Reviewerly/exHarmony huggingface dataset:
PREDICTION OF STAGE-BASED HUMAN IMMUNODEFICIENCY VIRUS (HIV) AND ACQUIRED IMMUNODEFICIENCY SYNDROME (AIDS) CASES IN SURABAYA FROM 2018 UNTIL 2022

Introduction: The development of the presence of HIV and AIDS in the world is increasingly alarming. The number of cases is increasing every year in all regions of Indonesia. Surabaya is one of the cities in East Java which has the highest number of HIV and AIDS cases. Early detection of HIV and AIDS cases will affect the quality of life of people with HIV and AIDS. The purpose of this study was to develop predictive models of HIV and AIDS cases diagnosed at the stages of HIV and AIDS from 2018 until 2022. Materials and Methods: The present research was a quantitative descriptive study with time series analysis as the research approach. Secondary data was used. The variable was the number of cases at the stages of HIV and AIDS. Results: The results of this study confirmed that prediction of HIV and AIDS cases found over the next five years always experienced an increase. It increases every year, both at the stages of HIV and AIDS.  Based on the results of the prediction, there is a higher number of cases found at the stage of HIV. This indicated that early cases findings in Surabaya were quite good. Conclusion: The present research reveals the prediction of HIV and AIDS cases in Surabaya has increased over the next five years. They are more commonly found at the stage of HIV, but there are still many cases found at the stage of AIDS.
Cloud Services Quality Enhancement through Control & Monitoring of Service Level Agreements at Server and Consumer Side

Several researches and case studies have been revealing a repetitive issue and that is monopolization of controlling, measuring, and metering of the consumer’s billing for the consumed Cloud Services. The Cloud Service Providers (CSPs) have been hiding, or monopolizing the controlling, metering tools and ignoring the calculation and consideration of the service defects like downtime, service outage, poor performance and client service migration delays during overloads of the servers. The main issues behind these discrepancies are the Service Level Agreement (SLA), its violation, and the monopolized Cloud Governance. Till now there is a lack of parallel monitoring and metering system of the consumed cloud services at the customer level under the provisions of SLAs. The Cloud Governance Tier has not given any flexibility or utility to monitor such consumed cloud services at the client tier in parallel to the CSP tier. This research is an effort to develop a SLA controlling/monitoring framework to solve the identified problems other than the CSP level. Further the E_Draw max was used for designing the framework. The framework consists of the components like; SLA_CSP, SLA_CSC, SLA_M&C and Cloud Services and Delivery Models. After designing the framework, a prototype was developed using CloudSim on java net beans, and MySQL database. This prototype was implemented using four scenarios to evaluate its performance and the functionalities. 1) Checking the SLA under CSP whether the resources to the consumer in the same manner with promised. 2) To check if the request from cloud consumer is less than what is expected in SLA. 3) To check by increasing the request of cloud consumer more than what is expected in SLA and 4) Using the SLA_M&C mechanism. After execution it was observed that, in the first and the second cases there is no violation, but there is violation in the third and fourth cases. This implied that there are chances of the discrepancies in resources provided to make the selves beneficiary beyond SLA under CSP and CSC was discriminated while receiving services from CSP.
Quantitative Comfort Evaluation of Eating Assistive Devices based on Human Effort Estimation using an Accelerometer

Quantitative Comfort Evaluation of Eating Assistive Devices based on Human Effort Estimation using an Accelerometer
Journey Through Technology

Author(s): Nielsen, David James | Abstract: This music score was submitted for the Kaleidoscope 2020 Call for Scores, an open access collaboration with the UCLA Music Library.
Application of Optimized BP Neural Network Based on Genetic Algorithm in Rugby Tackle Action Recognition

