Reviewerly/exHarmony huggingface dataset:
VOCABULARY LEARNING STRATEGY EMPLOYED BY HIGH-ACHIEVER UNIVERSITY STUDENTS AT MALANG

Learners should learn English language basic skills and also the components such as grammar, pronunciation, and vocabulary. The learners will have a difficulty in understanding interlocutor’s mean or expressing their ideas without having a sufficient vocabulary. In learning vocabulary, students may have various strategies used to improve their vocabulary. High-achiever students are considered as students who perform well in school and have a high academic achievement. This research is conducted to find out the most common strategy in learning vocabulary used by high achiever students and the reasons in choosing the strategy. Mixed methods, which combine quantitative and qualitative research design was applied for this research. Then, the instruments of this research are questionnaire and interview. The researcher takes seventh-semester high-achiever students as population and 26 high-achiever students who have Grade Point Average (GPA) 3, 51 - above were taken as the sample of this research. The result showed that the most common strategy employed by high-achiever student is cognitive strategy with mean score of 79.8. The strategies involved were note-taking, highlighting, analyzing, dictionary-use, etc. The high-achiever students preferred to use cognitive strategy because it helped them in learning English skill, easy to apply, to make them speak English well, effective and also it is comfortable to use during learning process Therefore, appropriate learning strategies would help the students perform their English well.
Sentimental Analysis of Twitter Health Data using Machine Learning Techniques

With the huge development of Internet, more users have occupied with wellbeing networks, for example, medicinal discussions to assemble wellbeing related data, to share encounters about medications, treatments, analysis or to associate with different clients with comparable condition in social media. A lot of lookup has focused on examining Twitter health tweets for subject matter modeling using quite a number clustering approaches, but few have mentioned it for sentiment analysis. The truth that such statistics carries potential information for revealing the opinion of humans about fitness services and behaviors make it an interesting study. In these paper, universal sentiments about Twitter health data was investigated. Twitter, measuring and monitoring the occurrence of social health problems. The approach is based on two stages: In first stage separating perhaps applicable tweets utilizing a lot of uniquely made standard articulations, and afterward arranges these underlying messages utilizing machine learning techniques. Using the Twitter search API and Twitter metadata geocoded content, social media tweets were selected to start filtering. Once Tweets are correctly identified, the classifier was applied to data in order to filter out the tweets. Classification results were improved by detecting the values of ROC and f-measure. This report indicates that such a method provides a viable solution for quantifying and tracking the progression of health status within society.
Text Independent Speaker Identification with Prosody Features in Presence of Noise

Automatic recognition of Meta data of a speaker apart from recognizing only his or her identity is a challenging task. It gives rich behavioral characteristics of a person.Maximum work have been done in speaker recognitionon low level spectral features. Which gives good accuracy with minimum error, but they ignore other information about the speaker. Also in spectral variations, in session variations and in channel variations these features give degraded performance. State-of-the-art systems for text-independent speaker identification use Mel Frequency cepstral coefficients (MFCCs) as main features. Generally this system performs very good under clean conditions and acceptable under matched conditions. Under mismatched conditions, however, performance significantly deteriorates. One of the principal reasons for poor performance in these conditions is because of the nature of low-level features; being spectral, they are susceptible to spectral variations due to noise and channel effects.Prosodic features are used successfully in these variation conditions as well as in presence of noise.In this paper multi SNR environment is considered. Recognition accuracy has been calculated at different SNR levels i.e. 15 dB, 25 dB and 35 dB.Also results are tested at different types of noise such as Traffic noise, cockpit noise, babble noise and fan noise. It has been found that combining prosodic features such as pitch, energy and formants gives improved performance.
Validation Techniques for Relating Ensemble Methods in Polyp Detection

Endoscopy is one of the most efficient colon screening technique through which the polyps are identified and treated. This manual process of identifying the polyp has chance of missing some polyps while diagnosis. To overcome this an efficient computer aided detection technique need to designed, there are several computing techniques and algorithms available, this research article tries compare different techniques used in polyp detection and also proposes several performance evaluation metrics which can be used to find the proficiency of methods in identifying polyps in endoscopy videos. The article presents various identification methods, evaluation methods include various parameters like performance metrics. The proposed ensemble approaches are discussed in polyp detection mechanism.
Single-Photon Frequency Conversion for Quantum Interface

