Reviewerly/exHarmony huggingface dataset:
Identification of Manual Alphabets Based Gestures Using s-EMG for Realizing User Authentication

At the present time, since mobile devices such as tablet-type PCs and smart phones have penetrated deeply into our daily lives, an authentication method that prevents shoulder surfing attacks comes to be important. We are investigating a new user authentication method for mobile devices that uses surface electromyogram (s-EMG) signals, not screen touching. The s-EMG signals, which are generated by the electrical activity of muscle fibers during contraction, can be detected over the skin surface, and muscle movement can be differentiated by analyzing the s-EMG signals. Taking advantage of the characteristics, we proposed a method that uses a list of gestures as a password in the previous study. In order to realize this method, we have to prepare a sufficient number of gestures that are used to compose passwords. In this paper, we adopted fingerspelling as candidates of such gestures. We introduced manual kana of the Japanese Sign Language syllabary and compared the identification performance of some candidate sets of feature values with adopting support vector machines.
Spelling Checker of Words in Rejang Language Using the N-Gram and Euclidean Distance Methods

Spelling mistakes of words in writing Rejang words are often found so it is difficult to understand. The method used in correcting word errors (spelling checkers) has been carried out by several researchers. In this research, words were improved in Rejang words based on the morphology of the Reajang language using the N-gram and Euclidean Distance methods. The process begins with forming the word practice with the N-gram method in cutting a number of words. In the testing process, the pre-process stages are carried out first and the training words are checked based on the existing dictionary. Words that are assumed to be wrong are corrected by looking for words similar to Euclidean Distance. The results of the lowest word resemblance are adjusted to the word training, if it is not appropriate then the word with the highest similarity is considered the correct word to be improved. In this study the experimental results of the words tested produce similarity levels of 20 words and the smallest 3. The results from the calculation of similarity can be directly to correct the wrong word. The results of the study can be seen that word improvement is very dependent on the dictionary word unigram and existing training words. This shows that the N-gram and Euclidean Distance methods are good in spelling checker Rejang language.
Time-Coordinating Protection Devices Over an Entire Reclose Sequence – A Generalized Approach

Time-current coordination of electrical distribution network protection equipment is necessary to minimize the extent of customer outages. Reclosing devices help clear temporary faults but complicate the coordination process for faults that last across several reclose attempts. Misoperations can occur if the reset characteristics of neighboring devices are not considered. This paper improves upon a method to ensure coordination between a fuse and recloser by making it easier to apply and extends it to coordination between two reclosing relays.
Comparative study of upsampling methods for super-resolution in remote sensing

Many remote sensing applications require high spatial resolution images, but the elevated cost of these images makes some studies unfeasible. Single-image super-resolution algorithms can improve the spatial resolution of a lowresolution image by recovering feature details learned from pairs of low-high resolution images. In this work, several configurations of ESRGAN, a state-of-the-art algorithm for image super-resolution, are tested. We make a comparison between several scenarios, with different modes of upsampling and channels involved. The best results are obtained training a model with RGB-IR channels and using progressive upsampling.
A Study on Access Control Scheme Based on ABE Using Searchable Encryption in Cloud Environment

Recently, with the development of cloud computing technology, data can be stored and shared in the cloud. However, because the cloud involves a network, various security threats can occur. Therefore, it is important to encrypt and store data on cloud servers, and CP-ABE (Ciphertext-Polly Attribute Based Encryption) based access control technology is used to access the encrypted data. However, some of the previously studied CP-ABE schemes are inefficient and vulnerable to security threats. This paper is a data access control technique using searchable encryption and attribute-based encryption in cloud environment, and aims to secure and efficient data sharing system in cloud environment. Searchable encryption system technology allows you to efficiently search for the desired data among the many encrypted data stored in the cloud environment. In addition, CP-ABE is used to access encrypted data, so that the user can access the encrypted data securely as a user attribute and decrypt the data.
Analysis Partition Clustering and Similarity Measure on Al-Quran Verses

Clustering text is an important application in data mining. This is related by grouping similar text documents together. In this study, several models are builts to classify Qur’anic verses on Surah Al-Baqarah using three clustering technique: kmeans, bisecting kmeans, and k-medoid. Every verse in Surah al-Baqarah represented as a document derived from the translation of the Qur’an in English. Three similarity measures are also used: cosine similarity, jaccard similarity, and correlation coefficient. Then, the cluster of each combination of clustering technique with similarity measure is evaluated using average within cluster distance and davies bouldin index. The result show that the best performance is achieved by using the hemodoidal combined with cosine similarity. Finally obtained the category verses in the Surah al-Baqarah that correlate with each other.
Data sharing method of power distribution and consumption network based on multi-chain