Rugby is a fair compete fullfed with human discipline, self-control, and mutual respect between athletes. Notably, tackle action is one of the most basic rugby actions and the smallest unit of body touch and confrontation when attacker and defender fight for possession of rugby. After match, trainer usually count the number of tackle action happened during match to analyze and discuss for adjusting rugby tactics. This paper studies a method that optimized BP neural network based on genetic algorithm and build a neural network classifier through extracting 10-dimension gray characteristics of samples and 12-dimension geometric characteristics of body to recognize and count the tackle action. After further optimizing network structure and weights, experimental results show that our model could tackle action happened in match to avoid the subjective and empirical nature of artificial viewing discrimination with strong practical value, and will greatly improve the efficiency of trainers' work.
The Performance Comparison of Models for Predicting the Risk of Losing Student Loan by Fuzzy Neural Network Method and Multiple Linear Regression Analysis Method

This research presents the comparing the performance of the prediction model of the risk of losing student loan by Fuzzy Neural Network (FNN) and Multiple Linear Regression Analysis (MLR) . We used the preparation of 296 samples, assigned the income variables, which are characterised by Fuzzy attribute and had problem-solving with Dummy variables. Also, we proposed the method of converting fuzzy data by making a fuzzy attribute matching of Neural Network (NN) and Multiple Linear Regression. It calculates the fuzzy linguistic term, the fuzzy language and the Crisp value, and after that have filtered variables with Pearson correlation technique and multiple linear regression which get eight independent variables and RiskForPay is a dependent variable. Results from this research, the appropriate model of Fuzzy Neural Network is a division of learning data, and cross-validation fold is 5 with an accuracy of 83.33% +/- 6.02% which models have 8-5-6 structure, momentum 0.2 and learning rate 0.3. The predictive model with multiple linear regression equations has Root Mean Squared Error: 1.513 +/- 0.000 and Squared Correlation: 0.081, respectively.Keywords: Fuzzy Neural Network, Multiple Linear Regression Analysis, Student Loan, Fuzzy logic
Accent neutralization for non-native speech using neural style transfer

Automatic speech recognition (ASR) has been an object of extensive research since the second half of the previous century. ASR systems achieve high accuracy rates, however, only when the system is used for recognizing the speech of native speakers. The score drops in case the ASR system is being used with a non-native speaker of the language to be recognized, as the pronunciation is affected by the patterns of the mother tongue. Traditional approaches for developing speech recognition classifiers are based on supervised learning, relying on the existence of large labeled datasets. In case of non-native speech such datasets do not always exist and even if they do, the number of samples is not always high enough to train accurate classifiers. We have dealt with the problem of the non-native speech in our previous research using different approach of dual-supervised learning [1]. This time, we try tackling the problem using the style transfer methodology. We designed a pipeline for modifying the non-native speech, so that it resembles the native one to a higher extent. In this research, we plan to tackle the problem of non-native accent, using style transfer methodology. We adjust style transfer to the domain of speech (double degree). He is focusing on the area of machine learning and deep learning, especially targeted for speech recognition problem for non-native speakers. He has published several conference and journal papers. Currently he is employed at the Waseda University as a Research Associate. Publication of speakers: 1. Radzikowski, K., Wang, L., Yoshie, O., Nowak, R.: Dual supervised learning for non-native speech recognition. EURASIP Journal on Audio, Speech and Music Processing 2019:3, 1–10 (2019), doi:10.1186/ s13636-018-0146-4 2. Alhindi, Tariq, Savvas Petridis, and Smaranda Muresan. “Where is your Evidence: Improving Fact-checkand sound, to create an algorithm for real-time accent modification. Such an approach could allow to modify non-native speaker’s voice on-the-fly, so that the ASR system can recognize the speech with higher accuracy. Our methodology could potentially be used as a wrapper for existing ASR system, reducing the necessity of training new algorithms for non-native speech.
A study on the Financial Competency, Financial Education, and Retirement Preparation of Professional Military Households

