Reviewerly/exHarmony huggingface dataset:
Chattering <b>‐</b> free hybrid adaptive neuro‐fuzzy inference system‐particle swarm optimisation data fusion‐based BG‐level control

In this study, a closed-loop control scheme is proposed for the glucose-insulin regulatory system in type-1 diabetic mellitus (T1DM) patients. Some innovative hybrid glucose-insulin regulators have combined artificial intelligence such as fuzzy logic and genetic algorithm with well known Palumbo model to regulate the blood glucose (BG) level in T1DM patients. However, most of these approaches have focused on the glucose reference tracking, and the qualitative of this tracking such as chattering reduction of insulin injection has not been well-studied. Higher-order sliding mode (HoSM) controllers have been employed to attenuate the effect of chattering. Owing to the delayed nature and non-linear property of glucose-insulin mechanism as well as various unmeasurable disturbances, even the HoSM methods are partly successful. In this study, data fusion of adaptive neuro-fuzzy inference systems optimised by particle swarm optimisation has been presented. The excellent performance of the proposed hybrid controller, i.e. desired BG-level tracking and chattering reduction in the presence of daily glucose-level disturbances is verified.
Spatiotemporal Modeling for Image Time Series with Appearance Change: Application to Early Brain Development

There has been considerable research effort into image registration and regression, which address the problem of determining correspondence primarily through estimating models of structural change. There has been far less focus into methods which model both structural and intensity change. However, medical images often exhibit intensity changes over time. Of particular interest is MRI of the early developing brain, where such intensity change encodes rich information about development, such as rapidly increasing white matter intensity during the first years of life. In this paper, we develop a new spatiotemporal model which takes into account both structural and appearance changes jointly. This will not only lead to improved regression accuracy and data-matching in the presence of longitudinal intensity changes, but also facilitate the study of development by direct analysis of appearance change models. We propose to combine a diffeomorphic model of structural change with a Gompertz intensity model, which captures intensity trajectories with 3 intuitive parameters of asymptote, delay, and speed. We propose an optimization scheme which allows to control the balance between structural and intensity change via two data-matching terms. We show that Gompertz parameter maps show great promise to characterize regional patterns of development.
Camera model for lens with strong distortion in automotive application

This document presents mathematical model of camera with strong lens distortion that can be used in calibration process and in a computer vision algorithms. Both radial and tangential aberration are taken into consideration. Moreover, a comparison of the use of the division and Brown-Conrady models has been presented.
Fusing Dialogue and Gaze From Discussions of 2D and 3D Scenes

Conversation partners rely on inference using each other’s gaze and utterances to negotiate shared meaning. In contrast, dialogue systems still operate mostly with unimodal question or command and response interactions. To realize systems that can intuitively discuss and collaborate with humans, we should consider other sensory information. We begin to address this limitation with an innovative study that acquires, analyzes, and fuses interlocutors’ discussion and gaze. Introducing a discussion-based elicitation task, we collect gaze with remote and wearable eye trackers alongside dialogue as interlocutors come to consensus on questions about an on-screen 2D image and a real-world 3D scene. We analyze the visual-linguistic patterns, and also map the modalities onto the visual environment by extending a multimodal image region annotation framework using statistical machine translation for multimodal fusion, applying three ways of fusing speakers’ gaze and discussion.
A Dynamic Height Analysis on Vision Based Fall Detection System

The senior citizens need support or help after a fall accident. But they may not be able to summon help due to the injuries or impairment of the fall accidient, and the cost of human based health care monitoring system is high. The artificial intelligence and image processing give a good solution of these problems, as it can monitor the room round-the-clock and detect the fall accident with low cost. Although a dataset is important for machine learning or deep learning based fall detection systems, few works construct a dataset with enough images. Moreover, the dataset in related works is taken by a sensor or camera at a low height; this is not very suitable for a fall detection system because the camera or sensor may be blocked by the furniture. In this study, images are taken by a depth camera, which is fixed at 1.7 meters, 1.9 meters and 2.1 meters height, as the height of roof should be no less than 2.1 meters according to the laws in many countries. This paper gives a brief dynamic camera height analysis and study how the camera height affects the detection accuracy.
English Pronunciation Recognition and Detection Based on HMM-DNN

