Reviewerly/exHarmony huggingface dataset:
Index of suicide risk in Mexico using Twitter

Objectives: To identify early suicide risk signs on depressive subjects, so that specialized care can be provided. Various studies have focused on studying expressions on social networks, where users pour their emotions, to determine if they show signs of depression or not. However, they have neglected the quantification of the risk of committing suicide. Therefore, this article proposes a new index for identifying suicide risk in Mexico. Methodology: The proposal index is constructed through opinion mining using Twitter and the Analytic Hierarchy Process. Contribution: Using R statistical package, a study is presented considering real data, making a classification of people according to the obtained index and using information from psychologists. The proposed methodology represents an innovative prevention alternative for suicide.
Personalized Movie Recommendation System Combining Data Mining with the k-Clique Method

Today, most approaches used in the recommendation system provide correct data prediction similar to the data that users need. The method that researchers are paying attention and apply as a model in the recommendation system is the communities’ detection in the big social network. The outputted result of this approach is effective in improving the exactness. Therefore, in this paper, the personalized movie recommendation system that combines data mining for the k-clique method is proposed as the best exactness data to the users. The proposed approach was compared with the existing approaches like k-clique, collaborative filtering, and collaborative filtering using k-nearest neighbor. The outputted result guarantees that the proposed method gives significant exactness data compared to the existing approach. In the experiment, the MovieLens data were used as practice and test data.
Implementation of Naïve Bayes updateable with modified absolute discount smoothing on Pamekasan Regent SMS center data classification

Abstract Classification is a grouping process based on a predetermined class. Previous research has classified Regent Pamekasan SMS Center using Naïve Bayes and Modified Absolute Discounting (MAD) Smoothing, but the average classification accuracy is still equal to 76.83%. to improve the accuracy of classification then in this study applied Naïve Bayes Updateable by using MAD Smoothing. The classes used remain 15 classes: Education, Health, Infrastructure, Crime, Administrative Services, Sports, Government, Agriculture, Small and Medium Enterprises, Order, Weak Economy, Religion, Art and Culture, Natural Disasters, and Others. Before doing the classification process first done pre-processing such as equating characters, deletion of punctuation, restore abbreviation, translation of the local language (Madura), deletion of numbers, deletion of words that are not important in SMS, and stemming to convert into a basic word. Results Some experiments obtained an average accuracy of 78.89%, with the accuracy of one test reached 87.65%. And Naïve Bayes Updateable can increase accuracy by 2.07% with the addition of 0.47-minute classification time.
White-Box Implementation of the KMAC Message Authentication Code

In 2016, US NIST released the KMAC message authentication code, which is actually a keyed variant of the new-generation hash function standard SHA-3. Following the increasing use of SHA-3, it is highly anticipated that KMAC will also be increasingly widely used in various security applications. Due to the distinctions between sponge hash functions and Merkle-Damgård hash functions, white-box implementations of KMAC and HMAC are rather different. In this paper, we present an efficient white-box implementation of KMAC with strong resistance against both key extraction and code lifting attacks, which can still work with an updated user key. It has a storage complexity of about 107.7 MB, and has a running time of about 1.5 ms on a DELL Precision T5610 workstation, about 375 times slower than the original KMAC implementation without white-box protection. There are implementation variants with different trade-offs between security and performance. This is the first published white-box implementation of KMAC to the best of our knowledge, and our implementation methods can be applied to similar sponge constructions.
Disablist hate speech online

This chapter discusses the nature of disablist hate speech over the internet, using examples of some of the most common words that are used as slurs against disabled people. It outlines some of the most common forms of disablist hate speech: trolling, flaming, RIP trolling, cyberbullying, cyberstalking, swatting, online impersonation, doxxing, online sexual solicitation, and the use of graphic images as weapons against people who have photosensitive epilepsy. The chapter highlights the case of Sophia Weaver and her mother Natalie, who have been relentlessly trolled by people with online disablist hate. It then examines the options open to individuals and groups who wish to respond to hate speech online. The chapter also focuses on four major social media companies which have adopted policies against hate speech: Facebook, Twitter, YouTube, and Instagram, and then it discusses the rise of “hate speech havens” – areas of the internet such as Gab and Reddit where hate speech flows unchecked.
Penerapan ECS Stemmer untuk Modifikasi Nazief &amp; Adriani Berbahasa Jawa