The purpose of this study was to provide implications for financial education programs by investigating the financial capacity of professional soldiers, analyzing the influencing factors of retirement preparation, with the recognition that military households need guidance in preparing for retirement. The results of analyzing the data of 267 professional soldiers by conducting online and offline surveys May-October 2019 are as follow.<BR> First, the respondents were poorly prepared for future difficulties and were less likely to be planning for the future, were not using a system that could protect households from financial risks, and had insufficient knowledge of emergency funds. They perceived themselves as practicing insufficient use of household account books, failing to properly save for retirement, and engaging in insufficient insurance management, a long-term financial product.<BR> Second, their experience in financial education was 24.3% of internal military education and 25.1% of external military education. The intention to participate in financial education in the future was higher in internal financial education.<BR> Third, the group above the average for retirement preparation had higher life attitudes and financial behavior than the group below the average, but there was no difference in the level of financial education experience and financial knowledge depending on retirement preparation.<BR> Fourth, the respondents with internal and external financial education experience showed higher levels of financial behavior than those without financial education experience.<BR> Fifth, as a result of the logistic regression analysis to assess the influence on retirement preparation, it was found that life attitude and financial behavior had significant effects on the retirement preparation.<BR> This study revealed that professional military households are experiencing difficulties with income management and asset formation, and that the demand for financial education inside the military is high. This study provided basic data and implications for financial education for the military.
Cognitive Neuroscience of Memory

Department of Neurology Vanderbilt University Nashville, Tennessee The reviewer declares no conflicts of interest.
The Analysis of the Track and Field Physical Training Approaches

The improvement of track and field performance depends on the comprehensive improvement of track and field athletes'physical fitness, and track and field athletes' physical fitness includes both physical strength, endurance, and speed, as well as physical sensitivity, coordination, and flexibility. Therefore, for track and field, the athletes' physical trainingmust be a comprehensive and targeted unity. Starting fromthe author's own work, this paper discusses the track and field physical fitness training approaches and related strategies to improve the training effect, hoping to promote the overall improvement of track and field physical fitness training level.
Enhanced Flush+Reload Attack on AES

In cloud computing, multiple users can share the same physical machine that can potentially leak secret information, in particular when the memory de-duplication is enabled. Flush+Reload attack is a cache-based attack that makes use of resource sharing. T-table implementation of AES is commonly used in the crypto libraries like OpenSSL. Several Flush+Reload attacks on T-table implementation of AES have been proposed in the literature which requires a notable number of encryptions. In this paper, we present a technique to enhance the Flush+Reload attack on AES in the ciphertext-only scenario by significantly reducing the number of needed encryptions in both native and cross-VM setups. In this paper, we focus on finding the wrong key candidates and keep the right key by considering only the cache miss event. Our attack is faster than previous Flush+Reload attacks. In particular, our method can speed-up the Flush+Reload attack in cross-VM environment significantly. To verify the theoretical model, we implemented the proposed attack.
A Socio-Cognitive Approach to the Discourses of Self-Identity and Public Perception about Transgender in District Mansehra

This study critically examines the discourses about the self-identity formation and public perception about transgender community in District Mansehra, Pakistan. The study is theoretically guided by socio-cognitive approach of discourse analysis. The data was collected in the participant setting through in-depth interviews from the twenty transgender and fifteen respondents from the general public. The views of transgender about their self-identity and public perception about transgender was analyzed in the light of the linguistic choices while using socio-cognitive model. It was found that the self-identity formation of transgender is socially constructed that was reflected in their discourse. Likewise, the status, acceptance and overall social existence of transgender are also potentially determined by the views and narratives of the general public. The study concludes that the identity formation and discrimination of transgender is socially and linguistically constructed and internalized by the transgender community in specific and society in general.
A Paleomagnetic Study of the Tectonic Deformation in Circum-Marmara Region, NW Anatolia, during the Late Cretaceous and Cenozoic Period

