Reviewerly/exHarmony huggingface dataset:
Real-time 3D Traffic Cone Detection for Autonomous Driving

Considerable progress has been made in semantic scene understanding of road scenes with monocular cameras. It is, however, mainly focused on certain specific classes such as cars, bicyclists and pedestrians. This work investigates traffic cones, an object category crucial for traffic control in the context of autonomous vehicles. 3D object detection using images from a monocular camera is intrinsically an ill-posed problem. In this work, we exploit the unique structure of traffic cones and propose a pipelined approach to solve this problem. Specifically, we first detect cones in images by a modified 2D object detector. Following which the keypoints on a traffic cone are recognized with the help of our deep structural regression network, here, the fact that the cross-ratio is projection invariant is leveraged for network regularization. Finally, the 3D position of cones is recovered via the classical Perspective n-Point algorithm using correspondences obtained from the keypoint regression. Extensive experiments show that our approach can accurately detect traffic cones and estimate their position in the 3D world in real time. The proposed method is also deployed on a real-time, autonomous system. It runs efficiently on the low-power Jetson TX2, providing accurate 3D position estimates, allowing a race-car to map and drive autonomously on an unseen track indicated by traffic cones. With the help of robust and accurate perception, our race-car won both Formula Student Competitions held in Italy and Germany in 2018, cruising at a top speed of 54 km/h on our driverless platform “gotthard driverless” https://youtu. be/HegmIXASKow?t==11694. Visualization of the complete pipeline, mapping and navigation can be found on our project page http://people.ee.ethz.ch/=tracczucrich/Trnfflcf.lonc/.
Automated Criminal Identification by Face Recognition using Open Computer Vision Classifiers

This paper presents a real time face recognition using a automated surveillance camera. The proposed system consists of 4 steps, including (1)training of real time images (2)face detection using Haar-classifier (3) comparison of trained real time images with images from the surveillance camera (4)result based on the comparison. An important application of interest is automated surveillance, where the objective is to recognize people who are on a watch list. The aspiration of this paper is to compare an image with several images which has been already trained. In this paper, we represent a methodology for face detection robustly in real time environment. Haar cascading is one of the algorithm for face detection. Here we use Haar like classifiers to track faces on OpenCV platform. The accuracy of the face recognition is very high. The proposed system can successfully recognize more than one face which is useful for quickly searching suspected persons as the computation time is very low. In India, we have a system for recognizing citizen called Aadhaar. If we use this as a citizenship database we can differentiate between citizen and foreigner and further investigate whether the identified person is criminal or not.
A Novel Blind Image Quality Assessment Method Based on Refined Natural Scene Statistics

Natural scene statistics (NSS) model has received considerable attention in the image quality assessment (IQA) community due to its high sensitivity to image distortion. However, most existing NSS-based IQA methods extract features either from spatial domain or from transform domain. There is little work to simultaneously consider the features from these two domains. In this paper, a novel blind IQA method (NBIQA) based on refined NSS is proposed. The proposed NBIQA first investigates the performance of a large number of candidate features from both the spatial and transform domains. Based on the investigation, we construct a refined NSS model by selecting competitive features from existing NSS models and adding three new features. Then the refined NSS is fed into SVM tool to learn a simple regression model. Finally, the trained regression model is used to predict the scalar quality score of the image. Experimental results tested on both LIVE IQA and LIVE-C databases show that the proposed NBIQA performs better in terms of synthetic and authentic image distortion than current mainstream IQA methods. The source code is available at https://github.com/GZU-Image-Video-Lab/NBIQA.
From Pairwise Comparisons and Rating to a Unified Quality Scale

The goal of psychometric scaling is the quantification of perceptual experiences, understanding the relationship between an external stimulus, the internal representation and the response. In this paper, we propose a probabilistic framework to fuse the outcome of different psychophysical experimental protocols, namely rating and pairwise comparisons experiments. Such a method can be used for merging existing datasets of subjective nature and for experiments in which both measurements are collected. We analyze and compare the outcomes of both types of experimental protocols in terms of time and accuracy in a set of simulations and experiments with benchmark and real-world image quality assessment datasets, showing the necessity of scaling and the advantages of each protocol and mixing. Although most of our examples focus on image quality assessment, our findings generalize to any other subjective quality-of-experience task.
TitAnt

