Reviewerly/exHarmony huggingface dataset:
Understanding the underlying algorithms and theories of machine learning

Machine learning is largely known as the basis for Artificial Intelligence (AI). The name itself describes exactly that, machines that learn. We are seeing an increasing amount of these machines in our day-to-day lives as well but we may not even realise it. The machines involved in machine learning are not exactly the robots learning to walk or work assembly lines. In fact, machine learning is the study of algorithms and statistical models that computer systems use so that they can perform tasks without being told exactly what to do. The algorithms are designed to find patterns and inferences in the data generated by the task at hand and use those to not only complete the task but learn how to perform said task more efficiently. In the age of big data machine learning has become an essential tool in interpreting massive datasets and developing new ways to use them. From the recommendations music and video streaming services make, to self-driving cars and speech recognition software, anywhere huge amounts of data require analysis and mining, machine learning algorithms are humming away in the background. While this field has become essential to our modern way of life and transformative for many industries, it is also a new and rapidly evolving field. Designing new algorithms that can handle increasing amounts of data with greater complexity is one area of active research, as is refining the statistical models involved in machine learning. Professor Yoshinobu Kawahara, who is based at the Institute of Mathematics for Industry at Kyushu University in Japan is carrying out research dedicated to studying the underlying algorithms and theories of machine learning.
Measuring Compensatory Eye Movements to Assess Sensory and Learning Properties

textabstractThis thesis deals with measuring compensatory eye movements to assess sensory and learning properties of humans and mice. We have used this method to determine the visual performance of humans and of mouse models of aging and congenital nystagmus and to measure the effect of direct stimulation of the brain and indirect stimulation by enriching the mouse environment on learning ability.
Improving Students’ Speaking Skill by Using Guessing Game at Grade VIII A of SMP Negeri 1 Tanjung Pinang

Students’ motivation in learning English as a Foreign language might be influenced by strategies used in the classroom. This article aims to find out to what extend guessing game improve students’ speaking skill and to find out the factors influence the students’ speaking skill. This is action research which the samples were taken from VIII A class consisted on 32 students at SMPN 1 Tanjungpinang. The instruments in this research were using test and observation.
Presburger Constraints in Trees

The fully enriched µ-calculus is an expressive propositional modal logic with least and greatest fixed-points, nominals, inverse programs and graded modalities. Several fragments of this logic are known to be decidable in EXPTIME. However, the full logic is undecidable. Nevertheless, it has been recently shown that the fully enriched µ-calculus is decidable in EXPTIME when its models are finite trees. In the present work, we study the fully-enriched µ-calculus for trees extended with Presburger constraints. These constraints generalize graded modalities by restricting the number of children nodes with respect to Presburger arithmetic expressions. We show that this extension is decidable in EXPTIME. In addition, we also identify decidable extensions of regular tree languages (XML schemas) with interleaving and counting operators. This is achieved by alinear characterization in terms of the logic. Regular path queries (XPath) with Presburger constraints on children paths are also characterized. These results imply new optimal reasoning (emptiness, containment, equivalence) bounds on counting extensions of XPathqueries and XML schemas.
Across the language barrier

Translation devices are getting better at making speech and text understandable in different languages.
The measurement process in micro and small software maintenance companies: empirical study

With the dissemination of quality models and the need for measurement-based management for software organizations, a variety of approaches and models are emerging to support organizations on this journey. According to a Systematic Review of Literature that selected 30 of 692 papers returned from search sources, it was noticed the need for guidelines and guides regarding the measurement process in a specific sector: micro and small companies that work with software maintenance. In this work, an empirical study was carried out to understand how the measurement process aligned with quality models can be implemented in these organizations. The field study followed the implementation of an improvement program based on CMMI® level 2 in 7 Brazilian companies from Maringá, Paraná. Finally, it was found that the process for the definition of a measurement guide can be common to a specific group of companies, taking steps and guidelines for its execution, always aligned with the organization’s strategy, giving top management an objective view work and process, supporting decision making.
Swimmer’s Head Detection Based on a Contrario and Scaled Composite JTC Approaches

