Reviewerly/exHarmony huggingface dataset:
Untampered Electronic Voting in Entertainment Industry

Digital services are gaining new meaning with the invention of blockchain technology. While blockchain still has a long way to go, it has achieved great expertise in the technical and legal aspect of the digital services. Digital services work well in terms of functionality without the involvement of blockchain, but there is always a risk of privacy and security, which mostly remains unanswered. Technologies leveraged through the blockchain system are efficient as well as maintain the secure and the anonymity of all the members involved. The chance of having a fair and transparent voting system increases with the involvement of blockchain-based electronic voting services. In this paper, we are leveraging the power of blockchain to implement a new electronic voting system to cast, transport, and count votes for singing competitions held on national televisions.
Bringing big sound to small devices: One module revolutionized how we listen - [Resources_Tools]

It's not often that one circuit revolutionizes a huge product category, but the Philips Universal class-D (UcD) module did just that for audio amplifiers. Before the UcD, there had been a few quirky and short-lived commercial class-D amps, which differed from previous amplifiers by using an intermediate stage in which transistors are rapidly switched on and off, versus directly trying to amplify a signal linearly.
Output Forms for Calculation Results in the Computing &amp; Geo-Information System

The paper discusses about special Computing and Geo-Information System with Object-Oriented Energy Database.The System is used to carries out the research of feasibility and prospects of Interstate Power Interconnection expansion.For this purpose, the mathematical model for Optimization of Power Systems Expansion and Operating Modes, which is the main part of the System, is used.An interface for working with the Model is developed.The output forms for calculation results by the Model in tabular, graphic and cartographic forms are presented.
The Challenges of Cloud Computing in Forensic Science

Cloud computing (CC) is rapidly growing new information technology (IT) in private, public and especially in Government sectors internationally. CC technology facilitates rather than deploy and manage an in-house physical IT infrastructure by having local servers or personal devices to manage their softwares application. This new technology helps to transfer their traditional IT services model into remote, virtualised environments, which are often hosted and managed by the third parties. Therefore, CC environment of any organisation turn prospective opportunity for cyber attackers, which is become a primary challenges of CC in the protection of valuable data from different types of attacks. CC posed a serious risk and major challenge to digital investigators, also offers sufficient opportunities to investigators for better refining the forensic science. This study summarises the key areas of CC forensics science challenges and analyses by the researched performed by other researchers. The challenges also presented along with associated literature that particularly reference them. Finally the discussion and analysis, which is based on the study finding to consider the challenges provoke by the CC forensics science on our findings.
Texture Analysis using GLCM & GLRLM Feature Extraction Methods

Textures are one of the most important features in computer vision for many applications. Texture Feature Extraction is a method of capturing visual content of images for indexing & retrieval. General features such as extraction of color, texture and shape or domain specific features. GLCM to extract statistical texture features such as Contrast, Correlation, Energy, Entropy and Homogeneity. GLRLM to extract run length features such as SRE, LRE, GLN, RLN, LGRE and HGRE. Constructing combinations of the various extraction methods (GLCM & GLRLM) to get the data with sufficient accuracy. The experimental results demonstrated that texture feature extraction based on the KNN technique achieved a better image recognition, and the accuracy of classification based on this method has been significantly improved and that it requires less computation time efficiently used for real time Object Tracking Applications.
Fast 3D Surface Measurement with Wrapped Phase and Pseudorandom Image

Balancing the accuracy and speed of three-dimensional (3D) surface measurement of objects is crucial in many important applications. In this paper, we present a wrapped phase and pseudorandom image method and develop an experimental system aiming to avoid the process of phase unwrapping. Our approach can reduce the length of image sequences and improve the speed of pattern projection and image acquisition and can be used as a good candidate for high-speed 3D measurement. The most critical step in our new methodology is using the wrapped phase and the epipolar constraint between one camera and a projector, which can obtain several candidate 3D points within the measurement volume (MV). The false points from the obtained candidate 3D points can be eliminated by the pseudorandom images. A systematic accuracy with MV better than 0.01 mm is achievable. 3D human body measurement results are given to confirm the fast speed of image acquisition capability.
Traffic model using a novel sniffer that ensures the user data privacy

Nowadays, the traffic over the networks is changing because of new protocols, devices and applications. Therefore, it is necessary to analyze the impact over services and resources. Traffic Classification of network is a very important prerequisite for tasks such as traffic engineering and provisioning quality of service. In this paper, we analyze the variable packet size of the traffic in an university campus network through the collected data using a novel sniffer that ensures the user data privacy. We separate the collected data by type of traffic, protocols and applications. Finally, we estimate the traffic model that represents this traffic by means of a Poisson process and compute its associated numerical parameters.
Automatic Monitoring Method for Surface Deformation of Coastal Area Based on Time Series Analysis

