Reviewerly/exHarmony huggingface dataset:
Multi-UAV Trajectory Optimization Utilizing a NURBS-Based Terrain Model for an Aerial Imaging Mission

Trajectory optimization precisely scanning an irregular terrain is a challenging problem since the trajectory optimizer needs to handle complex geometry topology, vehicle performance, and a sensor specification. To address these problems, this paper introduces a novel framework of a multi-UAV trajectory optimization method for an aerial imaging mission in an irregular terrain environment. The proposed framework consists of terrain modeling and multi-UAV trajectory optimization. The terrain modeling process employs a Non-Uniform Rational B-Spline (NURBS) surface fitting method based on point cloud information resulting from an airborne LiDAR sensor or other sensor systems. The NURBS-based surface model represents a computationally efficient terrain topology. In the trajectory optimization method, the framework introduces a multi-UAV vehicle routing problem enabling UAV to scan an entire area of interest, and obtains feasible trajectories based on given vehicle performance characteristics, and sensor specifications, and the approximated terrain model. The proposed multi-UAV trajectory optimization algorithm is tested by representative numerical simulations in a realistic aerial imaging environment, namely, San Diego and Death Valley, California.
Visual-Haptic Colocation in Robotic Rehabilitation Exercises Using a 2D Augmented-Reality Display

Haptics-based Virtual Reality (VR) games have been found to be effective in rehabilitation from disability. Augmented Reality (AR) has gained traction in recent years in various domains including gaming, entertainment, and education. In this paper, we integrate spatial AR into robotic rehabilitation to provide colocation between visual and haptic feedback as a human user participates in a rehabilitative game. A comparison between the effectiveness of VR vs AR (i.e., non-colocation vs colocation of vision) is done. Spatial AR is the colocation of vision through the use of projection. Visual-Haptic colocation is the combination of spatial AR and haptic interaction. We also compare each visualization technique in the absence and presence of haptic feedback and cognitive loading (CL) for the human user. The system was evaluated by having 10 able-bodied participants do all 8 different conditions lasting approximately 3 minutes per condition. The results show that spatial AR (corresponding to colocation of visual frame and hand frame) leads to the best user performance when doing the task regardless of the presence or the absence of haptics. It is also observed that for users undergoing cognitive loading, the combination of spatial AR and haptics produces the best result in terms of task completion time.
An improved adaptive weighted median filter algorithm

An improved adaptive weighted median filtering method is proposed to deal with the interference noise of ultrasonic RF signal. Firstly, edge pixel points are determined to be filtered by the method of extending edge points; secondly, mean value is used to replace the median value which considered to be noise points; finally, weighted smoothing processing is carried out. The final experimental results in this paper show that the proposed method has better effect on RF signal processing.
Active learning Rotation Forest for multiclass classification

Abstract Although the achievements of the computer science field have facilitated the tasks of collecting, storing, and accessing vast amounts of data efficiently, its annotation still remains a non–easily resolvable problem since no automated mechanism can perform reliably enough. This fact is even more profound when the objective is the high quality of generalization ability. Active learning constitutes a scheme that is exploited for tackling such problems, controlling the demanded human effort under a trade‐off assumption concerning the achievement of higher accuracy rates. In this work, the well‐known Rotation Forest algorithm is integrated with the active learning theory for constructing a robust and accurate classifier. Thus, apart from exploiting the collected labeled data, unlabeled data mining takes place through appropriate queries, whereas human expert decisions over the most questionable of them enrich the initial ones. Comprehensive comparisons of the proposed algorithm against four distinct learners inside the same learning scheme were executed. Moreover, the baseline strategy of random sampling and the corresponding supervised scenarios were included. During the evaluation stage, 13 publicly available multiclass datasets were assessed, and the obtained results verified our assumptions, regarding also the significant supremacy of the proposed algorithm against the majority of its rivals.
Image Content Enhancement Through Salient Regions Segmentation for People With Color Vision Deficiencies

Color vision deficiencies affect visual perception of colors and, more generally, color images. Several sciences such as genetics, biology, medicine, and computer vision are involved in studying and analyzing vision deficiencies. As we know from visual saliency findings, human visual system tends to fix some specific points and regions of the image in the first seconds of observation summing up the most important and meaningful parts of the scene. In this article, we provide some studies about human visual system behavior differences between normal and color vision-deficient visual systems. We eye-tracked the human fixations in first 3 seconds of observation of color images to build real fixation point maps. One of our contributions is to detect the main differences between the aforementioned human visual systems related to color vision deficiencies by analyzing real fixation maps among people with and without color vision deficiencies. Another contribution is to provide a method to enhance color regions of the image by using a detailed color mapping of the segmented salient regions of the given image. The segmentation is performed by using the difference between the original input image and the corresponding color blind altered image. A second eye-tracking of color blind people with the images enhanced by using recoloring of segmented salient regions reveals that the real fixation points are then more coherent (up to 10%) with the normal visual system. The eye-tracking data collected during our experiments are in a publicly available dataset called Eye-Tracking of Color Vision Deficiencies.
Review on Studies of Machine Learning Algorithms

