Reviewerly/exHarmony huggingface dataset:
Designing Time Series Crime Prediction Model using Long Short-Term Memory Recurrent Neural Network

Crime influences people in many ways. Prior studies have shown the relationship between time and crime incidence behavior. This research attempts to determine and examine the relationship between time, crime incidences types and locations by using one of the neural network models for time series data that is, Long Short-Term Memory network. The collected data is pre-processed, analyzed and tested using Long Short-Term Memory recurrent neural network model. R-square score is also used to test the accuracy. The study results show that applying Long Short-Term Memory Recurrent Neural Network (LSTM RNN) enables to come up with more accurate prediction about crime incidence occurrence with respect to time. Predicting crimes accurately helps to improve crime prevention and decision and advance the justice system.
Smartphone Based Language Learning through Mobile Apps

Mobile technology signifies an innovative approach to the modern education in the present times. In this era smartphones have been integrated into learning system by having different user friendly applications (APPs). The wide range of availability of smart phones connected to mobile network and the occurrence of APPs developed for the educational purposes provide us with the possibility and feasibility of mobile teaching and learning that turn out to be a boon in the educational sector nowadays. The wide-ranging use of Smartphones and different user-friendly gadgets has transformed the outmoded techniques of teaching and learning process entirely. Consequently the students are finding it more practical to understand through such devices reinforced by internet. This extensive usage of smartphones has given birth to loads of mobile application in Language teaching especially English. Abundant apps are available for language learners to refer through easy access to the internet to enhance their communication skills. Through such apps learning materials can be easily accessed and transferred due to the transportability and user-friendliness of such portable devices. The main objective of this paper is to identify the effectiveness of learning LSRW skills through mobile technology and to classify apps based on the learners’ level. The study also discusses the plan, technique, theory and instructive features that support the various mobile apps. Various apps are developed to increase the effectiveness of the communication skills of the learners wherein the prime emphasis is to be laid on the enhancing of basic communication skills such as listening, speaking, reading and writing skills with the help of Mobile phones.
Machine Translation (MT) Techniques for Indian Languages

Machine Translation (MT) is the process of converting the text from one language (source) to another language (TL). MT draws the idea of linguistics, computer science, artificial intelligence, sociology, psychology etc. The linguistically rich country like India has the demand to develop a full-fledged MT system to convert the text across different languages. Though the research has been made on MT for the past 60 years, still it is considered to be a challenging task. Building a fully automatic MT system is extremely difficult. This paper deals with the various ideas in MT systems for Indian Languages. Advantages and limitations of some of the important Dravidian Language translation systems developed using MT techniques are discussed.
Phish Alert: An Efficient Phishing URL Detection via Hybrid Methodology

In spite of various research endeavors, phishing assaults stay common and exceedingly successful in attracting clueless clients to uncover delicate data, including account details and government managed savings numbers. Misfortunes due to phishing are developing consistently. A solitary methodology isn't effective for distinguishing a wide range of phishing assaults. So we propose a hybrid approach to deal with the classification of URLs as phishing or real. The investigation aftereffects of our proposed methodology, in view of a dataset gathered from phishing and legitimate URLs, have demonstrated that PhishAlert framework can successfully counteract phishing assaults and can thus ensure system security.
Comparison of Cart and Naive Bayesian Algorithm Performance to Diagnose Diabetes Mellitus

Based on Indonesia's health profile in 2008, Diabetes Mellitus is the cause of the ranking of six for all ages in Indonesia with the proportion of deaths of 5.7% under stroke, TB, hypertension, injury and perinatal. This is reinforced by WHO (2003), Diabetes Mellitus disease reached 194 million people or 5.1 percent of the world's adult population and in 2025 is expected to increase to 333 million inhabitants. In particular, in Indonesia, people with Diabetes Mellitus are increasing. In 2000, Diabetes Mellitus sufferers have reached 8.4 million people and it is estimated that the prevalence of Diabetes Mellitus in 2030 in Indonesia reaches 21.3 million people.This allows researchers and practitioners to focus their attention on detecting/diagnosing diabetes mellitus and to prevent it because the disease can cause complications. The method used in this research was problem identification, data collection, pre-processing stage, classification method, validation and evaluation and conclusion. The algorithm used in this research was CART and Naïve Bayes using dataset taken from UCI Indian Pima database repository consisting of clinical data ofpatients who detected positive and negative diabetes mellitus. Validation and evaluation method used was 10-crossvalidation and confusion Matrix for the assessment of precision, recall and F-Measure. The result of calculation has been done, got the accuracy result on CART algorithm equaled to 76.9337% with precision 0.764%, recall 0.769%, and F-Measure 0.765%. Whilethe diabetes dataset was tested with the Naïve Bayes algorithm, got an accuracy of 73.7569% with precision 0.732%, recall 0.738%, and F-Measure 0.734%. From these results it can be concluded that to diagnose diabetes mellitus disease it is suggested to use CART algorithm.
Effects of Integrating Augmented Reality in Early Childhood Special Education

