Reviewerly/exHarmony huggingface dataset:
Blockchain Based Electronic Voting System Using Biometric

Electronic voting system aims to eliminate the possibility of human error, manipulation and tamplering which occurs in traditional voting system. Electronic voting systems require heavy encryption to ensure transparency, security, auditability and secrecy for a vote. The proposed system modeled utilizing the information block of currently available framework to join them to another increasingly secure framework. This uses decentralized distributed database called blockchain. This is highly cost efficient and helps to increase the trust in the voting process. For encrypting data, we have used Elliptic Curve Digital Signature Algorithm (ECDSA) and hash code. Fingerprint module is interfaced with Raspberry Pi to access finger impression of user.
An Efficient Scheme for Prototyping kNN in the Context of Real-Time Human Activity Recognition

The Classifier kNN is largely used in Human Activity Recognition systems. Research efforts have proposed methods to decrease the high computational costs of the original kNN by focusing, e.g., on approximate kNN solutions such as the ones relying on Locality-sensitive Hashing (LSH). However, embedded kNN implementations need to address the target device memory constraints and power/energy consumption savings. One of the important aspects is the constraint regarding the maximum number of instances stored in the kNN learning process (being it offline or online and incremental). This paper presents simple, energy/computationally efficient and real-time feasible schemes to maintain a maximum number of learning instances stored by kNN. Experiments in the context of HAR show the efficiency of our best approaches, and their capability to avoid the kNN storage runs out of training instances for a given activity, a situation not prevented by typical default schemes.
Designing an expert system model for pulmonary TB disease diagnose pre-screen laboratory process

Tuberculosis is a major public health problem in Ethiopia more than half a century ago. If the disease is not treated well and on time, it can lead to severe health problems like lungs, but can also affect other organs, including the central nervous system. Therefore, detecting those diseases at early stages enable us to overcome and treat them appropriately. However, among people in the developing countries like Ethiopia, permanent diseases are growing to be causes of death. These problems are becoming worse due to the scarcity of specialists, practitioners and health facilities. In Ethiopia, there has been observed a threat of increased prevalence of tuberculosis and the number of death rates imputed to tuberculosis reached above 29,000 deaths (35 per 100,000) due to TB, excluding HIV related deaths, during the same period, 2014. In an effort to address such problem, this study attempts to design an expert system model for tuberculosis disease diagnose pre-screen laboratory process that can provide advice for physicians and patients to facilitate the diagnosis before laboratory testing. To this end, knowledge is acquired using both structured and unstructured interviews from domain experts which are selected using purposive sampling technique from health agents. Relevant documents analysis method is also followed to capture explicit knowledge. Thereafter, the acquired knowledge is modeled using decision tree that represents concepts, procedures involved in diagnosis and treatment of tuberculosis and production rules are used to represent the domain knowledge, and knowledge-based system is developed using SWI Prolog editor tool. It uses backward chaining which begins with possible solutions or goals and tries to gather information that verifies the solution. Key words: Model, expert system, knowledge representation, knowledge acquisition, tuberculosis disease. &nbsp
Economic Impact of Class Attendance Systems on Universities

This paper aims to develop a platform that allows face features to be extracted faster using multiple algorithms for looking up people in a large database.We will be presenting an enhanced technique for human face recognition where we will be using an image-based approach (process of using two-dimensional images to create three-dimensional models) towards artificial intelligence by extracting features from face images by using Principle Component Analysis, Local Directional Pattern and SVM Machine Learning.Up until now, studies focusing on face recognition rely on the fusion of PCA (Principle Component Analysis) and LBP (Local Binary Pattern) for feature extraction, PCA and LBP were used for global feature extraction of the whole image and the features of the mouth area separately.Results show that this method was susceptible to random noise and resulted in a performance rate of 89.64% [1].Also, recent studies have shown the fusion of PCA (Principle Component Analysis) and LDP (Local Directional Pattern) for feature extraction [2]. First, PCA is adopted to extract global features of facial images, then LDP operator is used to extract local texture features of eyes and mouth area and these areas are calculated by comparing the relative edge response value of a pixel in different directions.This fusion resulted in a performance rate of 91.61%.The results of PCA and LDP method show that it is more effective than adopting the fusion of PCA and LBP.It's more robust to noise and improves the rate of facial recognition.However, both methods still suffer from changes in illumination, pose changes, random noise, and aging.In this paper, we propose using a set of trained images to make the facial recognition process faster and provide more accurate results.
Hy-FiX