This paper mainly introduces machine learning algorithms in the field of artificial intelligence. First, it describes the classification of such algorithms and their main application scenarios. Then the paper introduces the principles behind those algorithms and presents the author's views. Finally, the development trend of machine learning algorithms is envisioned.
CDL4CDRP: A Collaborative Deep Learning Approach for Clinical Decision and Risk Prediction

(1) Background: Recommendation algorithms have played a vital role in the prediction of personalized recommendation for clinical decision support systems (CDSSs). Machine learning methods are powerful tools for disease diagnosis. Unfortunately, they must deal with missing data, as this will result in data error and limit the potential patterns and features associated with obtaining a clinical decision; (2) Methods: Recent years, collaborative filtering (CF) have proven to be a valuable means of coping with missing data prediction. In order to address the challenge of missing data prediction and latent feature extraction, neighbor-based and latent features-based CF methods are presented for clinical disease diagnosis. The novel discriminative restricted Boltzmann machine (DRBM) model is proposed to extract the latent features, where the deep learning technique is adopted to analyze the clinical data; (3) Results: Proposed methods were compared to machine learning models, using two different publicly available clinical datasets, which has various types of inputs and different quantity of missing. We also evaluated the performance of our algorithm, using clinical datasets that were missing at random (MAR), which were missing at various degrees; and (4) Conclusions: The experimental results demonstrate that DRBM can effectively capture the latent features of real clinical data and exhibits excellent performance for predicting missing values and result classification.
Comparing Two Different Cursor Control Methods which Use Single-Site Surface Electromyography

Electromyography (EMG) can be used as an input signal for Human-Computer Interfaces, which are used in the disabled community. Typically, both the user-condition and the end-goal application dictate the number and locations of EMG sensors. We developed two cursor control methods intended for high-level spinal cord injury patients, where available muscle sites are limited to the head and neck areas. For patient comfort, it is desirable to minimize the number of sensors and their intrusiveness while maximizing the functionality of the control method. Both our control methods use a single, noninvasive, surface, differential EMG sensor at the temporalis muscle that controls a cursor in two degrees of freedom (DOF). Fourteen inexperienced able-bodied subjects completed a Fitts’s law task-based cursor-to-target paradigm using both control methods ("auto-rotate" and "manual rotate"). Subjects evaluated and compared performance between the two control methods, which both enabled the cursor to move in 2-DOF. They differed in the level of user control, where one allowed direction manipulation in 1-DOF and 2-DOF in the other. Subjects also completed pre- and post-session surveys and the National Aeronautics and Space Administration Task Load Index for workload assessment. In general, subjects’ performance improved with subsequent sessions within each control method. Subjects achieved a higher throughput (better performance) in the auto-rotate method, had lower workload scores, and tended to prefer this control method. However, about half the subjects felt the manual rotate method allowed them more control over cursor behavior. Our results suggest that a viable cursor control method can be achieved with only a single muscle site.
Blind image deblurring with reinforced use of edges

Blind image deblurring tries to restore a blurred image to a clear image without the blurring kernel known in advance, which is widely required in applications such as computer vision and medical image processing. With regard to this, the key issues here are to accurately estimate the blurring kernel for deconvolution of a blurred image, and avoid the ringing artifacts in the restored image, which are both related to high-quality detection of edge information in the blurred image. Though much endeavor has been made, it is still difficult to extract edge information well in blurred images and lacks investigation how edge information causes ringing artifacts. In this paper, we make a study on this and develop novel measures to optimize edge extraction and determine suitable width and weights for the extracted edges for reinforcing their use in deblurring, by which image deblurring can be improved with ringing artifacts considerably suppressed. Experimental results demonstrate our improvements over the existing methods.
Fusion by synthesizing: A multi-view deep neural network for zero-shot recognition