Liu, L.; Wang, C.; Zhang, H.; Tang, Y., and Zhang, Z., 2019. Automatic monitoring method for surface deformation of coastal area based on time series analysis. In: Guido-Aldana, P.A. and Mulahasan, S. (eds.), Advances in Water Resources and Exploration. Journal of Coastal Research, Special Issue No. 93, pp. 194–199. Coconut Creek (Florida), ISSN 0749-0208.The surface deformation monitoring in coastal areas is affected by the uncertainty of flow control, which leads to the low accuracy of surface deformation monitoring. In order to improve the accuracy of surface deformation monitoring in coastal areas, an automatic monitoring method of surface deforma tion in coastal areas based on time series analysis is proposed. The big data structure model of surface deformation in coastal areas is constructed, and the distributed information reconstruction of surface deformation in coastal areas is carried out by using phase space reconstruction and correlation coefficient mining methods. Big data fusion cluster analysis of surface deformation in coastal areas is carried out combined with time series analysis method. The dynamic distribution characteristics of surface deformation big data in coastal areas are extracted, and the clustering and information fusion results of surface deformation characteristics in coastal areas are obtained. The automatic monitoring of surface deformation dynamics in coastal areas is realized. The simulation results show that the accuracy of automatic monitoring of surface deformation in coastal areas is high, and the average confidence level of monitoring is better than that of traditional methods, which improves the adaptability of surface deformation monitoring in coastal areas.
Are We Ready for a VR Classroom?

With the advance of head-mounted virtual reality technology and their lower cost, it is foreseeable that an online course can be delivered in a virtual reality (VR) classroom. This paper introduces head-mounted VR technology, discusses the current designs of VR applications, and depicts a vision of future VR classrooms.
Evaluation and Comparison of Text Classifiers to Develop a Depression Detection Service

Depression is a mental disorder which can become a serious health problem. This research line is focused on creating a depression detection service from text analysis. Sentiment Analysis and Natural Language Processing methods will be used to develop this service. The service will classify text in positive or negative depending on the emotions inferred from user’s input texts. In this study, five classifiers have been evaluated to determine which one fits better for this purpose. Naïve Bayes, Decision Tree, Naïve Bayes based on Bernoulli model and Maximum Entropy are the classifiers analyzed. A specific corpus which includes sentences that fit research needs was designed for testing purposes, whereas Sentiment140’s corpus was used with training purposes. An evaluation methodology formed by three tests was designed. Results show a promising starting point, but further analysis will be needed. Future related works will focus on expanding classification to get user’s mood and not only a binary classification. It will allow to include this service as input for developing personalized intervention and education mHealth systems.
Exploring the Dimensions of Multicultural Education and its Implication in Teaching Learning

This article aims to present the concept and needs of multicultural education in Nepal. This paper is based on extensive review of literature on multicultural education in Nepal. Multicultural education provides equal educational opportunities for all races, nations and groups of different cultures. Cultural values, ethics, norms and practices are focused on multicultural education. It helps to find out the cultural knowledge and skills in the learners and communicate with other ethnic groups. Multicultural curriculum can prepare individuals and groups with diverse backgrounds to live peacefully in a society. Multicultural education promotes the strength and value of cultural diversity. Moreover, it also ensures human rights and respect to one another and the acceptance of alternative life styles of people, and the promotion of social justice.
A new model for study of quality attributes to components based development approach

Software development costs, time-to release and quality product are important factors affecting the construction of software. Different types of tools and techniques are suggested by researchers to improve in delivering quality software systems with lower cost and reduce time to delivery. One such practice is development of software using Component Based Software Development (CBSD) techniques[1]. CBSD recommended building software systems using existing reusable components, instead of writing from scratch. The main objective of CBSD is to writes once and reuse any number of time with no or modification. Some of the advantages that a company may available by adapting CBSD for the Software development are shorter development time which results in meet tight dead line, increase productivity and Quality Product. CBSD also, support reusability[3]. The aim of this paper is to develop the new model of software product and describe the characteristics of some selected of attributes of CBSD models that are widely practiced in software industries with explain about IT project. We proposed a complete model for Component Based Software Development for reuse. This Model will cover both component based software development as well as Component development phases for A-Model. This Model is represent one good solution for Component Based Development with reduce cost and time to deliverable and save the quality of product
GELİŞMEKTE OLAN EKONOMİLERDE YABANCI PARA MEVUDATININ DEĞİŞİMİNİ ETKİLEYEN FAKTÖRLER: TÜRKİYE ÖRNEĞİ