A hybrid speech recognition system based on deep neural network(HMM-DNN) is constructed with different acoustic modeling units in this paper. Using the idea of multitask learning, the input layer and hidden layer of DNN are shared, and the accuracy of modeling is improved by joint training. Then, the traditional classification method is improved from two aspects of input feature selection and classifier design. Using high-dimensional phonetics as the basic input feature, the binary classifier of each phoneme is embedded in the same neural network, and the multi-phoneme error detection problem is transformed into a multi-task learning problem. The experimental results show that the improved model can achieve better recognition performance than the single task learning model, and the output of the two systems is integrated to further reduce the word error rate.
The Univariate Flagging Algorithm (UFA): An interpretable approach for predictive modeling

In many data classification problems, a number of methods will give similar accuracy. However, when working with people who are not experts in data science such as doctors, lawyers, and judges among others, finding interpretable algorithms can be a critical success factor. Practitioners have a deep understanding of the individual input variables but far less insight into how they interact with each other. For example, there may be ranges of an input variable for which the observed outcome is significantly more or less likely. This paper describes an algorithm for automatic detection of such thresholds, called the Univariate Flagging Algorithm (UFA). The algorithm searches for a separation that optimizes the difference between separated areas while obtaining a high level of support. We evaluate its performance using six sample datasets and demonstrate that thresholds identified by the algorithm align well with published results and known physiological boundaries. We also introduce two classification approaches that use UFA and show that the performance attained on unseen test data is comparable to or better than traditional classifiers when confidence intervals are considered. We identify conditions under which UFA performs well, including applications with large amounts of missing or noisy data, applications with a large number of inputs relative to observations, and applications where incidence of the target is low. We argue that ease of explanation of the results, robustness to missing data and noise, and detection of low incidence adverse outcomes are desirable features for clinical applications that can be achieved with relatively simple classifier, like UFA.
Model of foresight work habits of agile software team members by personality traits

In order to support modern business environment, software engineering has a constant tendency to increase productivity and thus the quality of the software. When recruiting, a biography can tell a lot about an individual, about expertise to do a certain job, but that is often not enough. For this reason, a model for prediction of work habits based on personality types has been introduced in this paper. Through the results of the research, by analyzing the employees of the FitSoft company (Bosnia and Herzegovina), a statement has been made of the existence of a certain correlation between the types of personality and the way they perform their tasks. The purpose of testing personality types is to determine how an individual performs their daily activities, and which features give them the advantage of choosing and performing certain jobs. A proposed model for predicting employees' habits inside agile software teams based on personality estimates should present a clear insight of all factors that are related to employees, their productivity and business results.
Development of a Model that Detects Student's Disengagement during an Online Lecture Presentation through Eye Tracking and/or Head Movement

Technologies such as online lecture presentations are now provided to students to cater their learning through digital means. Unfortunately, student's engagement during online classes is uncertain. To partially address this problem, we developed models that can detect student disengagement using some well-known decision tree classifiers. Results showed that Random Forest using Information Gain criterion provided the highest accuracy rate of 91.73% and kappa statistic of 0.74. It was also observed that majority of the internal nodes of the tree models are features extracted from eye tracking. This includes: the time duration of the eye seen, the distance of the iris from the center of the eye, and the distance travelled by the iris. It implies that eye features are more likely to be determinants in detecting student disengagement rather than head movement features. Finally, we significantly noticed that: in every 450 video frames, if the eye is seen for less than 76 frames and an average angle of head pitch is less than or equal to 46.5 degrees with respect to the negative y-axis, the student is most likely disengaged. We suggest that the model should be embedded in a computational system that automatically provides feedback to teachers conducting online classes which could aid the teacher in maintaining the class' engagement and help pave way to future studies regarding any individuals' engagement.
An ergonomic evaluation method using a mobile depth sensor and pose estimation

An ergonomic evaluation is an observation of a person in order to identify musculoskeletal disorders (WMSDs) caused by prolonged or repeated harmful poses that a person adopts during work tasks. Nowadays, an ergonomist or other health professional perform such evaluations based on a set of posture rules and checklists, which can be subjective and thus lead to erroneous risk classifications. Moreover, this professional usually perform such evaluation in the patient work environment. In order to make those evaluations more objective and concise we propose a evaluation method using a mobile depth sensor. Different from other methods based in fixed depth sensors (e.g. Kinect), our method enable professionals easily perform it in the patient work environment. More precisely, we present an experiment that uses a smartphone from Google's Tango project and the Ovako Working Posture Analysing System (OWAS) method. To evaluate our approach, we also perform the ergonomic assessment using the Kinect sensor, a device that has a good reliability in the automated ergonomic evaluation. Both evaluations involved a set of 34 poses performed by 3 volunteers and annotated by an ergonomist. The Kinect has achieved accuracy of 57,08% on torso classification, 58,33% for arms and 25,00% for legs positions. While the approach using the mobile depth sensor has achieved 35,41% on torso classification, 93,05% for arms and 66,23% for legs positions on the same set of poses. Although the small sample, the achieved results may indicate that our mobile depth sensor approach can be as viable as methods based fixed depth sensor.
Is Face 3D or 2D on Stereo Images?