Hypervisors are critical components in cloud data centers. Maintaining up-to-date KVM hypervisors requires replacing the host kernel, hence rebooting the physical host with consequent termination of running virtual machines (VMs). We introduce Hy-FiX, an in-place upgrade tool for KVM hypervisors that applies host kernel and/or user-space components upgrades while transparently preserving running VMs, constituting a better alternative for platforms where VMs are inevitably terminated and restarted.
Web Server for Analysis and Visualization of Bitcoin Data

Most of the existing platform for Bitcoin data analysis or visualization, they perform data collection from one node in the main-net. Thus we can notice some differences in their provided data. In this research, and in order to provide global data visualization, we collect both the historical and realtime Bitcoin data. Our monitoring system is comprised of three main components; multiple full Bitcoin nodes were used to collect data, the analysis of data was done by the Analytic Engine, while the webserver was used to visualize both row-data and analyzed data.
Tracking Position and Status of Electric Control Switches Based on YOLO Detector

Tracking position and status of switches of electric control cabinets is the key to automatic polling and management systems for intelligent substation. It is a typical task of multi-target image detection and recognition. In this paper, we present an end-to-end switch position detection and state recognition system based on YOLO detector that can detect and recognize multiple targets in a single frame at one time. A four-category network based on YOLOv3-tiny is designed and optimized for real-time detection, then logistic regression is used to predict the probability of status of switches, and then an algorithm based on the prior information of the cabinet is developed to remove duplicate targets. Finally, the detected switches are sorted and numbered to compare with the information in the database. Experiments are reported to verify the proposed system.
Design Models for Developing Educational Virtual Reality Environments

Virtual reality, although not a new technology, has rapidly increased its popularity in the last few years. As a result, their use in educational environments has been a topic of interest in academic research. One of the first questions asked by education researchers has been, “How can we design an effective virtual reality environment?”, which led to the development of many design models for the preparation of educational virtual reality environments based on various approaches. These design models have been systematically examined in terms of themes and codes within this study, in hopes this chapter may be of importance for guiding researchers who want to design an educational virtual reality environment or create a design model.
Optimal motion planning of differential-drive mobile robots based on trapezoidal collocation method

Abstract This study focuses on optimal motion planning for nonholonomic constraints mobile robot. We formulate the dynamics model of a differential-drive mobile robot by using Lagrangian mechanics, where the nonholonomic constraints are accurately described through differential equations. The optimal motion planning of the system is constructed as an optimal control problem which is then converted to a nonlinear programming problem by introducing trapezoidal collocation method, and the formulated nonlinear programming is solved by interior-point method. Compared with the prevailing methods in the field of motion planning, our proposed method can handle different kinds of path constraints, terminal conditions and collision-avoidance requirements. Simulation results indicate that the proposed approach can efficiently deal with various user-specified requirements with advantage of high computing efficiency.
Deep Video‐Based Performance Synthesis from Sparse Multi‐View Capture

Abstract We present a deep learning based technique that enables novel‐view videos of human performances to be synthesized from sparse multi‐view captures. While performance capturing from a sparse set of videos has received significant attention, there has been relatively less progress which is about non‐rigid objects (e.g., human bodies). The rich articulation modes of human body make it rather challenging to synthesize and interpolate the model well. To address this problem, we propose a novel deep learning based framework that directly predicts novel‐view videos of human performances without explicit 3D reconstruction. Our method is a composition of two steps: novel‐view prediction and detail enhancement. We first learn a novel deep generative query network for view prediction. We synthesize novel‐view performances from a sparse set of just five or less camera videos. Then, we use a new generative adversarial network to enhance fine‐scale details of the first step results. This opens up the possibility of high‐quality low‐cost video‐based performance synthesis, which is gaining popularity for VA and AR applications. We demonstrate a variety of promising results, where our method is able to synthesis more robust and accurate performances than existing state‐of‐the‐art approaches when only sparse views are available.
Research on Question Answering Technology Based on Bi- LSTM

Abstract Question Answering (QA) is always the key issue in the Natural Language Process (NLP). This paper mainly researches the question answering model based on Bi-directional Long Short Term Memory networks (Bi-LSTM), with the use of the WebQA dataset to train the model. Experiment results show that ACC@1 in Bi-LSTM model is 55.00%, ACC@3 is 73.24%, and ACC@10 reaches to 86.64%. The Bi-LSTM model outperforms Best Match 25 (BM25) algorithm in all of the three metrics, which proves the superiority of Bi-LSTM model.
A Methodology for Generated Text Annotation for High Quality Speech Synthesis

