Reviewerly/exHarmony huggingface dataset:
Robust Landmark Selection for 3D Face Pose Estimation

Face pose estimation is an important computer vision problem that has many applications. Vehicle driver tracking, augmented reality, human-computer interaction, and face frontalization for recognition are among the examples of applications that benefit face pose estimation. One common way of face pose estimation is solving an optimization problem with given the 2D and 3D landmark locations as input. Estimated face pose should be stable in realtime applications, however jitter occurs due to noise and changes on facial expression. The resulting jitter has a negative effect on pose estimation applications. Therefore, reduction of jitter is an important requirement for face pose estimation. In this study, we aim to detect a set of robust facial landmarks that provides a stable pose estimation. We applied a feature selection scheme by using the variance of rotation vector as the accuracy metric which is computed from frames of face videos. In the experiments, we determined landmark subsets which reduce jitter for test videos with and without facial gestures, and provides a lower variance in rotation vector. As a result of this study, 29 landmark points that are positioned on the face are determined to be the most robust landmarks when the person has no facial expression. When the person has facial expression, 30 landmark points are selected on the face.
Effects of High Strength Resistance Exercise Program on Fall-related Fitness, HOMA-IR and HbA1c in Elderly Women

The purpose of this study is to investigate the effects of high intensity resistance exercise program on the fall fitness, HOMA-I and HbA1c of elderly women for 8 weeks. The participants were members of the fitness center located in D-gu, G-city, and 30 people with voluntary participation were selected after learning the purpose of the experiment. The selected 30 participants were divided into 15 groups for exercise and 15 for control group. The exercise group performed a high intensity resistance exercise program for 8 weeks. The results of this study showed that the high intensity resistance exercise program for 8 weeks significantly improved upper leg strength, lower extremity muscle strength, cardiopulmonary endurance, coordination and balance, and decreased insulin resistance. Taken together, the results of this study suggest that the program of 8 weeks of high intensity resistance exercise is an effective program to improve the fall-related physical fitness, HOMA-IR and HbA1c of elderly women. Therefore, this study aims to improve the cardiorespiratory endurance and muscular strength, decrease equilibrium and coordination, reduce the risk of falling, reduce insulin resistance, and to provide basic data for improving the quality of life and maintaining a healthy life of the elderly by maintaining body composition.
Intelligent Sense-Enabled Lexical Search on Text Documents

This paper proposes a new methodology for intelligent sense-enabled lexical search on text documents. The proposed methodology extracts words from an input text document which are semantically related to a particular sense of the query word. The entire methodology is divided in to two tasks namely, Word Sense disambiguation (WSD) of each word in the input text followed by semantic search i.e, extracting those words that are semantically related to a particular sense of the query word. The significance of the proposed methodology is that, to the best of our knowledge this is the first work that supports sense-enabled lexical search in a text document simultaneously considering the problems with polysemous words. Extraction of semantically related words to a given query word has role in many applications such as document indexing, vocabulary learning for humans, machine translation, etc. Experimental results show that the proposed system surpasses the existing system in terms of precision and computational time. This improved precision and execution time enhances the end user’s experience quality in using the system.
Scene Recognition with Deep Learning Methods Using Aerial Images

In this paper, two novel deep learning architectures are proposed to solve the scene classification problem using aerial images. The results that we get using the two models that are constructed utilizing a part of ResNET50 pretrained model are investigated. In model evaluations, we used one of the largest open access dataset, i.e. NWPU-RESIS45 dataset, that contains 45 different categories and in total 31500 samples. 95.7% accuracy that we get with the developped models show a competitive performance with the state-of-the-art methods. The proposed approaches are advantageous because they present a base model that requires low processing power and memory compared to the existing approaches. Within the scope of the study, the contribution of the regulation of the categories in a hierarchical structure to the classification performance is also investigated.
Differentially Private Markov Chain Monte Carlo

Recent developments in differentially private (DP) machine learning and DP Bayesian learning have enabled learning under strong privacy guarantees for the training data subjects. In this paper, we further extend the applicability of DP Bayesian learning by presenting the first general DP Markov chain Monte Carlo (MCMC) algorithm whose privacy-guarantees are not subject to unrealistic assumptions on Markov chain convergence and that is applicable to posterior inference in arbitrary models. Our algorithm is based on a decomposition of the Barker acceptance test that allows evaluating the Renyi DP privacy cost of the accept-reject choice. We further show how to improve the DP guarantee through data subsampling and approximate acceptance tests.
Analisa Tingkat Kepuasan Pengguna Elearning Menggunakan EUCS dan Model Delone and McLean