Single-photon frequency conversion for quantum interface plays an important role in quantum communications and networks, which is crucial for the realization of quantum memory, faithful entanglement swapping and quantum teleportation. In this chapter, we will present our recent experiments about single-photon frequency conversion based on quadratic nonlinear processes. Firstly, we demonstrated spectrum compression of broadband single photons at the telecom wavelength to the near-visible window, marking a critical step towards coherent photonic interface. Secondly, we demonstrated the nonlinear interaction between two chirped broadband single-photon-level coherent states, which may be utilized to achieve heralding entanglement at a distance. Finally, we theoretically introduced and experimentally demonstrated single-photon frequency conversion in the telecom band, enabling switching of single photons between dense wavelength-division multiplexing channels. Moreover, quantum entanglement between the photon pair is maintained after the frequency conversion. Our researches have realized three significant quantum interfaces via single-photon frequency conversion, which hold great promise for the development of quantum communications and networks.
Sistem Pakar Deteksi Penyakit pada Anak Menggunakan Metode Forward Chaining

Health is the most valuable thing for humans, because anyone is vulnerable to health problems. Especially in children, children are very susceptible to germs and sensitivity to symptoms of a disease is a fear for parents themselves. However, with the convenience of having an expert doctor, sometimes there are also weaknesses such as limited working hours or doctor's practice hours and the number of patients who have to wait for queues and also the many parents who do not know about the symptoms and types of children's diseases, a system is built to facilitate the medical team and system users. The purpose of this study was to detect children's diseases using the Forward Chaining method precisely and accurately. The data that were processed were 25 symptoms and 5 types of childhood diseases which were sourced from patient medical records and interviews with experts at the Ibnu Sina Simpang Empat Islamic Hospital. The symptoms and types of disease are entered into the Expert System using the rules of rules and the Forward Chaining method. To diagnose a child's disease, a Forward Chaining method is needed with the following stages: Preparing input data, determining decision tables, determining rules, tracking processes, making decision trees. The results of the study with 25 symptom data obtained as many as 5 decision rules, namely which type of childhood disease the patient has and the initial treatment that must be done. Based on the analysis carried out, it can be seen the types of diseases suffered by children so that it can be used as a reference for making decisions to diagnose diseases in children. This expert system calculation shows the percentage of success from the expert.
Critical Success Factors For Creation Of Technopolis Communities And Creation Of The Central Florida Technopolis

Technopolis communities are sprouting up all over the world and are creating a profound impact on global economic landscapes.Today's global economy can be characterized by increasing globalization, heightened interdependency and the emergence of a new paradigm of regional, institutional and technological clusters which facilitate innovation and its commercialization called the "Technopolis Phenomenon".This paper reviews research done on creation of Technopolis communities and highlights exemplary best practices from Technopolis communities around the world such as Silicon Valley, Boston, Austin, Ireland, Bangalore, Taiwan, Sophia-Antipolis, and others.The role of academia-industry-government collaboration in creation of Technopolis communities is discussed in detail.This involves sustained, collaborative efforts by academics, industry representatives, Entrepreneur Support Organizations (ESOs), Economic Development Organizations (EDOs), engineers, entrepreneurs, investors, and other practitioners to develop initiatives, plans, methodologies, infrastructure, and action items for Technopolis creation.The authors' experiences in attempting to create a Technopolis community in Central Florida from Florida Tech on the Space Coast, through UCF and the Orlando Metropolis to USF and Tampa on the Gulf Coast to add experiential insights for program implementation and effectiveness.Engineering education programming linking academic / continuing education programs with experiential entrepreneurship and community outreach activities is emphasized.Critical Success Factors for Technopolis creation are identified.
Heavy metal contamination of soil based on pollution, geo-accumulation indices and enrichment factor in Phan Me coal mine area, Thai Nguyen province, Vietnam

Soil samples around the Phan Me coal mine area, Thai Nguyen province, Vietnam had been analyzed for Fe, Mn, Cu, Zn, As, Cd, Hg, Pb and Ni. Single pollution index, geo-accumulation index, enrichment factor have been determined and used for assessing the soil quality. In average by pollution index, the soil heavy metal pollution sequence is Cu(PI=4.2)&gt;Cd(3.5)&gt;Zn(3.1) &gt;Pb(1.7)&gt;Ni(1.3). High concentrations of Cu, Zn and Cd in the soil in the study area are thanks to geo-accumulation and enrichment of the metals in the soil. The soil is from moderately to heavily contaminated by Cu, Zn and Cd: Cu and Zn concentrations are 1.5-2.8 times higher than the ecological risk values, and Cd concentration is about 3.5 times higher than allowable limits. The high concentration of Pb is thanks to moderate geo-accumulation and enrichment of Pb, and the soil mostly has Pb concentration higher than the allowable limit in about 1.7 times. There is a sign that the soil is contaminated by Ni: 22% of the samples have Ni concentration higher than the threshold value, however, the enrichment of Ni is only deficient to minimal. The results of the assessment highlight the need for a comprehensive and detailed study program on heavy metal content in different soil resources in a wider area to identify the magnitude and details of the problem associated with heavy metal contamination for the development of a remediation plan and more effective pollution preventing measures.
Dactylogram Based Voting System Using Image Encryption and Symmetric Key Encryption

