Reviewerly/exHarmony huggingface dataset:
MURAUER: Mapping Unlabeled Real Data for Label AUstERity

Data labeling for learning 3D hand pose estimation models is a huge effort. Readily available, accurately labeled synthetic data has the potential to reduce the effort. However, to successfully exploit synthetic data, current state-of-the-art methods still require a large amount of labeled real data. In this work, we remove this requirement by learning to map from the features of real data to the features of synthetic data mainly using a large amount of synthetic and unlabeled real data. We exploit unlabeled data using two auxiliary objectives, which enforce that (i) the mapped representation is pose specific and (ii) at the same time, the distributions of real and synthetic data are aligned. While pose specifity is enforced by a self-supervisory signal requiring that the representation is predictive for the appearance from different views, distributions are aligned by an adversarial term. In this way, we can significantly improve the results of the baseline system, which does not use unlabeled data and outperform many recent approaches already with about 1% of the labeled real data. This presents a step towards faster deployment of learning based hand pose estimation, making it accessible for a larger range of applications.
Generalized general access structure in secret image sharing

Generally speaking, the probability of every qualified set is the same and fixed in conventional secret image sharing (SIS) for general access structure (GAS). In this paper, first we introduce generalized GAS (GGAS), which allows the user to assign probability to every qualified set. Then we design a SIS scheme for GGAS by Chinese remainder theorem (CRT). On one hand, for any qualified set, we can decode the secret image at pre-assigned probability. When we collect all the shares, we can losslessly decode the secret image. On the other hand, for any forbidden set, we will decode nothing of the secret image. We only employ modular operation to decode the secret image, which may be suitable for real-time and green computing scenarios. Experimental results and analyses are realized to examine the effectiveness of our scheme.
Evaluation of color differences in natural scene color images

Since there is a wide range of applications requiring image color difference (CD) assessment (e.g. color quantization, color mapping), a number of CD measures for images have been proposed. However, the performance evaluation of such measures often suffers from the following major flaws: (1) test images contain primarily spatial- (e.g. blur) rather than color-specific distortions (e.g. quantization noise), (2) there are too few test images (lack of variability in color content), and (3) test images are not publicly available (difficult to reproduce and compare). Accordingly, the performance of CD measures reported in the state-of-the-art is ambiguous and therefore inconclusive to be used for any specific color-related application. In this work, we review a total of twenty four state-of-the-art CD measures. Then, based on the findings of our review, we propose a novel method to compute CDs in natural scene color images. We have tested our measure as well as the state-of-the-art measures on three color related distortions from a publicly available database (mean shift, change in color saturation and quantization noise). Our experimental results show that the correlation between the subjective scores and the proposed measure exceeds 85% which is better than the other twenty four CD measures tested in this work (for illustration the best performing state-of-the-art CD measures achieve correlations with humans lower than 80%).
Noise removal in medical mammography images using fast non-local means denoising algorithm for early breast cancer detection: a phantom study

Denoising plays a crucial role in the field of medical imaging in regard to the improvement of image quality. In this study, a fast nonlocal means (FNLM) denoising algorithm which utilizes neighborhood filtering is proposed and implemented for early breast cancer detection based on medical mammography. For comparison with conventional denoising methods, the Wiener filter and total variation (TV) denoising algorithm were used. The temporal resolution, coefficient of variation (COV), and contrast to noise ratio (CNR) were evaluated for three exposure conditions: (a) various tube voltages at fixed 40 mAs, (b) various tube currents at fixed 28 kVp, and (c) auto exposure control mode. The results showed that the proposed FNLM denoising algorithm can achieve a similar temporal resolution to the Wiener filter and may efficiently reduce image noise by using COV and CNR values in mammography.
Hardware Constructions for Error Detection of Number-Theoretic Transform Utilized in Secure Cryptographic Architectures

Polynomial multiplication is one of the most rigorous arithmetic construction of postquantum cryptosystems. Utilizing number-theoretic transformations, the product of such multiplication can be efficiently computed in quasi-linear time O(n.lgn). Error detection schemes of number-theoretic transform (NTT) architectures are essential to ensure correct mathematical operations, improved security, and thwart active side-channel attacks mounted through faults. NTT is not only significant to post-quantum cryptosystems, but the structure is also valuable to the already existing security protocols, e.g., signature schemes, hash functions, and the like. This paper, for the first time, introduces new error detection schemes of NTT architectures, successfully detecting both permanent and transient faults. Our schemes are based on recomputing with negated, scaled, and swapped operands. We have implemented the proposed schemes on the application-specific integrated circuit (ASIC). Performance and implementation metrics on this hardware platform show acceptable hardware overhead. As our schemes provide acceptable complexity and high efficiency, they can be utilized in compact hardware implementations of constrained applications, e.g., deeply embedded architectures.
Knowledge model for emergency response based on contingency planning system of China

