Reviewerly/exHarmony huggingface dataset:
Development of A Fingerprint Biometric Authentication System For Secure Electronic Voting Machines

Democratic government in the world today rely on electronic voting as the foremost means of providing credible, transparent and fair elections for the electorate. There is a need for developed electronic voting systems to be security enhanced to ensure the authenticity of the developed system. Traditional paper balloting systems suffer from vote tampering, multiple voting and illegal voting by unregistered voters. They are also, susceptible to under aged voting due to the difficulty in authenticating the identity of prospective voters. Manual collation and publication of vote results also leads to slow response times and inaccuracies in published results. This research paper proposes a system to combat the current challenges through the development of a fingerprint biometric authentication system for secure electronic voting machines. It uses a fingerprint biometric sensor, integrated via Python to verify users of the system. The inclusion of biometrics improves the security features of the system. The secure voting system is built using PHP and easy to use Graphical User Interface was designed using HTML and CSS. Users are required to interact with the machine via a 7” touchscreen interface. From the results, it shows that the developed machine has a minimum response time of 0.6 seconds for specific operation, an FAR of 2%, FRR of 10% and overall system accuracy of 94%. The developed machine is able to combat the challenges of authentication of users, thereby guaranteeing the transparency, credibility, integrity and vote authenticity of the elections.
A Voting Argumentation Framework: Considering the Reasoning behind Preferences

One of the most prominent ways to reach an acceptable collective decision in normal group settings is the employment of routines and methods of social choice theory.The classical social choice setting is the following: each agent involved in the decision expresses her preferences about a given set of alternatives in the form of a linear order on them.Then, the group's aggregated decision is the outcome of the application of a voting rule to the input's preferences.However, there are instances where social choice on its own cannot provide proper solutions.For example, there are decision problems where the outcome has to be based on the reasoning behind agents' preferences, rather than the unjustified preferences itself.Hence, our research motivation is the practical case where agents' rationale is needed for the decision outcome.In this paper, we explore how the agents' rationale can be formulated inside the classical voting setting.Therefore, we propose a decision-making procedure based on argumentation and preference aggregation which permits us to explore the effect of reasoning and deliberation along with voting for the decision process.We quantify the deliberation phase by defining a new voting argumentation framework, that uses vote and generic arguments, and its acceptability semantics based on the notion of pairwise comparisons between alternatives.We prove for these semantics some theoretical results regarding well-known properties from Argumentation and Social Choice Theory.
Fast Video Shot Boundary Detection Based on Visual Perception

Dividing video sequences into shots for video content analysis and video retrieval is very important, so shots are the beginning of these advanced analyses. In this paper, we proposed a new model based on visual perception, which can be expressed as "whole to local", following the concept of human vision. We first browse the video to remove the redundant frames of the video, which can reduce the computational cost. Then, the visual consistency feature of the video is used to construct the consistency function between frames to create pending shots. Finally, the shot boundary detection results are further optimized in conjunction with the motion feature. The new model which contains the inter-frame coherence and optical flow feature for shot boundary detection can shorten the calculation time and detect the shots boundary fast and accurately, without sacrificing detection performance. In terms of evaluation, the precision, recall and F1 value of the model shows good results.
Psychological and Pedagogical Factors of Activating the Masters Students Cognitive Interests to the Study of Foreign Languages

Motivation is a major factor in activity, including cognitive one. Pedagogical and psychological factors stimulate cognitive interest, cognitive and educational activity. Activating the cognitive interests of students to study foreign languages becomes successful due to the correspondence of the content of educational material organization, ways and methods of teaching, the provision of personal meaning educational material, the establishment of subject-subject relations in the educational process, the use of psychological and pedagogical methods of stimulation by the teacher of students’ cognitive interest and cognitive activity. The future professionals (students) successful training of foreign language, professionally oriented communication is impossible without defining a single concept that is the training clear strategy and tactics.Formation of foreign-language professional-oriented competence is possible through that modeling in the educational process typical situations of real professional communication that arise in different spheres of life and relate to different topics.The communicative activity of students during the training of professionally oriented communication in a foreign language is a system consisting of organizational and motivational, informational and procedural and criterion-effective components that derive from the social nature of human activity and create the best conditions for the improvement of the entire system of student communicative activities in relation to formation of foreign professional competence.
Exploring Various Levels of Parallelism in High-Performance CRC Algorithms

