Reviewerly/exHarmony huggingface dataset:
An analysis on the relationship between uncertainty and misclassification rate of classifiers

This paper provides new insight into the analysis on the relationship between uncertainty and misclassification of a classifier. We formulate the relationship explicitly by taking entropy as a measurement of uncertainty and by analyzing the misclassification rate based on the membership degree difference. Focusing on binary classification problems, this study theoretically and experimentally validates that the misclassification rate will definitely be upgrading with the increase of uncertainty if two conditions are satisfied: (1) the distributions of two classes based on membership degree difference are unimodal, and (2) these two distributions attain peaks when the membership degree difference is less and larger than zero, respectively. This work aims to provide some practical guidelines for improving classifier performance through clearly expressing and understanding the relationship between uncertainty and misclassification of a classifier.
A Survey On Face Recognition Algorithms

Facial Recognition System is a computer technology that uses a variety of algorithms that identify the human face in digital images, identify the person and then verify the captured images by comparing them with the facial images stored in the database. Facial recognition is an important topic in computer vision, and many researchers have studied this topic in many different ways; it is important especially in some applications such as surveillance systems. The main objective of this survey paper is to compare the multiple algorithms used for facial recognition.
An Enhanced Approach in Detecting Object Applied to Automotive Traffic Roads Signs

In this article, we use among and the best-known library is Open Computer Vision we call it for short OpenCV. It is used for image processing, to do all operations we want, to isolate and detect a specific object, which in our case are traffic road signs. We process to find the most efficient methods of detection of traffic road signs. Our objective is to demonstrate the links the elements for optimized and powerful to computer vision algorithms that are easy to use as typing in an image and video processing. Most of the techniques they employed the color selection, edge detection, a region of interest selection, and shapes transformation of detection. Many applications require the recognition of traffic road signs in urban areas. The automation of this task is necessary, for example, ADAS systems, and the application of vision robotics or an autonomous vehicle, consist of recognizing and identifying traffic road signs as quickly as possible with errors to be minimized, in images of their type acquired by an embedded camera on board a vehicle.
Automated Expansion of Abbreviations Based on Semantic Relation and Transfer Expansion

Although the negative impact of abbreviations in source code is well-recognized, abbreviations are common for various reasons. To this end, a number of approaches have been proposed to expand abbreviations in identifiers. However, such approaches are either inaccurate or confined to specific identifiers. To this end, in this paper, we propose a generic and accurate approach to expand identifier abbreviations by leveraging both semantic relation and transfer expansion. One of the key insights of the approach is that abbreviations in the name of software entity <inline-formula><tex-math notation="LaTeX">$e$</tex-math></inline-formula> have a great chance to find their full terms in names of software entities that are semantically related to <inline-formula><tex-math notation="LaTeX">$e$</tex-math></inline-formula> . Consequently, the proposed approach builds a knowledge graph to represent such entities and their relationships with <inline-formula><tex-math notation="LaTeX">$e$</tex-math></inline-formula> and searches the graph for full terms. Another key insight is that literally identical abbreviations within the same application are likely (but not necessary) to have identical expansions, and thus the semantics-based expansion in one place may be transferred to other places. To investigate when abbreviation expansion could be transferred safely, we conduct a case study on three open-source applications. The results suggest that a significant part (75 percent) of expansions could be transferred among lexically identical abbreviations within the same application. However, the risk of transfer varies according to various factors, e.g., length of abbreviations, the physical distance between abbreviations, and semantic relations between abbreviations. Based on these findings, we design nine heuristics for transfer expansion and propose a learning-based approach to prioritize both transfer heuristics and semantic-based expansion heuristics. Evaluation results on nine open-source applications suggest that the proposed approach significantly improves the state of the art, improving recall from 29 to 89 percent and precision from 39 to 92 percent.
Canceling Teleportation Error in Legacy IMC Code for Photonics (Without Tilts, With Simple Minimal Modifications)

Monte Carlo (MC) schemes for photonics have been intensively studied throughout the past decades. The recent ISMC scheme presents many advantages (no teleportation error, converging behavior with respect to the spatial and time discretisations). But it is rather different from the IMC one (it is based on a different linearization and needs a slightly different code architecture). On another hand, legacy codes are often based on IMC implementations. For this reason, it remains important to be able to cancel the teleportation error within IMC codes. Canceling the teleportation error within the IMC framework is also important for fair comparisons between both the IMC and the ISMC linearisations. This paper aims at suggesting some simple corrections to apply to an IMC implementation to completely cancel the teleportation error.
Adversarial-Based Domain Adaptation Networks for Unsupervised Tumour Detection in Histopathology

