Reviewerly/exHarmony huggingface dataset:
The Effect of Partner Reading Strategy on Reading Comprehension

This research is conducted in investigating whether Partner Reading Strategy affects on students’ Reading Comprehension ability in descriptive text. The problem of this research is “What is the effect of Partner Reading strategy significantly for students’ reading comprehension at grade eight of SMP N7 Pematangsiantar?”. To solve the problem of this research, the researchers used related theories ; Grellet (1997), Nunan (1991), Melanie and Paula (2008) and Patel, Jain (2008), Pammun (2017), Snow et al (2002), Dorn and Soffos (2005 ) and Grade (2014). This research was done in SMP N 7 Pematangsiantar. In this research, the researchers conducted an experimental quantitative research. The population of this research was the eight grade of SMP N 7Pematangsiantar The samples of this research were two classes, they were 62students The first class was 31 students of experimental class who had been taught by Partner Reading Strategy and the second was 31 students of control group who had been taught by conventional method. The instrument for collecting the data was 20 items of multiple choice test. The data were analyzed by using t-test formula in order to see whether Partner Reading Strategy significantly affect on students’ reading comprehension or not. The total score of pre-test in control group is 1065, mean 34.35 and the total score of post-test in control group is 1355, mean is 45,16, meanwhile the total score of pre-test in experimental group is1525, mean is 49.19 and the total score of post-test in experimental group is 2470, mean is79.67. The finding indicates that the value of t-test exceeds the value of t-table (8.77&gt; 1,67), at the level of significant p = 0,05 and the degree freedom df= 60. The finding implies that the alternative hypo research is accepted. In other words, there is a significant effect of Partner Reading Strategy on the students reading comprehension.
Population transfer via a finite temperature state

We study quantum population transfer via a common intermediate state initially in thermal equilibrium with a finite temperature $T$, exhibiting a multi-level Stimulated Raman adiabatic passage structure. We consider two situations for the common intermediate state, namely a discrete two-level spin and a bosonic continuum. In both cases we show that the finite temperature strongly affects the efficiency of the population transfer. We also show in the discrete case that strong coupling with the intermediate state, or a longer duration of the controlled pulse would suppress the effect of finite temperature. In the continuous case, we adapt the thermofield-based chain-mapping matrix product states algorithm to study the time evolution of the system plus the continuum under time-dependent controlled pulses, which shows a great potential to be used to solve open quantum system problems in quantum optics.
BWCNN: Blink to Word, a Real-Time Convolutional Neural Network Approach

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease of the brain and the spinal cord, which leads to paralysis of motor functions. Patients retain their ability to blink, which can be used for communication. Here, We present an Artificial Intelligence (AI) system that uses eye-blinks to communicate with the outside world, running on real-time Internet-of-Things (IoT) devices. The system uses a Convolutional Neural Network (CNN) to find the blinking pattern, which is defined as a series of Open and Closed states. Each pattern is mapped to a collection of words that manifest the patient's intent. To investigate the best trade-off between accuracy and latency, we investigated several Convolutional Network architectures, such as ResNet, SqueezeNet, DenseNet, and InceptionV3, and evaluated their performance. We found that the InceptionV3 architecture, after hyper-parameter fine-tuning on the specific task led to the best performance with an accuracy of 99.20% and 94 ms latency. This work demonstrates how the latest advances in deep learning architectures can be adapted for clinical systems that ameliorate the patient's quality of life regardless of the point-of-care.
Features of the Construction and Control of the Navigation System of a Mobile Robot

The use of robots is one of the promising areas for development in various industries, human activities.Mobile robots are of particular importance.These robots are able to replace humans in difficult and dangerous situations.Mobile robots are able to perform any tasks that have different levels of difficulty.An important element of mobile robots is the navigation system and the management of such a system.The navigation control system of a mobile robot determines the possibilities of using such a robot.This necessitated the importance of considering the features of the construction and control of the navigation system of a mobile robot.The paper highlights the key features of this consideration.
Design of Human Machine Interactive System based on Hand Gesture recognition