Modern processors have increased the capabilities of instruction-level parallelism (ILP) and thread-level parallelism (TLP).These resources, however, typically exhibit poor utilization on conventional cyclic redundancy check (CRC) algorithms.In this paper, various levels of parallelism in high-performance CRC algorithms are investigated.The main idea of the proposed algorithms is to make full utilization of modern processors, from the perspective of both instruction-level and thread-level parallelism.First, a finegrained algorithm executes the CRC computation in an interleaved manner, so that multiple independent data flows can be processed simultaneously.This algorithm allows instruction-level parallelism, which triples and doubles the performance of the existing slicing-by-4 and slicing-by-8 algorithms, respectively.Second, a coarse-grained algorithm can ideally deal with data in a parallel way by parallelizing a family of serial CRC generating algorithms.Therefore, this algorithm allows thread-level parallelism, which can make full use of multi-core computing capability.As a result, it achieves a speedup that is almost equal to the number of threads used.In addition, both fine-grained and coarse-grained algorithms can be applied together to achieve high throughput further.
Robust Cylinder Estimation in Point Clouds from Pairwise Axes Similarities

The ubiquitous presence of cylindrical shapes in both natural and man-made environments makes their automated extraction a pivotal task for a broad range of applications such as robot manipulation, reverse engineering and automated industrial inspection.Albeit conceptually simple, the task of fitting cylinders from 3D data can quickly become challenging if performed "in-the-wild", when no prior is given to the number of primitives to find or when the point cloud is noisy and not oriented.In this paper we introduce a new robust approach to iteratively extract cylindrical primitives from a 3D point cloud by exploiting mutual consensus of different cylinder candidates.First, a set of possible axes is generated by slicing the point cloud with multiple random planes.Then, a game-theoretic inlier selection process is performed to extract a subset of axes maximizing the fitness against a payoff function based on the shortest geodesic path in SE(3) between pairs of corresponding 3D lines.Finally, the probability distribution resulting from the previous selection step is used to weight the input candidates and robustly obtain the final cylinder coefficients.Compared to other methods, our approach does not require point normals, offers superior resilience to noise and does not depend on delicate tuning of multiple parameters.
Construction of Intangible Cultural Heritage Spot Based on AR Technology—Taking the Intangible Cultural Heritage of the Li Nationality in the Areca Valley as An Example

The tourist area is a display area mainly for tourism and cultural visit. With the continuous advancement of the digitization process of scenic spots in China, a number of information software products showing the digital achievements of science and technology have been emerging continuously, and the technology of virtual and real fusion has become the main technical means at present. AR technology is adopted to display the intangible cultural heritage of the scenic spot. This paper analyses the advantages and disadvantages of existing scenic spot construction and analyzes the advantages of AR technology in the construction of scenic spot. Taking penang valley li intangible cultural heritage scenic spot as an example, this paper expounds the display mode of AR technology in the scenic spot. The visit and tour of the scenic area based on AR technology brings real visual stereo effect, mobile convenience and interest to hands, and also provides beneficial reference for the wide application and rapid improvement of AR technology, as well as digital innovation and rapid popularization of the historical background of intangible cultural heritage.
Resolvable Group State Estimation with Maneuver Based on Labeled RFS and Graph Theory

In this paper, multiple resolvable group target tracking was considered in the frame of random finite sets. In particular, a group target model was introduced by combining graph theory with the labeled random finite sets (RFS). This accounted for dependence between group members. Simulations were presented to verify the proposed algorithm.
Face Recognition Based Attendance System

The objective of the attendance system is to provide an alternative means to the traditional attendance system which consumes 10 to 15 minutes of time in 50 minutes of lecture hour. It also aims at eliminating human errors and proxy in recording the attendance of the student. This can be achieved by using face recognition for monitoring the attendance of the students in a class. The face recognition process is carried out by using the Cognitive Face API which follows the Principal Component Analysis (PCA) algorithm. Initially, the dataset of the students in a class are collected. The dataset is collected in a manner that for each student, a set of 25 images in various angles is collected. The features are extracted from the images that are collected by using the cognitive face API and the database is formed. The image of the class in columns is acquired immediately, when the input image is acquired by using a mechanical set up which captures image based on hour, the number of faces in the input image is detected. The detected faces are cropped and then stored in a folder. The features of the cropped faces are also extracted and it is compared and matched with the features in the database. When the feature matches, the attendance is marked for the particular student in the spreadsheet and then the attendance report of the class is being uploaded in the web-page. Thus, the attendance of the student can be recorded in an effective manner. This paper also helps in avoiding human error which is unavoidable.
Specific Emitter Identification based on Power Amplifier