China is severely exposed to natural hazards. Currently, there are more than 5.5 million contingency plans for handling various incidents. Similar to those produced in other counties, the paper-based plans in China are limited in that emergency responders cannot easily extract helpful information for them. In this paper, a knowledge-based system will be proposed for providing different stakeholders with helpful information in the emergency response. The conceptual model is the core for the whole system, which can link plans in the physical world and the ontology in the cyber world.
Open-sector rapid-reactive collision avoidance: Application in aerial robot navigation through outdoor unstructured environments

A new reactive collision avoidance method for navigation of aerial robots (such as unmanned aerial vehicles (UAVs)) in unstructured urban/suburban environments is presented. Small form-factor aerial robots, such as quadcopters, often have limited payload capacity, flight time, processing power, and sensing capabilities. To enhance the capabilities of such vehicles without increasing weight or computing power, a reactive collision avoidance method based on open sectors is described. The method utilizes information from a two-dimensional laser scan of the environment and a short-term memory of past actions and can rapidly circumvent obstacles in outdoor urban/suburban environments. With no map required, the method enables the robot to react quickly and navigate even when the enivornment changes. Furthermore, the low computational requirement of the method allows the robot to quickly react to unknown obstacles that may be poorly represented in the scan, such as trees with branches and leaves. The method is validated in simulation results and through physical experiments on a prototype quadcopter system, where results show the robot flying smoothly around obstacles at a relatively high speed (3 m/s).
Determination of mode I dynamic fracture toughness of IM7-8552 composites by digital image correlation and machine learning

An optical experimental procedure for evaluating the J-Integral from full-field displacement fields under dynamic loading is proposed in this work. The methodology is applied to measure the J-integral in the dynamic compressive loading of fiber-reinforced composites and to calculate the dynamic fracture toughness associated with the propagation of a kink-band. A modified J-Integral that considers inertia effects is calculated over the full-field measurements obtained by digital image correlation, for double edge-notched specimen of IM7-8552 laminates dynamically loaded in a split-Hopkinson pressure bar (SHPB). A sensibility study is conducted to address the influence of the speckle parameters. The results show good agreement with experimental observations obtained by using a different data reduction method, suggesting the existence of a rising R-curve for the studied material under dynamic loading. Furthermore, it was noticed that the inertia effect can be negligible, indicating a state of dynamic equilibrium in which quasi-static approaches may comfortably be used.
Online Sparse Multi-Output Gaussian Process Regression and Learning

This paper proposes an approach for online training of a sparse multi-output Gaussian process (GP) model using sequentially obtained data. The considered model combines linearly multiple latent sparse GPs to produce correlated output variables. Each latent GP has its own set of inducing points to achieve sparsity. We show that given the model hyperparameters, the posterior over the inducing points is Gaussian under Gaussian noise since they are linearly related to the model outputs. However, the inducing points from different latent GPs would become correlated, leading to a full covariance matrix cumbersome to handle. Variational inference is thus applied and an approximate regression technique is obtained, with which the posteriors over different inducing point sets can always factorize. As the model outputs are non-linearly dependent on the hyperparameters, a novel marginalized particle filer (MPF)-based algorithm is proposed for the online inference of the inducing point values and hyperparameters. The approximate regression technique is incorporated in the MPF and its distributed realization is presented. Algorithm validation using synthetic and real data is conducted, and promising results are obtained.
ViPED: On-road vehicle passenger detection for autonomous vehicles

Abstract This paper is about detecting and counting the passengers of a tracking vehicle using on-car monocular vision. By having a model of nearby vehicle occupants, intelligent reasoning systems of autonomous cars will be provided with this additional knowledge needed in emergency situations such as those that many philosophers have recently raised. The on-road Vehicle PassengEr Detection (ViPED) system is based on the human perception model in terms of spatio-temporal reasoning, namely the slight movements of passenger shape silhouettes inside the cabin. The main challenges we face are the low light conditions of the cabin (no feature points), the subtle non-rigid motions of the occupants (possible artifactual transitions), and the puzzling discrimination problem of back or front seat occupants (lack of depth information inside the cabin). To overcome these challenges, we first track the detected car windshield and find the optimal affine warp. The registered windshield images are preprocessed in order to extract a feature matrix, which serves as input to a Convolutional Neural Network (CNN) for inferring the number and position of passengers. We demonstrate that our low-cost sensor system is able to detect in most cases successfully all the passengers in preceding moving vehicles at various distances and occupancies. Metrics and datasets are included for possible community future work on this new challenging task.
Fall Detection in Videos With Trajectory-Weighted Deep-Convolutional Rank-Pooling Descriptor

