Reviewerly/exHarmony huggingface dataset:
Parallel Demodulation Algorithm for processing independent windows of a fringe patterns using Simulating Annealing

We present a method for demodulating fringe patterns, which uses Simulated Annealing algorithm by setting an objective function and is capable of recovering the phase map without using an unwrapping algorithm. This method is based on the window fringe pattern demodulation method and introduces a recursive algorithm that performs an automatic partitioning of the pattern of fringes using a quad tree data structure and limiting each partition to a maximum number of fringes. The phase is approximated by means of a parametric estimation of a polynomial of the n-th degree in each window, independently, which facilitates parallel implementation, and ultimately, a gain in speed. The computer simulation and the experimental results have demonstrated that this method is able to recover the phase map in complex interferograms with results comparable to those obtained with soft computing techniques that address this problem ( Independent Windows Using a Genetic Algorithm), in terms of to quality, furthermore, our method employs parallel implementation, which makes the demodulation process more agile.
An enhanced random forest with canonical partial least squares for classification

Recently, several variants of random forest have been derived for the classification problems, among which the rotation forest is an important type to improve the model’s accuracy. In this article, we proposed a simple and effective variation of rotation forest, which the canonical partial least squares algorithm is employed to rotate the variable space of tree and then all the trees are combined being a “forest.” Results of an experiment on a sample of 20 benchmark datasets show our method has better prediction performance comparing with random forest and rotation forest.
Human facial expression recognition using deep learning technique

The emotion is recognized from facial expression by using static images. It is one of the categories in signal processing which is applied in various fields, similarly for human and computer interaction. Some sources are proposed to automatic emotion recognition, which uses machine learning approach. Many real-time problems have been solved by Deep learning technique. In this work we have defined Convolutional Neural Network (CNN) is used to identify 6 elementary emotions this technique has been implemented in MATLAB.
Achievement of Online Agile Manipulation Task for Aerial Transformable Multilink Robot

Transformable aerial robots are favorable in aerial manipulation tasks for their flexible ability to change configuration during the flight. By assuming robot keeping in the mild motion, the previous researches sacrifice aerial agility to simplify the complex non-linear system into a single rigid body with a linear controller. In this paper, we present a framework towards agile swing motion for the transformable multi-links aerial robot. We introduce a computational-efficient non-linear model predictive controller and joints motion primitive frame-work to achieve agile transforming motions and validate with a novel robot named HYRURS-X. Finally, we implement our framework under a table tennis task to validate the online and agile performance.Supplementary MaterialThis paper is accompanied by a experiment video: http://www.jsk.t.u-tokyo.ac.jp/%7eshifan/paper/iros19/video.mp4.
SignalCookie: Discovering Guard Relays of Hidden Services in Parallel

In this paper, we propose the SignalCookie attack which can reveal guard relays of multiple hidden services in parallel. The key insight of our method is utilizing Rendezvous Cookie and circuit watermark to deliver the hidden services' identifiers to our controlled relays. By conducting the attack in parallel, the speed of our method increases about 11.6 times compared with the previous work, so that we can continually monitor the guard relays of 13604 hidden services for 7 months. And we have an interesting finding by analyzing the distribution of hidden services binding on each guard relays. The distribution is quite uneven, that only 20% of guard relays serve about 89.32% hidden services. These guard relays would bind all hidden services at least one time in about 17 months. At last, we analyze several security problems aggravated by the uneven distribution, and find that just one corrupt guard relay may cause hundreds of hidden services being de-anonymized or eclipsed.
An Autonomous Quadrotor System for Robust High-Speed Flight Through Cluttered Environments Without GPS

Robust autonomous flight without GPS is key to many emerging drone applications, such as delivery, search and rescue, and warehouse inspection. These and other applications require accurate trajectory tracking through cluttered static environments, where GPS can be unreliable, while high-speed, agile, flight can increase efficiency. We describe the hardware and software of a quadrotor system that meets these requirements with onboard processing: a custom 300 mm wide quadrotor that uses two wide-field-of-view cameras for visual-inertial motion tracking and relocalization to a prior map. Collision-free trajectories are planned offline and tracked online with a custom tracking controller. This controller includes compensation for drag and variability in propeller performance, enabling accurate trajectory tracking, even at high speeds where aerodynamic effects are significant. We describe a system identification approach that identifies quadrotor-specific parameters via maximum likelihood estimation from flight data. Results from flight experiments are presented, which 1) validate the system identification method, 2) show that our controller with aerodynamic compensation reduces tracking error by more than 50% in both horizontal flights at up to 8.5 m/s and vertical flights at up to 3.1 m/s compared to the state-of-the-art, and 3) demonstrate our system tracking complex, aggressive, trajectories.
The effect of information quality, system quality, service quality on intention to use and user satisfaction, and their effect on net benefits primary care application at primary health facilities in Malang

