Reviewerly/exHarmony huggingface dataset:
A Novel Motion Plane-Based Approach to Vehicle Speed Estimation

Speed limit violation by vehicles is one of the most frequent reasons for road crashes, which take the lives of many people every year, resulting in an increasing demand for video-based vehicle speed estimation systems. One of the biggest challenges to achieve monocular video-based vehicle speed estimation is the projection displacement difference (PDD) problem, where there is a plane disparity between street-level indicators and above-plane feature points of vehicles, thus resulting in unreliable speed estimates. In this paper, a novel motion plane-based approach for vehicle speed estimation is proposed, which addresses the problem of PDD. In the proposed method, we consider the center of a vehicle license plate as the vehicle reference point and estimate the hypothetical plane (named motion plane), on which license plate moves. Subsequently, the plate position is mapped on the motion plane and the displacement is then calculated, thus mitigating the effects of PPD. To estimate the motion plane, a texture-based shape-from-template technique is used. Unlike existing methods, the proposed method needs neither to apply any indicator nor to use any information about extrinsic parameters of the camera. Furthermore, since all the license plates are approximately located on a flat plane, the motion plane can be estimated by using several extracted 3-D points. Experimental results show that the proposed method performs better than the state-of-the-art vehicle speed estimation methods, illustrating the efficacy of this approach for achieving reliable vehicle speed estimation.
A Hybrid Ensemble Model Based on ELM and Improved AdaBoost.RT Algorithm for Predicting the Iron Ore Sintering Characters

As energy efficiency becomes increasingly important to the steel industry, the iron ore sintering process is attracting more attention since it consumes the second large amount of energy in the iron and steel making processes. The present work aims to propose a prediction model for the iron ore sintering characters. A hybrid ensemble model combined the extreme learning machine (ELM) with an improved AdaBoost.RT algorithm is developed for regression problem. First, the factors that affect solid fuel consumption, gas fuel consumption, burn-through point (BTP), and tumbler index (TI) are ranked according to the attributes weightiness sequence by applying the RReliefF method. Second, the ELM network is selected as an ensemble predictor due to its fast learning speed and good generalization performance. Third, an improved AdaBoost.RT is established to overcome the limitation of conventional AdaBoost.RT by dynamically self-adjusting the threshold value. Then, an ensemble ELM is employed by using the improved AdaBoost.RT for better precision than individual predictor. Finally, this hybrid ensemble model is applied to predict the iron ore sintering characters by production data from No. 4 sintering machine in Baosteel. The results obtained show that the proposed model is effective and feasible for the practical sintering process. In addition, through analyzing the first superior factors, the energy efficiency and sinter quality could be obviously improved.
Nurses “Seeing Forest for the Trees” in the Age of Machine Learning

Although machine learning is increasingly being applied to support clinical decision making, there is a significant gap in understanding what it is and how nurses should adopt it in practice. The purpose of this case study is to show how one application of machine learning may support nursing work and to discuss how nurses can contribute to improving its relevance and performance. Using data from 130 specialized hospitals with 101 766 patients with diabetes, we applied various advanced statistical methods (known as machine learning algorithms) to predict early readmission. The best-performing machine learning algorithm showed modest predictive ability with opportunities for improvement. Nurses can contribute to machine learning algorithms by (1) filling data gaps with nursing-relevant data that provide personalized context about the patient, (2) improving data preprocessing techniques, and (3) evaluating potential value in practice. These findings suggest that nurses need to further process the information provided by machine learning and apply “Wisdom-in-Action” to make appropriate clinical decisions. Nurses play a pivotal role in ensuring that machine learning algorithms are shaped by their unique knowledge of each patient's personalized context. By combining machine learning with unique nursing knowledge, nurses can provide more visibility to nursing work, advance nursing science, and better individualize patient care. Therefore, to successfully integrate and maximize the benefits of machine learning, nurses must fully participate in its development, implementation, and evaluation.
Face Hallucination via Coarse-to-Fine Recursive Kernel Regression Structure