&amp;lt;p&amp;gt;The Marmara region is located on the Alpine Himalayan orogenic belt which experienced a active tectonic deformation. The region consists of tectonic units such as the Istanbul Zone, the Strandja Zone and the Sakarya Continent. It is reported in the previous geological studies that the Istanbul Zone began to move southwards appart from the Moesia Platform with the effect of West Blacksea Fault in the west and West Crimea Fault in the east after the the opening of the Black Sea in the Cretaceous. It is known that the Intra Pontide suture is formed after the closure of the Intra-Pontide ocean during the Early Eocene due to the collision between &amp;amp;#304;stanbulzone and the Sakarya continent which moved northwards. As a result of the continental collision, the region has completed its evolution under the influence of basin formation and the emplacement of North Anatolian Fault Zone from Miocene to the present.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;In this study, Upper Cretaceous-Oligocene sedimentary and volcanic rocks were sampled at 103 sites to investigate the tectonic deformation of the area. As a result of rock magnetism studies, it was shown that magnetic minerals in sedimentary and volcanic rocks are defined by titanium-rich titanomagnetite showing low coercivity, while in limestone samples, magnetization is defined by hematite showing high coercivity. As a result of anisotropy of magnetic susceptibility (AMS) measurements, it was observed that most of the samples show magnetic foliation and a deformation ellipsoid which is oblate. Paleomagnetic results show counterclockwise rotation of 19.9&amp;amp;#176;&amp;amp;#177;10.9&amp;amp;#176; for the Sakarya continent, 27.4&amp;amp;#176;&amp;amp;#177;11.6&amp;amp;#176;for the Pontides and 15.6&amp;amp;#176;&amp;amp;#177;11.8&amp;amp;#176;for the Strandja Zone from Eocene to present. The results indicate that the region has completed the collision in Eocene and rotated counterclockwise as a large block. Deformation due to basin development or fault bounded block rotations which developed after Miocene could not been detected in this study. Miocene paleomagnetic data from previous studies in the study area are compatible with counterclockwise rotations in Upper Cretaceous-Oligocene which shows that different blocks emplaced in the study area moved together as a single plate during Eocene-Miocene time.&amp;lt;/p&amp;gt;
Subject Guides: ANTH 495: Anthropology Capstone: Browse Journals

Research Guide for the Capstone Course in Anthropology Use Browzine app to find journals in your area
Scalable Association Rule Learning Algorithm for Very Large Dataset

Author(s): Li, Haosong | Advisor(s): Sheu, Phillip | Abstract: Many algorithms have been proposed to solve the association rule learning problem. However, most of them suffer from the problem of scalability either because of unacceptable time complexity or tremendous memory usage, especially when the dataset is enormous and the minimum support (minsup) is low. This paper introduces a new approach that follows the divide-and-conquer paradigm, which can exponentially reduce both the time complexity and memory usage, even on a single machine.
A CGM-Based Parallel Algorithm Using the Four-Russians Speedup for the 1-D Sequence Alignment Problem

The CGM model is one of the most widely used models for the design of parallel algorithms. It has shown its efficiency in solving several problems modeled by dynamic programming such as the longest common subsequence problem, which is a special case of the one-dimensional sequence alignment problem. This problem consists in aligning two strings of length n to measure their similarity. It is widely used in many fields, particularly in Bioinformatics where n is usually very large. Brubach and Ghurye proposed a sequential solution based on the Four-Russians speed-up that requires O(n^2 / log n) execution time. To the best of our knowledge, there are not yet parallel solutions on the CGM model to solve this problem. This paper is exclusively dedicated to this task. Our solution applies to both local and global similarity computations and is based on Brubach and Ghurye’s sequential algorithm. It requires O(n^2 / p log n) execution time with O(p) communication rounds on p processors.
Study on the Countermeasures of School-Enterprise Cooperation in Wenzhou under the Mode of New Apprenticeship of Enterprises

