Reviewerly/exHarmony huggingface dataset:
Sensitivity reduction of degree histogram publication under node differential privacy via mean filtering

Summary Publication of nodes' degree information in the form of histogram provides useful information about the graph as well as the risk of privacy disclosure. Under the robust protection of node‐differential privacy (node‐DP), publishing result's accuracy mainly depends on the global sensitivity of this publishing task. Thus, the reduction of sensitivity is of great importance. Existing methods for degree histogram publication under node‐DP are mostly based on limitation of maximum degree, whose sensitivity is still high, leading an unbearable noise scale. In this paper, we innovatively propose a method to tackle this issue. Firstly, we introduce mean filtering to process the histogram, almost halve the original sensitivity. Then, we use a series of techniques to further improve publishing accuracy, instituting a complete workflow for degree histogram publication under node‐DP. Experimental results show that our method effectively improves the accuracy.
Efficient Graph Processing with Invalid Update Filtration

Most of existing graph processing systems essentially follow pull-based computation model to handle compute-intensive parts of graph iteration for high parallelism. Considering all vertices and edges are processed in each iteration, pull model may suffers from a large number of invalid (vertex/edge) operations that do not contribute to graph convergence, leading to potential performance degradation. In this paper, we have the insight that these invalid operations can be filtered by leveraging a small fraction of critical information. However, most of critical information are often beyond the visibility of active vertices being processed. We present two novel filtration approaches to (cooperatively) identify out-of-visibility critical information with boundary-cut heuristics and speculative prediction for many graph algorithms. We have integrated both approaches and their hybrid solution into three state-of-art graph processing systems (including Ligra, Gemini, and Polymer). Experimental results using a wide variety of graph algorithms on both real-world and synthetic graph datasets show that neither of these approaches can have an absolute win for all graph algorithms. Boundary-cut, predictive, and hybrid approaches can improve the performance by 115.1, 38.1, and 136.6 percent on average.
Simple Additive Weighting algorithm to aid administrator decision making of the underprivileged scholarship

Abstract Underprivileged scholarships are assistance provided by government or institutions to students from poor families. Scholarships can be given to all students at the education level from elementary school to higher education with certain conditions and criteria. However, in reality many scholarships are given that are not on target. This is because of the number of students who register as scholarship recipients. Therefore, it is necessary to have a decision support system in order to aid the selection process. This study aims to find appropriate system to aid the underprivileged scholarship selection. This study proposed a system which was built using PHP for programming languages and using MySQL as its database using the Simple Additive Weighting method. Data is taken from SMK Sultan Agung 1 Jombang. Data is then analyzed and tested using the Blackbox testing method. Results of this research obtained a list of the best ranks of prospective scholarship recipients which are result of processing from system. The results obtained from system will be validated by means of comparisons with manual calculations as in Table 1. The validation results show same results, so that system can be said to be valid and can replace process that is done manually.
Applications of finite dimensional sampling theories

When modeling physical processes, phenomena in radio-physics, optics, we propose to use Finite-Dimensional Sampling Theories (FDST) instead of the Wiener-Kotelnikov theorem and methods based on difference schemes, integral sums. Accurate FDST operations are compatible with convolution, analytical calculations, estimation of multiple integrals and are the basis for creating new devices with Signal Processing Systems (SPS) in various fields.
Modelling of Dependence of Mechanical Properties of Cast Iron on Chemical Composition of Raw Materials

The problem of the influence of the chemical composition of pig iron on its mechanical properties, such as ultimate tensile strength, elongation, impact test and hardness by Brinell solved. Based on the combinatorial GMDH algorithm, linear and nonlinear models of dependence of each of the indicators describing the quality of the casting on the mass fraction of components of raw materials are constructed. The obtained models allow us to analyze how changes in the mass fraction of each of the components affect each of the variables of mechanical properties.
Signature Based Authentication for Ephemeral Setup Attacks in Vehicular Sensor Networks

In this paper, we focus on the communication security perspective for connected vehicles that can be categorized into: V2V (Vehicle to Vehicle), V2I (Vehicle to Infrastructure - such as with road side units or a cloud) and the internal vehicle network connections. V2V and V2I improve driving safety and comfort through the dissemination of critical warning messages. We propose a scheme resistant to ephemeral key leakage attacks that imposes pre-computation threat with respect to the static secret key. In particular, the pseudorandom generators that are implemented into the hardware are non-verifiable with respect to sustaining the true randomness while choosing ephemeral keys. We present a formal security model in which the adversary has power to inject ephemeral values of her choice to the protocol and still cannot deduce the static secret keys.
Topic sentiment analysis using words embeddings dependency in edge social system