Financial dollarization has increased substantially in&#x0D; emerging markets in recent years. A material increase in financial&#x0D; dollarization may concern policy makers but it also serves as a cushion for&#x0D; banks and the states. Increase in foreign Exchange deposits may arise from the&#x0D; need to preserve purchasing power, speculation or liquidity concerns. This&#x0D; paper aims to shed light to the relation between foreign exchange deposits and&#x0D; factors such as sight FX deposits, USD/TRY parity, CPI, confidence ındex, VIX,&#x0D; TL deposits, dollar index via a VECM model. Sight FX deposits are found to be a&#x0D; positive and a statistically significant influencing factor on FX deposits;&#x0D; whereas, confidence index and CPI have a negative but a significant relation with&#x0D; FX deposits. Factors that have an influence on the increase in foreign Exchange&#x0D; deposits shall be closely monitored to mitigate the adverse effects of it.&amp;nbsp;
Simplifying the Structural Complexity of Software Systems

Abstract Simplification of execution traces is peculiarly important in the case of software comprehension. The objective is to make execution traces in ways that are more tractable and less difficult. However, the simplification process is a difficult task, particularly, in object-oriented contexts. Due to coupling, execution traces of object-oriented systems involve the Spaghetti Architectures phenomenon, which is a very complicated structure of dependencies. Therefore, the simplification process needs a well-established approach to be helpful for software comprehension. Otherwise, the simplified execution traces will be informative as their structures will involve several gaps that lead to a misunderstanding process. This research uses decoupling to guide the simplification of object-oriented execution traces. Specifically, decoupling truthfully can decrease the complexity of execution traces without eliminating the trace components and making numerous gaps in the trace structure. Then, decoupling can solve the problem of the Spaghetti Architectures phenomenon. A controlled experiment was conducted to empirically validate the usefulness and effectivity of the suggested work. There was a significant statistical added value demonstrated in the time required and the accurate solutions of the tasks being solved. More precisely, 25% less time required with a 62% more correct solutions were achieved solving the experiment’s comprehension tasks.
Toward a Neural-Symbolic Framework for Automated Workflow Analysis in Surgery

Learning production rules from continuous data streams, e.g. surgical videos, is a challenging problem. To learn production rules, we present a novel framework consisting of deep learning models and inductive logic programming (ILP) system for learning surgical workflow entities that are needed in subsequent surgical tasks, e.g. “what kind of instruments will be needed in the next step?” As a prototypical scenario, we analyzed the Robot-Assisted Partial Nephrectomy (RAPN) workflow. To verify our framework, first consistent and complete rules were learnt from the video annotations which can classify RAPN surgical workflow and temporal sequence at high-granularity e.g. steps. After we found that RAPN workflow is hierarchical, we used combination of learned predicates, presenting workflow hierarchy, to predict the information on the next step followed by a classification of step sequences with deep learning models. The predicted rules on the RAPN workflow was verified by an expert urologist and conforms with the standard workflow of RAPN.
Study on Feature Selection in Data Mining

Feature selection is an important term in machine learning tasks as it can efficiently improve the performance of the model by eliminating the redundant and irrelevant attributes.Feature selection not only improves the quality of the model, it also makes the process of modeling more efficient.Due to irrelevant and huge dimensions data the quality of the model may degrade.This paper presents the importance of feature selection on various classification algorithms.In this study, the feature selection techniques like attribute evaluator and the best first search is used for reducing the number of features.The dimension is reduced from eight to four.The dataset used is Pima diabetic dataset from UCI repository.The substantial increase is given in terms of accuracy.
G-Match: A Fast GPU-Friendly Data Compression Algorithm

Data compression plays an important role in the era of big data; however, such compression is typically one of the bottlenecks of a massive data processing system due to intensive computing and memory access. In this paper, we propose a high-speed GPU-friendly data compression algorithm called G-match that takes full advantage of the GPU parallel computing power to speed up the compression process. The greatest challenge here is to solve the contradiction between the high data dependency inherent in the compression algorithm and the GPU single-instruction multiple-thread operating model. G-match achieves a high parallel degree by eliminating fine-grained data dependency and all path divergences in the algorithm. Compared with other, similar work on GPUs, G-match is the first thoroughly parallelized data compression algorithm. Experiments comparing other GPU compression algorithms show that G-match achieves approximately 33% speedup over the current fastest implementation and the highest compression ratio.
Robust Correlation Clustering.

