Reviewerly/exHarmony huggingface dataset:
Penerapan Aplikasi Android Pengenalan Huruf Hijaiyah Metode Waterfall Pada Paud Al Fina

Recognition and mastery of the hijaiyah letters which are the basis for reading and studying the Holy Qur'an from an early age is very important, because if at the time of reading the hijaiyah letters you are accustomed to the wrong words it will cause different meanings so that we are required to read properly and correctly. The method in learning hijaiyah letters is usually given by traditional (conventional) reciting teachers or people often call it the sequential method. With this kind of teaching, children tend to be passive in accepting their teaching lessons, because the Koran teacher explains the children in learning to read hijaiyah letters orally, in writing and in body language. The application of recognizing hijaiyah letters is very effective and easy to use for students of paud al fina in recognizing and writing hijaiyah letters because the design of the learning aid program to read hijaiyah letters is a method in the form of visualization in the form of images, text and sound. To implement this method, the author uses Android Studio programming which acts as a tool (programming system) used to build an application, this application program is expected to generate interest for early childhood or adults who want to learn how to learn to read hijaiyah letters properly and correctly. accommodated.
Torso Control System with A Sensory Safety Bar for a Standing Mobility Device

With a focus on lower-body mobility impairment, we have developed a device for standing mobility: Qolo, which allows a user with full lower-body paralysis to displace around in a standing posture. In this paper, we propose a torso control system for allowing a hands-free operation through what we hypothesize to be a natural motion of the upper body. We introduce a new interface of motion control of the intended users through an ergonomic bar at the user's waist level with an array of force sensitive resistor (FSR) on the surface. We present the interface and the user's intention detection algorithms and evaluate it with a set of five healthy participants to examine and compare the motion interface with a traditional bi-dimensional joystick. Results show that users are able to adapt and use the proposed interface at a cost of longer training and task completion time; pointing out a higher level of skill required in compensation for achieving hands-free control.
An Improved Privacy-Preserving Stochastic Gradient Descent Algorithm

Deep learning techniques based on neural network have made significant achievements in various fields of Artificial Intelligence. However, model training requires large-scale datasets, these datasets are crowd-sourced and model parameters will contain the encoding of private information, resulting in the risk of privacy leakage. With the trend towards sharing pre-trained models, the risk of stealing training datasets through member inference attack and model inversion attack is further heightened. To tackle this problem, we propose an improved Differential Privacy Stochastic Gradient Descent algorithm, using simulated annealing algorithm and denoising mechanism to optimize the allocation method of privacy loss and improve model accuracy. We also analyze privacy cost under random shuffle data batch processing methods in detail within the framework of Subsampled Rényi Differential Privacy. Compared with existing methods, our experiments show that we can train deep neural networks with non-convex objective function more efficiently with moderate privacy budgets.
Revisiting Uncertainty Propagation in Robotics

This paper revisits the problem of uncertainty propagation in robotic systems. Standard approaches use Kalman filtering, either Extended (linearized) (EKF) or Unscented Transform-based (UKF) Kalman Filters to handle state estimation of robotic systems. These dynamic filters operate on predict-correct cycle where the current state is predicted based on prior state, and then corrected based on current observations using calculated correction weight. These approaches provide both mean and uncertainty (covariance) of the state estimates. Alternative state estimation techniques include Monte-Carlo based particle filters. The system state estimate is often used in composition with other transforms to localize objects in a world frame. Often these internal transforms are treated as known nominal quantities, and their uncertainty is ignored. In this paper, we revisit the handling of errors and compare several representations to highlight limitations of current approaches.
Fish-Eye Image Based Cross Traffic Alert System

In the automotive industry, the use of advanced driver assisted systems (ADAS) is gaining a lot of traction. ADAS systems are designed to assist the driver by providing information regarding road users, lane information, traffic infrastructures etc., and improve the road safety aspects of an automobile. In this paper, we propose a real-time cross traffic alert (CTA) system based on fisheye camera images having ~180° field of view (FOV). The main purpose of CTA is to avoid unwanted collision with any approaching target by issuing an alert to the driver at a T-junction. The main components of the proposed CTA algorithm are, sparse optical flow vector tracking, object cluster formation and estimation of time to collision (TTC) for each object cluster. The TTC calculation can be performed without explicit depth reconstruction. We compute TTC based on homography estimation between similar features of consecutive image frames. Under good weather condition, the performance of the proposed CTA algorithm in detecting approaching target is reasonably good with a true positive rate of nearly 88%, but with significant false positive rate to the tune of 19%. A major contributing factor to this high false positive is identified to be the inability to distinguish a real crossing object and objects moving parallel to the host vehicle just using TTC estimates. To suppress the unintended false positive cases, we propose a novel solution based on statistics of the flow vector cluster and showcase its efficacy. The classification result for the proposed approach indicates that we have achieved to reduce the false positive rate to nearly 10% while maintaining the true positive rate.
The Robot Path Planning Algorithm In Indoor Environment