The topic sentiment analysis is really fundamental in detecting potential cyber threats and cyber attacks in edge social systems. We can detect potential cyber threats and cyber attacks by identifying the sentiment orientation and topics of public opinion information in the edge social system. Topic sentiment joint model is an extended model, which aims to deal with the problem of detecting sentiments and topics simultaneously from the online comment. Most existing topic sentiment joint models ignore the dependency among words so that they lose rich semantic information and the resulting distribution might be not very satisfactory. In this paper, we propose a novel topic sentiment joint model with word embeddings dependency based on recurrent neural network. The model introduces the dependency among word embedding and delivers topic information and sentiment information of words by a recurrent neural network. It fully extends the semantic information and redefines the topic sentiment-word distribution. Moreover, we obtain more accurate topic detection and sentiment analysis. Experimental results on online review data set show that the proposed model significantly improves the sentiment classification accuracy and achieved better topic detection compared with previous methods.
CLUB COMMUNICATION AS THE BASIS FOR SOCIAL AND CULTURAL ACTIVITIES

Purpose of the Study: The article considers the phenomenon of club communication and possibilities of realizing its pedagogical potential by means of social and cultural activities.&#x0D; Methodology: The differentiated analysis of various directions of club communication in the aspect of improving the performance of cultural and art institutions has been carried out. Club communication in this regard is considered as an alternative to the dominance of formalized and regulated communications, blocking the development of an individual as a creative subject.&#x0D; Main Findings: The main characteristics of the organization of club communication processes for solving various psychological problems are identified. Various types of club communication are offered that form the basis of socio-cultural activities.&#x0D; Originality: The problem areas for further improvement of socio-cultural activities using the pedagogical content of created situations of interpersonal communication are identified.
RSA Cryptographic Algorithm using Cubic Congruential Generator

Abstract In the field of cryptography, the key and procedures for converting messages into secret messages have a very important role. The RSA key is built with a random number generator. The RSA key is made as strong as possible to increase ciphertext robustness. A key that is difficult to crack and requires a lot of time certainly has higher robustness. Two initial variables that are very important in building RSA keys are prime number p and prime number q. In this research, the p and q prime numbers are generated by Cubic Congruential Generator algorithm. The results of this study can be seen by testing the ciphertext robustness. Ciphertext test results that are resistant to cryptanalytic attacks certainly show the strength of the key that is built, including the selection of primes p and primes q. The ciphertext test shows that 90% of the greater primes p and primes q will achieve a higher percentage of ciphertext robustness, and 10 % of the combination from small prime number p with large prime number q is also able to achieve the highest percentage of ciphertext robustness. This proves that the selection of prime number p and prime number q in generating RSA keys really can affect the ciphertext robustness.
Recommendations for Biometric Access Control System Deployment in a Vehicle Context in South Africa

Motor vehicles are valuable assets to us in modern life. Those of us that are fortunate enough to own one, have the ability to quickly obtain transport to wherever we need to be. Since these are such prized possessions, they are also in high demand with criminals who would break into vehicles to steal either the vehicle contents or the vehicles themselves. High rates of vehicle break-ins and theft show that vehicles remain vulnerable in terms of access control. This purpose of this paper is to find out whether there is something we can do to improve the security of vehicle access control. We look at existing access control implementations and discuss the results of the survey we distributed in order to get high-level respondent preferences and opinions for token and biometric-based vehicle access control. Among our results, we find that roughly a third of our respondents have experienced vehicle-related crime, and that there is a marked respondent preference for fingerprint biometrics in improving the security of access control to their vehicles. Based on our findings, we provide recommendations for a token and biometric authentication approach for improving access control in vehicles.
Convolutional neural network based face recognition approach

Face recognition is the process of assignment of correct label to the face under consideration. Process of face recognition comprises of extraction of features from underlying face and feeding the extracted features to classifier to identify the corresponding individual. The Accuracy of classifier is greatly affected by the nature of features extracted. Conventional face recognition system relies on manually engineered, handcrafted features. Convolutional neural network is a deep learning model which automatically extract features from the raw data in the process of end to end classification. The automated feature extraction property of convolutional neural network not only saves the effort in manually extracting features but also solve the dilemma of set of features to be used for classification. In this work, we present a face recognition system based on convolutional neural network. We create our own dataset to test the efficiency of the proposed system. The accuracy of around 96% is achieved on the test dataset consisting of around 1900 images with 10 different classes.
Implementation of Web-Based Weighted Product Use Decision Support System to Determine the Post-Disaster Damage and Loss