In Online, Security plays an important role including payments, social activities, electoral application educational sites, etc.The Secure voting system and its process are ensured by an encryption and decryption process and its main goal is to provide high security and authorization to the data involved in the process and also the data that has been stored in the database.Security is the priority needed in every activity that is involved in the process.Using Triple-DES encryption and decryption algorithm, the fingerprint of the user has been captured from the dataset and it undergoes Triple-DES encryption and decryption to provide security to data of each user.Here Dataset is used to store the fingerprint of each user after obtaining the fingerprint from the fingerprint scanner.The advantage of this security process is to secure data that cannot be breached by any unauthorized users and to provide ease of use and easy maintenance.The proposed method DBVS is efficient compared to RSA based algorithms.
KECERDASAN EMOSIONAL PESERTA DIDIK SEKOLAH DASAR

This study aimed to describe emotional intelligence of Grade IV Students of elementary school . This study used descriptive method with a qualitative approach. The data collection using emotional intelligence questionnaires and interview. Based on the results t he students’ emotional intelligence charactheristic was variated. The student ability to recognize and manage emotions was lacked. In addition, the ability to motivate oneself, recognize the emotions of others and the ability to develop relationships was good. Nevertheless there was tendency for the characteristics of good emotional intelligence to contribute to the learning achievements of students. This is indicated by 63.64% of students characterized by good emotional intelligence also supported by learning outcomes with 54.55% of students whose learning outcomes are complete.
Biometric Based Online Signature Identification to Determine the Fraudulent Behavior

Signature verification is the promising technique for one’s own personal identification in day to day life activities like banking transactions, credit card payments and all kind of legal documents. Nowadays wifi credit cards offered by banks are in major threat as it doesn’t need any sort of verification. The customer can be verified either through online or offline depending on the input. In this paper, i built self database after collecting the details of 20 persons like their pen speed, pen angle, pen acceleration, x,y coordinates of the pen from the way of writing. Fuzzy logic is used to compute the accuracy of the recognition rate and the implementation is accomplished in Matlab. It is found that the results are remarkable and the accuracy rate is noticeable.
Energy Efficiency Analysis Between Green Computing and Cloud Computing

Usually cloud computing is based under effective data computing but green cloud computing focus on energy efficiency of device and computing mainly based on computing architecture. Here cloud computing is also known as green computing due its better effective energy. The purpose of green computing is to decrease the usage of harmful resources, maximizing energy efficiency throughout the living span of the good and also to promote the recyclability or bio degradability of useless goods plus misuse materials of the factory. In order to simulate the hardware by utilizing software, green computing plays a major role for various technologies like virtualization, green data center, cloud and grid computing and power optimization. Here computer resources are effectively utilized by replacing the data center standalone server system by artificial server so as to run the software by less number for large computers called as virtualized. Finally this paper is fulfilled by the advancement of recognized energy efficient computing
Hybrid Method of Document Image Encryption using ECC and Multiple Chaotic Maps

Document imaging is one of the most important technology used to preserve, retrieve and to transmit old documents over the internet. Since open network threatens the security of such documents from being intercepted, the proposed scheme intends to provide enhanced security for such documents. The proposed hybrid algorithm includes Elliptic Curve Cryptography (ECC), and mixed chaotic system. The ECC encrypts the plain image based on the elliptic points generated by selecting a prime number. The mixed chaotic map includes two-dimensional logistic map and three-dimensional Lorenz map. The 2D logistic map is used for confusing the ECC encrypted image and 3D Lorenz map is used for diffusing the confused image. The two levels of confusion, first with byte level and second with bit level are introduced. The two levels of diffusion, first level with Lorenz map and second level with Fibonacci sequences are introduced. Better net pixel change rate and unified average change intensity with flat histogram and low correlation are achieved in this method. The key Sensitivity, Entropy, Mean Square Error, and Peak Signal to Noise Ratio analysis reveal that the proposed method is resistant to any external invasion. The encryption and decryption speed is increased by using programming techniques. The robustness of the algorithm is also checked and found to be robust against noise and loss of information.
Oppositional Multi Objective Particle Swarm Based Re source Optimized Job Scheduler f or Load Balanced Cloud Service Provisioning