The sweeping robots used to clean the floor by random collision and infrared detection, which is inefficient and time-consuming. The path of those robots is disorder and some area may be cleaned for several times. Due to the fact that the robot needs to avoid the obstacles on their path, it may cause low coverage problem. A elastic path planning algorithm based on U-shaped trajectory is designed to allow the robot to fit int the indoor environment. The robot can change the length of the trajectory to avoid collision. Instead of using obstacle-avoidance algorithm, a kind of smart algorithm is designed to recover the area that is left due to the obstacle-avoidance. In addition, to prevent the robot from cleaning the area that has been cleaned before, a mechanism is built to allow the robot to memory the cleaned area. In practice, after being tested by the experiments, the coverage rate of the robot can reach 94.61 %, which is more than the robots that use random cleaning mode, and the robot uses less electricity compared with the conventional ones.
Experimental study of oil free scroll vacuum pump parameters

Mathematical model of working process of scroll vacuum pumps was developed earlier to improve scroll vacuum pumps. For its verification experimental database for the object under study was required. For this purpose experimental unit to test oil free scroll vacuum pump is developed. Relationships such as pressure in pump working chambers vs rotation angle of the drive shaft, pump ultimate pressure vs drive shaft rotation speed, pumping speed vs pump inlet pressure, power consumption vs pump inlet pressure, temperature of the housing, orbiting and fixed scroll elements, gas at the inlet and outlet of the pump vs pump inlet pressure are obtained. Behavior of these parameters depending on pump working regimes is discussed. Experimental data will be used for verification of mathematical model of scroll vacuum pump.
The Non-linear Tracking of IMM- PHD Filter for Rader-Infrared Sensor Data Fusion

Abstract In order to improve the accuracy of multi-target tracking, a feasible dada fusion system is proposed for radar and infrared sensor used together, to overcome the defects both small detection range and no distance measurement for infrared sensor, and to avoid the problem of large error in measurement information of single sensor. Rader and infrared dada fusion can achieve complementary information. An optimal weighted fusion method for measurement of radar and infrared sensors is derived, and an interactive multi-model PHD nonlinear filtering algorithm is used to improve the accuracy of target tracking. The computer simulation results show that the integrated system and multi-sensor measurement fusion method can improve the accuracy of target state estimation. It is of great practical significance to solve the problem of target tracking estimation of heterogeneous multi-sensor.
On the Use of a Low-Cost Embedded System for Face Detection and Recognition

This paper explores the feasibility of using commercially available off-the-shelf components to implement a low-cost embedded system as the core of a facial detection and recognition system. The system is composed of a Raspberry Pi camera module and a Raspberry Pi B+ enhanced by an Intel Neural Compute Stick 2. Four supervised learning models were implemented on the embedded system for face recognition under different conditions to determine the limitations and capabilities of the system, and the best operational conditions. Best results were achieved when using a Multilayer Perceptron (MLP) algorithm and the distance of the subject to the camera was between 0.3 to 1 meters, the illumination factor in the range from 115 to 130 lux and the horizontal face rotation between -5° to +5°.
Block chain Based Medical Reports Monitoring System

These days the healthcare frameworks are attempting to provide central healthcare general administrations. Frequently the frameworks are consistently on the verge of breakdown which was inferable from the high costs, enormous scope measurements and rare assets. In the current scenario, a centralized cloud-based system is used. When a centralized system is used, the stored data is at risk as security is the main concern. If there's a single breach in the centralized system, the entire data becomes easily accessible. A Blockchain, being a distributed network for storage of data can overcome the mentioned problem. The data blocks in the Block chain are immutable and secure. In this research a system is introduced where the patient is given complete control over his medical records. It mainly comprises three roles namely Doctor, Patient and Management. The patient has monopoly over his medical records which provide him the authority to grant access over his medical records. The Medical records are securely stored using the Inter Planetary File System. Thus, one can completely rule out the part of a mediator and the patients have complete command over their medical information.
Review of Iterative Numerical Methods Preferred in Technical Application to Increase Efficiency