In recent years, patch-based face hallucination algorithms have attracted considerable interest due to their effectiveness. These approaches produce a high-resolution (HR) face image according to the corresponding low-resolution (LR) input by learning a reconstruction model from the given training image set. The critical problem in these algorithms is establishing the underlying relationship between LR and HR patch pairs. Most previous methods aim to denote each input LR patch by the linear combination of the training set in the LR space while utilizing the combination weights to reconstruct the target HR patch. However, this assumes that the same combination weights should be shared between various resolution spaces, which is truly difficult to satisfy because of the one-to-many mapping relation between LR and HR patches. In this paper, we directly train a series of adaptive kernel regression mappings for predicting the lost high-frequency information from the LR patch, which avoids dealing with the above difficult problem. During the training process, we first establish a local optimization function on each LR/HR training pair according to the geometric structure of neighboring patches. The objective of local optimization can be presented in two aspects: 1) ensure the reconstruction consistency between each LR patch and the corresponding HR patch and 2) preserve the intrinsic geometry between each HR training patch and its original neighbors after the reconstruction process. The local optimizations are finally incorporated as the global optimization for calculating the optimal kernel regression function. To better approximate the target HR patch, we further propose a recursive structure to compensate for the residual reconstruction error of high-frequency details by a series of regression mappings. The proposed method is rather fast yet very effective in producing HR face images. Experimental results show that the proposed approach achieves superior performance with reasonable computational time compared with the state-of-the-art methods.
High-Secure Fingerprint Authentication System Using Ring-LWE Cryptography

This paper presents a high-secure fingerprint authentication system using ring learning with errors (ring-LWE) cryptography to protect users' fingerprint data more securely. A delay-optimized high-accuracy scheme for a fingerprint-features extraction approach is proposed to collect necessary features' information from fingerprint images. In addition, a ring-LWE cryptography scheme using low-latency number theoretic transform (NTT) polynomial multiplications is deployed to speed up the ring-LWE encryption and decryption times. As a result, the processing time of the fingerprint authentication system is significantly reduced, and the fingerprint data are effectively protected. The simulation results show that the proposed NTT multiplication-based ring-LWE cryptography scheme for fingerprint features outperforms the existing works up to 46% and 44% in terms of encryption time and decryption time, respectively. The latency of the whole fingerprint authentication system is less than 160 ms, which makes it suitable for practical applications. Furthermore, performance analysis on entropy and similarity of the encrypted fingerprint features proves the domination of the proposed system compared with the previous systems in terms of confidentiality.
A Comparison of Techniques for Sign Language Alphabet Recognition Using Armband Wearables

Recent research has shown that reliable recognition of sign language words and phrases using user-friendly and noninvasive armbands is feasible and desirable. This work provides an analysis and implementation of including fingerspelling recognition (FR) in such systems, which is a much harder problem due to lack of distinctive hand movements. A novel algorithm called DyFAV (Dynamic Feature Selection and Voting) is proposed for this purpose that exploits the fact that fingerspelling has a finite corpus (26 alphabets for the American Sign Language (ASL)). Detailed analysis of the algorithm used as well as comparisons with other traditional machine-learning algorithms is provided. The system uses an independent multiple-agent voting approach to identify letters with high accuracy. The independent voting of the agents ensures that the algorithm is highly parallelizable and thus recognition times can be kept low to suit real-time mobile applications. A thorough explanation and analysis is presented on results obtained on the ASL alphabet corpus for nine people with limited training. An average recognition accuracy of 95.36% is reported and compared with recognition results from other machine-learning techniques. This result is extended by including six additional validation users with data collected under similar settings as the previous dataset. Furthermore, a feature selection schema using a subset of the sensors is proposed and the results are evaluated. The mobile, noninvasive, and real-time nature of the technology is demonstrated by evaluating performance on various types of Android phones and remote server configurations. A brief discussion of the user interface is provided along with guidelines for best practices.
Faster R-CNN with Inception V2 for Fingertip Detection in Homogenous Background Image

Fingertip Detection is a versatile research field in computer vision, since it has multiple purpose such as natural user interface, robotic, 3D simulation etc. It is a challenging research field in computer vision. There are some hand segmentation methods, pre-processing phase, was conducted to provide area of fingertip detection in image. However, in this research, fingertip detection can be done by directly find the fingertip itself. This approach cut off preprocessing phase by using Faster R-CNN method and inception V2 architechture directly to find the fingertip in image. With a homogenous background as a simple input image, this approach showed a good accuracy in its performance. It has 90% and 91% accuracy in way to detect fingertip for both male and female hand datasets. More over, exchanging male and female model toward to male and female dataset gave 94% and 92% accuracy that showed the different pattern between both.
Uncertainty-Aware Principal Component Analysis