Due to the wide application of human-computer interaction, gesture recognition technology has received more and more attention in recent years. At present, the most common human-computer interaction systems often do not have hand gesture interaction functions. Therefore, on the basis of the traditional interactive system function, this paper adds a depth-image capture device to make the system interactive function with bare hands reality. The whole interactive system mainly consists of three modules: preprocessing, hand gesture detection and recognition, tracking and interaction. The preprocessing module is mainly responsible for detecting and locating the interaction area. The gesture detection and recognition module are mainly responsible for detecting and identifying gestures appearing in the interaction area. The tracking and interaction module perform tracking of user gestures through Kalman tracking and operates virtual hardware according to the gesture recognition result to realize human-computer interaction function. The system has been tested in complex environments, showing robustness to lighting changes and complex backgrounds. By using CPU-GPU parallel computing, the average processing speed is achieved more than 50 fps. The proposed system uses Kinect camera and using Python open source tools for image and video processing. This paper establishes a gesture interaction system with Kinect camera, and controls the virtual hardware through gestures to realize real-time human-computer interaction in complex background.
Mosaic Packing to Visualize Large-Scale Electric Grid Data

For large power systems, a continual challenge is to display wide-area data in a way that maximizes human users’ situational awareness. This paper describes a new visualization technique that draws a mosaic of colored tiles to represent multiple data fields for electric grid objects, arranged to preserve geographic context. The key problem in creating these diagrams is packing the tiles onto the display space, minimizing the total displacement while forbidding overlaps. This paper formulates that problem and presents a horizontal-packing algorithm which is able to produce a feasible, quality solution at an interactive time scale. Illustrative examples are shown for using mosaics to monitor wide-area generator status and dispatch, bus voltages, and line and transformer limits. Mosaics can be customized in numerous ways to show different aspects of the system state, providing for human users a simultaneous sense of the wide-area summary, regional trends, and prominent outliers.
Simultaneous Demosaicing and Chromatic Aberration Correction through Spectral Reconstruction

We present an algorithm for simultaneously demosaicing digital images, and correcting chromatic aberration, that operates in terms of spectral bands. Chromatic aberration depends on both the camera’s optical system, and on the spectral characteristics of the light entering the camera. Previous works on calibrating chromatic aberration produce models of chromatic aberration that assume fixed relationships between image channels, an assumption that is only valid when the image channels capture narrow regions of the electromagnetic spectrum. When the camera has wideband channels, as is the case for conventional trichromatic (RGB) cameras, the aberration observed both within and between channels can only be accurately predicted given the spectral irradiance of the theoretical, aberration-free image. For an RGB camera, we use bandpass-filtered light to calibrate its chromatic aberration in terms of image position and light wavelength. Inspired by literature on reconstructing spectral images from RGB images, we then correct images for chromatic aberration by estimating aberration-free, spectral images. As we model within-channel chromatic aberration, our reconstructed images are sharper than those obtained by calibrated warping of color channels, yet we avoid artifacts commonly produced by explicit deblurring algorithms.
A numerical method to compute Fisher information for a special case of heterogeneous negative binomial regression

Negative binomial regression has been widely applied in various research settings to account for counts with overdispersion. Yet, when the gamma scale parameter, $ \nu $, is parameterized, there is no direct algorithmic solution to the Fisher Information matrix of the associated heterogeneous negative binomial regression, which seriously limits its applications to a wide range of complex problems. In this research, we propose a numerical method to calculate the Fisher information of heterogeneous negative binomial regression and accordingly develop a preliminary framework for analyzing incomplete counts with overdispersion. This method is implemented in R and illustrated using an empirical example of teenage drug use in America.
Internet of things–based vital sign monitoring system

Wireless network technology-based internet of things (IoT) has increased significantly and exciting to study, especially vital sign monitoring (body temperature, heart rate, and blood pressure). Vital sign monitoring is crucial to carry out to strengthen medical diagnoses and the continuity of patient health. Vital sign monitoring conducted by medical personnel to diagnose the patient's health condition is still manual. Medical staff must visit patients in each room, and the equipment used is still cable-based. Vital sign examination like this is certainly not practical because it requires a long time in the process of diagnosis. The proposed vital sign monitoring system design aims to assist medical personnel in diagnosing the patient's illness. Vital sign monitoring system uses HRM-2511E sensor for heart detection, DS18b20 sensor for body temperature detection, and MPX5050DP sensor for blood pressure detection. Vital sign data processing uses a raspberry pi as a data delivery media-based internet of things (IoT). Based on the results of the vital sign data retrieval shows that the tool designed functioning correctly. The accuracy of the proposed device for body temperature is 99.51%, heart rate is 97.90%, and blood pressure is 97.69%.
Feature Keypoint-Based Image Compression Technique Using a Well-Posed Nonlinear Fourth-Order PDE-Based Model