Computational methods (CM) are involved in the realization of many modern industrial applications. Increasing efficiency of the practical application contributes to the widespread use of CM. Also, the increased speed of the processors which is available at a reasonable cost, makes CM possible to use widely. Iterative methods are one of the main computational methods preferred in the literature and industrial applications to increase efficiency. One of the main contributions of the iterative method is to increase efficiency without increasing the production cost. Today's harsh market conditions are forcing companies to obtain high-quality products at low cost in every field. Therefore, the use of iterative methods takes part in more field industrial applications. Iterative methods mostly preferred in practical applications are examined in this study and summarized in four tables. The focus of this paper is to be a starting point for using iterative methods in practical application and create a framework on the subject for researchers that want to use iterative methods in their application.
Radio and WhatsApp. Public Space among the Eastern Khanty and the Asiatic Yupik

The article focuses on the virtual public space created in Siberian Indigenous villages via WhatsApp chats and radio communication. These media are breaking boundaries and are creating a unique space for communication. I explore how these media form virtual public space and how they change everyday practices. Both practices create new public space, essential in the context of a lack of real public space.
Use of Functional Linear Models to Detect Associations between Characteristics of Walking and Continuous Responses Using Accelerometry Data

Various methods exist to measure physical activity. Subjective methods, such as diaries and surveys, are relatively inexpensive ways of measuring one’s physical activity; however, they are prone to measurement error and bias due to self-reporting. Wearable accelerometers offer a non-invasive and objective measure of one’s physical activity and are now widely used in observational studies. Accelerometers record high frequency data and each produce an unlabeled time series at the sub-second level. An important activity to identify from the data collected is walking, since it is often the only form of activity for certain populations. Currently, most methods use an activity summary which ignores the nuances of walking data. We propose methodology to model specific continuous responses with a functional linear model utilizing spectra obtained from the local fast Fourier transform (FFT) of walking as a predictor. Utilizing prior knowledge of the mechanics of walking, we incorporate this as additional information for the structure of our transformed walking spectra. The methods were applied to the in-the-laboratory data obtained from the Developmental Epidemiologic Cohort Study (DECOS).
PBFT Consensus Performance Optimization Method for Fusing C4.5 Decision Tree in Blockchain

Data management methods using blockchain have the advantages of being traceable and not easy to tamper. Therefore, blockchain is widely used in supply chain finance, credit reporting and other fields. However, in the actual blockchain system application, there are two main problems in the Hyperledger Fabric license alliance chain based on the Practical Byzantine Fault Tolerance (PBFT) consensus mechanism: 1) The non-honesty node acts as the primary node to interfere with the consensus process; 2) The network bandwidth resource consumption caused by the flooded message broadcast of the consensus node is too large. To solve these problems, a blockchain PBFT consensus performance optimization method for fusing C4.5 decision tree is proposed. This method uses the C4.5 decision tree with high model classification accuracy to evaluate the trust degree of the consensus nodes in the blockchain network, and effectively reduces the non-honesty node as the primary node. On this basis, the voting weight is introduced. Consistency verification can be completed only by considering a small number of trusted nodes voting weights, thereby reducing the number of messages broadcasted in the network. The experimental results show that compared with the existing methods, the trust node classification and voting weights of the consensus nodes are improved in the consensus performance of throughput, delay and fault tolerance, and the effectiveness of the proposed method is verified.
Improving Big Data Technologies with Visual Faceted Search

Because of the quantity, complexity and speed measurements of big data, access to the data needed for big data applications becomes ever more challenging for both end-users and IT-experts. access. This has however led to problems of overloading information. While progress has been made in search engines, finding the right information still is a struggle due to the exponential increase in the everyday information provided. The need for Big Data Analytic now powers other aspects of modern society, because they can build new linkages and discoveries that help drive tomorrow's innovation. Data visualization of big data applications is an important tool to interpret and understand immense data, making the technology more enticing to use. Visualization of a search result with faceted search is increasingly popular in search engines. This seeks to allow users to easily and effectively find their way through large document collections. FS strategies presume that correct information is required so that the value, importance, and expense of achieving the requested information is optimized. In this work, we propose a new FS framework for visualizing browsing and refinements of search results to allow users to visually build complex search queries. The proposed FS can also solve the problem of lexical uncertainty in current search engines and give users more interest.
An analysis of the proportion of feature subsampling on XGBoost - A case study of claim prediction in car insurance