Abstract The Indonesian government has serious attention to facilitate the health of its people, one of which is by establishing Badan Penyelenggara Jaminan Sosial (BPJS) hereinafter BPJS. The Government of Indonesia collaborates with several parties including primary health facilities, General and Regional Hospitals, as well as general practitioners and specialists who have been appointed by BPJS. The aim of this study is the user’s perception of Primary Care applications at primary health facilities BPJS in Malang. This study uses the DeLone &amp; McLean (D&amp;M) Update IS Success Model to assess the success of an information system where its use is mandatory at primary health facilities. The model contains 6 dimensions that can measure the success of a system, i.e. Information Quality, System Quality, Service Quality, Use, User Satisfaction and Net Benefits. This research is a quantitative study using a questionnaire with a Likert scale. Purposive sampling is a method used in sampling. The respondents consist of doctors or admins who operate primary care applications at primary health facilities. The results of this study indicate that primary care applications can be categorized as successful information systems for each dimension of success in the D&amp;M Update IS Success Model.
Image Forgery detection on cloud

Every day many digital documents are produced and distributed by televisions, newspapers, magazines, and websites. In all these communication channels images play an important role in communicating the information. Today, it is very easy to manipulate digital images with the help of advanced computer, photo editing software, image processing techniques etc. So, verification of the authenticity of digital images has become a challenging problem. This paper is based on image forgery detection on cloud. Image forger detection is classified into two: passive and active. Active approach includes digital signature and watermarking whereas the Passive approach includes detection of the tampered area using copy move and splicing techniques. Here we are using Canny Edge Detection Algorithm which mainly uses splicing. In this paper, we are also comparing canny edge detection and SSIM. SSIM is more efficient compared to canny edge detector and it gives above 90 % accuracy.
Deep Learning Models for Face Recognition: A Comparative Analysis

Deep learning networks have established themselves as a promising model for face recognition. Their success is attributed towards multiple processing layers in order to learn data representations with several feature extraction levels. Convolutional neural networks have been present as the deep learning tool in almost all face recognition systems. The significant breakthrough made by DeepIDs, DeepFace, Face++, FaceNet, and Baidu has changed the entire investigation scope. The deep face recognition techniques leverage hierarchical architecture in order to learn discriminative face representation. It has improved the system's performance appreciably which has led to the growth of several successful applications. In this chapter, comparative analysis for important deep learning models towards face recognition is presented. A multi-stage strategy is used in order to prepare the experimental datasets. These datasets are developed from readily available web knowledge sources. The datasets are used for model training as well as evaluation. The experimental results are presented considering their comparative analysis against benchmark datasets. Several deep face recognition issues and other open questions in deep face recognition are also discussed with directions towards future research.
An IoT-Enabled Telerobotic-Assisted Healthcare System Based on Inertial Motion Capture

Evolution of smart sensing technologies provide an increasingly utilization for IoT-enabled healthcare. In the context of the aging population, the demand for elderly-assistant robots is increasing. At the same time, more and more attention has been paid to the more intuitive way of remote human-robot interaction. In this article, we present the design, implementation, and evaluation of a telerobotic-assisted healthcare system with the ability to achieve the remote elderly assistant and healthcare application. In this work, a remote operation interface using wearable inertial motion capture suit is proposed to control the YuMi robot remotely. The motion capture subsystem and the robot control subsystem are all based on robot operation system (ROS) to carry out the distributed design and integration. The robot arm is controlled by the position and orientation data of the operator's hand acquired by the motion capture suit. Furthermore, the robot's gripper is controlled by the finger bending signal acquired by a data glove. The achievement and performance of the introduced system was verified by experiments.
Classification of Moving Crowd Based on Motion Pattern