In the existing network system of power distribution and consumption, each type of service system of power distribution and consumption collects and stores the data of power distribution and consumption by using a channel for electric communication alone. In the case of different open interface protocols customized between each two business systems to achieve data sharing, such a situation has the problem of low data sharing efficiency. By proposing a data sharing method of power distribution and consumption based on multi-chain, we designed a system to realize the coexistence of multiple blockchains after the access to the platform for different IoT services. Evaluation shows that the system we designed could ensure the trusted access and sharing of data belonging to the same blockchain.
CLASSIFICATION OF PRINTED TEXT AND HANDWRITTEN CHARACTERS WITH NEURAL NETWORKS

Various scanned documents consist of machine printed text as well as handwritten alphabet. The classification of handwritten alphabet and machine printed alphabet in document images is the required process afore character recognition. We can distinguish these two types of alphabet images by their shape structural, statistical and visual difference features. This work proposes the artificial neural network based classification technique for machine printed and handwritten text classification at character level using a set of new features which are combination of statistical, shape structural and visual impression features. The projected technique attained remarkable classification efficiency on two databases; IAM dataset and prepared dataset.
Analyzing Social Media Opinions Using Hybrid Machine Learning Model Based on Artificial Neural Network Optimized by Particle Swarm Optimization

Sentiment Analysis (SA) is one of the concepts of Natural Language Processing, also called Opinion Mining. This area of computer science is used to extract the feeling of a text to give useful information about the author’s tendency towards a specific subject. This paper presents an implementation of a sentiment analyzer for Twitter. We analyze tweets to extract feelings on a specific subject. We used a data set of 100000 tweets with 98469 unique values, collected on Twitter. The collected tweets were analyzed using a hybrid Machine Learning model based on Artificial Neural Network optimized by Particle Swarm Optimization (PS-ANN). The results are compared with three algorithms, Artificial Neural Network (ANN), Support Vector Machine (SVM) and Naïve Bayes. The results obtained by cross-validation on the same data set confirm the effectiveness of the proposed approach (PS-ANN) compared to existing methods.
Multimedia Ontology of the Tunisian Archaeology Field

For an information retrieval project, we are in the process of developing an ontology of the Tunisian archeology, a termino-ontological resource that ensures (a) the model role of the domain that list all the relevant concepts and (b) the link between the concepts and the way they are named in the archaeologist’s documents. This double function allows the annotation and the indexing of images of an archaeological site and the search for information in annotated images. The development of ontology will be realized in 6 steps among of which we describe the principal ones in this paper. This project shows that (1) the viability of such resource presupposes a precise articulation of the concepts and terms, and (2) such prerequisite can be attained by the implementation of procedures based on the architecture of meta-modeling, which enables to model the set of the Knowledge Organization Systems and the structures of the necessary knowledge.
Deep RBF Value Functions for Continuous Control.

A core operation in reinforcement learning (RL) is finding an action that is optimal with respect to a learned state-action value function. This operation is often challenging when the learned value function takes continuous actions as input. We introduce deep RBF value functions: state-action value functions learned using a deep neural network with a radial-basis function (RBF) output layer. We show that the optimal action with respect to a deep RBF value function can be easily approximated up to any desired accuracy. Moreover, deep RBF value functions can represent any true value function up to any desired accuracy owing to their support for universal function approximation. By learning a deep RBF value function, we extend the standard DQN algorithm to continuous control, and demonstrate that the resultant agent, RBF-DQN, outperforms standard baselines on a set of continuous-action RL problems.
Load Balance of Cloud Computing Center Based on Energy Awareness

[Purpose] Due to the high energy consumption of Cloud Computing Center, a load balance method based on energy-awareness is proposed in order to optimize energy consumption. [Method] Firstly, the energy consumption of the server when it is in activation state, suspended state and close state is studied. Secondly, the key factors affecting energy consumption are analyzed and the mathematical model is established. Finally, load balance method based on historical load data is proposed with energy consumption as the optimization target. [Result] Simulation experiments on ContainerCloudSim platform show that the proposed method can effectively reduce the energy consumption of Cloud Computing Center. [Conclusions] Based on the prediction of historical load data and in order to reduce the energy consumption of Cloud Computing Center, this paper puts forward a load balance method based on energy-awareness, which is simple, easy to implement and worthy of promotion.
Computable metrics of personal performance in software development teams