Various research studies showed the praiseful impact of technology tools in the education process of the special kids. The intention of this comparative study was to explore how Augmented Reality (AR) based educational application would support mild and moderate Intellectually Disabled (ID) children of age 5-10. Eight ID students from a Block Resource Center in Kerala, India were participated in this study. They were divided into two groups. Children with hyperactivity were selected as experimental group, and they had extra support from AR-based tool via smartphone. The control group students were trained by using conventional method. After measuring the parameters such as learning outcome, response to the learning material, learning time and memory capacity, the data has been analyzed. From the analysis results, it noticed that the participants from the experimental group exhibited improvements in all the parame-ters than those in the control group. In addition to the experi-ment, a survey was conducted among their parents and the re-source teachers to know their feedback about the effectiveness of the application. The observed parameters and the survey results indicate that the implementation of AR-based educational ap-plications in early childhood special education system is worth to explore further.
Portfolio Optimization of the Mean-Absolute Deviation Model of Some Stocks using the Singular Covariance Matrix

Investing in the stock sector, investors often face risk problems. Usually, forming an investment portfolio is done to minimize risk. In this research, investment portfolio optimization is discussed. The data analyzed are 8 shares traded on the capital market in Indonesia through the Indonesia Stock Exchange (IDX). Optimization is performed using the Mean-Absolute Deviation model with the singular covariance matrix to determine the optimal weights. The results of portfolio optimization Mean-Absolute Deviation model with singular covariance matrix method, was obtained optimal portfolio weights that is of 17.22% for BBCA shares; 26.64% for TKIM shares; 9.96% for BBRI shares; 9.96% for BBNI shares; 8.70% for BMRI shares; 3.75% for ADRO shares; 6.52% for GGRM shares; and 17.25% for UNTR shares. Where the optimal portfolio composition is obtained the expected rate of return (expected return) of 0.18% with a portfolio risk level (standard deviation) of 0.07%.
Analysis of recycling performance in Poisson multi-Bernoulli mixture filters

In a multi-target tracking (MTT) scenario, the computational cost of usual Poisson multi-Bernoulli mixture (PMBM) filter will rise rapidly as the increasing number of global hypotheses. In order to lower computational cost, this paper presents to apply recycling algorithm to PMBM filter. The proposed method is done by recycling Bernoulli components which are less than a fixed threshold, approximate them as Poisson point process (PPP), thus add the intensity to the undetected PPP intensity. In the numerical experiment, we apply recycling algorithm to PMBM, Poisson multi-Bernoulli (PMB) and multi-Bernoulli mixture (MBM), respectively. The result shows that the Bernoulli recycling algorithm leads to lower computational cost in a simulated scenario.
A Business Operation Stability by Improving the Speed of Restoration on Software Application Service

Tatana Zitkova
Exploration of geothermal potential using integrated fuzzy logic and analytic hierarchy process (AHP) in Ağrı, Eastern Turkey

Hot water, steam and gases, which are caused by the heat at different depths in the earth's crust and contain various chemicals, are defined as "geothermal fluids" (Akkuş and Alan, 2016).Geothermal energy can use direct and indirect depending on these fluids.Geothermal energy is frequently used today in energy, heating and cooling processes where electrical production and district heating and cooling applications are required.In addition, some industrial applications require drying in the production of chemicals such as fertilizer, lithium, hydrogen or carbon dioxide, in the tourism sector, and in the production of mineral drinking water (Teke and Yaşar, 2018;Yan et al., 2017;Wang et al., 2018;Meng et al., 2021).The most important advantage of geothermal is that it is a renewable energy source.At the same time, it is a clean energy source as it is natural.Countries around the world are turning to alternative energy sources in order to meet the energy needed by the rapidly increasing population and developing industry (Noorollahi et al., 2008;Yousefi et al., 2010;Cambazoğlu et al., 2019).In Turkey, comprehensive researches are carried out to discover geothermal resources, which are a renewable energy sources that can replace conventional energy resources, and to find ways to ake maximum use of them (Şimşek, 2003; Sanlıyüksel and Baba, 2011;
A Framework for Digital Image Encryption using Chaotic Baker Map with SHA Algorithm

Recently, security for the image is becoming very important. Image Encryption is the only choice in protecting the digital image by transforming the image into an unreadable format. There are many methods used to protect against unauthorized access. This research proposes a framework for image encryption using Chaos Baker map with SHA-1 algorithm. The Chaotic Baker map is a randomization technique used to make the pixels more shuffled. Key generation is essential part of image encryption, which will be carried out by SHA-1 algorithm. Experimental results show that the proposed method is well suited for high security, key sensitivity and resists various attacks.
On upper and lower generalized semi-continuous fuzzy multifunctions