This paper presents two algorithms to distinguish real faces from flat photos in stereo camera images. The first algorithm based on geometric relations between certain face landmarks. All points of the face photo will lie on the same plane, but for the real face - no. The second uses the generated face relief pattern. The face is 2D or 3D dependent on the similarity of the generated normalization pattern to the pretrained template. Proposed approaches allow obtaining a number of advantages of 3D face recognition systems with minimal costs to modernization the existing 2D systems. Some recommendations and limitations to the usage of the considered approaches are determined.
People Detection in Downward-Viewing Fisheye Camera Networks Using Fuzzy Integral

In this paper we study techniques for accurate detection and localization of multiple people in an indoor scene covered by multiple downward-viewing fisheye cameras. In particular, we propose a fuzzy integral based method for aggregating the information from the multiple cameras to form a combined occupancy map of people in the scene. The experimental results on test videos exhibit good performance for practical use. We also propose methods to account for occlusion by scene objects at different stages of the algorithm that lead to improved results.
TaskDo: A Daily Task Recommender System

Many individuals like working professionals, students, and house makers often find lack of time and time management as problems forsuccessful task accomplishment. One of the key reasons for failure in task accomplishment is inefficient planning of the tasks. There are many task management and to-do-list applications, but most of them do not advise on optimal task management and guidance for optimal performance. This problem has driven us to contribute a task recommender system which suggests a specific type of tasks to users based on their history of tasks and various factors at that specific time. This system not only suggests a specific type of task for the user but also collects feedback from the user to make the recommender system learn on how to provide useful recommendations thus making the users time much productive.
Single Image Dehazing Using Improved Gray World Theory and Dark Channel Prior

The images captured outdoor are usually influenced by inclement weather conditions severely, bringing a great deal of inconvenience to the automatic data processing system. The widely used dehazing method based on dark channel prior (DCP) is not effective on the image with color distortion. In order to solve this problem, we improve the gray world theory (GWT) and propose a single image dehazing method using our improved gray world theory and the dark channel prior. Experiments show that our method can restore the hazy image with color distortion effectively and outperforms the state-of-art results.
Registration of Untracked 2D Laparoscopic Ultrasound Liver Images to CT Using Content-Based Retrieval and Kinematic Priors

Laparoscopic Ultrasound (LUS) can enhance the safety of laparoscopic liver resection by providing information on the location of major blood vessels and tumours. Since many tumours are not visible in ultrasound, registration to a pre-operative CT has been proposed as a guidance method. In addition to being multi-modal, this registration problem is greatly affected by the differences in field of view between CT and LUS, and thus requires an accurate initialisation. We propose a novel method of registering smaller field of view slices to a larger volume globally using a Content-based retrieval framework. This problem is under-constrained for a single slice registration, resulting in non-unique solutions. Therefore, we introduce kinematic priors in a Bayesian framework in order to jointly register groups of ultrasound images. Our method then produces an estimate of the most likely sequence of CT images to represent the ultrasound acquisition and does not require tracking information nor an accurate initialisation. We demonstrate the feasibility of this approach in multiple LUS acquisitions taken from three sets of clinical data.
Automatic identification of standard template algorithms in raw loops

This paper explains a tool-based approach to detect sourcecode patterns that can be substituted with calls to the C++ standard template library (STL). The goal of the tool is to support developers in the process of refactoring a legacy code base to make use of modern library interfaces and standardized algorithms. This way, the intention of the programmer is encoded more explicitly in the code to increase readability. In addition, the STL is well tested, i.e., its use can improve robustness. We show early results from applying our tool to the High-Performance Conjugate Gradient (HPCG) benchmark. The current prototype creates roughly 50% false positives, all of which a human can easily identify.
Perceptually guided contrast enhancement based on viewing distance