The present article describes setup, configuration and usage of the key performance indicators (KPIs) of members of project teams involved into the software development life cycle. Key performance indicators are described for the full software development life cycle and imply the deep integration with both task tracking systems and project code management systems, as well as a software product quality testing system. To illustrate, we used the extremely popular products - Atlassian Jira (tracking development tasks and bugs tracking system) and git (code management system). The calculation of key performance indicators is given for a team of three developers, two testing engineers responsible for product quality, one designer, one system administrator, one product manager (responsible for setting business requirements) and one project manager. For the key members of the team, it is suggested to use one integral key performance indicator per the role / team member, which reflects the quality of the fulfillment of the corresponding role of the tasks. The model of performance indicators is inverse positive - the initial value of each of the indicators is zero and increases in the case of certain deviations from the standard performance of official duties inherent in a particular role. The calculation of the proposed key performance indicators can be fully automated (in particular, using Atlassian Jira and Atlassian Bitbucket (git) or any other systems, like Redmine, GitLab or TestLink), which eliminates the human factor and, after the automation, does not require any additional effort to calculate. Using such a tool as the key performance indicators allows project managers to completely eliminate bias, reduce the emotional component and provide objective data for the project manager. The described key performance indicators can be used to reduce the time required to resolve conflicts in the team, increase productivity and improve the quality of the software product.
Complexity Limitations on One-turn Quantum Refereed Games

This paper studies complexity theoretic aspects of quantum refereed games, which are abstract games between two competing players that send quantum states to a referee, who performs an efficiently implementable joint measurement on the two states to determine which of the player wins. The complexity class QRG(1) contains those decision problems for which one of the players can always win with high probability on yes-instances and the other player can always win with high probability on no-instances, regardless of the opposing player’s strategy. This class trivially contains QMA ∪co-QMA and is known to be contained in PSPACE. We prove stronger containments on two restricted variants of this class. Specifically, if one of the players is limited to sending a classical (probabilistic) state rather than a quantum state, the resulting complexity class CQRG(1) is contained in ∃⋅PP (the nondeterministic polynomial-time operator applied to PP); while if both players send quantum states but the referee is forced to measure one of the states first, and incorporates the classical outcome of this measurement into a measurement of the second state, the resulting class MQRG(1) is contained in P ⋅PP (the unbounded-error probabilistic polynomial-time operator applied to PP).
A Novel Multi-vehicle Tracking Method Based on Linked List of Associated Vehicles

Vehicle tracking is a hot research direction in the Intelligent Transportation System (ITS). To deal with the traditional Camshift algorithm can not achieve multi-vehicle tracking, and tracking-lost of the vehicle under large proportion occlusion, a novel multi-vehicle tracking method based on linked list of associated vehicles is proposed in this paper. In order to initialize the search window, vehicle detection is used to obtain the target vehicle's location and size. Each tracked vehicle establishes a track list to realize multi-vehicle tracking. The algorithm uses Bhattacharyya distance to occlusion identification, and uses Kalman filter to predict the location of vehicles, accurate vehicle tracking can be realized under occlusion. Experimental results show that the algorithm can achieve a robust and accurate vehicle tracking under different traffic.
Requirement Engineering for Traffic Information Application Using GPS Based on Motivations to Contribute

This study identified the motivation to contribute traffic information based on the theory of Helping Behaviour using quantitative research methods and questionnaires as data collection method. Samples were randomly selected from 100 drivers in the city of Palembang. Data analysis was performed using Structural Equation Modelling technique with the help of SmartPLS application, indicating that the hypothesis which are variables from Helping Behaviour theory i.e. attention, evaluation and impetus to respond, will influence the motivation of someone’s contribution, all of the hypothesis are accepted with T Statistics values of 2.165, 2.883 and 2.892 respectively. From these three variables, this study do requirement engineering to proposes features for traffic information sharing applications based on user generated content, namely Need for Help, We are One, Top Contributor, Like, Share, Point and Redeem, Periodic GPS, and Privacy.
Verbalizing Decision Model and Notation