Claim prediction is one of the important elements in the insurance. The increasing frequency of claim makes the data volume also increases to become big data. So, we need the right machine learning method to help insurance companies manage big data more efficiently. XGBoost is a machine learning model based on decision trees. XGBoost can be applied for claim prediction case in the form of two-class or multi-class classification. We may select a subset of features in building the XGBoost model especially for data with a large number of features. In this paper, we examine the influence of the proportion of features on the accuracy of the XGBoost model. Our simulations show that by randomly using 1/5 of features, the XGBoost model can produce accuracy comparable to the model that uses all features. It means that the XGBoost model is scalable in terms of the proportion of features.
Standoff mid-infrared reflectance spectroscopy using quantum cascade laser for mineral identification

In this work, quantum cascade laser (QCL) mid-infrared (MIR) reflectance spectroscopy is used to discriminate silicate and carbonate minerals in a standoff measurement setting. The tunable external cavity QCL source that was used allows measurements from 5.2 μm to 13.4 μm wavelength, where the fundamental vibrational bands of silicates and carbonates are observed. Spectra measured from a half-core sample were analyzed using multivariate analysis to extract and identify the end-member spectra from the mixtures. The end-member spectra were compared and validated using the ASTER database spectra and the spectra measured on reference samples with the same QCL MIR reflectance spectroscopy setup. Spectra of minerals commonly found in the mining industry were compared: quartz, microcline, albite, chlorite, muscovite, biotite, calcite and dolomite. MIR reflectance spectroscopy using compact QCL sources allow rapid spectral measurements at standoff distances and high spatial resolution. All these advantages show the potential of QCL MIR reflectance spectroscopy for in-the-field mining applications.
Entropy of LOO Predictable Horizons to Select a Learning Machine and a Representative Prediction of Chaotic Time Series

Recently, we have presented several methods to select representative predictions of chaotic time series. Here, the methods employ strong learners capable of making predictions with small error, where usual ensemble mean does not work well owing to an ensemble member with short term predictable horizon because the ensemble prediction error of chaotic time series grows exponentially after the smallest predictable horizon of the ensemble member. Here, we refer to 'predictable horizon' as the first point in time after which the prediction error exceeds a certain error threshold. So far, we have developed several methods to select representative predictions from a set of many predictions by means of using a LOOCV (leave-one-out cross-validation) measure to estimate predictable horizon. From the analysis of the methods showing that the method works well with sufficiently large number of predictions generated by sufficiently strong learning machines, this paper presents a method to generate a large number of predictions and select a learning machine and a representative prediction using the entropy of LOO predictable horizons. By means of numerical experiments, we show and examine the effectiveness of the present method.
K-means, HAC and FCM Which Clustering Approach for Arabic Text?

Today, we are witnessing rapid growth in Web resources that allow Internet users to express and share their ideas, opinions, and judgments on a variety of issues. Several classification approaches have been proposed to classify textual data. But all these approaches require us to label the clusters we want to obtain. Which, in reality, is not available because we do not know in advance the information that can be proposed through these opinions. To overcome this constraint, clustering approaches such as K-mean, HAC or FCM can be exploited. In this paper, we present and compare these approaches. And to show the importance of exploiting clustering algorithms, to classify and analyze textual data in Arabic. By applying them to a real case that has created a great debate in Morocco, which is the case of teachers contracting with academies.
Depression discovery in cancer communities using deep learning

We are said to be living in the “information age,” and data are the capital of the new economy. With the continuous explosion in the extent of data being created every day on online portals and social networking websites, industries today are collecting and analyzing more data than ever before. Data are readily available, finding valuable insights are the struggle. The easy accessibility of data, new cutting-edge technologies, and a cultural shift toward data-driven decision-making drive the demand for sentiment analysis (SA) and make it relevant in each and every domain such as politics, marketing, healthcare, and so on. In the healthcare domain, cancer is a deadly disease that claims almost 10 million lives every year. The alarming numbers of fatalities are caused due to privation of timely cancer detection, tardy medical attention, or in some cases from patients losing the will to live due to a protracted and unending treatment procedure. Governments across the world are taking steps to ensure timely cancer detection and treatment. However, little attention is being paid to the seemingly unending treatment course taking a toll on the patient's mental health, thus crushing the patient's spirit to continue. In this chapter, we investigate the use of different deep neural network architectures and natural language processing for depression detection in cancer communities. Depression detection using SA can be of great assistance to the doctors treating cancer patients and aid them in deciding whether along with the cancer treatment their patients need help from psychologists or psychiatrists.