Reviewerly/exHarmony huggingface dataset:
A Novel Recursive Smooth Trajectory Generation Method for Unmanned Vehicles

This article presents a novel recursive smooth trajectory (RST) generation algorithm for application in robotics and in particular for unmanned aerial vehicles (UAVs). RST builds the trajectory recursively as a smooth polynomial path, thus a closed form trajectory satisfying any arbitrary dynamic limitation that translates into kinematic constraints (e.g., position, velocity, acceleration, etc.). Uncertainties and perturbations in the constraints are also discussed, modeled, and finally included in the calculation of the polynomial trajectory coefficients. Moreover, due to its recursive formulation, RST enables an immediate extension toward the solution of a trajectory optimization problem. In particular, it is shown that the minimum-snap piecewise polynomial trajectory can be interpreted as a special case of RST. The effectiveness of the proposed algorithm is demonstrated numerically via two illustrative scenarios. Its application to a UAV structure is also discussed to highlight the advantage of a smooth path over a piecewise one. Finally, the computational complexity and memory requirements are analyzed.
Speeding Up SpMV for Power-Law Graph Analytics by Enhancing Locality &amp; Vectorization

Graph analytics applications often target large-scale web and social networks, which are typically power-law graphs. Graph algorithms can often be recast as generalized Sparse Matrix-Vector multiplication (SpMV) operations, making SpMV optimization important for graph analytics. However, executing SpMV on large-scale power-law graphs results in highly irregular memory access patterns with poor cache utilization. Worse, we find that existing SpMV locality and vectorization optimizations are largely ineffective on modern out-of-order (OOO) processors-they are not faster (or only marginally so) than the standard Compressed Sparse Row (CSR) SpMV implementation. To improve performance for power-law graphs on modern OOO processors, we propose Locality-Aware Vectorization (LAV). LAV is a new approach that leverages a graph's power-law nature to extract locality and enable effective vectorization for SpMV-like memory access patterns. LAV splits the input matrix into a dense and a sparse portion. The dense portion is stored in a new representation, which is vectorization-friendly and exploits data locality. The sparse portion is processed using the standard CSR algorithm. We evaluate LAV with several graphs on an Intel Skylake-SP processor, and find that it is faster than CSR (and prior approaches) by an average of 1.5x. LAV reduces the number of DRAM accesses by 35% on average, with only a 3.3% memory overhead.
Defending Support Vector Machines Against Data Poisoning Attacks

Support Vector Machines (SVMs) are vulnerable to targeted training data manipulations such as poisoning attacks and label flips. By carefully manipulating a subset of training samples, the attacker forces the learner to compute an incorrect decision boundary, thereby causing misclassifications. Considering the increased importance of SVMs in engineering and life-critical applications, we develop a novel defense algorithm that improves resistance against such attacks. Local Intrinsic Dimensionality (LID) is a promising metric that characterizes the outlierness of data samples. In this work, we introduce a new approximation of LID called K-LID that uses kernel distance in the LID calculation, which allows LID to be calculated in high dimensional transformed spaces. We introduce a weighted SVM against such attacks using K-LID as a distinguishing characteristic that de-emphasizes the effect of suspicious data samples on the SVM decision boundary. Each sample is weighted on how likely its K-LID value is from the benign K-LID distribution rather than the attacked K-LID distribution. Experiments with benchmark data sets show that the proposed defense reduces classification error rates substantially (10% on average).
Depth Completion via Inductive Fusion of Planar LIDAR and Monocular Camera

Modern high-definition LIDAR is expensive for commercial autonomous driving vehicles and small indoor robots. An affordable solution to this problem is fusion of planar LIDAR with RGB images to provide a similar level of perception capability. Even though state-of-the-art methods provide approaches to predict depth information from limited sensor input, they are usually a simple concatenation of sparse LIDAR features and dense RGB features through an end-to-end fusion architecture. In this paper, we introduce an inductive late-fusion block which better fuses different sensor modalities inspired by a probability model. The proposed demonstration and aggregation network propagates the mixed context and depth features to the prediction network and serves as a prior knowledge of the depth completion. This late-fusion block uses the dense context features to guide the depth prediction based on demonstrations by sparse depth features. In addition to evaluating the proposed method on benchmark depth completion datasets including NYUDepthV2 and KITTI, we also test the proposed method on a simulated planar LIDAR dataset. Our method shows promising results compared to previous approaches on both the benchmark datasets and simulated dataset with various 3D densities.
Improvement of decision tree classifier accuracy for healthcare insurance fraud prediction by using Extreme Gradient Boosting algorithm