In this paper, the concepts of upper and lower generalized continuous (resp.semi-generalized continuous and generalized semi-continuous) fuzzy multifunctions are defined where the domain of these multifunctions is a classical topological space with their values as arbitrary fuzzy sets in a fuzzy topological space.Main properties of these multifunctions along with their mutual relationships are discussed with the help of examples.Also, a new approach to these multifunctions between two fuzzy topological spaces (X, τ) and (Y, η) based on the sense of Šostak are introduced.Some properties of these multifunctions along with their mutual relationships are investigated with the help of examples.
Gene expression data classification using genetic algorithm-based feature selection

In this study, hybrid methods are proposed for feature selection and classification of gene expression datasets.In the proposed genetic algorithm/support vector machine (GA-SVM) and genetic algorithm/k nearest neighbor (GA-KNN) hybrid methods, genetic algorithm is improved using Pearson's correlation coefficient, Relief-F, or mutual information.Crossover and selection operations of the genetic algorithm are specialized.Eight different gene expression datasets are used for classification process.The classification performances of the proposed methods are compared with the traditional GA-KNN and GA-SVM wrapper methods and other studies in the literature.Classification results demonstrate that higher accuracy rates are obtained with the proposed methods compared to the other methods for all datasets.
XOR Codes and Sparse Learning Parity with Noise

A k-LIN instance is a system of m equations over n variables of the form s i 1 + • • • + s i k = 0 or 1 modulo 2 (each involving k variables).We consider two distributions on instances in which the variables are chosen independently and uniformly but the righthand sides are different.In a noisy planted instance, the right-hand side is obtained by evaluating the system on a random planted solution and adding independent noise with some constant bias to each equation; whereas in a random instance, the righthand side is uniformly random.Alekhnovich (FOCS 2003) conjectured that the two are hard to distinguish when k = 3 and m = O(n).We give a sample-efficient reduction from solving noisy planted k-LIN instances (a sparse-equation version of the Learning Parity with Noise problem) to distinguishing them from random instances.Suppose that m-equation, n-variable instances of the two types are efficiently distinguishable with advantage ε.Then, we show that O(m • (m/ε) 2/k )-equation, nvariable noisy planted k-LIN instances are efficiently solvable with probability exp -O((m/ε) 6/k ).Our solver has worse success probability but better sample complexity than Applebaum's (SICOMP 2013).We extend our techniques to show that this can generalize to (possibly non-linear) k-CSPs.The solver is based on a new approximate local list-decoding algorithm for the k-XOR code at large distances.The k-XOR encoding of a functionGiven oracle access to a function G that µ-correlates with F k , our algorithm, say for constant k, outputs the descrip-
A QoS-enhanced data replication service in virtualised cloud environments

In recent cloud systems, data-intensive applications have been widely deployed for solving non-trivial problems, which often involve large amount of dataset. As a standard service for offering data availability and reliability, data replication service plays an important role and is proven to be effective in many real-world cloud environments. However, many existing replication services are designed for improving the system-oriented metrics instead of user-oriented metrics. In this paper, we first formulate the quality-of-service (QoS) constrained replication placing problem in different manners and then a novel replication service is proposed which uses multi-objective technique to solve the replica placement problem via a time-efficient approach. A set of experiments are conducted to evaluate the effectiveness and efficiency of the proposed service and the results indicate it can significantly reduce the average response time metric comparing with other existing replication services and therefore improve the QoS for user applications. In addition, it also exhibits a good robustness when the cloud storage system is facing intensive replication workloads.
Evaluation and Applying Feature Extraction Techniques for Face Detection and Recognition

Detecting the image and identifying the face has become important in the field of computer vision for recognizing and analyzing, reconstructing into 3D, and labelling the image. Feature extraction is usually the first stage in detection and recognition of the image processing and computer vision. It supports the conversion of the image into a quantitative data. Later, this converted data can be used for labelling, classifying and recognizing a model. In this paper, performance of such feature extraction techniques viz. Local Binary Pattern (LBP), Histogram of Oriented Gradients (HOG) and Convolutional Neural Network (CNN) technique is applied to detect and recognize the face. The experiments conducted with a data set addressing the issues like pose variation, facial expression and intensity of light. The efficiency of the algorithms were evaluated based on the computational time and accuracy rate.
Anomaly event detection and localization of video clips using global and local outliers

