ratio correlation as advocated in Pearce element ratio diagrams, which is not recommended. Applications to trace elements and radiogenic isotope correlations are discussed. The details of discriminant analysis are outlined as a prelude to a more detailed discussion of tectonic discrimination diagrams considered in Chapter 5.
A Lossless Image Processing Method with Contrast and Saturation Enhancement

This paper proposes a lossless image processing method for color images that simultaneously enhances brightness contrast and saturation. Most of the previous studies focus on lossless contrast enhancement (CE) for grayscale images, where the ideas of reversible data hiding (RDH) are introduced. Meanwhile, a lossless CE method for color images has been proposed. This method, however, never ensures perfect reversibility, while the quality of the restored images is adequately high. In contrast, our method can fully restore the original images. It enhances not only brightness contrast but also saturation. Additionally, the enhancement level is variable, and thus, we can flexibly adjust the visual effect. The experimental results demonstrate that our method controls both brightness contrast and saturation and guarantees reversibility.
Fuzzy Classification with Comprehensive Learning Gravitational Search Algorithm in Breast Tumor Detection

The research paper herewith presents an effectual diagnosis classification system using fuzzy classifier and a very efficient heuristics algorithm comprehensive learning gravitational search algorithm (CLGSA) which has a good ability to search and finding optimal solutions. The effectiveness of the proposed model is estimating on Wisconsin breast cancer data set available in the UCI Machine learning source in the University of California, Irvine. We testify the data over the parameters of classification of accurateness, sensitivity as well as specificity with a much better and more responsive 10-fold cross validation method; which is considered as a reliable diagnostics model in the medical field. Experiment results have clearly shown that the proposed approach will turn out to be a calculative and decisive medium for cancer detection in the field of medicine.
Introduction

Intelligence science studies the basic theory and implementation technology of intelligence, which is a frontier interdisciplinary subject created by brain science, cognitive science, artificial intelligence, etc.
Impact of Virtualization Technologies in the Development and Management of Cloud Applications

Today most of the consumer services ranging from education to banking, hospital management to ticket booking are made online.The online services are hosted in cloud and they are mostly time-critical applications.The cloud-based applications depend on datacenter (DC) resources for computation, communication, and storage.The resource utilization in the cloud needs to cope with the dynamic workload and stochastic request spikes.Virtualization is the key technology for effective resource utilization in the cloud data center.The type of virtualization technology (VT) that is adapted for the delivery of cloud application ensures the quality of service.The goal of this paper is to compare and contrast the performance measures of various virtualization technologies for heterogeneous workloads.The paper presents the impact of VT in the development of application in the cloud.Each virtualization technology outperforms the other in some or other performance metrics.In spite of the differences, certain virtualization technology dominates depending upon the application requirements in the software development sector.
Federated Reinforcement Distillation with Proxy Experience Memory

This paper is presented at 28th International Joint Conference on Artificial Intelligence (IJCAI-19) 1st Wksp. Federated Machine Learning for User Privacy and Data Confidentiality (FML'19), Macau, August 2019.
Evaluating Performance of Asymmetric Homomorphic Encryption Algorithms for Privacy Preservation in Location Based Services

The recent advancement in mobile technology &amp; wireless communication has change the way of people’s communication &amp; execution of tasks. In location based services (LBS), there are large number of LBS users who are available to get their location based information. In LBS, Users work together mutually to compute the centroid by per-forming large number of data aggregation operation that aggregate user’s location before sending it to the LBS provider. Users have to submit their personalized information to the LBS provider. Location privacy is the one of the most critical issue. Homomorphic encryption technique ensures the secure data aggregation by encrypting the user’s location using Homomorphic encryption algorithm. For privacy requirement, semantic security is a standard for any encryption schema. Many Homomorphic encryption algorithm are available, so it’s require to investigate the performance of those that are semantically secure. In this paper, we will discuss homomorphic encryption algorithm and also attempt to evaluate the performance of various additive asymmetric Homomorphic encryption algorithms. Our work is inspired to recognize an asymmetric homomorphic encryption algorithms for LBS that offers strongest location privacy.
Beautiful Visualizations Slain by Ugly Facts: Redesigning the National Hurricane Center’s ‘Cone of Uncertainty’ Map