Crowd behavior analysis is a significant task in the context of surveillance and crowd management. For a moving crowd, analyzing the motion pattern is very important. In this work, we present a simple scheme to categorize such crowds as structured, semi-structured and unstructured ones. The categorization is achieved based on the regularity of the motion pattern of the collection of objects (humans, in this case). In case of structured one, the movement is coherent and uniform in nature. It is expected that the crowd as a whole or individual segment of it reflects consistent orientation and speed of movement. For unstructured crowd, on the other hand, the movement is random. Hence, diversity is there in terms of orientation and speed. The semi-structured one stands in between and makes the classification problem difficult. In this work motion orientation based feature is computed to represent the motion pattern. A set of interest points detected in the initial frame are tracked over the sequence using optical flow. Thus, motion orientations are obtained. A frame is divided into blocks, and distribution of the orientation of motion of the interest points in each block is summarized in a four dimensional histogram. Block level histograms are concatenated to form the frame level descriptor. Finally, frame level descriptors are taken together to represent the sequence. In this experiment, artificial neural network (ANN) is used as classifier. Experiment is carried out on collectiveness dataset. Proposed method provides better classification accuracy in comparison to state-of-the-art techniques.
Improved Feature Based Sentiment Analysis for Online Customer Reviews

The evolution of E-commerce site tends to produce a huge amount of data nowadays. These data consist of very novel knowledge to compete with the other E-commerce sites in terms of business perspective. Customers often use these E-commerce sites to manage decision on the purchase of products based on comments or reviews given by the existing customer who bought the same product. The concept of Opinion Mining enables these processes of selection and decision easier. Several techniques have been proposed for the opinion mining and provided their own advantages. However, those techniques contain certain drawbacks in the selection of features and opinions with respect to the priority of product feature given by the individual user. This paper proposes a novel idea of incorporating weight which is automatically calculated according to the attributes evolved. The reason to do certain weight calculation is an assumption of weight and weight based on priority may differ from person to person. Experimental results show the performance of the proposed with various techniques for the online review collected from different sites.
Hierarchical Reinforcement Learning for Concurrent Discovery of Compound and Composable Policies

A common strategy to deal with the expensive reinforcement learning (RL) of complex tasks is to decompose them into a collection of subtasks that are usually simpler to learn as well as reusable for new problems. However, when a robot learns the policies for these subtasks, common approaches treat every policy learning process separately. Therefore, all these individual (composable) policies need to be learned before tackling the learning process of the complex task through policies composition. Moreover, such composition of individual policies is usually performed sequentially, which is not suitable for tasks that require to perform the subtasks concurrently. In this paper, we propose to combine a set of composable Gaussian policies corresponding to these subtasks using a set of activation vectors, resulting in a complex Gaussian policy that is a function of the means and covariances matrices of the composable policies. Moreover, we propose an algorithm for learning both compound and composable policies within the same learning process by exploiting the off-policy data generated from the compound policy. The algorithm is built on a maximum entropy RL approach to favor exploration during the learning process. The results of the experiments show that the experience collected with the compound policy permits not only to solve the complex task but also to obtain useful composable policies that successfully perform in their corresponding subtasks.
CyRA: A Real-Time Risk-Based Security Assessment Framework for Cyber Attacks Prevention in Industrial Control Systems

Insufficient authentication and authorization of interconnected components are major risks in the Industrial Control System (ICS). To address this, we introduce CyRA, a realtime risk-based security assessment framework that consists of a Nested-ICS security architecture, secure registration protocol, and risk-based multi-factor authentication protocol by which every component is authenticated and authorized to ensure secure communications and prevent cyber attacks in the ICS. Our proposed framework applies Zero-Knowledge Proof of Knowledge (ZKPK) to perform risk-based multi-factor authentication and authorization using a digitally signed identity that encodes secrets provided by the component. Our approach is based on Threat Modeling (TM), Vulnerability Identification (VI), and Consequence Analysis (CA) to provide adequate and efficient authentication and authorization in the ICS. The resilience of our framework is evaluated against recent well-known cyber attacks. Specifically, we conduct a risk-based security assessment for a Safety Instrumentation System (SIS) communication protocol, known as TriStation. The results show that our framework enhances the security of the protocol in dealing with real-time uncertainty of threats, vulnerabilities, and consequences from a new cyber-attack, known as TRITON malware.
Information Extraction from Echocardiography Reports for a Clinical Follow-up Study—Comparison of Extracted Variables Intended for General Use in a Data Warehouse with Those Intended Specifically for the Study