With the explosive growth of e-commerce and the booming of e-payment, detecting online transaction fraud in real time has become increasingly important to Fintech business. To tackle this problem, we introduce the TitAnt, a transaction fraud detection system deployed in Ant Financial, one of the largest Fintech companies in the world. The system is able to predict online real-time transaction fraud in mere milliseconds. We present the problem definition, feature extraction, detection methods, implementation and deployment of the system, as well as empirical effectiveness. Extensive experiments have been conducted on large real-world transaction data to show the effectiveness and the efficiency of the proposed system.
A Hybrid L<sub>2</sub> −L<sub>P</sub> Variational Model For Single Low-Light Image Enhancement With Bright Channel Prior

In this paper, we consider and study the norm variable and propose a hybrid L2-L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> variational model with bright channel prior based on Retinex to decompose an observed image into a reflectance layer and an illumination layer. Different from the existing methods, our proposed model can preserve the reflectance layer with more fine details while enforcing the illumination layer to be texture-less, avoiding the texture-copy problem. Moreover, for solving our non-linear optimization, we adopt an alternating minimization scheme to find the optimal. Finally, we test our algorithm on a large number of images and the experimental results illustrate that the proposed method has achieved the better result than other state-of-the-art methods both qualitatively and quantitatively.
Modeling the Role of Sensory Feedback in Speech Motor Control and Learning

Purpose While the speech motor system is sensitive to feedback perturbations, sensory feedback does not seem to be critical to speech motor production. How the speech motor system is able to be so flexible in its use of sensory feedback remains an open question. Method We draw on evidence from a variety of disciplines to summarize current understanding of the sensory systems' role in speech motor control, including both online control and motor learning. We focus particularly on computational models of speech motor control that incorporate sensory feedback, as these models provide clear encapsulations of different theories of sensory systems' function in speech production. These computational models include the well-established directions into velocities of articulators model and computational models that we have been developing in our labs based on the domain-general theory of state feedback control (feedback aware control of tasks in speech model). Results After establishing the architecture of the models, we show that both the directions into velocities of articulators and state feedback control/feedback aware control of tasks models can replicate key behaviors related to sensory feedback in the speech motor system. Although the models agree on many points, the underlying architecture of the 2 models differs in a few key ways, leading to different predictions in certain areas. We cover key disagreements between the models to show the limits of our current understanding and point toward areas where future experimental studies can resolve these questions. Conclusions Understanding the role of sensory information in the speech motor system is critical to understanding speech motor production and sensorimotor learning in healthy speakers as well as in disordered populations. Computational models, with their concrete implementations and testable predictions, are an important tool to understand this process. Comparison of different models can highlight areas of agreement and disagreement in the field and point toward future experiments to resolve important outstanding questions about the speech motor control system.
Sonny, Cerca! Evaluating the Impact of Using a Vocal Assistant to Search at School

Children struggle with translating their information needs into effective queries to initiate the search process. In this paper, we explore the degree to which the use of a Vocal Assistant (VA) as an intermediary between a child and a search engine can ease query formulation and foster completion of successful searches. We also examine the potential influence VA can have on the search process when compared to a traditional keyboard-driven approach. This comparison motivates the second contribution of our work, an evaluation framework that covers 4 dimensions: (1) a new search strategy (VA) for (2) a specific user group (children) given (3) a particular task (answering questions) in (4) a defined environment (school). The proposed framework can be adopted by the research community to conduct comprehensive assessments of search systems given new interaction methods, user groups, contexts, and tasks.
Stock market prediction using Firefly algorithm with evolutionary framework optimized feature reduction for OSELM method

Forecasting future trends of the stock market using the historical data is the exigent demand in the field of academia as well as business. This work has explored the feature optimization capacity of firefly with an evolutionary framework considering the biochemical and social aspects of Firefly algorithm, along with the selection procedure of objective value in evolutionary notion. The performance of the proposed model is evaluated using four different stock market datasets, such as BSE Sensex, NSE Sensex, S&P 500 index and FTSE index. The datasets are regenerated using the proper mathematical formulation of the fundamental part belonging to technical analysis, such as technical indicators and statistical measures. The feature reduction through transformation is carried out on the enhanced dataset before employing the experimented dataset to the prediction models such as Extreme Learning Machine (ELM), Online Sequential Extreme Learning Machine (OSELM) and Recurrent Back Propagation Neural Network (RBPNN). For feature reduction, both statistical and optimized based feature reduction strategies are considered, where Principal Component Analysis (PCA) and Factor Analysis (FA) are examined for statistical based feature reduction and Firefly Optimization (FO), Genetic Algorithm (GA) and Firefly algorithm with evolutionary framework are well thought out for optimized feature reduction techniques. An empirical comparison is established among the experimented prediction models considering all the feature reduction techniques for the time horizon of 1 day, 3 days, 5 days, 7 days, 5 days and 30 days in advance, applying on all the datasets used in this study. From the simulation result, it can be clearly figured out that firefly with evolutionary framework optimized feature reduction applying to OSELM prediction model outperformed over the rest experimented models.
Distributed fusion cubature Kalman filters for nonlinear systems