With the implementation of the thought of socialism with Chinese characteristics, the economic level of our country has gradually improved, and the problem of employment has been solved to a certain extent. Promoting the transformation and upgrading of enterprises can create more employment opportunities and create conditions for enterprises to gain more kinetic energy. The development of modern enterprises has an urgent demand for talents. The mode of new apprenticeship is more and more widely used in enterprises. On this basis, the construction of school-enterprise cooperation relationship enables enterprises and schools to achieve a win-win situation in terms of talents. This paper mainly analyzes and discusses the school-enterprise cooperation in Wenzhou, and provides countermeasures for the development of school-enterprise cooperation in Wenzhou under the mode of new apprenticeship of enterprises, hoping to provide reference for relevant personnel.
Comparative Analysis of Machine Learning Techniques Using Predictive Modeling

Introduction: Machine learning is an intelligent technology that works as a bridge between businesses and data science. With the involvement of data science, the business goal focuses on findings to get valuable insights on available data. The large part of Indian Cinema is Bollywood which is a multi-million dollar industry. This paper attempts to predict whether the upcoming Bollywood Movie would be Blockbuster, Superhit, Hit, Average or Flop. For this Machine Learning techniques (classification and prediction) will be applied. To make classifier or prediction model first step is the learning stage in which we need to give the training data set to train the model by applying some technique or algorithm and after that different rules are generated which helps to make a model and predict future trends in different types of organizations. Methods: All the techniques related to classification and Prediction such as Support Vector Machine(SVM), Random Forest, Decision Tree, Naïve Bayes, Logistic Regression, Adaboost, and KNN will be applied and try to find out efficient and effective results. All these functionalities can be applied with GUI Based workflows available with various categories such as data, Visualize, Model, and Evaluate. Result: To make classifier or prediction model first step is learning stage in which we need to give the training data set to train the model by applying some technique or algorithm and after that different rules are generated which helps to make a model and predict future trends in different types of organizations Conclusion: This paper focuses on Comparative Analysis that would be performed based on different parameters such as Accuracy, Confusion Matrix to identify the best possible model for predicting the movie Success. By using Advertisement Propaganda, they can plan for the best time to release the movie according to the predicted success rate to gain higher benefits. Discussion: Data Mining is the process of discovering different patterns from large data sets and from that various relationships are also discovered to solve various problems that come in business and helps to predict the forthcoming trends. This Prediction can help Production Houses for Advertisement Propaganda and also they can plan their costs and by assuring these factors they can make the movie more profitable.
Self-understanding Support Tool Using Twitter Sentiment Analysis

Self-understanding, which is important for such daily aspects as decision making, requires grasping one’s strengths and weaknesses, likes and dislikes, joys and disappointments. However, gathering such useful data for self-understanding is difficult. The aim of this research is to develop a system to support self-understanding using sentiment analysis of Twitter posts. We developed a tool that displays bar graphs, tweet log lists, pie charts, and word lists that indicate the positive/negative emotions of a user’s tweets. Preliminary experiments revealed that our approach and tool are promising for self-understanding, understanding emotions, and recalling memories.
Digital Forensics in private Seafile Cloud Storage from both client and server side

Nowadays, some people and organisations are unwilling to store their information on public clouds due to security and privacy problems. With increased use of established cloud services by cloud software, it is possible for malicious users to use these services for criminal purposes, so digital forensic investigations of these cloud services are necessary. Seafile cloud storage is one of popular cloud that is free and open source. In this paper, we document a series of digital forensic experiments on Seafile cloud storage service with the aim of providing forensic researchers and practitioners on both the client and server sides of Seafile. Data remnants are checked on client systems in virtual machines which running Windows 10 Home operating system and on a server with Windows Server 2012 R2 operating system. Different circumstances are created for digital forensic examinations. Correspondingly, Seafile client software and multiple browsers are used for interaction with Seafile cloud server.
LibGuides: Research Output Metrics: Home

Displays research outputs (journals articles, meeting papers, etc.) from Web of Science, Compendex and other resources