Fraud in the healthcare sector is prevalent and very cumbersome. Fraud generally involves intentional disappointment, and frustration or misrepresentation usually leads to an unfair benefit. Such exciting demand for insurance services has led to manipulative and inappropriate behaviour. Based on the report published by the United States Government Accountability Office, healthcare insurance fraud contributes to a 10% unexpected rise of annual health expenditure, which amounts to US$ 100 billion per year. In order to identify and avoid fraud, the scientific state of the art is applied. This paper seeks to analyze statistical modelling approaches for the assessment of fake health benefits using state-of-the-art techniques. Once the data is collected and the study of exploratory data is completed, it can use random forest regression and the classification of trees algorithm with extreme gradient boost (XGB) to determine the most efficient models. Compared to the Random Forest Method that reaches 81% accuracy with for class 1 recall, XGB Tree method of random sub-sampling was successfully achieved by 86% overall accuracy and 87% with illegitimate providers. Refer to the result, XGB method produce more accuracy for clean data that has been tuned with several adjustment.
The effect of financial sector development on economic growth of selected<scp>sub‐Saharan</scp>Africa countries

Abstract The effect of financial sector development on economic growth is among the main debatable issue in economics and policymaking. Thus, by using different financial sector dimensions, this research tried to look at the effect of financial sector development on the economic growth of 25 sub‐Saharan Africa countries for the period 2010–2017. Precisely, three dynamic panel data models that look at the effect of financial sector depth, access, and efficiency on economic growth were estimated by two‐step system GMM estimation. In this research, credit extended to the private sector per GDP, commercial bank branch per 100,000 adult population, and Return to assets were used as a proxy and measures for financial sector depth, access, and efficiency respectively. Accordingly, the results revealed that financial sector depth, access, and efficiency have a positive and statistically significant effect on these countries' economic growth. Therefore, it is recommended that the concerned bodies that broadening the depth of financial institutions by giving more credit to the private sector is essential. Besides, the financial institutions will have to be expanded to increase their accessibility to the mass and take some measures to promote their efficiency.
Meta-Learning Deep Visual Words for Fast Video Object Segmentation

Personal robots and driverless cars need to be able to operate in novel environments and thus quickly and efficiently learn to recognise new object classes. We address this problem by considering the task of video object segmentation. Previous accurate methods for this task finetune a model using the first annotated frame, and/or use additional inputs such as optical flow and complex post-processing. In contrast, we develop a fast, causal algorithm that requires no finetuning, auxiliary inputs or post-processing, and segments a variable number of objects in a single forward-pass. We represent an object with clusters, or "visual words", in the embedding space, which correspond to object parts in the image space. This allows us to robustly match to the reference objects throughout the video, because although the global appearance of an object changes as it undergoes occlusions and deformations, the appearance of more local parts may stay consistent. We learn these visual words in an unsupervised manner, using meta-learning to ensure that our training objective matches our inference procedure. We achieve comparable accuracy to finetuning based methods (whilst being 1 to 2 orders of magnitude faster), and state-of-the-art in terms of speed/accuracy trade-offs on four video segmentation datasets. Code is available at https://github.com/harkiratbehl/MetaVOS.
Weighted Multiview Possibilistic C-Means Clustering With L2 Regularization

Since social media, virtual communities and networks rapidly grow, multiview data become more popular. In general, multiview data always contain different feature components in different views. Although these data are extracted in different ways (views) from diverse settings and domains, they are used to describe the same samples, which make them highly related. Hence, applying (single-view) clustering methods for multiview data poses difficulty in achieving desirable clustering results. Thus, multiview clustering methods should be developed that will utilize available multiview information. Most of multiview clustering techniques currently use k-means due to its conceptual simplicity, and use fuzzy c-means (FCM) that the datapoints can belong to more than one cluster based on their membership degrees from 0 to 1. However, the use of k-means or FCM may degrade its performance due to the presence of noise and outliers, especially on large or high-dimensional datasets. The constraint imposed on the membership degrees of k-means and FCM tends to assign a corresponding high membership value to an outlier or a noisy data point. To address these drawbacks, possibilistic c-means (PCM) relaxes the membership constraint of k-means and FCM so that outliers and noisy datapoints can be properly identified. On the other hand, there are various extensions of k-means and FCM for multiview data, but no extension of PCM for multiview data was made in the literature. Thus, we use PCM in our proposed multiview clustering model. In this article, we propose novel weighted multiview PCM algorithms designed for clustering multiview data as well as view and feature weights on PCM approaches, called W-MV-PCM and W-MV-PCM with L2 regularization (W-MV-PCM-L2). In multiview clustering, different views may vary with respect to its importance and each view may contain some irrelevant features. In the proposed algorithms, a learning scheme is constructed to compute for the view weights, and feature weights within each view. This scheme will be able to identify the importance of each view and, at the same time, it will also identify and select relevant features in each view. Comparisons of W-MV-PCM-L2 with existing multiview clustering algorithms are made on both synthetic and real datasets. The experimental results are evaluated using accuracy rate (AR) and external validity indexes, such as Rand index (RI) and normalized mutual information (NMI). The proposed W-MV-PCM-L2 algorithm with comparisons of existing algorithms under criteria of AR, RI, and NMI shows that it is a feasible and effective multiview clustering algorithm.
Lynceus: Cost-efficient Tuning and Provisioning of Data Analytic Jobs