The automatic detection of anomaly events in video sequence has become a critical issue and essential demand for the extensive deployment of computer vision systems such as video surveillance applications. An anomaly event in video can be denoted as outlier behavior within video frames which formulated by a deviation from the stable scene. In this paper, an anomaly event detection and localization method in video sequence is presented including multilevel strategy as temporal frames differences estimation, modelling of normal and abnormal behavior using regression model and finally density–based clustering to detect the outliers (abnormal event) at clips level. Hence, outlier score is obtained at the segment or clip level along video frames sequences. The proposed method seplits video frames into nonoverlapped clips using global outlier detection process. Afterward, at each clip, the local outliers are determined based on density of each clip. Extensive experiments were conducted upon two public video datasets which include dense and scattered outliers along video sequence. The experiments were performed on two common public datasets (Avenue) and University of California, San Diego (UCSD). The experimental results exhibited that the proposed method detects well outlier frames at clip level with lower computational complexity comparing to the state-of-the-art methods.
The moderating role of knowledge sharing and mediating role of employee creative self-efficacy on the association of empowering leadership and employee creativity

The study's purpose is to explore the relationship among empowering leadership (EL) and employee creativity through knowledge sharing and employee creative self-efficacy. A standard questionnaire is distributed among 352 employees and their leadership for data collection of the study. The study participants are taken from top management, including lower-level employees working in the telecommunication industry. We analyse data using the structural equation modelling technique. The results show that empowering leadership significantly affect employees' creativity and creative self-efficacy mediates the relation between empowering leadership and employee creativity and knowledge sharing to strengthen empowering leadership and employee creativity relationships. Our findings explain that leadership has a strong role in employees' creativity. Additionally, the implications of the study inform practitioners to investigate this phenomenon in other areas.
Communicative Language Teaching in Malaysian ESL Context: A Qualitative Exploration into In-service Teachers' Beliefs and Practices

In contrast to the plethora of research on teachers' beliefs and practices to implement communicative language teaching (CLT), research into in-service primary school teachers' beliefs has earned less focus to evaluate its impacts on their instructional and evaluation practices, particularly in Malaysian ESL context.This qualitative study intended to mitigate the knowledge gap by investigating prevalent communicative language teaching practices in the primary schools in Malaysia.Analysis of our survey and interview data reveals some common problems that affect the proper implementation of CLT.Although teachers faced challenges and had their shortcomings, they endeavored to integrate various instructional and assessment practices to improve students' competency on all four language skills.The study also highlights some key issues which may provide practical implications for the application of CLT in Malaysian primary schools.
Data sharing and data citation: join the movement!

If you attended the EASE Conference in Bucharest in July 2018, you might remember a session on data citation, presented by Bart Verberk from Springer Nature, Heather Tierney from ACS & COPE and me, from Crossref.If you weren't there, we talked about data sharing policies and support for these, ethical issues around research data, and the practicalities of citing data and registering these citations with Crossref.In the months since that event, we've seen the conversation around data publication and data citation continue to grow.Enabling data to be clearly and persistently identified and linked to the journal articles it supports is key for the data to be properly acknowledged, credited and tracked within the research process.As an editor, it's valuable to be able to look at the data associated with an article you're reading.This can help provide more details behind the ideas the the paper puts forward.As a researcher, you may want to share this information to allow others to check your processes, to help them reproduce, extend or avoid duplicating your research, and also to see who is citing, downloading or viewing the research you are publishing.To bring the sharing and citation of data into the mainstream, authors, editors and publishers need to take steps to include this in their regular workflows.This is easily said, but what does this look like in practice?The research community has been motivated to share ideas and experiences on how to introduce and implement a policy on data sharing and data citation.A recent article 1 by Jones, Grant and Hrynaszkiewicz (2019) explores the approaches taken by two publishers, Taylor & Francis and Springer Nature, in communicating and integrating their data sharing policies into standard journal workflows.There are similarities in the approaches taken by these two publishers which may prove helpful to those considering implementing policies of their own.Their publisher policies are tiered, from a basic approach which encourages data sharing, citation and the provision of a data availability statement, 2 through to an open and fully FAIR 3 approach where data must be made freely and openly available under a suitable licence, and data citation and data availability statements are mandatory.Both publishers have journals adopting different levels of data sharing within these tiers, and they acknowledge that there are differences in willingness to share data across different disciplines.Springer Nature found that "life scientists were the most likely to make their data available in a repository, while those in the physical sciences were least likely to". 1 There are also differences in the perceptions
Teach Machine Learning with Excel

In the fourth industrial revolution, data becomes as important as energy and materials.The major tool to deal with the huge amount of data is machine learning, which can analyze audio and visual information effectively.The fundamentals of machine learning were introduced to Engineering Physics students in a one-credit introductory course.Since many concepts in machine learning are related to physics and engineering, the students have no trouble understanding the principles.In order to avoid the challenges of programming, Excel was used to demonstrate the training processes.