In order to accompany the swimming coaches in evaluating high-level swimmers, we developed a prototype for instantaneous speed estimation. To achieve this, we proposed and validated, in a previous work, a swimmer tracking system based on data fusion. However, the initialization phase is done manually, and our aim, in this paper, is to automate this process. First, we propose a region of interest localization module that allows the detection of the first appearance of the swimmer in the lane as well as the restriction of the region of interest around him. This module is based on the method a contrario which consists of modeling the random noise corresponding to the water and detecting the structured movement relative to the swimmer motion. To do that, we calibrate the pool using DLT (Direct Linear Transform) technique, extract the concerned lane, apply the frame difference approach to detect the moving objects, and then decompose the lane into blocs and classify them into swimmer motion or noise. Second, in order to detect the swimmer’s head, we propose the Scaled Composite JTC which is based on the NL-JTC correlation technique. The input plane of this latter includes a target and a reference image. The first is the region of interest detected by the method a contrario. The second consists of a Scaled Composite Reference. The tests conducted on real video sequences of French swimming championships (Limoges 2015) showed very good results in terms of region of interest localization and swimmer’s head detection which allows a reliable initialization for the tracking system.
Opening a new university offers opportunities to try new approaches and reach new populations

Brand recognition, funding, and language barriers are common challenges.
SVM Ensemble-based Noise Detection Method for Image Denoising

Background: To preserve sharp edges and image details while removing noise, this paper presents a denoising method based on Support Vector Machine (SVM) ensemble for detecting noise. Methods: The proposed method ISVM can be divided into two stages: noise detection and noise recovery. In the first stage, local binary features and weighted difference features are extracted as input features vector of ISVM, and multiple sub-SVM classifiers are integrated to form the noise classification model of ISVM by iteratively updating the sample weight. The pixels are divided into noise points and signal points. In the noise recovery stage, according to the classification results of the previous stage, only the gray value of the noise point is replaced, and the replacement value is the weighted mean value with the reciprocal of the quadratic square of the distance as the weight. Results: Finally, the replacement value at the noise point and the original pixel value of the signal point are reconstructed to get the denoised image. Conclusion: The experiments demonstrate that ISVM can achieve a noise detection rate of up to 99.68%. ISVM is highly effective in the denoising task, produces a visually pleasing denoised image with clear edge information, and offers remarkable improvement compared to that of the BPDF and DAMF.
A Method for Human Facial Image Annotation on Low Power Consumption Autonomous Devices

This paper proposes a classifier designed for human facial feature annotation, which is capable of running on relatively cheap, low power consumption autonomous microcomputer systems. An autonomous system is one that depends only on locally available hardware and software—for example, it does not use remote services available through the Internet. The proposed solution, which consists of a Histogram of Oriented Gradients (HOG) face detector and a set of neural networks, has comparable average accuracy and average true positive and true negative ratio to state-of-the-art deep neural network (DNN) architectures. However, contrary to DNNs, it is possible to easily implement the proposed method in a microcomputer with very limited RAM memory and without the use of additional coprocessors. The proposed method was trained and evaluated on a large 200,000 image face data set and compared with results obtained by other researchers. Further evaluation proves that it is possible to perform facial image attribute classification using the proposed algorithm on incoming video data captured by an RGB camera sensor of the microcomputer. The obtained results can be easily reproduced, as both the data set and source code can be downloaded. Developing and evaluating the proposed facial image annotation algorithm and its implementation, which is easily portable between various hardware and operating systems (virtually the same code works both on high-end PCs and microcomputers using the Windows and Linux platforms) and which is dedicated for low power consumption devices without coprocessors, is the main and novel contribution of this research.
Cylindrical image mosaic method based on fast camera calibration in multi-scene

A cylindrical image mosaic method based on fast camera calibration in multi-scene is proposed to solve the problems of scene limitation and complex calibration process in image mosaic using camera calibration parameter. Firstly, the accurate corner feature of checkerboard calibration board is used to make it in the overlapping field of view of two adjacent images. Then, the image sequence is pre-processed by corner extraction, precision and matching, so that the registration parameters between the images to be stitched can be solved accurately and quickly. After that, the cylindrical projection is used to maintain the visual consistency of the images, and the multi-band fusion is used to retain the details of the images. Subsequently, the images are stitched using registration parameters obtained by calibration. Finally, the whole system is built on a low-power embedded platform to accomplish fast calibration and mosaic process based on calibration parameters in multi-scene. The experiment results show that the proposed method can accomplish camera calibration quickly and accurately in indoor and tunnel scenarios, and the image mosaic process is time-consuming. Meanwhile, it can ensure better stitching accuracy and imaging effect, and has strong robustness.
A Speaker Recognition System Based on Deep Learning

This paper lies in the field of digital signal processing. This is a speech recognition system that identifies the different speakers based on deep learning. The invention consists of the following steps: Firstly, we collect the voice data from different people. Secondly, the data having been selected is preprocessed by extracting their Mel Frequency Cepstral Coefficients (MFCC) and is divided into training set and test set randomly. Thirdly, we cut the training set into batches, and put them into the convolutional neural network which consists of convolutional layers, max pooling layers and fully connected layers. After repeatedly adjusting the parameters of the network such as learning rate, dropout rate and decay rate, the model will reach the optimal performance. Finally, the testing set is also cut into batches and put into the trained neural network. The final recognition accuracy rate is 70.23%. In brief, the research can automatically recognize different speakers efficiently.
Rephrasing Visual Questions by Specifying the Entropy of the Answer Distribution