The Track Forecast Cone, commonly known as the “cone of uncertainty”, is the most popular hurricane and tropical storm forecast product that the National Hurricane Center produces. However, it is often misinterpreted by non-experts. In this study we first explored the most common misconceptions about the cone and produced two alternative redesigns that we expected to be more attractive to and easier to understand by non-expert readers. Our results were mixed, but reveal promising paths for future efforts.
REORGANIZATION AND MODERNIZATION OF THE PUBLISHING SEGMENT OF THE PRINTING INDUSTRY OF SIBERIA AND THE FAR EAST AT THE BEGINNING OF THE XXI CENTURY

The course of privatization of state-owned printing enterprises in the period of 2008–2017 is presented as well as its consequences in Siberia and the Far East. The characteristic of the most promising areas of modernization and diversification of printing houses in the situation of reduction of total volume of publishing products output is given.
Computational Thinking and Block-Based Programming for Beginning Engineering Students: Systematic Review of the Literature

In the article, the authors conduct a systematic review of computational thinking in higher education. For this review, the following research topics were taken into account: computational thinking skills in higher education, programming teaching methods in beginning engineering students, and block-based programming tools in higher education.
STEAM Education: background, framework, and characteristics

This article elaborates at the background of STEAM education, analyzes its framework, and summarize the characteristics of STEAM education, so as to offer some lessons for primary education in Indonesia.
Client/Server Model of Data Privacy using Extended Playfair Cipher for SaaS Applications on the Cloud

Cloud Computing is a paradigm of distributed computing that delivers on-demand and utility-based services to its customers. It provides a set of shared computing resources such as networking, servers, storage, and applications in the form of services to an organization or an individual. The major benefits of cloud computing include on-demand self-service and cost-effectiveness. For the customer, there is no up-front cost for setting up and running the applications on the cloud. Despite the benefits provided by various cloud services, the outsourcing of data storage and computation raise many new security issues. One of such security issues that have to be addressed before uploading our sensitive data to the cloud is data privacy. With the cloud model, end-users lose control over the physical location of data, because data will be stored and processed elsewhere on the globe and not in the local computer. Hence, we need an algorithm for encrypting the data that can be stored and retrieved from a database managed by the public cloud.
IMPROVING STUDENTS’ READING COMPREHENSION THROUGH THE USE OF NUMBERED HEAD TOGETHER (NHT) TECHNIQUE

This research is a Classroom Action Research (CAR). Numbered Head Together (NHT) was used as the technique to improve students’ reading comprehension which was conducted in two cycles. The subject of this research were the tenth grade students of social 3 class in SMA Negeri 2 Sungai Kakap in the academic year of 2017/2018. The class consisted of 35 students. The data collection used observation and measurement technique. The results showed that Numbered Head Together (NHT) technique could improve students’ ability in reading comprehension. Moreover, it could influence the students’ attention, and also motivated them in learning activities as the result of the groups’ discussion. Through the use of Numbered Head Together (NHT) technique, the students were able to participate in teaching learning process. They became serious in listening to the teacher’s explanations and worked cooperatively with their friends in each group. They were also more active, enthusiastic, and more confident in following class activities which resulted on the better atmosphere of the classroom that was more enjoyable and alive. Keywords:Reading Comprehension, Numbered Head Together and Classroom Action Research
Stable Confident Rating Prediction in Collaborative Filtering

Stability of matrix factorization is estimated in terms of gradient descent estimation at each iteration which ultimately defines the stability of recommendations in collaborative filtering techniques. Also, stability is inversely proportional to total number of iterations used for estimating ratings. This gives rise to the need of the method which possesses better rating predictability within less number of iterations. The accuracy of rating prediction is found to be better when user to user trust score is estimated using similarity of individual rated items. The trust estimation is also prone to sparsity due to irregularity of ratings in large volume of data sets (big data). Based on the experimentation strategy and platform requirements, the method of trust evaluation is proposed in this positional paper. The Lyapunov stability solver functions can be used directly in obtaining solution for the trust score amongst users which can bring sufficient stability in learning stages of matrix factorization process and hence better performance in predicting the ratings of non-rated items. Here, the results obtained possess the sufficient gravity for consideration of predicted ratings which also keeps errors in prediction at lowest level for rated items. The papers are addressed from similar domain in related work section to compare proposed work in terms of novelty and performance. The results obtained are satisfactory, which are assessed in terms of mean absolute error (MAE).