Natural Language Generators may generate texts that are linguistically enriched. These may result in significantly improved synthetic speech. At the same time, the generators produce pieces of plain text that may span between a single word to a full sentence. Additionally, traditional natural language generators have limited domain coverage, resulting in restricted language analysis of the generated texts. For those cases the enriched input to the speech synthesizer, required for high quality speech synthesis, can be provided by analysing the plain text. This work reports on the method for automatic domain dependent annotation of plain text, through the utilisation of the linguistic information from rich generated text. The synthetic speech from the resulting prosody models is evaluated by human participants showing annotation results for plain text quite on par with the rich generated text. This leads to improved perceived naturalness of the synthesized speech.
Learning Attention Regularization Correlation Filter for Visual Tracking

Spatial regularization can effectively solve the unwanted boundary effect of discriminative correlation filters (DCF). However, the predefined mask is independent of the feature, which limits the performance improvement. In this paper, we take the mask as a variable that plays the same role as the filter, and an attention regularization correlation filter (ARCF) is proposed for visual tracking. Especially, the mask is no longer a binary but a real value between 0 and 1, used as the weight of the corresponding feature. Additionally, the temporal coherence is also considered when the filter and the mask are simultaneously optimizing via ADMM algorithm, so the filter can fit the variation of the target in the temporal domain. Extensive experiments on the OTB100 database prove that our algorithm is much better than the traditional SRDCF algorithm both in the performance and speed.
Virtual Machine Allocation in Heterogeneous Cloud for Load Balancing Based on Virtual Machine Classification

Many organizations are turning to cloud users because of the potential benefits of the cloud. The increasing popularity of cloud services has brought several difficulties as well. Balancing the workload among the available resources at cloud datacenter is one of them and becomes a crucial task. The cloud service provider needs an effective mechanism for achieving workload balance to meet the demands of large numbers of users. To overcome this, many different approaches are suggested in the literature. But still, there is scope to improve the performance of the heterogeneous cloud. The method of distribution of workload among resources needs to consider the processing capability of each resource. Here, in this work, we propose a method “VAHC (VM Allocation in Heterogeneous Cloud for Load Balancing Based on VM Classification)” for allocation of VM based on its classification. The median is used for effective classification of VMs into two groups based on their capacities. This work focuses on minimizing the response time and time required for processing the request in the heterogeneous cloud. The performance of this work is analyzed and compared with “Equally Spread Current Execution (ESCE)”, “Throttled”, and “Round Robin (RR)” Algorithms. The results of the proposed method showed a considerable reduction of 16% in response time whereas 29% in time required processing the request at the datacenter.
An Active and Deep Semantic Matching Framework for Query Rewrite in E-Commercial Search Engine

In order to make the query retrieve much more related products, some query rewrite methods have been proposed to obtain a set of candidate queries which can infer users' search intents and reduce the vocabulary gap between the original query and title of related products. However, previous studies ignore that some candidate queries may change users' search intents and retrieve irrelevant products. As a result, users' search experience will be impacted significantly. To reduce this influence, we need to design a semantic matching model to determine whether the candidate query change the original query's search intents (semantics). In addition, building a semantic matching model faces the following challenges: 1) Queries are usually very short and have limited information. It is very hard to learn an effective semantic matching model with the textual information of queries and candidate queries. 2) In order to get a generalized and effective mode, sufficient data samples are required to train the model. However, the cost of labeling is very huge. In order to address the above challenges, we propose an active and deep semantic matching framework (ActiveMatch) which is composed of two components. One component is the deep semantic matching (DSM) model which can make full use of the search log information to enhance the representation of queries and candidate queries. Then, it can estimate the semantic similarity between the original query and the candidate query more accurately. The other component is an uncertainty and novelty sampling (UNS) strategy which selects the samples to label based on the difficulty of the model estimating and the probability of the occurrence of new words. It not only reduces the cost of labeling but also ensures the effectiveness of the model. The experimental results on the Taobao e-commercial search platform verify the effectiveness of our framework.
SCiJP: Solving Computing Issues by Java Design Pattern