Specific emitter identification is the process of identifying or discriminating different emitters based on the radio frequency fingerprints extracted from the received signal.Due to the inherent non-linearities of the power amplifiers of emitters, these features are extracted from the power amplifiers for specific emitter identification.In this paper, we take the 433MHz power amplifier module BLT54A as the research object, eight circuit modules with the same specifications and batches were tested.The experimental contents include: S parameters, AM-AM curve, AM-PM curve, input power and output power curve, AM-AM curve when bias voltage is different, AM-PM curve and input power and output power curve.We extract the energy characteristics of the HHT spectrum of the amplifier output signal and recognize it by classifier.The results show that the eight amplifiers can reach 75%.Meanwhile, we evaluate performance of specific emitter identification in several aspects including different signal sample length and different SNR.
Développement financier et transformation structurelle des pays africains de la zone Franc : une approche panel-VAR

The African countries of the Franc Zone are on the margin of the observed industrialization process in several developing countries around the world. This research examines the relationship between the development of the financial sector and the industrialization of African economies in the Franc Zone. A vector auto regressive model (VAR) with errors corrections is used in order to better determine the effects of a financial innovation on industrialization over time. The results show that a shock on bank credit allocated to the private sector has a negative short-term and a positive long-term effect on the manufacturing sector. This negative effect takes longer before easing in the CAEMC (CEMAC) zone compared to the WAEMU (UEMOA) zone. Moreover, the dynamics of the manufacturing sector remains insensitive to the shocks on the monetary aggregate M2 and the inflation.
Dual-Channel Attention Model for Text Sentiment Analysis

Focused on the issue that text information cannot be fully extracted by the single-channel neural network model, the Dual-Channel Attention Model (DCAM) is proposed for text sentiment analysis.Firstly, text is represented in the form of a matrix using a word vector trained by Word2Vec.Secondly, the matrix is used as input data and sent to Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) networks for feature extraction.Thirdly, an attention model is introduced to extract important feature information.Finally, the text features are merged, and the classification layer is used to classify the sentiment.The model is evaluated on a Chinese corpus.According to the experimental results, the accuracy of the proposed model can reach 92.7%, which is obviously superior to other single-channel neural network models.
A deadline constrained scheduling algorithm for cloud computing system based on the driver of dynamic essential path

To solve the problem of the deadline-constrained task scheduling in the cloud computing system, this paper proposes a deadline-constrained scheduling algorithm for cloud computing based on the driver of dynamic essential path (Deadline-DDEP). According to the changes of the dynamic essential path of each task node in the scheduling process, the dynamic sub-deadline strategy is proposed. The strategy assigns different sub-deadline values to every task node to meet the constraint relations among task nodes and the user’s defined deadline. The strategy fully considers the dynamic sub-deadline affected by the dynamic essential path of task node in the scheduling process. The paper proposed the quality assessment of optimization cost strategy to solve the problem of selecting server for each task node. Based on the sub-deadline urgency and the relative execution cost in the scheduling process, the strategy selects the server that not only meets the sub-deadline but also obtains much lower execution cost. In this way, the proposed algorithm will make the task graph complete within its deadline, and minimize its total execution cost. Finally, we demonstrate the proposed algorithm via the simulation experiments using Matlab tools. The experimental results show that, the proposed algorithm produces remarkable performance improvement rate on the total execution cost that ranges between 10.3% and 30.8% under meeting the deadline constraint. In view of the experimental results, the proposed algorithm provides better-quality scheduling solution that is suitable for scientific application task execution in the cloud computing environment than IC-PCP, DCCP and CD-PCP.
An imperceptible &amp; robust digital image watermarking scheme based on DWT, entropy and neural network

image watermarking scheme based on DWT, entropy and neural network entropy and neural network
An Efficient Super-Resolution Network Based on Aggregated Residual Transformations

In this paper, we propose an efficient multibranch residual network for single image super-resolution. Based on the idea of aggregated transformations, the split-transform-merge strategy is exploited to implement the multibranch architecture in an easy, extensible way. By this means, both the number of parameters and the time complexity are significantly reduced. In addition, to ensure the high-performance of super-resolution reconstruction, the residual block is modified and simplified with reference to the enhanced deep super-resolution network (EDSR) model. Moreover, our developed method possesses advantages of flexibility and extendibility, which are helpful to establish a specific network according to practical demands. Experimental results on both the Diverse 2K (DIV2K) and other standard datasets show that the proposed method can achieve a good performance in comparison with EDSR under the same number of convolution layers.
Maternal health in underserved tribal India