Abstract Determination of damage and losses after natural disasters is carried out to determine the type of damage and the amount of loss after natural disasters that must be borne by the government. In order for the type of damage and the magnitude of the loss after natural disasters according to the data in the field, a study is carried out that implements the Decision Support System (DSS) with the Weighted Product (WP) method. The results obtained are tests of pattern data which are calculated using Weighted Product (WP) compared to 3 different test data, namely using damage and loss data after the natural disaster in East Java in 2010, 2011. The results of the 3 test data used the first amount of 373 data, which produces precision 56.50%, recall 50.50%, accuracy 53.30%, and f-measure 39.10%. For the second data using the data 77, which produces precision 52.80%, recall 36.30%, accuracy 43.02%, and f-measure 36.30%. And for the third data using data from data 24, which produces 50% precision, 50% recall, 50% accuracy, and 50% f-measure. From the 3 types of test data used can be concluded that the Weighted Product (WP) method has a high level of precision, f-measure, recall, and accuracy if the amount of data used is increasing.
Competing eBook acquisition models: which model best serves a community college library?

Purpose Academic libraries continue to invest in eBooks to ensure access to content in various formats. This paper aims to examine eBook acquisition models, including patron-driven acquisitions, one-time purchases, focused collection subscriptions or large-scale subscriptions, to better understand how users engage with this content based on usage data. Design/methodology/approach Usage data provide insights into eBook acquisition and how access models influence use. This study defines the acquisition model for each eBook purchase. Data were examined to determine usage by acquisition model and cost-per-use. Findings This paper finds that for a large suburban community college, a large-scale subscription model has the lowest cost-per-use and serves the largest portion of students. Focused collection subscriptions supported small, specialized programs in the Allied Health, Emergency Services, and Nursing fields. Originality/value This paper examines how eBooks are acquired to determine which model best serves an academic library community, specifically a community college library, which is currently underrepresented in the literature.
Potential Applications of Smart Contract Technology in Corporate Business Processes

There is more to blockchain technology than just cryptocurrencies – its more interesting and promising applications include powering so-called “smart contracts”. Smart contracts are special programs running on the blockchain that define relations (usually monetary) between parties and enforce them without needing to involve a third party. However, designing smart contracts can be difficult due to a dearth of experienced specialists and tools that enable their rapid development. In this paper, a prototype of one such tool is proposed and described – a tool designed to enable the development of smart contracts by non-experts in the field.
NIST Randomness Tests on the Extended Key of Quantum Noise Random Stream Cipher

This paper focuses on the key expansion of quantum noise random stream cipher (QNRC). We performed NIST randomness tests on the extended key. Also, a new Hash expansion algorithm with higher efficiency in counter mode was proposed. Test results showed that the extended key via advanced encryption standard (AES) and Hash both in counter mode can satisfy the randomness requirement. It conformed to the premise of using the key safely, while realizing efficient expansion.
Mobile Gaming for Medical Students and Learners

Libraries and medical libraries should be aware of the different delivery methods of medical knowledge and information. In today’s gaming society, even toddlers are adept at playing games on mobile devices and often have great fun learning from the educational games provided by such juggernauts as PBS and Toka Boka. Having fun while learning shouldnot be limited to children, anyone with a mobile device and a desire to learn something new could benefit from discovering the best available medical gaming and reference apps.
A Data Management Scheme for Micro-Level Modular Computation-Intensive Programs in Big Data Platforms

Big Data analytics or systems developed with parallel distributed processing frameworks (e.g., Hadoop and Spark) are becoming popular for finding important insights from a huge amount of heterogeneous data (e.g., image, text, and sensor data). These systems offer a wide range of tools and connect them to form workflows for processing Big Data. Independent schemes from different studies for managing programs and data of workflows have been already proposed by many researchers and most of the systems have been presented with data or metadata management. However, to the best of our knowledge, no study particularly discusses the performance implications of utilizing intermediate states of data and programs generated at various execution steps of a workflow in distributed platforms. In order to address the shortcomings, we propose a scheme of Big Data management for micro-level modular computation-intensive programs in a Spark and Hadoop-based platform. In this paper, we investigate whether management of the intermediate states can speed up the execution of an image processing pipeline consisting of various image processing tools/APIs in Hadoop Distributed File System (HDFS) while ensuring appropriate reusability and error monitoring. From our experiments, we obtained prominent results, e.g., we have reported that with the intermediate data management, we can gain up to 87% computation time for an image processing job.
A fast and accurate iterative method for the camera pose estimation problem