Stemming Javanese affix words using Nazief &amp; Adriani modifications still has problems that cannot be solved such as overstemming, understemming, and unchange. Then there needs to be improvements to improve the performance of Nazief &amp; Adriani modifications. This study aims to improve the performance of Nazief &amp; Adriani modifications using the Enahnced Confix Stripping (ECS) modification method. The results of this study indicate that Enhanced Confix Stripping can improve performance that previously had an accuracy of only 78.2% to 97.9% with an error rate of 2.1%. And fixing errors that originally numbered 98 to 9 errors. But Enhanced Confix Stripping still has problems with the words "ngetan, kumanggah, kumarut, kumasis, kumareg, kumadul, kumaras, katawakake, and pangenan". The next research is expected to be able to solve this problem.
Effects of electromagnetic radiation on the energy state of water molecules

The impact of electromagnetic fields of both natural and man-made origin in public places and at work due to the growing tendency to use electronic devices can have an adverse effect on human health. This article proposes a possible mechanism for the action of electromagnetic radiation of low power on a living organism through a liquid medium.
Generating Phishing Emails Using Graph Database

We need Phishing Awareness Tools to train employees because existing anti-phishing filters are not 100% capable of detecting phishing attacks, especially zero-day attacks. Current awareness tools can make phishing campaigns targeting the employees, but they contain an only limited number of predefined email templates. In this work, we designed a framework and built a tool generating new phishing emails automatically from a graph database perspective. Then, we conducted a three-round experiment. We sent the automatically-generated emails to some uninformed members of our community. On average, 72.85% of victims opened the emails, the click-through rate was 54.05% among who opened the emails, and all recipients who completed the survey stated that the content of emails was meaningful. In this experiment, we also showed which parts of the email are more luring and what the result might be if emails are carefully-crafted or from a person of authority.
Link Weight Prediction for Directed WSN Using Features from Network and Its Dual

Link prediction problem in social networks is a very popular problem that has been addressed as an unsupervised as well as supervised classification problem. Recently a related problem called link weight prediction problem has been proposed. Link weight prediction on Weighted Signed Networks (WSNs) holds great significance as these are semantically meaningful networks. We consider two groups of features from the literature - edge-to-vertex dual graph features and fairness-goodness scores in order to propose a supervised framework for weight prediction that uses fewer features than those used in the literature. Experimentation has been done using three different feature sets and on three real world weighted signed networks. Rigorous assessment of performance using (i) Leave-one-out cross validation and (ii) N% edge removal methods has been carried out. We show that the performance of Gradient Boosted Decision Tree (GBDT) regression model is superior to the results presented in the literature. Further the model is able to achieve superior weight prediction scores with significantly lower number of features.
Practical deep learning ANN demonstrations

Several practical DL demonstrations are shown in this chapter. You will be prepared to follow along with the demonstrations provided you have read the previous chapter or have acquired previous experience with DL techniques and concepts. I had two goals in mind when writing this chapter. The first was to clearly show how a complete ANN project could be accomplished to produce realistic and useful results. The second was to point out some potential pitfalls and unrealistic assumptions that are common in ANN development.
Off-road Terrain Path Planning for Differential Steering Vehicles Based on Artificial Potential Field Gradient

Motion planning of unmanned tracked vehicle (UTV) involves not only highly nonlinear vehicle dynamic constraints, but also challenges from complex rough off-road terrain with dynamic and static obstacles. In view of the UTV motion planning, this paper systematically consider vehicle kinematic model, off-road terrain and obstacle factors, by introducing different types of potential energy function models corresponding to two kinds of obstacles and ground. Regarding potential field gradient as a condition of optimization, a multi-stage optimization Channel Path Planning (CPP) is designed to avoid the impracticable path formulated by Linear Path Planning (LPP), and converted to nonlinear programming problem through the pseudo-spectral method. By means of multi-stage optimization strategy, the computation of the original optimization algorithm can be reduced while ensuring global optimal approximate solution. Simulation experiments show that this method can catch feasible paths effectively in the process of path planning even with respect to vehicle dynamic constraints, obstacle and off-road terrain.
ICTs and Geometry

The purpose of this paper is to present researches on how to teach students Geometry through ICTs methods from 2014 to 2019. The articles were searched through Google Scholar. At first, the focus of the article is on the teaching of geometry with software available on the Internet for PCs. Sec-ondly, educational games for geometry are presented that a student can in-stall on any device, such as a smartphone or tablet and are available to the user at any time. Almost all of the described researches have tested the ef-fectiveness of ICTs teaching methods for geometry in relation to classical teaching methods such as paper-and-pencil. Based on findings, the teaching of geometry with ICTs had better educational results than the paper-and-pencil method of teaching.
Data Mining Solution for Assessing the Secondary School Students of Brazilian Federal Institutes