Developing effective deep learning models for histopathology applications is challenging, as the performance depends on large amounts of labelled training data, which is often unavailable. In this work, we address this issue by leveraging previously annotated histopathology images from unrelated source domains to build a model for the unlabelled target domain. Specifically, we propose the adversarial-based domain adaptation networks (ABDA-Net) for performing the tumour detection task in histopathology in a purely unsupervised manner. This methodology successfully promoted the alignment of the source and target feature distributions among independent datasets of three tumour types - Breast, Lung and Colon - to achieve an improvement of at least 17.51% in accuracy and 18.22% in area under the curve (AUC) when compared to a classifier trained on the source data only.
Animal Localization in Camera-Trap Images with Complex Backgrounds

Motion-sensor camera traps help collect images of animals in the wild without intruding upon their native habitat. To obtain key insights about animal health and population densities, accurate counting, detection and classification of animals is important. Deep convolution neural networks perform well on these tasks when the background is free from dense vegetation, shadows, occlusions and rapid illumination changes. However, when the camera traps are located in regions with extremely complex backgrounds, performance of these models degrades significantly. This is due to the fact that the models learn to focus on aspects of the image that are unrelated to the animals. In this paper, we propose a system based on Robust Principal Component Analysis (Robust PCA) that spatially localizes the animals in the image. This localization can then be integrated into existing models to improve classification and detection accuracy. We demonstrate that our system creates better localizations than those of a pre-trained R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> Net.
Spatial coherence properties of down converted biphoton field generated using partially coherent pump beam

We report a theoretical study of spatial coherence properties of down converted photons generated using partially spatially coherent pump beam. We investigate coincidence rate and biphoton visibility in two geometries, in which a double-slit is either placed in the path of pump beam or in the path of the signal and idler field. The study infers that the visibility of the interference fringes is strongly influenced by the pump parameters; pump size and pump spatial coherence length. Specifically, the visibility of interference fringes increases as the pump spatial coherence or the transverse size of the pump increases at the crystal plane. The increase in biphoton visibility with the propagation distance verifies the van Cittert-Zernike theorem for the two cases. Downconverted photons with controllable pump spatial coherence, which in turn controls the spatial coherence and entanglement of the downconverted photons, could find potential applications in quantum imaging, quantum communication and non-linear interferometry.
A Multi‐Array Back‐Projection Approach for Tsunami Warning

Abstract Recent development of dense strong‐motion networks and seismic array processing enables rapid tsunami predictions based on the back‐projection (BP) approach. We develop a multi‐array local BP method (MLBP) using seismic networks with epicentral distance from 0.7° to 3.5°. The local BPs using individual arrays are first calculated and are then merged into a single image of the rupture process. This multi‐array approach circumvents the issue of artifact in single‐array BP caused by the overlapping of multiple phases and coda waves. Based on the local BP approach, tsunami predictions are available 7 min after the origin time. Case studies of the 2003 Mw 8.1 Tokachi‐oki and the 2011 Mw 9.0 Tohoku earthquakes show that their rupture zones are well resolved and are comparable with principal slip areas inferred from tsunami observations. The amplitude and arrival time errors of the predicted tsunami waves are within −1.59 to 3.74 m and −10.0 to 10.0 min, which are sufficiently small for warning purposes.
Bayesian analysis of hidden Markov structural equation models with an unknown number of hidden states

Hidden Markov models (HMMs) are widely used to analyze heterogeneous longitudinal data owing to their capability to model dynamic heterogeneity. Early advancements in HMMs have mainly assumed that the number of hidden states is fixed and predetermined based on the knowledge of the subjects or a certain criterion. However, as a limitation, this approach determines the number of hidden states on a pairwise basis, which becomes increasingly tedious when the state space is enlarged. Moreover, criterion-based statistics tend to select complex models with overestimated numbers of components in mixture modeling. A full Bayesian approach is developed to analyze hidden Markov structural equation models with an unknown number of hidden states. An efficient and hybrid algorithm that combines the reversible jump Markov chain Monte Carlo (RJMCMC) algorithm, the forward filtering and backward sampling scheme, and the Metropolis-Hastings algorithm is proposed to simultaneously select the number of hidden states and perform parameter estimation. The simulation study shows the satisfactory performance of the proposed method. Two real datasets collected from the UCLA Drug Abuse Research Center and National Longitudinal Survey of Youth are analyzed.
Analysis of the solar sail deformation based on the point cloud method