This paper presents a fast and accurate iterative method for camera pose estimation problem. The dependence on initial values is reduced by replacing unknown angular parameters with three independent non-angular parameters. Image point coordinates are treated as observations with errors and a new model is built using a conditional adjustment with parameters for relative orientation. This model allows for the estimation of the errors in the observations. The estimated observation errors are then used iteratively to detect and eliminate gross errors in the adjustment. A total of 22 synthetic datasets and 10 real datasets are used to compare the proposed method with the traditional iterative method, the 5-point-RANSAC and the state-of-the-art 5-point-USAC methods. Preliminary results show that our proposed method is not only faster than the other methods, but also more accurate and stable.
Description of non-Gaussian Random Processes, Signals and Noise Using the Statistical Linearization Method

Using stochastic differential equations to describe non-Gaussian signals and noise makes it unnecessary to determine multidimensional probability density function (PDF) of random processes. The paper describes the use of statistical linearization method to describe non-Gaussian random processes, signals and noise. In this case, if a large number of terms of the sequence are used, an acceptable error of the description can be obtained. The method involves finding the best probability approximation of an instantaneous non-linear transformation by a linear dependence. Here, the initial and approximating functions should have similar mathematical expectation and correlation functions. It is noted that there are two ways to determine the parameters of a statistically linearized dependence; one of them allows analyzing vector random processes. Connection between the cumulant method and statistical linearization is analyzed. It is shown that the method of statistical linearization is a particular case of the method of semiinvariants (cumulants). The dependences of PDFs of the steady-state non-Gaussian random process are obtained based on the criteria for determining parameters of statistical linearization. These PDFs emphasize advantages of approximation methods considered in the article.
On the Hermite Series-Based Generalized Regression Neural Networks for Stream Data Mining

In the paper, we develop the mathematically justified stream data mining algorithm for solving regression problems. The algorithm is based on the Hermite expansions of drifting regression functions. The global convergence, in the $$L_2$$ space, is proved both in probability and with probability one. The examples of several concept drifts to be handled by our algorithm, and the illustrative simulations are presented.
Developers' Eyes on the Changes of Apps: An Exploratory Study on App Changelogs

Release planning for mobile apps has only recently become an area of active research.As a result, little is known about the types of requirements that app developers pay the most attention to when releasing an app.This research uses the changelogs of apps to shed light on this.We report the results of an exploratory study in which we analyzed the requirements that dominate the changes of apps, according to a set of 3000 changelogs collected from 120 apps from three categories in the Apple App Store: Travel, Social networking, and Books.We analyzed the changelogs in terms of functional and non-functional requirements, from a developers' perspective.Our results suggest that developers' releases are by far more concerned with non-functional requirements than with functional requirements.We also found that usability and maintainability are the most frequently mentioned non-functional requirements (NFRs) in the changelogs.Surprisingly, reliability requirements formed only a fraction of the total number of NFRs addressed in all changelogs of apps in the three selected App Store categories.
Mining Undergraduate Students’ Code Repositories: Insights from Interdisciplinary Software Projects

Due to its multidisciplinary and dynamic nature, it is challenging to design Software Engineering (SE) educational material. To do so, universities must consider the complex working environments; recent technologies; and tools and skills, in order to prepare students to fulfill the expectations of the software industry. This study was carried out in a Brazilian private university, specifically in courses called Interdisciplinary Software Project (ISP) of the SE major. These courses are project-based, conducted by two professors at the same time in classroom, following a Scrum-like process. The objective of this study was to characterize how students work and collaborate in a group environment where agile development is used as well as how their behavior reflect on the use of Software Configuration Management (SCM) practices. In order to achieve this objective, the study analyzed 38 students' code repositories. This study has found out that students procrastinate sprint work, since 51% of the commits are performed when less than 20% of the sprint time is left. We have also observed that in 87% of the groups just one member is a top contributor and would harm the projects' outcomes if he/she left the project. In terms of SCM practices, we have identified that most commits comprised changes in up to three files. Moreover, most commit messages are less than 10 words long. This could mean that students do not commit many alterations at the same time but could make better use of messages in order to facilitate the comprehension of alterations by other members.