There have been constant debates on several aspects of the Brazilian secondary education including student's selection process and engagement, and quality of learning among other issues. The Federal Institutes (IFs) have played an important role on increasing the offer of integrated technical education to secondary education. Thus, their managers need tools to help in decision-making and evaluation of their services. This paper presents a data mining solution developed with Domain-Driven Data Mining (D3M) for predicting the performance of high school students of IFs and explaining the factors and niches which influence that. Logistic regression, Decision trees and Classification rule induction are the techniques applied for knowledge extraction and validation.
HEAVY METALS IN TOPSOILS OF THE REPUBLIC OF TATARSTAN, RUSSIA: MULTIVARIATE ANALYSIS AND POLLUTION EVALUATION

Background.The study is conducted to investigate the content and spatial distribution of Fe and eight heavy metals (Cd, Co, Cr, Cu, Mn, Ni, Pb, and Zn) in soils of the industrially developed region.Materials and methods.A total of 1,170 soil samples of different land use (natural, agricultural, urban) were collected from topsoils in the territory of the Republic of Tatarstan, Russia.Heavy metals concentrations in soil samples were determined using atomic absorption spectrometry after 5M HNO 3 extraction.Multivariate and geostatistical analyses were used to investigate the current state of soil heavy metal contamination and to identify spatial patterns and possible sources of heavy metals on the regional scale.Results.Zonal soil types of a natural land use were used to assess the regional background values of the heavy metals: Cd -0.44±0.24mg/kg, Co -10.4±3.6 mg/kg, Cr -23.3±12.7 mg/kg, Cu -16.4±7.8 mg/kg, Fe -15275.4±5178.3mg/kg, Mn -652.4±228.4mg/kg, Ni -29.8±18.8mg/kg, Pb -11.5±3.2 mg/kg, Zn -43.3±12.8mg/kg.The results of the pollution evaluation showed the absence of regionalscale contamination directly related to agriculture.Urban soils were contaminated by Cu, Pb and Zn.Geostatistical analysis revealed several patterns of regional distribution of heavy metals and suggested an anthropogenic impact to the Cu, Pb and Zn distribution.Principal component analysis allowed distinguishing three regional geochemical groups of heavy metals and showed that at the regional scale the distribution of Cu, Mn and Ni is controlled by the element richness in soil parent material, overlayed by the soil forming factors; the distribution of the Co, Cr and Fe is controlled mainly by lithology; and the distribution of the Pb, Zn and Cd is strongly influenced by the anthropogenic sources.Conclusion.This case study demonstrates that a combination of multivariate statistics and geostatistical analysis together with the pollution assessment allows comprehensive characterizing heavy metals spatial distribution and determining their sources.
Geometric Pólya-Aeppli process

In this paper, motivated by the risk process, we introduce and study a new point process called geometric Pólya-Aeppli process (GPAP), with underlying exponential distribution. We give two equivalent definitions of the process and discuss some of its properties, such as the distribution of the number of GPAP events up to time t, the distribution of the waiting times, etc. The new process is an extension of the well-known Pólya-Aeppli process, as well as the standard geometric process with underlying exponential distribution.
Techniques used to formulate confidential data by means of fragmentation and hybrid encryption

Cloud computing is a concept shifting in the approach how computing resources are deployed and purchased. Even though the cloud has a capable, elastic, and consistent design, several security concerns restrain customers to completely accept this novel technology and move from traditional computing to cloud computing. In the article, we aspire to present a form of a novel architectural model for offering protection to numerous cloud service providers with the intention to devise and extend security means for cloud computing. In this work, we presented a two-tier architecture for security in multi-clouds; one at the client side, and other at the server side. The article presented a security domination outline for multi-clouds and supports security needs like Confidentiality, Integrity, Availability, Authorization, and Non-repudiation for cloud storage. Through this document we have anticipated, HBDaSeC, a secure-computation protocol to ease the challenges of enforcing the protection of data for information security in the cloud.
Speeding Up Revocable Group Signature with Compact Revocation List Using Vector Commitments

In ID-based user authentications, a privacy problem can occur, since the service provider (SP) can accumulate the user's use history from the user ID. As a solution to that problem, group signatures are researched. One of important issues in the group signatures is the user revocation. Previously, an efficient revocable scheme with signing/verification of constant complexity was proposed. In this scheme, users are managed by a binary tree, and a list of revoked user information, called a revocation list (RL), is used for revocation. However, the scheme suffers from the large RL. Recently, an extended scheme has been proposed, where the RL size is reduced by compressing RL. On the other hand, there is a problem that some overhead occurs in the authentication as a price for reducing the size of RL. In this research, we propose an extended scheme where the authentication is sped up by reducing the number of zero-knowledge proofs. Furthermore, we implemented it on a PC and shows the effectiveness. The verification time is about 30% shorter than the previous scheme.
Graph-Based Interpretation of L-System Encodings Toward Aeroelastic Topology Optimization of a Morphing Airfoil in Supersonic Flow