The deformation of the solar-sail membrane is an important factor for causing inaccuracies in the solar-sail missions. This paper describes the solar sail under deformation by using a new modelling technique based on point cloud and triangular mesh generation. Two types of deformation, stemming from wrinkling and billowing, are modelled. The changes in the solar radiation pressure force and the moment caused by deformation are calculated and compared to the ideal non-deformed case. The heliocentric spiral trajectory and the orbital angular momentum reversal trajectory are taken as examples to quantify the influence of the deformation from an orbit point of view. Additionally, point cloud simplification, based on the normal vector and bounding box, is utilized to simplify the original deformed-sail model. It involves a reasonable reduction and renewal of the points in the model considering the variation of surface curvature. The simplification and its modelling accuracy are numerically investigated as well as computational efficiency.
An Enhanced Extreme Learning Machine Based on Liu Regression

Extreme learning machine (ELM) is one of the most remarkable machine learning algorithm in consequence of superior properties particularly its speed. ELM algorithm tends to have some drawbacks like instability and poor generalization performance in the presence of perturbation and multicollinearity. This paper introduces a novel algorithm based on Liu regression estimator (L-ELM) to handle these drawbacks. Different selection approaches have been used to determine the appropriate Liu biasing parameter. The new algorithm is tested against the basic ELM, RR-ELM, AUR-ELM and OP-ELM on nine well-known benchmark data sets. Statistical significance tests have been carried out. Experimental results show that L-ELM for at least one Liu biasing parameter generally outperforms basic ELM, RR-ELM, AUR-ELM and OP-ELM in terms of stability and generalization performance with a little lost of speed. Conversely, the training time of L-ELM is generally much slower than RR-ELM, AUR-ELM and OP-ELM. Consequently, the proposed algorithm can be considered a powerful alternative to avoid the loss of performance in regression studies
A 3D Fully Convolutional Neural Network With Top-Down Attention-Guided Refinement for Accurate and Robust Automatic Segmentation of Amygdala and Its Subnuclei

Recent advances in deep learning have improved the segmentation accuracy of subcortical brain structures, which would be useful in neuroimaging studies of many neurological disorders. However, most existing deep learning based approaches in neuroimaging do not investigate the specific difficulties that exist in segmenting extremely small but important brain regions such as the subnuclei of the amygdala. To tackle this challenging task, we developed a dual-branch dilated residual 3D fully convolutional network with parallel convolutions to extract more global context and alleviate the class imbalance issue by maintaining a small receptive field that is just the size of the regions of interest (ROIs). We also conduct multi-scale feature fusion in both parallel and series to compensate the potential information loss during convolutions, which has been shown to be important for small objects. The serial feature fusion enabled by residual connections is further enhanced by a proposed top-down attention-guided refinement unit, where the high-resolution low-level spatial details are selectively integrated to complement the high-level but coarse semantic information, enriching the final feature representations. As a result, the segmentations resulting from our method are more accurate both volumetrically and morphologically, compared with other deep learning based approaches. To the best of our knowledge, this work is the first deep learning-based approach that targets the subregions of the amygdala. We also demonstrated the feasibility of using a cycle-consistent generative adversarial network (CycleGAN) to harmonize multi-site MRI data, and show that our method generalizes well to challenging traumatic brain injury (TBI) datasets collected from multiple centers. This appears to be a promising strategy for image segmentation for multiple site studies and increased morphological variability from significant brain pathology.
Real-time multiple spatiotemporal action localization and prediction approach using deep learning

Detecting the locations of multiple actions in videos and classifying them in real-time are challenging problems termed "action localization and prediction" problem. Convolutional neural networks (ConvNets) have achieved great success for action localization and prediction in still images. A major advance occurred when the AlexNet architecture was introduced in the ImageNet competition. ConvNets have since achieved state-of-the-art performances across a wide variety of machine vision tasks, including object detection, image segmentation, image classification, facial recognition, human pose estimation, and tracking. However, few works exist that address action localization and prediction in videos. The current action localization research primarily focuses on the classification of temporally trimmed videos in which only one action occurs per frame. Moreover, nearly all the current approaches work only offline and are too slow to be useful in real-world environments. In this work, we propose a fast and accurate deep-learning approach to perform real-time action localization and prediction. The proposed approach uses convolutional neural networks to localize multiple actions and predict their classes in real time. This approach starts by using appearance and motion detection networks (known as "you only look once" (YOLO) networks) to localize and classify actions from RGB frames and optical flow frames using a two-stream model. We then propose a fusion step that increases the localization accuracy of the proposed approach. Moreover, we generate an action tube based on frame level detection. The frame by frame processing introduces an early action detection and prediction with top performance in terms of detection speed and precision. The experimental results demonstrate this superiority of our proposed approach in terms of both processing time and accuracy compared to recent offline and online action localization and prediction approaches on the challenging UCF-101-24 and J-HMDB-21 benchmarks.
Data Sharing via Differentially Private Coupled Matrix Factorization