In this paper, we introduce and study the Robust-Correlation-Clustering problem: given a graph G = (V,E) where every edge is either labeled + or - (denoting similar or dissimilar pairs of vertices), and a parameter m, the goal is to delete a set D of m vertices, and partition the remaining vertices V  D into clusters to minimize the cost of the clustering, which is the sum of the number of + edges with end-points in different clusters and the number of - edges with end-points in the same cluster. This generalizes the classical Correlation-Clustering problem which is the special case when m = 0. Correlation clustering is useful when we have (only) qualitative information about the similarity or dissimilarity of pairs of points, and Robust-Correlation-Clustering equips this model with the capability to handle noise in datasets.In this work, we present a constant-factor bi-criteria algorithm for Robust-Correlation-Clustering on complete graphs (where our solution is O(1)-approximate w.r.t the cost while however discarding O(1) m points as outliers), and also complement this by showing that no finite approximation is possible if we do not violate the outlier budget. Our algorithm is very simple in that it first does a simple LP-based pre-processing to delete O(m) vertices, and subsequently runs a particular Correlation-Clustering algorithm ACNAlg [Ailon et al., 2005] on the residual instance. We then consider general graphs, and show (O(log n), O(log^2 n)) bi-criteria algorithms while also showing a hardness of alpha_MC on both the cost and the outlier violation, where alpha_MC is the lower bound for the Minimum-Multicut problem.
Hesitation strategies in the Spanish Spontaneous Speech of Hungarian learners of Spanish

This paper examines what native Spanish speakers find most disturbing in the pronunciation of Hungarian language learners of Spanish.Former research (Baditzné Pálvölgyi, 2019) showed that in spontaneous Spanish speech of at least threshold level Hungarian learners, one of the aspects that Spanish native speakers least tolerated was the way Hungarians hesitated.So the present paper focuses primarily on hesitation phenomena-lengthening and filled pauses-assuming that Hungarians hesitate more, and the lengthened segments are longer than the Spanish ones.In order to validate the hypothesis, an investigation comparing a corpus of Northern Spanish spontaneous speech to another corpus of advanced Hungarian learners of Spanish was conducted.
A Neural Attack Model for Cracking Passwords in Adversarial Environments

In many scenarios, one has to enter her text or graphical password in a public area, such as unlocking the smartphone on the street, and entering the password when she pays with a debit card in a shopping mall. However, the environment where the password is entered may be adversarial as it is almost impossible to prevent adversaries from premeditated installation of surveillance and/or eavesdropping equipment in public areas. In this work, we investigate password security in such extreme adversarial environments in which every single interaction between humans (provers) and input terminals (verifiers) is transparent to the attacker. We first present a neural network-based attack model, which consists of a feature extraction model and a prediction model. Experimental results show that the neural model attains an accuracy of more than 80% in password prediction in three real-world authentication systems. We also propose a risk alert system based on the attack model. It can issue a timely warning notice when the password in use is at high security risk.
Development of a 3D Grading Method for Shoe Lasts Based on Scanned 3D Foot Data

In the shoe manufacturing sector, products are typically designed for a small number of sample sizes, whereas all other shoe sizes in the range (for example, 2 to 4 shoe sizes below and above) are scaled using grading methods.However, the grading process significantly affects the fit and thus consumer acceptance.In this context, the shoe last forms the basis for all additional components to be used, such as insoles, midsoles, outsoles and heels.The entire shaft construction is based on the last as well.It also determines the fit of shoes.However, a method once established in the 1930s and still used today for the grading of shoe lasts is limited to only two dimensions, i.e. last length and technical ball circumference.The entire last is enlarged or reduced on the basis of these dimensions.The 3D CAD systems available today still employ the principle of mechanical last copying based on machines from the 1930s.Thus, the technical potential of these CAD systems is far from exhausted.An innovative grading process allows shoe companies to better utilize and significantly expand the performance potential and technical capabilities of their 3D CAD systems.As the required amount of manual work in preparation for grading and last generation is considerably decreased, development times are shortened.
Assessing agreement of hemoglobin and three- fold conversion of hematocrit as methods for detecting anemia in children living in malaria-endemic areas of Calabar, Nigeria

One of the major causes of anemia, defined as the reduction in the level of hemoglobin or red blood cells (RBCs) in the blood, in children in sub-Saharan Africa is malaria. Anemia is diagnosed by using either the hematocrit method or by measuring the hemoglobin concentration.To evaluate the relationship and agreement between hemoglobin and three-fold conversion of hematocrit results of participants in a clinical trial.This is a cross-sectional study that obtained data from a multi-center clinical trial that took place from 2007 to 2008 in public health facilities in Calabar, Nigeria. The hemoglobin and hematocrit results of 494 children who had ≥2000 parasite density recruited were pooled to evaluate the relationship and agreement between the two methods. The difference between the measures against the mean of the two measures was plotted according to the theory of Bland and Altman.The mean age of the children was 34 months, with approximately equal number of boys and girls. The measured hemoglobin was lower than the calculated hemoglobin in 84.5% of the children. The result showed that lower the hemoglobin concentration, the higher the chances that the three-fold hematocrit conversion overestimates hemoglobin levels in the participants.The three-fold hematocrit conversion of hemoglobin estimation is a less reliable method than the measured hemoglobin in anemic children in the study setting.