PCA 0 0 0 (b) s = 0.5 0 0 0 (c) s = 0.8 0 0 0 (d) s = 1.0 Fig. 1.Data uncertainty can have a significant influence on the outcome of dimensionality reduction techniques.We propose a generalization of principal component analysis (PCA) that takes into account the uncertainty in the input.The top row shows the dataset with varying degrees of uncertainty and the corresponding principal components, whereas the bottom row shows the projection of the dataset, using our method, onto the first principal component.In Figures (a) and (b), with relatively low uncertainty, the blue and the orange distributions are comprised by the red and the green distributions.In Figures (c) and (d), with a larger amount of uncertainty, the projection changes drastically: now the orange and blue distributions encompass the red and the green distributions.Abstract-We present a technique to perform dimensionality reduction on data that is subject to uncertainty.Our method is a generalization of traditional principal component analysis (PCA) to multivariate probability distributions.In comparison to non-linear methods, linear dimensionality reduction techniques have the advantage that the characteristics of such probability distributions remain intact after projection.We derive a representation of the PCA sample covariance matrix that respects potential uncertainty in each of the inputs, building the mathematical foundation of our new method: uncertainty-aware PCA.In addition to the accuracy and performance gained by our approach over sampling-based strategies, our formulation allows us to perform sensitivity analysis with regard to the uncertainty in the data.For this, we propose factor traces as a novel visualization that enables to better understand the influence of uncertainty on the chosen principal components.We provide multiple examples of our technique using real-world datasets.As a special case, we show how to propagate multivariate normal distributions through PCA in closed form.Furthermore, we discuss extensions and limitations of our approach.
RLWE-Oriented High-Speed Polynomial Multiplier Utilizing Multi-Lane Stockham NTT Algorithm

Cryptography based on ring learning with error (RLWE) problem has become increasingly popular due to its resistance against quantum analysis. The most time-consuming operation in RLWE cryptosystem is polynomial multiplication. This brief presents a novel polynomial multiplier based on Stockham fast Fourier transform (FFT) algorithm. We propose a multi-lane number theoretic transform (NTT) algorithm which can achieve n-degree polynomial multiplication in (nlgn)/d + 2n/d clock cycles with d lanes of butterfly units. In addition, we also customize a memory addressing strategy and a round constant managing scheme for the proposed multi-lane NTT algorithm. Based on our proposed algorithm, a high-speed polynomial multiplier is accomplished on FPGA platform. Implementation results on Spantan-6 FPGA show that our proposed algorithm can achieve a speed up factor of no less than 2.7 times compared with the state of art designs.
Automated Speech Recognition in Adult Stroke Survivors: Comparing Human and Computer Transcriptions

Speech sound errors are common in people with a variety of communication disorders and can result in impaired message transmission to listeners. Valid and reliable metrics exist to quantify this problem, but they are rarely used in clinical settings due to the time-intensive nature of speech transcription by humans. Automated speech recognition (ASR) technologies have advanced substantially in recent years, enabling them to serve as realistic proxies for human listeners. This study aimed to determine how closely transcription scores from human listeners correspond to scores from an ASR system.Sentence recordings from 10 stroke survivors with aphasia and apraxia of speech were transcribed orthographically by 3 listeners and a web-based ASR service. Adjusted transcription scores were calculated for all samples based on accuracy of transcribed content words.As expected, transcription scores were significantly higher for the humans than for ASR. However, intraclass correlations revealed excellent agreement among the humans and ASR systems, and the systematically lower scores for computer speech recognition were effectively equalized simply by adding the regression intercept.The results suggest the clinical feasibility of supplementing or substituting human transcriptions with computer-generated scores, though extension to other speech disorders requires further research.
Spatial distribution and trace element geochemistry of laterites in Kunche area: Implication for gold exploration targets in NW, Ghana

Kunche area is located in NW Ghana within the Wa-Lawra greenstone Birimian belt and is composed mainly of volcaniclastics, metasediments and some intrusive granitoids. The area is covered with laterites making it difficult to identify exploration targets. In this study, we describe a new methodology based on statistical tools and trace element geochemistry to map the pathfinder elements of gold deposits in lateritic context. However, the results of this study are specific to a particular context and cannot be extrapolated without supplementary studies to all the lateritic areas. In this respect, a total of 67 lateritic samples were collected from residually weathered materials and their spatial distribution was determined by means of the GIS-based kriging interpolation method. The samples collected vary from detrital to residual laterites/duricrusts and are hosted in volcaniclastic rocks. ICP-MS and XRF techniques were used to determine the element concentrations of the samples. The trace element geochemical data were analyzed using bivariate and multivariate geostatistical analysis to establish relationships among elements. Fe-oxides such as goethite and hematite and clay minerals like kaolinite are the main secondary minerals of the concretionary reddish lateritic samples. All the analyzed elements showed asymmetrical distribution rather than normal distribution. Spearman correlation shows that Cu, Pb, S, As, and Ag have moderate to strong positive correlation with Au. From the multivariate geostatistical analysis, three element associations; a) Fe, Pb, S, Co, Cr; b) Ni, Y, Rb, Sr, Zn, and c) Ca, Cu, Mn, Ti, Zr, As, Au, Ag were observed. Threshold values of selected elements were determined using the median absolute deviation (MAD) method, which indicates possible anomalous concentrations in the laterites for Pb (≥48 ppm), Cu (≥46 ppm), As (≥134.2 ppm), and Ag (≥0.42 ppm). Multi-element mapping indicates that Pb + Cu + As + Ag is the most ideal association in the exploration of gold deposits. It reveals ellipsoidal anomalies comparable to the Au distribution map that suggest dispersion and accumulation of the pathfinder elements in the area. The geochemical anomalies are mainly restricted to the environment of the residual laterites in the Kunche area and we recommend that exploration programs should be focused in such areas.
Embodying their own wheelchair modifies extrapersonal space perception in people with spinal cord injury