Visual question answering (VQA) is a task of answering a visual question that is a pair of question and image. Some visual questions are ambiguous and some are clear, and it may be appropriate to change the ambiguity of questions from situation to situation. However, this issue has not been addressed by any prior work. We propose a novel task, rephrasing the questions by controlling the ambiguity of the questions. The ambiguity of a visual question is defined by the use of the entropy of the answer distribution predicted by a VQA model. The proposed model rephrases a source question given with an image so that the rephrased question has the ambiguity (or entropy) specified by users. We propose two learning strategies to train the proposed model with the VQA v2 dataset, which has no ambiguity information. We demonstrate the advantage of our approach that can control the ambiguity of the rephrased questions, and an interesting observation that it is harder to increase than to reduce ambiguity.
Atrial Natriuretic Peptide Prevented Lipid‐Induced Kidney Injuries by Inhibiting Endoplasmic Reticulum Stress

Hyperlipidemia plays an important role in the progression of kidney injury, likely due to renal lipotoxicity. The purpose of the present study was to investigate whether atrial natriuretic peptide (ANP) prevented lipid‐induced injury through inhibiting endoplasmic reticulum (ER) stress and apoptosis in the kidney. In human proximal tubule HK2 cells, saturated fatty acid palmitic acid (PA) treatment (0.4mM) for 24h markedly increased protein abundance of two ER stress makers BiP (3.7‐folds of controls) and CHOP (5.2‐folds of controls), which was associated with upregulated cleaved‐caspase 3 expression (3.2‐folds of controls) and decreased ratio of Bcl2/Bax (0.76‐folds of controls), two markers of apoptosis. ANP treatment (10pM) markedly decreased protein abundance of BiP (2.8‐folds of controls), CHOP (3.7‐folds of controls), and cleaved‐caspase 3 (1.7‐folds of controls), and increased the ratio of Bcl2/Bax (1.2‐folds of controls) in HK2 cells treated by PA. Co‐treatment with ANP and LBQ657, a neprilysin inhibitor preventing hydrolysis of ANP, showed better protective effects than ANP treatment alone in HK2 cells treated with PA. ANP is known to stimulate intracellular cGMP‐PKG signaling pathway, the role of cGMP and PKG in ANP‐induced protection after PA treatment in HK2 cells was thus investigated. Sildenafil, an inhibitor of phosphodiesterase‐5 (PDE5), and exogenous 8‐Br‐cGMP markedly suppressed PA‐induced ER stress and apoptosis, whereas KT‐5823, a selective cGMP‐dependent PKG inhibitor, blocked protection of ANP or cGMP in HK2 cells treated with PA. A synthesized, specific PDE5 inhibitor LW1646 showed slightly better prevention than sildenafil on PA‐induced HK2 cell injury. ANP attenuated PA‐induced upregulation of inositol 1,4,5‐triphate receptor (IP3R) that is involved in calcium efflux from ER to cytoplasm. ANP decreased PA‐induced intracellular calcium oscillation, prevented PA‐induced mitochondrial depolarization, and promoted mitochondrial biogenesis in HK2 cells. Compared with controls, in mice fed with high‐fat diet for 8wk, the protein expression of BiP and CHOP in the kidney cortex showed 2.8‐fold and 2.3‐fold increases, respectively, which was markedly prevented by sildenafil (15mg/kg·BW/day, gavage). In conclusion, these findings demonstrated that ANP inhibited lipid‐induced ER stress and apoptosis, likely via activating the cGMP‐PKG signaling pathway and maintaining mitochondrial function, it may be a potentially therapeutic target for lipid‐induced kidney injury.
Internet of Things enabled Smart Activity Recognition System for energy conservation

Activity recognition and energy conservation are the key enablers in context-aware for a smart home. Conflict among user preferences is challenging especially when there are more than a single resident living in a smart home. Along with this focus has to be devoted to assisting conservation system exiting approach focuses on a multi-agent environment. Researchers propose a Context model. Each user activity is aggregated with its related appliances and ignores the user comfort indices. This paper presents context-aware application and rule-based approach to achieve energy Conservation while considering comfort indices in multiuser resident.
Prediction Analysis of Employee Resignation Level using K-Nearest Neighbor Algorithm