Maternal mortality has declined in many countries across the world over the decades, including in India, but the decline has been sub-optimal in resource poor and tribal-rural regions. 1,2 Remotene...
Digital transformation of academic libraries: Opportunities and challenges

The times are changing. With Internet access and electronic reading devices, visiting the library is no longer a necessity for today’s students. The library has changed a great deal over the past decade, due to changing demands from researchers, teachers, and learners and the onset of a digital revolution of library holdings. Digital transformation is powered by disruptive digital technologies, insights, and processes. The key focus of digital transformation is on transforming for the digital age by influencing customer experience, innovation, and efficiency. The big challenge with digital transformation is ‘how fast and how far should organizations go on their digital transformation path’. Digital transformation journey is complicated and involves varied objectives, complexities, and covers a vast area. It requires a coherent and well-organized digital strategy to effectively address technology and process transformation together with supporting governance and delivery models. Keywords: Digital age, Academic libraries, Transformation, Digital transformation.
Wacana Berita PT. Djarum dan Bulu Tangkis Indonesia

PT. Djarum as a company known as a cigarette producing company is doing one of the CSR program which is very contradictory.PT.Djarum seeks to distract people from the knowledge of the dangers of smoking by caring for the community through the Badminton support program.Or in other words Djarum Foundation provides CSR as compensation for guilt to the community.The purpose of this study is to know the construction of discourse on the CSR news PT.Djarum sports field especially Badminton.This research uses qualitative method by using Critical Discourse Analysis perspective which try to dismantle the structure of discourse in text.Researchers will take each of 2 news on every online portal (Tribunnews.com,Detik.com, and Kompas.com) in the period of 1 year starting from January 1, 2017 until
Implementation of Jigsaw Type Cooperative Learning Model to Improve Economics Learning Results

This study aims to determine the application of the Jigsaw type cooperative learning model to&#x0D; students and whether or not there is an increase in learning outcomes in economic subjects. Jigsaw&#x0D; cooperative learning is learning that focuses on discussion by groups of experts and groups of origin.&#x0D; Discussions conducted by students aim to understand the material being studied. The problem&#x0D; posed is about economic subject matter. This research is a classroom action research consisting of&#x0D; two cycles. This classroom action research is focused on improving student learning outcomes. The&#x0D; results showed that the application of the Jigsaw type cooperative learning model was able to&#x0D; improve the learning outcomes of economic subjects in each cycle
Development of Sales Modules for Agricultural E-Commerce Using Dynamic System Development Method

E-Commerce is a process model of selling and buying goods using information and communication technology. This business model offers a broad market for products owned by the seller. However this sales model is not yet common for agricultural products. Most of the process of buying and selling agricultural products is still done conventionally. The lack of agricultural e-commerce sites is the main reason for the research conducted. This research will develop an agricultural e-commerce system. This system has two main modules that are sales module and purchasing module. This article specifically develops sales modules for agricultural e-commerce. In the development of modules applied Dynamic System Development Method. The used of this model is based on characteristic of the project which have limited number of development team and short development time. The development stages consist of pre-project, feasibility study, business study, functional model iteration, and design and build iteration. In this study a prototype has been produced for the sales module on agricultural e-commerce that is being developed
Application of the Least Square Support Vector Machine for point-to-point forecasting of the PV Power

&lt;p&gt;In today's industrial world, the growing capacity of renewable energy sources is a crucial factor for sustainable power generation. The application of solar photovoltaic (PV) energy sources, as a clean and safe renewable energy resource has found great attention among the consumers in the recent decades. Accurate forecasting of the generated PV power is an important task for scheduling the generators and planning the consumption patterns of customers to save electricity costs. To this end, it is necessary to develop a global model of the generated power based on the effective factors which are mainly the solar radiation intensity and the ambient weather temperature. As a result of the wide numerical range of these parameters and various weather conditions, a large training database must be used for developing the models, which results in high-computational complexity of the algorithms used for training the models. In this paper, a novel algorithm for point to point prediction of the generated power based on the least squares support vector machine (LS-SVM) has been proposed which can handle the large training database with a very fewer deal of computation and benefits from reasonable accuracy and generalization capability. &lt;/p&gt;