Summary This paper is concerned with the distributed fusion estimation problem for multisensor nonlinear systems. Based on the Kalman filtering framework and the spherical cubature rule, a general method for calculating the cross‐covariance matrices between any two local estimators is presented for multisensor nonlinear systems. In the linear unbiased minimum variance sense, based on the cross‐covariance matrices, a distributed fusion cubature Kalman filter weighted by matrices (MW‐CKF) is presented. The proposed MW‐CKF has better accuracy and robustness. An example verifies the effectiveness of the proposed algorithms.
A hybrid design of shadowed type-2 fuzzy inference systems applied in diagnosis problems

Computer aided systems have been frequently used in recent times and the evolution of artificial intelligence allows the inclusion of this kind of systems in most complex problems, for example in diagnosis problems. Some of the most powerful algorithms or methods that have been applied to diagnosis problems are: Artificial Neural Networks, Support Vector Machines, Decision Threes, Fuzzy Inference Systems and hybrids of these algorithms. The present paper explores the applications of a special approximation of the Type-2 Fuzzy Inference System, which is the Shadowed Type-2 Fuzzy Inference System, and the reason for using this approach (and not another one) is because it provides a good approximation to General Type-2 Fuzzy Inference Systems, but with a computational cost reduction. Shadowed Type-2 Fuzzy Inference Systems are inspired on the Shadows Sets that simplify the traditional Type-1 Membership Function using two optimal α-cuts, which in our case are applied in the secondary membership function of the General Type-2 Fuzzy Inference System (modeled by the α-planes representation) in order to model the system with only two α-planes. Experiments were realized with eleven benchmark datasets in order to evaluate the accuracy of the proposed system. The obtained results demonstrate the advantages of using this approach over the conventional General Type-2 Fuzzy Inference Systems, and this is because we obtain better performance in most of the cases and with less computational resources.
Review and Implementation of Topic Modeling in Hindi

Formulae display:?Mathematical formulae have been encoded as MathML and are displayed in this HTML version using MathJax in order to improve their display. Uncheck the box to turn MathJax off. This feature requires Javascript. Click on a formula to zoom.
Financial Access and Productivity Dynamics in Sub-Saharan Africa

The purpose of this study is to investigate whether enhancing financial access influences productivity in Sub-Saharan Africa. The research focuses on 25 countries in the region with data for the period 1980–2014. The adopted empirical strategy is the Generalised Method of Moments. The credit channel of financial access is considered and proxied by private domestic credit while four main total factor productivity (TFP) dynamics are adopted for the study, namely: TFP, real TFP, welfare TFP and real welfare TFP. It is apparent from the findings that enhancing financial access positively affects welfare TFP whereas the effect is not significant on TFP, real TFP and welfare TFP. Policy implications are discussed. The study complements the extant literature by engaging hitherto unemployed dynamics of TFP in Sub-Saharan Africa.
DPRF: A Differential Privacy Protection Random Forest

Providing privacy protection for classification algorithms has become a research hotspot in current data mining. In this paper, differential privacy is applied to the random forest classification algorithm, and a random forest algorithm based on differential privacy is proposed to protect the privacy information in the data classification process. Firstly, differential privacy provides privacy protection by adding perturbation noise, which leads to a decrease in the classification accuracy of random forest algorithms. In order to reduce the impact of differential privacy protection on the accuracy of random forest classification, a hybrid decision tree algorithm is proposed. For the construction of a single decision tree in a random forest, the information gain ratio in the ID3 algorithm and the information gain ratio in the C4.5 are combined to generate a new attribute metric IG_GR to improve the classification accuracy of a single decision tree. Secondly, a new privacy budget allocation strategy is proposed. For nodes of different depths in the decision tree, the privacy budget is allocated to its counting query and attribute query by weight, which is used to balance the signal-to-noise ratio of differential privacy technology to nodes of different depths in the decision tree. At the same time, the hybrid decision tree algorithm is applied to the construction of random forest, which balances the privacy and classification accuracy of the random forest algorithm based on differential privacy. Finally, this paper conducted experiments on UCI's Adult and Mushroom datasets. The results show that compared with the traditional decision tree algorithm, the algorithm proposed in this paper has better classification accuracy; and the DPRF can provide effective privacy protection under the premise of ensuring high classification performance. The work of this paper achieves a balance between privacy and classification accuracy, and has practical application value.
Modeling Embedding Dimension Correlations via Convolutional Neural Collaborative Filtering