Zero-shot learning (ZSL) aims to recognize objects without seeing any visual instances by learning knowledge transfer between seen and unseen classes. Attributes, which denote high-level visual entities or visual characteristics, have been widely utilized as intermediate embedding space for knowledge transfer in a majority of existing ZSL approaches and have shown impressive performance. Attribute based ZSL approaches, which introduce an intermediate embedding space of attributes for knowledge transfer, have shown impressive performance. However, providing attribute annotations for unseen classes at test time is time-consuming and labor-intensive. Besides, directly using attributes as intermediation (embedding space) for knowledge transfer and zero-shot prediction inevitably leads to the projection domain shift and hubness problems. In this paper, we propose a novel multi-view deep neural network, termed Fusion by Synthesis (FS), which leverages word embeddings of classes as complementary for attributes and performs zero-shot prediction by fusing the word embeddings of unseen classes and the synthesized attributes in the visual feature space. Specifically, in the training phase, by considering the visual features, attributes and word embeddings as three different views of visual instances, FS allocates each view with a denoising auto-encoder to simultaneously ensures robust view-specific reconstructions and cross-view synthesizing, while preserving the discrimination of class labels. During testing, FS can synthesize the absent attributes for unseen classes and fuse them with word embeddings to the visual feature space to perform zero-shot prediction. Besides, FS is flexible to learn with partial views, where either attribute view or word embedding view is missing during training. Moreover, FS is flexible to synthesize either the missing view of attributes or word embeddings from the provided view. Extensive experiments on six benchmark datasets on both image classification and action recognition show that FS is advantaged to fuse multi-view data by synthesis and achieves superior performance compared with the state-of-the-art ZSL methods.
An Intelligent Approach of Text-To-Speech Synthesizers for English and Sinhala Languages

This paper attempts to investigate novel Text-to-Speech algorithm based on Deep voice which is an attention based, fully convolutional mechanism. The procedure of producing speech synthesis involves with learning statistical model of the human vocal production mechanism which is eligible of taking some text and vocalize that as speech. This paper would reveal the route of the attempt where there is the destination of accuracy and realism. Serenity and fluency are the most important qualities which expect from a TTS. The idea is to give an outline of discourse amalgamation in the Sinhala language, compresses and replicates about the characteristics of different blend procedures utilized. The proposed TTS synthesizing with the neural network based approach to perform phonetic-to-acoustic mapping has described by the purpose of applying for multilingual synthesizers.
A privacy‐preserving framework for smart context‐aware healthcare applications

Abstract Smart connected devices are widely used in healthcare to achieve improved well‐being, quality of life, and security of citizens. While improving quality of healthcare, such devices generate data containing sensitive patient information where unauthorized access constitutes breach of privacy leading to catastrophic outcomes for an individual as well as financial loss to the governing body via regulations such as the General Data Protection Regulation. Furthermore, while mobility afforded by smart devices enables ease of monitoring, portability, and pervasive processing, it introduces challenges with respect to scalability, reliability, and context awareness. This paper is focused on privacy preservation within smart context‐aware healthcare emphasizing privacy assurance challenges within Electronic Transfer of Prescription. We present a case for a comprehensive, coherent, and dynamic privacy‐preserving system for smart healthcare to protect sensitive user data. Based on a thorough analysis of existing privacy preservation models, we propose an enhancement to the widely used Salford model to achieve privacy preservation against masquerading and impersonation threats. The proposed model therefore improves privacy assurance for smart healthcare while addressing unique challenges with respect to context‐aware mobility of such applications.
Impact of Adversarial Examples on the Efficiency of Interpretation and Use of Information from High-Tech Medical Images

In this paper we discuss the possibility of adversarial examples appearance in high-tech medical images (Computer tomography and Magnetic resonance imaging), due to the noise inherent in the technology of their formation, and therefore we suggest ways to counteract this effect. As the idea of the paper we put two questions: 1. Can individual instances of real high-tech medical images work as AE when being analyzed with the use of neural networks? 2. Is it possible to defend oneself against such "natural" adversarial attacks with the simplest possible means? In our research, we tried the following defence methods: adversarial training, Gaussian data augmentation and bounded RELU (see section 3 for a detailed description). We conducted the experiment with the use of the neural network - a variant of convolutional network structure combining U-Net with the region proposal networks. As the source data two datasets were chosen - the Lung Image Database Consortium image collection containing 1018 lung cancer screening thoracic CT scans and Brain MRI DataSet containing clinical imaging data of glioma patients (a total of 274 cases). The experiments showed that the degree of manifestation of AE varies depending on the type of training model. When training a model not using techniques of defences on adversarial examples, the number of incorrectly recognized images is quite large (200 per 10,000 for CT and 285 per 10,000 for MRI). By proper selecting of the activation function of CNN, it can be reduced to 60 and 68, respectively. With augmentation of training dataset by Gaussian noised images, this number drops to 21 and 26. An even greater reduction in the number of incorrectly recognized images is achieved using the Adversarial Training method -12 and 15. Thus, it is shown that the adversarial effect is possible after the application of adversarial training techniques, but the degree of noise in such an image will be much higher than before using these techniques, and it will be easy enough for the doctor to recognize them visually and exclude them from further consideration.
Why Do These Match? Explaining the Behavior of Image Similarity Models