Abstract Background The interest in information extraction from clinical reports for secondary data use is increasing. But experience with the productive use of information extraction processes over time is scarce. A clinical data warehouse has been in use at our university hospital for several years, which also provides an information extraction of echocardiography reports developed for general use. Objectives This study aims to illustrate the difficulties encountered, while using data from a preexisting information extraction process for a large clinical study. To compare the data from the preexisting process with the data obtained from a specially developed process designed to improve the quality and completeness of the study data. Methods We extracted the echocardiography variables for 440 patients from the general-use information extraction of the data warehouse (678 reports). Then we developed an information extraction process for the same variables but specifically for this study, with the aim to extract as much information as possible from the text. The extracted data of both processes were compared with a newly created gold standard defined by a cardiologist with long-standing experience in heart failure. Results Among 57 echocardiography variables considered relevant for the study, 50 were documented in the routine text reports and could be extracted. Twenty of the required variables were not provided by the general-use extraction process, some others were not provided correctly. The median macro F1-score (precision, recall) across the 30 variables for which values were extracted was 0.81 (0.94, 0.77). Across all 50 variables, as relevant for the study, median macro F1-score was only 0.49 (0.56, 0.46). Employing the study-specific approach considerably improved the quality and completeness of the variables, resulting in F1-scores of 0.97 (0.98, 0.96) across all variables. Conclusion Data from information extractions can be used for large clinical studies. However, preexisting information extraction processes should be treated with caution, as the time and effort spent defining each variable in the information extraction process may not be clear.
A Model-Agnostic Approach for Explaining the Predictions on Clustered Data

Machine learning models especially deep neural network models have shown great potential in making decisions when analyzing clustered or longitudinal data. However, lack of model transparency is a major concern in risk sensitive domains such as social science and medical diagnosis. Despite the early success of explaining machine learning models, there is a lack of explanation methods that can be applied to any predictors on clustered data since most of the existing models assume that all observations are independent of each other. In this paper, we address this deficiency and propose to use a linear mixed model to mimic the local behavior of any complex model on clustered data, which can also improve the fidelity of the explanation method to the complex models. We apply our method to explain several models including a deep neural network model on two tasks including movie recommendation and medical record diagnosis. Experiment results show that our model outperforms the baseline models on several metrics such as fidelity and exactness.
WiseMed: Medication reminder for seniors

Today, many people are very busy with their daily lives, so they may forget to take their medications on schedule, and their medication treatments may not be as effective as they are supposed to be. Several medication reminder applications for smartphones are already available in Thailand, but most of them share a general limitation that users need to enter their medication information and schedule by themselves. This limitation can lead to a new, serious problem: users inputting erroneous medication information or schedules into their smart-phones, especially elderly users. This paper proposes a medication reminder application that overcomes this problem. To eliminate this kind of critical data entry error, we retrieves information on the user's medication and schedule by scanning the QR code on the medication package. This application is not only able to automatically remind users to take their medication on schedule but also provides the user with basic and crucial medication information such as directions for use, precautions, and side effects. Moreover, the application also keeps track of a user's medication-taking history over time, so the physician can modify the medication schedule or the user behavior in his or her follow-up visit in order for the treatment to be as effective as possible. The evaluations demonstrate that QR code mitigate the seniors' errors by 42%. Additionally, our pipeline can identify the user's medication-taking behavior that violate the time-critical scheduled medications with almost perfect correlation with a standard guideline. Finally, all medical professionals confirm that our medication-taking report is crucial and very helpful.
Performance Comparison Between SURF and SIFT for Content-Based Image Retrieval

Speeded-Up Robust Feature (SURF) and Scale Invariant Feature Transform (SIFT) have been two well-known methods used in extracting features. This paper presents and analyzes performance comparison between the SURF approach and the SIFT technique for content-based image retrieval (CBIR) application. In particular, we are interested in comparing the accuracy and the response time between these two methods. For the testing purposes, we make use sample images obtained for the Pennsylvania State College of Information Science and Technology database. As it turns out, in this paper, we will demonstrate that in terms of accuracy and speed, SURF shows superior performance compared to SIFT.
Leukemia Diagnosis using Computational Intelligence

Leukemia is a fatal disease that is commonly found in children and also in adults above 55 years of age. It is also known as cancer of blood or bone marrow. [1] It can be categorized into myelogenous leukemia or lymphocytic on the basis of the cells affected by the disease. As the symptoms of the disease are very common like fever, fatigue and body ache, it is not easily detectable at early stages which prove fatal at later stages. So diagnosing it at early stage is crucial for the better prognosis of disease. The paper presents a comparative analysis of extensively used machine learning (ML) algorithm SVM and the relatively new ML algorithm i.e., extreme learning machine for predicting Leukemia. The classification is based on the segmentation of blood smear images publically available dataset ALL-IDB1. The results shows that ELM with an accuracy of 92.2448% outperforms SVM with accuracy 86.3636%.