We address the privacy-preserving data-sharing problem in a distributed multiparty setting. In this setting, each data site owns a distinct part of a dataset and the aim is to estimate the parameters of a statistical model conditioned on the complete data without any site revealing any information about the individuals in their own parts. The sites want to maximize the utility of the collective data analysis while providing privacy guarantees for their own portion of the data as well as for each participating individual . Our first contribution is to classify these different privacy requirements as (i) site-level and (ii) user-level differential privacy and present formal privacy guarantees for these two cases under the model of differential privacy. To satisfy a stronger form of differential privacy, we use a variant of differential privacy which is local differential privacy where the sensitive data is perturbed with a randomized response mechanism prior to the estimation. In this study, we assume that the data instances that are partitioned between several parties are arranged as matrices. A natural statistical model for this distributed scenario is coupled matrix factorization. We present two generic frameworks for privatizing Bayesian inference for coupled matrix factorization models that are able to guarantee proposed differential privacy notions based on the privacy requirements of the model. To privatize Bayesian inference, we first exploit the connection between differential privacy and sampling from a Bayesian posterior via stochastic gradient Langevin dynamics and then derive an efficient coupled matrix factorization method. In the local privacy context, we propose two models that have an additional privatization mechanism to achieve a stronger measure of privacy and introduce a Gibbs sampling based algorithm. We demonstrate that the proposed methods are able to provide good prediction accuracy on synthetic and real datasets while adhering to the introduced privacy constraints.
Cost effective stream workflow scheduling to handle application structural changes

Stream workflow is a network of big data streaming applications that acts as key enabler for real-time analysis from Internet of Things data. Smart traffic management and smart grid are examples of stream workflow. The focus of existing work is on streaming operator graphs which differs from stream workflow and handling data fluctuations without significant consideration of different dynamic forms that could happen in the structure of data pipelines. This paper investigates the scheduling problem of stream workflow to support runtime alterations of stream workflow deployment, so that scheduling plans will be revised to handle stream workflow applications with continuously changing characteristics. It proposes a pluggable dynamic scheduling technique that accepts user-defined algorithms to handle stream workflow runtime changes. It also presents three different plug-in algorithms and methods to enable auto-scaling of this workflow in a Multicloud environment. The experimental results of the quality of the solution showed that the proposed plug-in optimisation technique is more efficient than baseline and dynamic fair-share techniques to handle runtime changes.
Mitigating Algorithmic Bias: Evolving an Augmentation Policy that is Non-Biasing

Artificial Intelligence promises to make the world a safer place through automation. Automobiles can be steered between traffic lines, spoken words can be translated into textual commands, and wanted persons can be identified by law enforcement. These tasks, once only surmountable by humans, can now be performed by AIs with great speed and precision. If these algorithms are negatively biased against certain groups, what unforeseen harm may come to society? This work focuses on the classification of gender and age, a problem known to have systemic negative bias for certain subgroups, to investigate the role of data augmentation in the mitigation of such bias. A novel approach is proposed for mitigating bias in a deep learning algorithm that estimates age and gender. Settings for numerous data augmentation techniques are learned through an evolutionary process that optimizes data augmentation for specific subgroups. This approach proves to reduce systemic bias while also generalizing models and obtaining results that are state-of-the-art. The tools we use for determining human biometrics must be fair and non-discriminatory. This work examines not only bias, but also the insights gleaned from successful and unsuccessful policies in certain scenarios.
Blockchain for Data Science

Nowadays, the development of social information and network leads to the explosive growth of data. The increasing amount and diversity of data have also encouraged researchers to make business decisions by analyzing the big data generated. This has also caused the rapid development of the data science industry. However, there are still many challenges to be solved, especially data security and privacy. Data security and privacy threat permeate every link of data science industry chain, such as data production, collection, processing and sharing, and the causes of risk are complex and interwoven. Blockchain technology is highly praised and recognized for its decentralized infrastructure, anonymity, security and other characteristics. It will change the way we access and share information. We believe that blockchain technology can overcome some limitations in data science and promote the development of data science, but it may also bring some other problems. Therefore, it is necessary to explore the relationship between blockchain technology and data science. In this paper, we investigate the researches and applications of blockchain technology in the field of data science and give the potential advantages and challenges that blockchain technology may bring to data science.