Explaining a deep learning model can help users understand its behavior and allow researchers to discern its shortcomings. Recent work has primarily focused on explaining models for tasks like image classification or visual question answering. In this paper, we introduce Salient Attributes for Network Explanation (SANE) to explain image similarity models, where a model's output is a score measuring the similarity of two inputs rather than a classification score. In this task, an explanation depends on both of the input images, so standard methods do not apply. Our SANE explanations pairs a saliency map identifying important image regions with an attribute that best explains the match. We find that our explanations provide additional information not typically captured by saliency maps alone, and can also improve performance on the classic task of attribute recognition. Our approach's ability to generalize is demonstrated on two datasets from diverse domains, Polyvore Outfits and Animals with Attributes 2. Code available at: https://github.com/VisionLearningGroup/SANE .
Evaluation of Phonexia automatic speaker recognition software under conditions reflecting those of a real forensic voice comparison case (forensic_eval_01)

As part of the Speech Communication virtual special issue “Multi-laboratory evaluation of forensic voice comparison systems under conditions reflecting those of a real forensic case (forensic_eval_01)” two automatic speaker recognition systems developed by the company Phonexia were tested. The first named SID (Speaker Identification)-XL3 is an i-vector PLDA system that works with two streams of features, one of them using MFCCs in a classical sense, the other using DNN-Stacked Bottle-Neck features based on correlated spectral-domain features as well as on information from voice/voiceless detection and fundamental frequency. The second system that was tested is called SID-BETA4. It uses MFCCs as input features (without deltas and double deltas) and employs a DNN-based speaker embedding architecture. Each of the two systems was tested in two variants. In the first, the system was used without including any domain-specific data, i.e. data from the training set of forensic_eval_01. In the second variant, training set data were used with a method called 10% FAR calibration. With this method scores are shifted in a way that 10% of the scores in the non-target distribution (based on training data) will have LLR > 0 and 90% will have LLR < 0. Results showed that the speaker embedding system SID-BETA4 leads to clear improvement over the use of the SID-XL3 in terms of accuracy, discrimination and precision measures. Use of the FAR calibration method turned out to leave precision unaffected but lead to improvement in discrimination. The accuracy measure Cllrpooled improved with the use vs. non-use of FAR calibration in SID-XL3 but not SID-BETA4.
Prioritizing the Needs of Caregivers of Older Adults to Support Their Help-Seeking Process as a First Step to Developing an eHealth Tool: The Technique for Research of Information by Animation of a Group of Experts (TRIAGE) Method

Caregivers of functionally dependent older persons sometimes seek formal services to support their relatives. However, this process of help-seeking is complex.The overall aim of the study was to use a co-design approach to develop an electronic health (eHealth) tool to support caregivers in their process of help-seeking. This study presents the first step of the design phase, which aimed to prioritize the user needs to be considered during the development of an eHealth tool.A total of 3 groups of caregivers, community workers, and health and social service professionals participated in either a co-design session (1 or 2) or an advisory committee in 2 rural areas and 1 urban area. The needs identified in the academic literature and during a previous study were sorted (Technique for Research of Information by Animation of a Group of Experts [TRIAGE] method) by the participants (referred to in this study as co-designers) to obtain a consensus on those to be prioritized. Needs identified, grouped, and removed were ranked and compared.Of the initial list of 32 needs, 12 were modified or merged, 3 added, and 7 deleted as the co-designers felt that the needs were poorly formulated, redundant, irrelevant, or impossible to meet. In the end, 19 needs were identified for the design of the eHealth tool.Many of the identified needs are informational (eg, having access to up-to-date information) and are probably met by existing tools. However, many others are emotional (eg, being encouraged to use the services) and offer an interesting challenge to eHealth tool development.RR2-10.2196/11634.
A Theoretical Framework and Conceptual Design for Engaging Children in Therapy at Home—The Design of a Wearable Breathing Trainer

Wearable technologies are being implemented in the health and medical context with increasing frequency. Such technologies offer valuable opportunities to stimulate self-management in these domains. In this context, engagement plays a crucial role. An engaged patient is a patient who is emotionally involved and committed to the therapy or care process. Particularly for children who have to follow some sort of therapy, engagement is important to ensure a successful outcome of the therapy. To design for engagement, a framework based on theories of motivation in child therapy was developed. This framework was applied to the design of a wearable breathing trainer for children with asthma and dysfunctional breathing. As such, the present paper provides knowledge about the implementation of theory on engagement and motivation in design. Expert and first user evaluations found that the resulting prototype is appealing, perceived as useful, and may engage children in breathing training and stimulate self-management.