PT. Micro Madani Institute is a company engaged in the training and recruitment of employees, the company is given the mandate to recruit Mekaar employees.In 2018 along with the rapid growth of the Mekaar Branch Office from 1,100 units to 2,500 units, the number of HR management will automatically increase by 27,500 people.With this increase in HR management targets, PT.Micro Madani Institute takes a strategic step to accelerate employee fulfillment.The level of fulfillment and resignation of employees is highly considered by the company especially with the number of employee resignations every year continues to increase constantly hencethe process of achieving targets for employee fulfillment is hampered as many employees resigns.One way to deal with this is to implement data mining to assist in decision making.In this study the author implemented the K-Nearest Neighbor method to assist analysts in predicting employee resignation rates.The K-NN analysis process in this study uses 4 variables: position, distance traveled, years of service, and resignation reason.The study applied the euclidian distance to calculate the distance between training data and test data sorted according to the smallest distance value.The study used the 5 nearest neighbors (k) namely 1,3,5,7 and 11 which were used to see the cluster patterns of the neighbors.Reduction of employee resignation analysis implemented with the K-Nearest Neighbor method can generate resign reasoning patterns from the results of euclidian distance.The results expected to facilitate the company's management in the process of analyzing the rate of employee reesignation by examining previous data in order to create the right decision in minimizing employee resignations.
An Unmediated Consortia Interlibrary Loan Service: The Data and Discovery of CSU+

In July 2017 the California State University system of twenty-four libraries started an unmediated interlibrary loan service for print materials, known as CSU+. This paper examines four years of interlibrary loan data, from two years before CSU+’s launch to two years after, to explore the impact of the new service on interlibrary loan usage. The data shows that CSU + dramatically increased interlibrary loan use across the California State University system.
ENGAGING TO NURTURING: ENGLISH LANGUAGE TEACHING STRATEGIES AND CONSTRAINTS FOR VERY YOUNG MUSLIM LEARNERS AT KINDERGARTEN IN ACEH

The use of English language for very young learners in kindergarten has become fundamental in recent global teaching development, including in Aceh, Indonesia. Therefore, this qualitative research is aimed to discover the English language teaching strategies applied for very young Muslim learners’ at Kiddos English School (KES) Kindergarten, Banda Aceh. The research participants were selected by using non-probability sampling method. Furthermore, semi-structured interview towards four English teachers and participant observation was conducted in two classes of KES. The result of this study showed that KES teachers implemented at least seven strategies to teach English for their students from engaging learners in daily oral language activity, to establishing a nurturing environment, playing games, storytelling, audio-visual, singing, and teaching face to face. Furthermore, the teachers faced four constraints in applying those strategies, which are: students’ misbehavior, inconsistency of students’ attendance, lack of parental involvement, and insufficient number of teacher.
Penerapan Steganografi File Gambar Menggunakan Metode Least Significant Bit (LSB) dan Algoritma Kriptografi Advanced Encryption Standard (AES) Berbasis Android

Sending messages in the form of image files among the public or business people using the SMS feature from time to time is increasingly easy and often done. Messages can be sent from business partners or from clients. If the file is used for business purposes, it is an important secret for the company, and very dangerous if it falls into the hands of an undue. Therefore the security and confidentiality of the files sent are important and are very necessary in the exchange of information in this case in the form of image files through internet media. The method used to overcome this problem applies a combination of Advanced Encrytion Standard (AES) Cryptographic Algorithm techniques integrated with the Least Significant Bit (LSB) method. The integration of these techniques aims to provide protection or security systems on secret file messages in the form of images sent. The results of this study are applications called "StegoKripto" which is a combination of steganography and cryptography. The application runs on the Android platform which can encrypt text messages and hide the messages in pictures in the format .jpg and .png.
Pengujian Black Box pada Sistem Informasi Penjualan HI Shoe Store Menggunakan Teknik Equivalence Partitions

Testing software that is incomplete and ineffective can cause various problems that cause losses, especially for users, this test is carried out to ensure that if it does not match what is expected then the system fails. The sales information system at the Hi Shoe Store is then carried out using the Black Box test based on Equivalence Partitions, so it will be known weaknesses in the information system after testing. The system test results show that testing can improve quality and guarantee error free.
sEMG-based lip shapes recognition

With the development of intelligent technology, the need for more effective and practical human-computer interaction becomes more and more urgent. The objective of this paper is to use the facial surface electromyography (sEMG) for lip shapes recognition. We recorded the sEMG signals of eight mouth movements from five subjects, used various feature extraction methods to do classification and used a feature selection method. . Results show that the best classification performance was obtained from Support Vector Machines and the efficient number of features is five. Our research would be a potential method to implentate a robust human computer interface further. High recognition accuracy means that we can send commands to control external devices by collecting sEMG signals and predicting their expressed actions to achieve effective human-computer interaction.