Abstract - Elearning is now a necessity to support the implementation of learning in various educational institutions. End User Computing Satisfaction (EUCS) is a method used to measure the level of satisfaction of users of an application system by comparing expectations and reality of an information system. In this study 5 EUCS indicators were measured which would determine the level of Elearning user satisfaction, namely content, accuracy, shape, ease of use and timeliness. In the Delone And McLean Model there are six factors which are indicators, namely system quality, information quality, service quality, usage, user satisfaction, net benefits. Of the two methods will be measured how the level of elearning user satisfaction at Bina Sarana Informatics University. This study uses random samples collected through electronic questionnaires. There are several limitations in this study and further studies are needed so that the level of elearning user satisfaction can be described correctly.&#x0D; Keywords: elearning, eucs, endusercomputingsatisfaction, delonemclean
Machine Learning for Geoscience Applications

Summary With roots in Artificial Intelligence (AI), machine learning has evolved over several decades with contributions from various scientific disciplines. In the nineties, remarkable techniques such as probabilistic graphical models, kernel, boosting, and random forest methods emerged and since the mid-2000s, with availability of large datasets and improvements in computational power led to advances in neural network based methods with various deep learning architectures. The latter has resulted in some remarkable innovations in the recent years, and led to wide and visible successes for a spectrum of scientific and commercial applications. With these modern methods, it is now feasible to solve problems with significant underlying complexity; and that too with remarkable accuracy and flexibility. Oil and gas industry acquires large and complex datasets for exploration and field development purposes. However, these datasets are not being optimally used to extract useful information. We believe that with the recent advances in machine learning and computational power, advanced machine learning methods can be used to not only extract useful information from these complex datasets but also reduce the man power costs to process and makes sense of these datasets. Recognizing this potential, over the past several years, we have been actively researching and developing numerous modern machine-learning applications in various domains, including the geosciences. Through examples, our focus will be on the potential of machine learning to address complex geoscientific problems such as well log processing, interpretation, correlation and seismic interpretation.
Stochastic Variance Reduced Primal Dual Algorithms for Empirical Composition Optimization

We consider a generic empirical composition optimization problem, where there are empirical averages present both outside and inside nonlinear loss functions. Such a problem is of interest in various machine learning applications, and cannot be directly solved by standard methods such as stochastic gradient descent (SGD). We take a novel approach to solving this problem by reformulating the original minimization objective into an equivalent min-max objective, which brings out all the empirical averages that are originally inside the nonlinear loss functions. We exploit the rich structures of the reformulated problem and develop a stochastic primal-dual algorithms, SVRPDA-I, to solve the problem efficiently. We carry out extensive theoretical analysis of the proposed algorithm, obtaining the convergence rate, the total computation complexity and the storage complexity. In particular, the algorithm is shown to converge at a linear rate when the problem is strongly convex. Moreover, we also develop an approximate version of the algorithm, named SVRPDA-II, which further reduces the memory requirement. Finally, we evaluate the performance of our algorithms on several real-world benchmarks and experimental results show that they significantly outperform existing techniques.
Multi-task Image Clustering through Correlation Propagation

Traditional image clustering algorithms deal with single-task clustering (STC) problem on a single domain. However, with the increasing number of related images on the Web, it is challenging for STCs to perform related image clustering tasks independently without considering the between-task relationship, which mainly consists of similar visual features and image patterns among tasks. Therefore, it is intuitive to resort to multi-task clustering (MTC) algorithms. However, most existing MTCs learn a shared feature subspace, which may lead to negative transfer when facing the image clustering tasks that are not strongly related. In this paper, we propose a novel multi-task image clustering algorithm, which performs multiple image clustering tasks simultaneously and propagates the task correlation to improve clustering performance. Specifically, we first extend the information bottleneck method to cluster tasks independently. The related and unrelated images between the pairwise clusters of different tasks are then discovered. Meanwhile, two corresponding types of correlations are propagated among the tasks, where only the positive correlation benefits the clustering of each task. A sequential and collaborative method is further designed to ensure an optimal solution. Moreover, we perform a theoretical analysis of the properties on correlation propagation and the convergence of our algorithm. The experimental results demonstrate that the proposed algorithm outperforms the state-of-the-art clustering methods.
Faster Boosting with Smaller Memory

State-of-the-art implementations of boosting, such as XGBoost and LightGBM, can process large training sets extremely fast. However, this performance requires that the memory size is sufficient to hold a 2-3 multiple of the training set size. This paper presents an alternative approach to implementing the boosted trees, which achieves a significant speedup over XGBoost and LightGBM, especially when the memory size is small. This is achieved using a combination of three techniques: early stopping, effective sample size, and stratified sampling. Our experiments demonstrate a 10-100 speedup over XGBoost when the training data is too large to fit in memory.
Implementation of Finding Shortest Route for Pharmacy Location Using A-star Algorithm