Despite the many links between body representation, acting and perceiving the environment, no research has to date explored whether specific tool embodiment in conditions of sensorimotor deprivation influences extrapersonal space perception. We tested 20 spinal cord injured (SCI) individuals to investigate whether specific wheelchair embodiment interacts with extrapersonal space representation. As a measure of wheelchair embodiment, we used a Body View Enhancement Task in which participants (either sitting in their own wheelchair or in one which they had never used before) were asked to respond promptly to flashing lights presented on their above- and below-lesion body parts. Similar or slower reaction times (RT) to stimuli on the body and wheelchair indicate, respectively, the presence or absence of tool embodiment. The RTs showed that the participants embodied their own wheelchair but not the other one. Moreover, they coded their deprived lower limbs as external objects and, when not in their own wheelchair, also showed disownership of their intact upper limbs. To measure extrapersonal space perception, we used a novel, ad hoc designed paradigm in which the participants were asked to observe a 3D scenario by means of immersive virtual reality and estimate the distance of a flag positioned on a ramp. In healthy subjects, errors in estimation increased as the distance increased, suggesting that they mentally represent the physical distance. The same occurred with the SCI participants, but only when they were in their own wheelchair. The results demonstrate for the first time that tool embodiment modifies extrapersonal space estimations.
The GH-EXIN neural network for hierarchical clustering

Hierarchical clustering is an important tool for extracting information from data in a multi-resolution way. It is more meaningful if driven by data, as in the case of divisive algorithms, which split data until no more division is allowed. However, they have the drawback of the splitting threshold setting. The neural networks can address this problem, because they basically depend on data. The growing hierarchical GH-EXIN neural network builds a hierarchical tree in an incremental (data-driven architecture) and self-organized way. It is a top-down technique which defines the horizontal growth by means of an anisotropic region of influence, based on the novel idea of neighborhood convex hull. It also reallocates data and detects outliers by using a novel approach on all the leaves, simultaneously. Its complexity is estimated and an analysis of its user-dependent parameters is given. The advantages of the proposed approach, with regard to the best existing networks, are shown and analyzed, qualitatively and quantitatively, both in benchmark synthetic problems and in a real application (image recognition from video), in order to test the performance in building hierarchical trees. Furthermore, an important and very promising application of GH-EXIN in two-way hierarchical clustering, for the analysis of gene expression data in the study of the colorectal cancer is described.
Multiple-image double-encryption via 2D rotations of a random phase mask with spatially incoherent illumination

Optical image encryption technique has become extremely important in these years. However, most of the proposed multiple-image encryption systems are illuminated with coherent light source. Here we present a multiple-image double-encryption method with spatially incoherent illumination. The first-encryption of multiple images is based on the speckle rotation decorrelation property, and the second-encryption of images' order is based on the speckle shift decorrelation out of the angular memory-effect range. The double-encryption via two-dimensional rotations of the random phase mask enhances the security and keeps the simplicity of the cryptosystem. The capacity of the ciphertext is greatly increased by multiplexing, and further increased after crosstalk noise removal. The use of incoherent light source reduces the requirements for experimental conditions, and makes the cryptosystem easy to implement in various application scenarios.
Compression with Flows via Local Bits-Back Coding

Likelihood-based generative models are the backbones of lossless compression due to the guaranteed existence of codes with lengths close to negative log likelihood. However, there is no guaranteed existence of computationally efficient codes that achieve these lengths, and coding algorithms must be hand-tailored to specific types of generative models to ensure computational efficiency. Such coding algorithms are known for autoregressive models and variational autoencoders, but not for general types of flow models. To fill in this gap, we introduce local bits-back coding, a new compression technique for flow models. We present efficient algorithms that instantiate our technique for many popular types of flows, and we demonstrate that our algorithms closely achieve theoretical codelengths for state-of-the-art flow models on high-dimensional data.