The ability to effectively communicate decision rules becomes increasingly important, as the stake of multidisciplinary teams within organizations increases. With Decision Model and Notation (DMN) turning out to become one of the international standards for modelling decision rules, the need for effective communication of decision rules modelled using the standard grows. In order to facilitate this communication we present a structured verbalization for DMN decision tables, using principles of fact based thinking and modelling theory. This verbalization is designed to be fully interpretable by anyone that has knowledge about the described domain. The key to accommodating this interpretability is the use of a structured natural language as the foundation for the verbalization.
Complexity Analysis and Performance of the Madenda Filter Algorithm

There are many algorithms used to do edge detection. Algorithms on edge detection, in general, can be applied to detect the edge of the image that is affected by noise and not exposed to noise. However, it has not been able to filter for images with noise problems that also experience blurry effects. The Madenda filter algorithm develops from the Canny filter algorithm and Shen-Castan algorithm so that it can overcome this problem. This study aims to measure the time complexity of the Madenda filter algorithm and perform a performance evaluation on the filter. The analysis of the complexity of the Madenda filter algorithm uses the Big O notation. Whereas the performance evaluation is carried out with the help of MATLAB software (trial version). Based on the running time of the Madenda filter algorithm is obtained as follows from the results of time complexity calculations, it is obtained if the Madenda filter algorithm has the complexity of the O (n2). Based on the implementation of program-code implemented in the MATLAB programming language shows that the Madenda filter algorithm can do edge detection where images other than experiencing noise disturbances also experience blurry effects. But the condition that must be fulfilled is that the $a$ value must be smaller and the blur controller parameter $(\pmb{\beta})$ approaches 1.
A Comprehensive View for Providing the Decision on Medicare Data

Decision making toward Medicare in the running environment has been taken topmost priority. Processing and understanding of Medicare data are a tricky challenge. The recommended system design carries in various stages to work insight to the problem. In this paper, we use machine learning algorithms for accurate decision on Medicare data. Specifically, the paper analyzes heterogeneous-typed data with the use of hierarchical grouping (HG) mechanismin at the preprocessing phase. Application of metrics like multiple aggregate, grouping and re-indexing simplifies the process in the both the phases. While in the development phase, detection outlier with the perspective of claims provided by the provider given at prior (history). Prior cost acts as good indicator for the decision. Use of statistical-based approach the outlier amount is detected. Random forest (RF) algorithm generates RF trees, and they were able to generate accurate results to choose cost of surgery (disease) from the provided data. Our system is useful to evaluate with reasonably low costs and error free, as demonstrated in experimentation on real-world datasets which are publicly available.
An Adaptive Image Zooming Technique with the aid of correlation matrix and hybrid interpolation

Zooming technology is basically a necessary part of Personal Digital Assistants (PDAs) for today’s information concentrated world. It plays a fundamental role on image processing (IP) and also machine vision, and has an assortment of applications in the digital cameras, printing industry, electronic publishing, medical imaging and sampling, images on web pages, license plate recognition (LPR), along with face recognition. Edge’s Resolution, be short of blurred along with extra artifacts are two significant factors involved in the zoomed images’ quality. This paper offers an adaptive image zooming technique by utilizing correlation matrix (CM) and hybrid interpolation, wherein the interpolation algorithms, for instance, Bilinear Interpolation (BI) Algorithm and Adaptive Edge Preserving (AEP) algorithm are combined. The proposed work is analysed and weighted against the other conventional algorithms to bear out the dominance of this technique.
PERANCANGAN APLIKASI PENGOLAHAN DATA BUKU DAN PENGUNJUNG PERPUSTAKAAN PADA TAMAN BACAAN MASYARAKAT AR-RASYID ACEH BESAR

This research is motivated by several places or educational institutions that still use manual methods in processing books, members' data, visits and loans that are less effective and time consuming. TBM Ar-Rasyid in the process of filling in the guest book, and data collection on books, loans, members are still done manually. So that a lot of energy and resources will be wasted, both in terms of material and in terms of time, this research is conducted to build a data processing system that can be used by library staff in data collection of books, members, visits and loans to be easier and more effective. The research method used is R &amp; D and for system development using the waterfall method. Then after testing the system, namely in the form of black box testing and continued with the user acceptance test (User Acceptend Test) to users, namely members, library officers, the test results obtained with an average value of 4.31 with a percentage of 86% which means it is very suitable or very easy. Users feel satisfied with the system built.