The number of pharmacies in the Pangkalpinang City has increased rapidly. This is directly proportional to the needs of the people of Pangkalpinang City to fulfill the health needs, buy medicine or divert doctor's prescriptions. This is a debate for people who do not know the location of pharmacies in this city. Practically, people can just go to a pharmacy with a location that far from user location, even though there is a pharmacy near the user. This is because there is no pharmacy information location in the Pangkalpinang City. To solve this problem, proposed a system that can display the location of all pharmacies in the Pangkalpinang City. In addition, the system can search the shortest path from the user's location to the nearest pharmacy location around using the A * algorithm. This algorithm was chosen because its advantages that are able to produce optimal solutions according to the expected heuristic functions. The results of the testing are that the system can optimize and be able to search the shortest path from the user's location to the nearest pharmacy.
Policy-Based De-Identification Test Framework

Protecting privacy of individuals is a basic right, which has to be considered in our data-centered society in which new technologies emerge rapidly. To preserve the privacy of individuals de-identifying technologies have been developed including pseudonymization, personal privacy anonymization, and privacy models. Each having several variations with different properties and contexts which poses the challenge for the proper selection and application of de-identification methods. We tackle this challenge proposing a policy-based de-identification test framework for a systematic approach to experimenting and evaluation of various combinations of methods and their interplay. Evaluation of the experimental results regarding performance and utility is considered within the framework. We propose a domain-specific language, expressing the required complex configuration options, including data-set, policy generator, and various de-identification methods.
Improved ORB Algorithm used in Image Mosaic

The classical SIFT, SURF and other algorithms based on linear scale decomposition are robust, but they lose local precision, which is easy to cause boundary blur and detail loss, and the real-time performance is generally not good. To overcome the above problems, and considering that ORB is not sensitive to the scale transformation of feature points, an improved algorithm is proposed. After the image is preprocessed, the feature points are first proposed by ORB algorithm, Then use the FREAK algorithm to obtain the binary code descriptor of the feature point for matching, and then adopts KNN algorithm to rough match these points. Then it uses the Random Sample Consensus Algorithm to remove the fault matching and to realize fine match. Finally, it uses the improved weighted average method for image fusion and stitching. Experimental results show that the proposed algorithm is effective for image stitching, moreover the stitching quality and stitching speed are much better than other image stitching algorithms.
All About Phishing: Exploring User Research through a Systematic Literature Review.

Phishing is a well-known cybersecurity attack that has rapidly increased in recent years. It poses legitimate risks to businesses, government agencies, and all users due to sensitive data breaches, subsequent financial and productivity losses, and social and personal inconvenience. Often, these attacks use social engineering techniques to deceive end-users, indicating the importance of user-focused studies to help prevent future attacks. We provide a detailed overview of phishing research that has focused on users by conducting a systematic literature review of peer-reviewed academic papers published in ACM Digital Library. Although published work on phishing appears in this data set as early as 2004, we found that of the total number of papers on phishing (N = 367) only 13.9% (n = 51) focus on users by employing user study methodologies such as interviews, surveys, and in-lab studies. Even within this small subset of papers, we note a striking lack of attention to reporting important information about methods and participants (e.g., the number and nature of participants), along with crucial recruitment biases in some of the research.
The Overall Goal of Education and General Purpose

His physical mental moral and social development through creative development of the person's innate ability and qualities. To give the student the opportunity to develop according to his own talent and tendency and to nurture his creativity. Making skilled, productive and manpower necessary for the socioeconomic development and progress of the country by applying education. To help the student acquire the necessary qualifications and skills in order to earn a living according to his talents and trends. To inspire the student in the open expression of basic thought and to develop liberal thinking and life-oriented, objective and positive outlook in the society.
A Cataloger’s Perspective on Cataloging Backlogs

Backlogs in cataloging are an unpleasant fact for many library technical service departments. While backlogs may be strategic in nature and smooth out the workflow from acquisitions to cataloging, many are simply uncataloged gifts, unfinished retrospective conversion projects, or low-priority uncataloged collections—all waiting to be addressed when everything else is caught up. Catalogers are often stereotyped as perfectionists; in reality, the nature of our work requires us to be very pragmatic. By substituting a less than full record, progress on reducing backlogs is possible.
SECURITY CONFIGURATION AND PERFORMANCE ANALYSIS OF FTP SERVER 

File Transfer Protocol is used for transferring files to clients over networks using client-server architecture. FTP provides two mechanisms for file transfer; one is anonymous method and another one is password authentication mechanism. All the communication between client and server is without encryption means data is transferred in clear text whether it is password or ftp commands. There are some requirements which are considered important while file transfers and these are Authentication, Integrity and Confidentiality. For implementing security in file transfer protocol we use FTPS rather than FTP as it is more secure. As FTPS uses some encryption mechanisms, it adds some extra process which effects the performance. FTP and FTPS are configured according to the security requirements for file transfer. Adding some extra process overhead in FTPS like encryption, it affects the performance. This research paper compares both FTPS and FTP on Linux and Windows server.
Relations among Conceptions of Learning, Approaches to Learning and Academic Achievement: A Comparison between College and High School Contexts