Job scheduling is a key problem to be resolved in cloud service provisioning for balancing load and improving resource optimization performance. Recently, many research works have been designed for performing job scheduling using different techniques. However, job scheduling efficiency (SE) was not sufficient. In order to addresses the above limitations, Oppositional Multi-Objective Particle Swarm Based Resource Optimized Job Scheduling (OMPS-ROJS) technique is proposed. The designed OMPS-ROJS technique balances the load on computer resources by distributing tasks to available resources with higher efficiency. The OMPS-ROJS technique at first takes number of incoming user requested jobs to cloud server (CS) as input. Then, OMPS-ROJS technique develops Oppositional Particle Swarm Multi-Objective Optimization (OPSMO) algorithm in order to determine the optimal virtual machines for each input user requested jobs with a minimal amount of time. On the contrary to conventional works, OPSMO algorithm assume multi-objective such as energy, makespan, bandwidth, memory and cost for fitness function evaluation. This helps for OMPS-ROJS technique to find out the virtual machine which contains maximum resource availability as best to carry out the user requested jobs. Therefore, OMPS-ROJS technique efficientlybalancedynamic loads on CS through scheduling user requested jobs with a minimal time.Thus, OMPS-ROJS technique enhances the cloud service provisioning performance as compared to conventional works. Experimental result evident that OMPS-ROJS technique enhances the SE and lessen the EC as compared to conventional works.
A Hybrid Technique for Health Insurance Fraud Detection on Highly Imbalanced Dataset

Health Insurance industry is producing a massive amount of heterogeneous data. Detecting fraud from these data is a challenging task. Highly imbalanced data causes huge challenge to the Insurance Data Analysis. Classification of imbalanced data is a critical issue faced by the fraud detection methodologies. Fraud only covers less than 10% of the whole data. In this study, we use highly imbalanced data and propose a hybrid method for fixing class imbalance problem by using a combination of SMOTE, Cross Validation, and Random Forest. We used Medicare data, which will be applied to various sampling techniques, and further a classification model was built. We observed that SMOTE with Random forest with cross validation produced excellent results. Our model should be capable of identifying all the relevant(fraud) instances, i.e., the model should have a high recall value. SMOTE with Random forest had average recall of 86% and an overall accuracy of 90%, which could be considered as good among the existing models.
Introduction to the Minitrack on Games and Gaming

The Games & Gaming mini-track is now in its fourth year in the Digital and Social Media track at HICSS.Games and play are an important part of the human foundation, but despite the digital nature of the games in this minitrack they often mirror real-world social issues.More broadly, games and play deeply reflect who we are as a species.
Stereo Matching with Cost Combination and Disparity Refinement

This paper presents an approach that use combined cost method and disparity refinement for stereo matching. Here disparity is obtained using the intensity, gradient, histogram of gradients for cost computation. Later disparity refinement is performed using Weiner filter. The correctness of matching is enhanced in the proposed method. The combined use of cost measure ensures the effective evaluation. The proposed approach produces good quality disparity map.
A New Cygnus Optimization Algorithm for Prediction Of Cardio Vascular Disease

Machine learning is an emerging field in the present day due to a massive improvement in the size of data. However, with the current studies, the prologue of artificial intelligence and medical sciences, help in averting any such category of disease. With the intention to take good decision in health care, data mining methodology and technology plays a foremost role to transmit the enormous data into valuable information. Emerging as a primary source of fatality in the early 20th century and peaking in prevalence from 1980s, Cardio-Vascular Disease (CVD) remains a major global threat. Early prediction of CVD is most vital for sensible preclusion and treatment. This paper is to investigate the work on the attribute selection approach and propose an improved Cygnus Optimization Algorithm by carrying a random search through the whole attributes, which was obtained from UCI machine learning repository. The proposed method shows 97% of accuracy, which is better than early.
Fake Comments Detection with Sentiment Anatomy using Iterative Sequential Minimal Optimization Algorithm

It is significant to create electronicon stream markets,on stream communication networks, peer-to-peer functions, social media providerson stream and convenience customers. In reality, web based amenities are specially designed to overcome the risk of uncertainties &amp; distrust inherent in the main concern of ecommerce applications &amp; to increase the robustness of the system&amp; resistance against fake clients &amp; unbelievers. The aim of the Ecommerce platform is, moreover, to embrace one of the most efficient methods for understanding and evaluating user attempts to expose fraudsters. Or else, the fundamental objective of ecommerce amenities to exploit the profit &amp; purchase rate, will be endangered &amp; deteriorated through fake and ill-intentioned users. Individuals and organizations need to detect fake Comments. With disappointing and hidden features, it is difficult to identify counterfeit Comments simply by looking at a single Comments text. It is also why it is a difficult task to identify falsified Comments.This paper uses the sentiment anatomy (SA) tool for the identification of fake Comments to analyzeon stream film Comments. The texts and the SA system are used for a specific dataset of film Comments. We particularly compared the supervised SVM &amp; SMO machine-learning process with the feeling classification methods of the analyzes in two different cases, without stopping phrases. Measured outcomes display that SMO process compared to the SVM process for both methodes, &amp;it arrives at the maximum precision not only in the classification of text but also for finding duplicate analyses.