We propose an image-space contrast enhancement method for color-encoded visualization. The contrast of an image is enhanced through a perceptually guided approach that interfaces with the user with a single and intuitive parameter of the virtual viewing distance. To this end, we analyze a multiscale contrast model of the input image and test the visibility of bandpass images of all scales at a virtual viewing distance. By adapting weights of bandpass images with a threshold model of spatial vision, this image-based method enhances contrast to compensate for contrast loss caused by viewing the image at a certain distance. Relevant features in the color image can be further emphasized by the user using overcompensation. The weights can be assigned with a simple band-based approach, or with an efficient pixel-based approach that reduces ringing artifacts. The method is efficient and can be integrated into any visualization tool as it is a generic image-based post-processing technique. Using highly diverse datasets, we show the usefulness of perception compensation across a wide range of typical visualizations.
Exploration into Gray Area: Toward Efficient Labeling for Detecting Malicious Domain Names

In this paper, we propose a method to reduce the labeling cost while acquiring training data for a malicious domain name detection system using supervised machine learning. In the conventional systems, to train a classifier with high classification accuracy, large quantities of benign and malicious domain names need to be prepared as training data. In general, malicious domain names are observed less frequently than benign domain names. Therefore, it is difficult to acquire a large number of malicious domain names without a dedicated labeling method. We propose a method based on active learning that labels data around the decision boundary of classification, i.e., in the gray area, and we show that the classification accuracy can be improved by using approximately 1% of the training data used by the conventional systems. Another disadvantage of the conventional system is that if the classifier is trained with a small amount of training data, its generalization ability cannot be guaranteed. We propose a method based on ensemble learning that integrates multiple classifiers, and we show that the classification accuracy can be stabilized and improved. The combination of the two methods proposed here allows us to develop a new system for malicious domain name detection with high classification accuracy and generalization ability by labeling a small amount of training data.
Compositionality of Component Fault Trees

In order to deal with the rising complexity of safety-critical systems, model-based systems engineering (MBSE) approaches are becoming popular due to their promise to improve consistency between different views of the system model. Component Fault Trees (CFTs) are one particular technique to integrate the well-known Fault Tree Analysis (FTA) with a model of the system. CFTs decompose the specification of fault propagation on component level, which results in smaller, easier to manage models and leads to a safety analysis view that is consistent with the system model. However, although CFTs gain more and more popularity, their semantics is not well defined and the compositionality of CFTs is not formally proven to the best of our knowledge. In this paper, we provide a formal basis for CFTs, formalize semantics of CFTs and formally prove compositionality of CFTs by mapping them to information flow semantics, which is well-researched in the security analysis domain. Our results allow insights in the compositionality of CFTs, showing a high potential for validation techniques of CFTs and discuss these consequences in detail. We claim that this proof is crucial for the use of CFTs in assurance cases for safety-critical systems and one fundamental approach to integrate safety and security engineering.
Stereo-Vision Based System For Object Detection And Recognition

This paper proposes a method for detecting and recognizing the object using Stereo Vision, Scale-Invariant Feature Transform (SIFT) and Fast library for approximate Nearest Neighbors (FLANN) concept with its implementation on an embedded system. Using stereo vision on the microprocessor Raspberry Pi, the implemented system takes the two images produced as input, calculates the disparity map which provides the relative depth information. Using this map and the Scale-Invariant Feature Transform (SIFT), features are obtained and matched with a database having large collection of images. This implementation uses Fast Library for Approximate Nearest Neighbors (FLANN), which unlike the Brute-Force matching algorithm can support large databases. This system gives a voice output when the object is recognized by text to speech conversion.
Tree-LSTM: Using LSTM to Encode Memory in Anatomical Tree Prediction from 3D Images

Extraction and analysis of anatomical trees, such as vasculatures and airways, is important for many clinical applications. However, most tracking methods so far intrinsically embedded a first-order Markovian property, where no memory beyond one tracking step was utilized in the tree extraction process. Motivated by the inherent sequential construction of anatomical trees, vis-à-vis the flow of nutrients through branches and bifurcations, we propose Tree-LSTM, the first LSTM neural network to learn to encode such sequential priors into a deep learning based tree extraction method. We also show that mathematically, by using LSTM, the variational lower bound of a higher order Markovian stochastic process could be approximated, which enables the encoding of a long term memory. Our experiments on a CT airway dataset show that, by adding the LSTM component, the results are improved by at least $$11\%$$ in mean direction prediction accuracy relative to state-of-the-art, and the correlation between bifurcation classification accuracy and evidence is improved by at least $$15\%$$ , which demonstrate the advantage of a unified deep model for sequential tree structure tracking and bifurcation detection.