이 연구는 학생들이 경험적으로 구성한 학습개념과 학습에 접근하는 방식이 학교급에 따라 달라지는 지를 살펴보고, 이러한 학습개념과 학습접근이 학업성취를 예측해주는지를 분석하고자 하였다. 연구를 위해 경기지역의 5개 고등학교에 재학 중인 2학년 학생과 서울에 있는 1개 대학의 교직수업을 듣는 대학생 등 총 236명이 편의표집되었다. 학습개념과 학습접근에서 집단 간 차이분석 결과, 고등학생은 학습을 ‘정보의 습득’으로 보는 경향이 대학생보다 높은 반면, 대학생들은 ‘관점의 변화’, ‘개인의 성장’, ‘의무’, ‘시공간을 초월하는 과정’으로 학습을 개념화하는 경향이 더 높았다. 상관분석 결과, 고등학생과 대학생 집단에서 모두 의미 이해, 개인의 성장, 시공간 초월 등의 질적인 학습개념을 가진 학습자와 심층적인 학습접근을 사용하는 학습자의 학업성취가 높은 것으로 나타났다. 회귀분석 결과, 고등학생 집단의 경우 국어와 영어교과목에서는 성취에 대한 학습개념의 예측력이 유의하지 않았지만 수학교과에서는 질적인 학습개념이 수학성취를 예측하였고, 심층 접근은 국어와 수학교과목에서 학업성취를 예측하는 것으로 나타났다. 대학의 교직과목의 경우, 의미 이해와 의무로서의 학습개념만이 학업성취를 예측하는 것으로 나타났다. 이러한 연구결과는 학교급(고등학교 대 대학교), 교과의 특성(순수 대 응용), 평가 지향(기억 파지 대 이해ㆍ탐구)과 관련하여 논의되었고, 이들 변인을 체계적으로 변이시켜 독립적인 효과를 분석하는 추후연구에의 시사점이 제언되었다.The aim of the study is twofold: 1) to analyze the differences between high school and college students in their conceptions of learning and approaches to learning, and 2) to examine the relationships among conceptions of learning, approaches to learning and academic achievement for these two groups. A total of 236 students from five high schools located in the Kyung-Ki province and one university located in Seoul participated in the study. The results of the t-test analysis indicated that high school students scored higher than college students on the conception of learning as acquisition of information while college students scored higher than high school students on the conceptions of learning as change of perspectives, personal change, duty, and processes not bound by time and space. For both groups, qualitative conceptions of learning were correlated with a deep learning approach, and qualitative conceptions of learning and a deep learning approach were correlated with academic achievement. Additionally, the regression analyses revealed that in the high school context, qualitative conceptions of learning predicted only math achievement while a deep learning approach predicted language arts and math achievement. In the college context, conceptions of learning as understanding and duty predicted course achievement while a deep learning approach did not predict course achievement. The findings and implications for future research were discussed in relation to the educational context, the characteristics of subject areas, and the purpose of evaluation.
Kurdish speaker identification based on one dimensional convolutional neural network

Voice is one of the vital biometrics in human identification and/or verification area. In this paper, two different models are proposed for speaker identification which are a 1D convolutional neural network (CNN) and feature based model. In the feature based model, three global spectral based features including Mel Frequency Cepstral Coefficient (MFCC), Linear Prediction Code (LPC) and Local Binary pattern (LBP) are fed to an SVM and k-NN classifiers. Results show that MFCC is the best feature among the others. Consequently, local MFCC features is extracted from the framed signal and used to both the proposed models. The result shows that the local based MFCC improved the accuracy of the CNN based model.
Blindfolded Evaluation of Random Forests with Multi-Key Homomorphic Encryption.

Decision tree and its generalization of random forests are a simple yet powerful machine learning model for many classification and regression problems. Recent works propose how to privately evaluate a decision tree in a two-party setting where the feature vector of the client or the decision tree model (such as the threshold values of its nodes) is kept secret from another party. However, these works cannot be extended trivially to support the outsourcing setting where a third-party who should not have access to the model or the query. Furthermore, their use of an interactive comparison protocol does not support branching program, hence requires interactions with the client to determine the comparison result before resuming the evaluation task. In this paper, we propose the first secure protocol for collaborative evaluation of random forests contributed by multiple owners. They outsource evaluation tasks to a third-party evaluator. Upon receiving the client's encrypted inputs, the cloud evaluates obliviously on individually encrypted random forest models and calculates the aggregated result. The system is based on our new secure comparison protocol, secure counting protocol, and a multi-key somewhat homomorphic encryption on top of symmetric-key encryption. This allows us to reduce communication overheads while achieving round complexity lower than existing work.