Automatic fall detection in videos could enable timely delivery of medical service to the injured elders who have fallen and live alone. Deep ConvNets have been used to detect fall actions. However, there still remain problems in deep video representations for fall detection. First, video frames are directly inputted to deep ConvNets. The visual features of human actions may be interfered with surrounding environments. Second, redundant frames increase the difficulty of time encoding for human actions. To address these problems, this paper presents trajectory-weighted deep-convolutional rank-pooling descriptor (TDRD) for fall detection, which is robust to surrounding environments and can describe the dynamics of human actions in long time videos effectively. First, CNN feature map of each frame is extracted through a deep ConvNet. Then, we present a new kind of trajectory attention map which is built with improved dense trajectories to optimally localize the subject area. Next, the CNN feature map of each frame is weighted with its corresponding trajectory attention map to get trajectory-weighted convolutional visual feature of human region. Further, we propose a cluster pooling method to reduce the redundancy of the trajectory-weighted convolutional features of a video in the time sequence. Finally, rank pooling method is used to encode the dynamic of the cluster-pooled sequence to get our TDRD. With TDRD, we get superior result on SDUFall dataset and get comparable performances on UR dataset and Multiple cameras dataset with SVM classifiers.
Feedback polynomial filtering and control of non-Gaussian linear time-varying systems

This paper deals with the optimal filtering and optimal output-feedback control of discrete-time, linear time-varying non-Gaussian systems. In the hypothesis that the time-varying and non-Gaussian distributions of the state and measurement noises have bounded and known moments up to a given order, this work extends previous results about polynomial filtering and optimal control to the time-varying case. The properties of the resulting filtering and control algorithms are discussed in the light of a stable recursive representation of the Kronecker powers of the system obtained through a suitable rewriting of the system with an output injection term. The resulting sub-optimal algorithm inherits the structure and the properties of the classical LQG approach but with enhanced performance.
Fuzzy Network Based Framework for Software Maintainability Prediction

Software metrics based maintainability prediction is leading to development of new sophisticated techniques to construct prediction models. This paper proposes a new software maintainability prediction framework, which bases on Fuzzy Network, a novel exploratory modeling technique. The proposed framework utilizes both the metric data collected from software system and the subjective appraisals from experts. An application example of the framework is shown. In comparison to the Standard Fuzzy System based models, Fuzzy Network based models improves the transparency more than 71.3% and the accuracy more than 11.0%. It is confirmed that Fuzzy Network based framework is more appropriate for constructing SMP model.
Optimal Accuracy-Privacy Trade-Off for Secure Computations

The purpose of secure multi-party computation is to enable protocol participants to compute a public function of their private inputs while keeping their inputs secret, without resorting to any trusted third party. However, opening the public output of such computations inevitably reveals some information about the private inputs. We propose a measure generalizing both Rényi entropy and g -entropy so as to quantify this information leakage. In order to control and restrain such information flows, we introduce the notion of function substitution, which replaces the computation of a function that reveals sensitive information with that of an approximate function. We exhibit theoretical bounds for the privacy gains that this approach provides and experimentally show that this enhances the confidentiality of the inputs while controlling the distortion of computed output values. Finally, we investigate the inherent compromise between accuracy of computation and privacy of inputs and we demonstrate how to realize such optimal trade-offs.
SVM-CNN-Based Fusion Algorithm for Vehicle Navigation Considering Atypical Observations

Modern intelligent transport systems focus on the integration of multiple sensors to obtain hybrid navigation schemes. A key issue of a hybrid scheme is distribution of the information sharing coefficients (ISCs) of subsystems and the fusion of parallel multiple observations of navigation sensors. Recently, deep learning methods, particularly convolutional neural networks (CNNs), have achieved great success in image processing tasks. However, there has been limited work in using deep learning for multisensor-based integrated navigation solutions. In this letter, we propose an ensemble learner-based classification and information fusion method, in which estimation error covariance matrices provided by local adaptive filters are used as input for the classifier, and the triple numbers of ISCs are determined by the proposed scheme. The results validate the effectiveness of the proposed scheme, in which the adequately trained ensemble learner can detect the degradation of a subsystem that may suffer atypical observations or faults and consequently can adjust the corresponding ISC in real time.
Experimental evaluation of dynamic performance of three‐phase AC–DC PWM rectifier with PD‐type‐2 fuzzy neural network controller