As the core of recommender systems, collaborative filtering (CF) models the affinity between a user and an item from historical user-item interactions, such as clicks, purchases, and so on. Benefiting from the strong representation power, neural networks have recently revolutionized the recommendation research, setting up a new standard for CF. However, existing neural recommender models do not explicitly consider the correlations among embedding dimensions, making them less effective in modeling the interaction function between users and items. In this work, we emphasize on modeling the correlations among embedding dimensions in neural networks to pursue higher effectiveness for CF. We propose a novel and general neural collaborative filtering framework—namely, ConvNCF, which is featured with two designs: (1) applying outer product on user embedding and item embedding to explicitly model the pairwise correlations between embedding dimensions, and (2) employing convolutional neural network above the outer product to learn the high-order correlations among embedding dimensions. To justify our proposal, we present three instantiations of ConvNCF by using different inputs to represent a user and conduct experiments on two real-world datasets. Extensive results verify the utility of modeling embedding dimension correlations with ConvNCF, which outperforms several competitive CF methods.
Computerized Classification of Prostate Cancer Gleason Scores from Whole Slide Images

Histological Gleason grading of tumor patterns is one of the most powerful prognostic predictors in prostate cancer. However, manual analysis and grading performed by pathologists are typically subjective and time-consuming. In this paper, we present an automatic technique for Gleason grading of prostate cancer from H&E stained whole slide pathology images using a set of novel completed and statistical local binary pattern (CSLBP) descriptors. First, the technique divides the whole slide image (WSI) into a set of small image tiles, where salient tumor tiles with high nuclei densities are selected for analysis. The CSLBP texture features that encode pixel intensity variations from circularly surrounding neighborhoods are extracted from salient image tiles to characterize different Gleason patterns. Finally, the CSLBP texture features computed from all tiles are integrated and utilized by the multi-class support vector machine (SVM) that assigns patient slides with different Gleason scores such as 6, 7, or ≥ 8. Experiments have been performed on 312 different patient cases selected from the cancer genome atlas (TCGA) and have achieved superior performances over state-of-the-art texture descriptors and baseline methods including deep learning models for prostate cancer Gleason grading.
Explainable Multi-Agent Systems Through Blockchain Technology

Advances in Artificial Intelligence (AI) are contributing to a broad set of domains. In particular, Multi-Agent Systems (MAS) are increasingly approaching critical areas such as medicine, autonomous vehicles, criminal justice, and financial markets. Such a trend is producing a growing AI-Human society entanglement. Thus, several concerns are raised around user acceptance of AI agents. Trust issues, mainly due to their lack of explainability, are the most relevant. In recent decades, the priority has been pursuing the optimal performance at the expenses of the interpretability. It led to remarkable achievements in fields such as computer vision, natural language processing, and decision-making systems. However, the crucial questions driven by the social reluctance to accept AI-based decisions may lead to entirely new dynamics and technologies fostering explainability, authenticity, and user-centricity. This paper proposes a joint approach employing both blockchain technology (BCT) and explainability in the decision-making process of MAS. By doing so, current opaque decision-making processes can be made more transparent and secure and thereby trustworthy from the human user standpoint. Moreover, several case studies involving Unmanned Aerial Vehicles (UAV) are discussed. Finally, the paper discusses roles, balance, and trade-offs between explainability and BCT in trust-dependent systems.
Nearshore Ship Detection on High-Resolution Remote Sensing Image via Scene-Mask R-CNN

Deep convolutional neural network (DCNN) can achieve ship detection mission on the highresolution remote sensing images.However, the false alarms caused by the onshore ship-like objects may decrease the accuracy and feasibility of these DCNN-based detection frameworks.In our work, an end-toend method, named as Scene Mask R-CNN, is proposed to reduce the onshore false alarms.The scene mask extraction network (SMEN), as a network branch for scene segmentation, is innovatively introduced into the detection framework.The non-target area is marked out by an inferenced scene mask which is used to assist the ship detection.Combining the feature map originated from feature extraction network (FEN) with the inferenced scene mask by using the edge probability weighted (EPW) merging method, the false candidate targets in the non-target area are excluded.This novel mechanism of DCNN-based ship detection not only maintains the detection accuracy, but also effectively suppresses the false alarms in the non-target area.Finally, the validity and accuracy of this method are verified on a ship dataset generated by the highresolution optical remote sensing images.