Abstract This work details the preliminary design of a morphing airfoil in supersonic flow using evolutionary design principles. The structural topology of the airfoil includes a fixed outer mold line, fixed spars, and designable internal stiffeners and actuators. The designable components are generated using a bio-inspired model known as a Lindenmayer System (L-System), which encodes design variables and governs the development of a structural topology when coupled with an interpretation algorithm. Here, we utilize a graph-based interpretation scheme known as Spatial Interpretation for the Development of Reconfigurable Structures (SPIDRS), which has been shown to effectively explore the mechanism design space using a limited number of design variables. The optimization process behind this preliminary design problem is discussed, and optimal airfoil topologies capable of meeting specified aerodynamic performance criteria are presented in hopes of gaining a better understanding of how actuation systems could be integrated into the next generation of aircraft.
CodeSonar (R) Extension for Copy-Paste-(Mis) Adapt Error Detection

Copy-paste programming is risky. One danger is that it may introduce hard-to-spot bugs in programs due to incomplete or inconsistent adaptation of the original code in the copy. In this tool demo paper we present an extension of the CodeSonar (R) static analysis tool that detects this kind of problems in C/C++ systems. To tune its accuracy, the tool was equipped with an extensible, rich set of novel filtering heuristics. Using it, we have identified several bugs in multiple open-source projects, errors that were confirmed by the maintainers.
High-Performance Light Field Reconstruction with Channel-wise and SAI-wise Attention

Light field (LF) images provide rich information and are suitable for high-level computer vision applications. To acquire capabilities of modeling the correlated information of LF, most of the previous methods have to stack several convolutional layers to improve the feature representation and result in heavy computation and large model sizes. In this paper, we propose channel-wise and SAI-wise attention modules to enhance the feature representation at a low cost. The channel-wise attention module helps to focus on important channels while the SAI-wise attention module guides the network to pay more attention to informative SAIs. The experimental results demonstrate that the baseline network can achieve better performance with the aid of the attention modules.
Exploring Motor and Sensory Devices in a Bicycle Simulator

This study presents the SimBike (Bicycle Simulator with Non-conventional Devices), a virtual reality simulator that uses motor and sensory devices to provide greater user involvement and comfort. The SimBike seeks to recreate the activity of riding a bicycle by taking full advantage of the efforts made in its execution, such as making curves with the handlebar or body weight, pedaling, braking, among others. It offers feedbacks commonly expected by the rider, such as the wind sensation depending on speed and the sensation of trepidation on uneven terrain. We evaluated the simulator with 16 users, obtaining satisfactory results regarding comfort, immersion, usability, and user experience. The results also suggest improvements to make the simulator suitable for all types of users, regardless of their characteristics such as weight and height, and also propose future studies.
The Research on Task Scheduling Algorithm for the Cloud Management Platform of Mimic Common Operating Environment

Scheduling algorithm is an important step in cloud computing, which determines the effectiveness of the system. Focus on the business requirement of mimic common operating environment (MCOE), especially the incomplete consideration of load balancing and heterogeneous in traditional scheduling algorithms, this paper presents an entropy weight clustering scheduling (EWCS) algorithm, which combines the dynamic heterogeneous redundancy (DHR) architecture of mimetic defense theory and K-Means clustering of machine learning to complete the nodes selection on the cloud platform. This algorithm consists of four steps: risk value screening, load balancing, entropy weight calculation and clustering optimization. The simulation results show that the algorithm is reasonable and can serve MCOE well. It is also an effective attempt to apply machine learning method to scheduling problem.
An Innovative Queue Management System Based on Bluetooth Beacon Technology

Most of the existing queue management systems are based on printed paper tickets with a queue number. When a specific customer is called, his/her queue number is shown on a screen accompanied by an acoustic signal. We observed the trend that nowadays most people spend their waiting time using smartphones, which distracts them from noticing the call. In this research, we take advantage of the fact that people pay more attention to their smartphones to design an innovative queue management system which announces the call for a specific customer directly to his/her smartphone by using the Bluetooth beacon technology. Our innovation lies in that beacon devices need not advertise fixed identifiers all the time - we allow a single beacon device to change its advertised identifier dynamically to represent different customers. The paperless queue management system not only protects the environment but also sets customers' mind at ease of using smartphones during their waiting time.