Diode and thyristor-based rectifier circuits have been widely used in the industry. Due to non-linear structures of these circuits, they draw non-sinusoidal current from AC network as well as cause a low power factor in the AC side. The DC-link voltage of rectifier is affected by the changes in AC network or by the load variations on the DC side. Pulse-width modulated (PWM) rectifiers can eliminate the mentioned power quality problems if they control properly. This study proposes a controller with an adaptive and robust structure based on proportional + derivative type-2 fuzzy neural network (PD-T2FNN) for DC-link voltage control of PWM rectifier. Dynamic performance of PWM rectifier using the proposed controller is evaluated via dSPACE based experimental setup under different operation conditions: set-point change, step load change in the DC side of the rectifier, set-point change under load and capacitive operation mode. The experimental results are given for traditional PD and proportional + integral and T2FNN controllers to validity performance of the proposed controller. Performances of controllers are evaluated regarding settling time, overshoot, steady-state error and total harmonic distortion. PWM rectifier with PD-T2FNN DC-link voltage controller has superior performance for all operating conditions according to performance criteria when compared with other controllers.
Learning New Words from Keystroke Data with Local Differential Privacy

Keystroke data collected from smart devices includes various sensitive information about users. Collecting and analyzing such data raise serious privacy concerns. Google and Apple have recently applied local differential privacy (LDP) to address privacy issue on learning new words from users' keystroke data. However, these solutions require multiple LDP reports for a single word, which result in inefficient use of privacy budget and high computational cost. In this paper, we develop a novel algorithm for learning new words under LDP. Unlike the existing solutions, the proposed method generates only one LDP report for a single word. This enables the proposed method to use full privacy budget for generating a report and brings the benefit that the proposed method provides better utility at the same privacy degree than the existing methods. In our algorithm, each user appends a hash value to new word and sends only one LDP report of an n-gram selected randomly from the string packed by each new word and its hash value. The server then decodes frequent n-grams at each position of the string and discovers the candidate words by exploring graph-theoretic links between n-grams and checking integrity of candidates with hash values. Frequencies of frequent new words discovered are estimated from distribution estimates of n-grams by robust regression. We theoretically show that our algorithm can recover popular new words even though the server does not know the domain of the raw data. In addition, we theoretically and empirically demonstrate that our algorithm achieves higher accuracy compared to the existing solutions.
Redundant measurement-based second order mutual difference adaptive Kalman filter

Noise distribution plays an essential role in state estimation using Kalman filter. However, statistical characteristics of the noise are often unknown in most practical applications. A second order mutual difference (SOMD) algorithm has been proposed to generate an estimation of the measurement noise covariance matrix R by calculating the autocorrelation of SOMD of redundant measurements, and thus it can avoid coupling with the state estimation error; however, the algorithm cannot be applied directly for a majority of practical systems due to the requirement of redundant measurements. In this paper, the SOMD algorithm is expanded to the system with single measurement by constructing a pseudo measurement. A non-zero estimation bias detection algorithm is presented to address the inconsistency between the mathematical model and the real. A modified robust adaptive Kalman filter (RAKF) is also developed to tackle this inconsistency and improve filtering accuracy by activating adaptive operation properly. The efficacy of the approach is demonstrated via a target tracking problem. Simulation results indicate that the proposed algorithm can reflect the noise properties accurately and outperform several reference algorithms in precision and robustness.
An Intersectional Technofeminist Framework for Community-Driven Technology Innovation

In this article, we describe the philosophy, objectives, and development of Multilingual User-Experience (Multilingual UX), a community-driven initiative for supporting technology innovation with marginalized communities. We highlight how community-based mentorship can guide innovative technology design through an intersectional technofeminist perspective. We begin with a discussion of the impetus for building this initiative before discussing how we are collaboratively designing a research center to facilitate technology design with and for marginalized communities. We both theorize and enact the intersectional technofeminist principles of our work by telling the story of our project with our collaborators and community partners, in the form of vignettes from a symposium. We conclude by looking ahead to our next steps and by offering strategies for intersectional technofeminist community building and technology innovation, in the hope that our experiences can be further developed and localized to support similar initiatives that highlight the value of feminist collaboration in technology design.
Speech-to-Singing Voice Conversion: The Challenges and Strategies for Improving Vocal Conversion Processes

Speech-to-singing (STS) conversion is the task of converting the read lyrics of a song, spoken in natural manner, to proper singing. The most important aspect of the task is to change the prosody of the natural speech to match with that of proper singing, while retaining the linguistic content and the speaker's identity. STS conversion is a challenging task because speaking and singing are different in many ways.