A digital image compression framework based on nonlinear partial differential equations (PDEs) is proposed in this research article. First, a feature keypoint-based sparsification algorithm is proposed for the image coding stage. The interest keypoints corresponding to various scale-invariant image feature descriptors, such as SIFT, SURF, MSER, ORB, and BRIEF, are extracted, and the points from their neighborhoods are then used as sparse pixels and coded using a lossless encoding scheme. An effective nonlinear fourth-order PDE-based scattered data interpolation is proposed for solving the decompression task. A rigorous mathematical investigation of the considered PDE model is also performed, with the well-posedness of this model being demonstrated. It is then solved numerically by applying a consistent finite difference method-based numerical approximation algorithm that is next successfully applied in the image compression and decompression experiments, which are also discussed in this work.
Detection of Phishing Websites using an Efficient Machine Learning Framework

Phishing attack is one of the commonly known attack where the information from the internet users are stolen by the intruder.The internet users are losses their sensitive information such as Protected passwords, personal information and their transactions to the intruders.The Phishing attack is normally carried by the attackers where the legitimate frequently used websites are manipulated and masked to gather the personal information of the users.The Intruders use the personal information and can manipulate the transactions and get definite from them.From the literature there are various anti-Phishing websites by the various authors.Some of the techniques are Blacklist or Whitelist and heuristic and visual similarity based methods.In spite of the users using these techniques most of the users are getting attacked by the intruders by means of Phishing to gather their sensitive information.A novel Machine Learning based classification algorithm has been proposed in this paper which uses heuristic features where feature selection can be extracted from the attributes such as Uniform Resource Locator, Source Code, Session, Type of security involve, Protocol used, type of website.The proposed model has been evaluated using five machine learning algorithms such as random forest, K Nearest Neighbor, Decision Tree, Support Vector Machine, Logistic regression.Out of these models, the random forest algorithm performs better with attack detection accuracy of 91.4%.Moreover the Random Forest Model uses orthogonal and oblique classifiers to select the best classifiers for accurate detection of Phishing attacks in the websites.
Big Data Analytics Application Model Based on Data Quality Dimensions and Big Data Traits in Public Sector

Big data analytics (BDA) represents a new technological paradigm with its ability to extract valuable knowledge from high amounts of data.Exploring the effect of data quality dimensions (DQD) and big data traits (BDT) on BDA application is a relatively new research trend that has not been featured in the existing literature.Thus, this study was conducted to build a new model by integrating the DQD and BDT to examine the BDA application in the context of the Malaysian public sector.This study proposes that the DQD should include intrinsic, contextual, representational, and accessibility dimensions, while variety, validity, and veracity should be considered as the main characteristics of BDT.For this purpose, this study employed theory analysis on on-going research related to DQD and BDT, along with the development of the BDA application model.The proposed model would create new research fields related to DQD and BDT in the BDA domain.Finally, in line with the Public Sector Big Data (DRSA) platform initiated by the Malaysian Administrative Modernisation and Management Planning Unit (MAMPU), the new model is expected to provide a benchmark for the development and application of BDA for the Malaysian public sector.
I-feed: A robotic platform of an assistive feeding robot for the disabled elderly population

BACKGROUND Over time, scholars have invented various types of feeding robots to help patients with hand disabilities. However, most commercially available feeding robots are functionally simple or expensive. OBJECTIVE The purpose of this study is to develop a cheap, multi-functional feeding robot with excellent performance to help disabled elderly eat independently. METHODS Our feeding robot (called 'I-feed') uses human-computer interaction based on voice recognition. The feeding system we developed with a four-degree-of-freedom robotic arm is capable of completing the two tasks of food selection and feeding through speech recognition, but also simultaneously meets users' diverse needs with three bowls. We also designed a U-shaped table to adjust the height of the feeding robot. RESULTS This newly developed feeding robot can not only select bowls with different foods by efficient voice commands, but also adapts to users of different heights through a U-shaped table with an adjustable height. CONCLUSIONS The experimental results show that the accuracy of speech recognition is excellent, and the robot arm can perform the corresponding tasks successfully.
PDMLP: Phishing Detection using Multilayer Perceptron