The types, needs, advantages and disadvantagesGautam, Hanu of JavaJohari, Rahul DesignBhatia, Riya Patterns have been explained, and Java Cryptography Architecture (JCA) case study has been discussed. The broad survey cross the spectrum of high-end programming language shows Java is the only programming language that offers concept and features which can be used extensively to design applications and software to harness the advantages of exascale computing.
An overview of the NFAIS Conference: Artificial intelligence: Finding its place in research, discovery, and scholarly publishing

This paper offers an overview of the highlights of the NFAIS Conference, Artificial Intelligence: Finding Its Place in Research, Discovery, and Scholarly Publishing, that was held in Alexandria, VA from May 15-16, 2019.The goal of this conference was to explore the application and implication of Artificial Intelligence (AI) across all sectors of scholarship.Topics covered were, among others: the amount of data, computing power, and the technical infrastructure required for AI and Machine Learning (ML) processes; the challenges to building effective AI and ML models; how publishers are using AI and ML in order to improve discovery and the overall user search experience; what libraries and universities are doing to foster an awareness of AI in higher education; and an actual case study of using AI and ML in the development of a recommendation engine.There was something for everyone.
Deep Metric Learning using Similarities from Nonlinear Rank Approximations

In recent years, deep metric learning has achieved promising results in learning high dimensional semantic feature embeddings where the spatial relationships of the feature vectors match the visual similarities of the images. Similarity search for images is performed by determining the vectors with the smallest distances to a query vector. However, high retrieval quality does not depend on the actual distances of the feature vectors, but rather on the ranking order of the feature vectors from similar images. In this paper, we introduce a metric learning algorithm that focuses on identifying and modifying those feature vectors that most strongly affect the retrieval quality. We compute normalized approximated ranks and convert them to similarities by applying a nonlinear transfer function. These similarities are used in a newly proposed loss function that better contracts similar and disperses dissimilar samples. Experiments demonstrate significant improvement over existing deep feature embedding methods on the CUB-200-2011, Cars196, and Stanford Online Products data sets for all embedding sizes.
Incremental Subclass Support Vector Machine

Support Vector Machine (SVM) is a very competitive linear classifier based on convex optimization problem, were support vectors fully describe decision boundary. Hence, SVM is sensitive to data spread and does not take into account the existence of class subclasses, nor minimizes data dispersion for classification performance improvement. Thus, Kernel subclass SVM (KSSVM) was proposed to handle multimodal data and to minimize data dispersion. Nevertheless, KSSVM has difficulties in classifying sequentially obtained data and handling large scale datasets, since it is based on batch learning. For this reason, we propose a novel incremental KSSVM (iKSSVM) which handles dynamic and large data in a proper manner. The iKSSVM is still based on convex optimization problem and minimizes data dispersion within and between data subclasses incrementally, in order to improve discriminative power and classification performance. An extensive comparative evaluation of the iKSSVM to batch KSSVM, as well as, other contemporary incremental classifiers, on real world datasets, has shown clearly its superiority in terms of classification accuracy.
An Attribute-Weighted Bayes Classifier Based on Asymmetric Correlation Coefficient

In this research, an attribute-weighted one-dependence Bayes estimation algorithm based on the asymmetric correlation coefficient is proposed. The asymmetric correlation coefficients Tau_y and Lambda_y, respectively, are used to calculate the correlation between parent attributes and category labels, then the result of calculation is regarded as weight to the parent attribute. The algorithm is applied to eight types of different datasets including binary classification and multiple classification from the UCI database. By comparing the time complexity and classification accuracy, experimental results show that the algorithm can significantly improve the classification performance with less prediction error. In addition, several baseline methods such as KNN, ANN, logistic regression and SVM are used for comparison with the proposed method.
Modern libraries and the future of science communications

To what extent do academic and research libraries contribute to the work of their mother organizations, i.e. research institutes and universities? What is the role of libraries in scientific research? Which problems do hinder the development of librarianship? These and related questions were in the focus of an analytical study conducted in 2018 by Springer Nature, which sent many questionnaires to the libraries around the world with a supplementary limiting interview of 20 librarians. Some part of the responses was quite predictable, and respondents specified the library problems as lacking money, underestimation of the library services and libraries themselves, toughening requirements of university community, and expanding needs of research teams. The respondents often mentioned new, positive approaches in the work of libraries – support of researchers willing to publishing their papers, building and maintaining repositories, increasing independence of libraries in acquisition process and collection development. The study findings offer some solutions to the dispute about the status of libraries and their prospects, which is held by our reputable experts, e.g. V. K. Stepanov, Yu. N. Stolyarov, etc.