A phishing website is a significant problem on the internet.It's one of the Cyber-attack types where attackers try to obtain sensitive information such as username and password or credit card information.The recent growth in deploying a Detection phishing URL system on many websites has resulted in a massive amount of available data to predict phishing websites.In this paper, we purpose a new method to develop a phishing detection system called phishing detection based on a multilayer perceptron (PDMLP), which used on two types of datasets.The performance of these mechanisms evaluated in terms of Accuracy, Precision, Recall, and F-measure.Results showed that PDMLP provides better performance in comparison to KNN, SVM, C4.5 Decision Tree, RF, and RoF to classifiers.
Continuously Indexed Domain Adaptation

Existing domain adaptation focuses on transferring knowledge between domains with categorical indices (e.g., between datasets A and B). However, many tasks involve continuously indexed domains. For example, in medical applications, one often needs to transfer disease analysis and prediction across patients of different ages, where age acts as a continuous domain index. Such tasks are challenging for prior domain adaptation methods since they ignore the underlying relation among domains. In this paper, we propose the first method for continuously indexed domain adaptation. Our approach combines traditional adversarial adaptation with a novel discriminator that models the encoding-conditioned domain index distribution. Our theoretical analysis demonstrates the value of leveraging the domain index to generate invariant features across a continuous range of domains. Our empirical results show that our approach outperforms the state-of-the-art domain adaption methods on both synthetic and real-world medical datasets.
Cone Semantics for Logics with Negation

This paper presents an embedding of ontologies expressed in the ALC description logic into a real-valued vector space, comprising restricted existential and universal quantifiers, as well as concept negation and concept disjunction. Our main result states that an ALC ontology is satisfiable in the classical sense iff it is satisfiable by a partial faithful geometric model based on cones. The line of work to which we contribute aims to integrate knowledge representation techniques and machine learning. The new cone-model of ALC proposed in this work gives rise to conic optimization techniques for machine learning, extending previous approaches by its ability to model full ALC.
JIT2R: A Joint Framework for Item Tagging and Tag-based Recommendation

Predicting tags for a given item and leveraging tags to assist item recommendation are two popular research topics in the field of recommender system. Previous studies mostly focus only one of them to make contributions. However, we believe that these tasks are inherently correlated with each other: tags can provide additional information to profile items for more accurate recommendation; user behaviors can help to infer item relationships to benefit the item tagging process. In order to take the advantages of such mutually influential signals, we propose to integrate item tagging and tag-based recommendation into a unified model. We firstly design a basic framework, where the user-item interaction signals are leveraged to supervise the item tagging process. Then we extend the basic model with a bootstrapping technique to circulate such mutual improvements between different tasks. We conduct extensive experiments based on real-word datasets to demonstrate our model's superiorities.
Batch Decorrelation for Active Metric Learning

We present an active learning strategy for training parametric models of distance metrics, given triplet-based similarity assessments: object $x_i$ is more similar to object $x_j$ than to $x_k$. In contrast to prior work on class-based learning, where the fundamental goal is classification and any implicit or explicit metric is binary, we focus on perceptual metrics that express the degree of (dis)similarity between objects. We find that standard active learning approaches degrade when annotations are requested for batches of triplets at a time: our studies suggest that correlation among triplets is responsible. In this work, we propose a novel method to decorrelate batches of triplets, that jointly balances informativeness and diversity while decoupling the choice of heuristic for each criterion. Experiments indicate our method is general, adaptable, and outperforms the state-of-the-art.
Density Peak Clustering Based on Relative Density Optimization

Among numerous clustering algorithms, clustering by fast search and find of density peaks (DPC) is favoured because it is less affected by shapes and density structures of the data set. However, DPC still shows some limitations in clustering of data set with heterogeneity clusters and easily makes mistakes in assignment of remaining points. The new algorithm, density peak clustering based on relative density optimization (RDO-DPC), is proposed to settle these problems and try obtaining better results. With the help of neighborhood information of sample points, the proposed algorithm defines relative density of the sample data and searches and recognizes density peaks of the nonhomogeneous distribution as cluster centers. A new assignment strategy is proposed to solve the abundance classification problem. The experiments on synthetic and real data sets show good performance of the proposed algorithm.
Modeling Morphological Typology for Unsupervised Learning of Language Morphology

This paper describes a language-independent model for fully unsupervised morphological analysis that exploits a universal framework leveraging morphological typology.By modeling morphological processes including suffixation, prefixation, infixation, and full and partial reduplication with constrained stem change rules, our system effectively constrains the search space and offers a wide coverage in terms of morphological typology.The system is tested on nine typologically and genetically diverse languages, and shows superior performance over leading systems.We also investigate the effect of an oracle that provides only a handful of bits per language to signal morphological type.