Modern data analytic and machine learning jobs find in the cloud a natural deployment platform to satisfy their notoriously large resource requirements. Yet, to achieve cost efficiency, it is crucial to identify a deployment configuration that satisfies user-defined QoS constraints (e.g., on execution time), while avoiding unnecessary over-provisioning.This paper introduces Lynceus, a new approach for the optimization of cloud-based data analytic jobs that improves over state-of-the-art approaches by enabling significant cost savings both in terms of the final recommended configuration and of the optimization process used to recommend configurations.Unlike existing solutions, Lynceus optimizes in a joint fashion both the cloud-related (i.e., which and how many machines to provision) and the application-level (e.g. the hyper-parameters of a machine learning algorithm) parameters. This allows for a reduction of the cost of recommended configurations by up to 3.7× at the 90-th percentile with respect to existing approaches, which treat the optimization of cloud-related and application- level parameters as two independent problems.Further, Lynceus reduces the cost of the optimization process (i.e., the cloud cost incurred for testing configurations) by up to 11×. Such an improvement is achieved thanks to two mechanisms: i) a timeout approach which allows to abort the exploration of configurations that are deemed suboptimal, while still extracting useful information to guide future explorations and to improve its predictive model - differently from recent works, which either incur the full cost for testing suboptimal configurations or are unable to extract any knowledge from aborted runs; ii) a long-sighted and budget-aware technique that determines which configurations to test by predicting the long-term impact of each exploration - unlike state-of-the-art approaches for the optimization of cloud jobs, which adopt greedy optimization methods.
Robust Feedback Stabilization of <i>N</i>-Level Quantum Spin Systems

Related DatabasesWeb of Science You must be logged in with an active subscription to view this.Article DataHistorySubmitted: 8 July 2020Accepted: 18 November 2020Published online: 22 February 2021Keywordsstochastic stability, exponential stability, quantum control and filtering, Lyapunov techniques, robustnessAMS Subject Headings81Q93, 93D15, 60H10Publication DataISSN (print): 0363-0129ISSN (online): 1095-7138Publisher: Society for Industrial and Applied MathematicsCODEN: sjcodc
Balancing accuracy and diversity in ensemble learning using a two-phase artificial bee colony approach

In ensemble learning, it is necessary to build a balancing mechanism to balance the accuracy of individual learners with the diversity between individual learners to achieve excellent ensemble learning performance. In previous studies, diversity was regarded only as a regularization term, which does not sufficiently indicate that diversity should implicitly be treated as an accuracy factor. In this study, an ensemble learning approach based on balanced accuracy and diversity (ELBAD) that uses a two-phase artificial bee colony (ABC) algorithm is proposed to balance the accuracy and diversity of ensemble learners. In the first phase, the ABC algorithm is used to generate an ensemble classifier with appropriate diversity. In the second phase, the ABC algorithm is used to generate a weighted ensemble classifier. The ELBAD ensemble learning algorithm is significantly superior to other state-of-the-art popular ensemble learning algorithms, including AdaBoost, Bagging, Decorate, extremely randomized trees (ET), gradient boosting decision tree (GBDT), random forest (RF), and rotation forest (RoF) on 30 UCI datasets. In addition, this study proposes a systematic parameter tuning procedure for the ELBAD algorithm that reduces the time required to generate an ensemble classifier.
Coding-Net: A multi-purpose neural network for Fringe Projection Profilometry

Fringe Projection Profilometry is one of the important techniques in three-dimensional vision. However, traditional Fringe Projection Profilometry employing a lot of patterns has significant challenges for efficiently recovering the absolute phase. In this paper, a multi-purpose neural network combining with code-based patterns is proposed to recover the absolute phase firstly, which can greatly decrease the number of patterns with high accuracy. Different from the traditional approach of trigonometric calculation, the well-trained multi-purpose network can learn the principle of extracting absolute phase from few patterns. Experiments demonstrate that the proposed method can acquire high accuracy for complex texture objects, indicating potential application in precision engineering with high speed.
Towards digital cognitive clones for the decision-makers: adversarial training experiments

There can be many reasons for anyone to make a digital copy (clone) of own decision-making behavior. This enables virtual presence of a professional decision-maker simultaneously in many places and processes of Industry 4.0. Such clone can be used as one's responsible representative when the human is not available. Pi-Mind ("Patented Intelligence") is a technology, which enables "cloning" cognitive skills of humans using adversarial machine learning. In this paper, we present a cyber-physical environment as an adversarial learning ecosystem for cloning image classification skills. The physical component of the environment is provided by the logistic laboratory with camera-surveillance over the conveyors. The digital component of the environment contains special modifications of Generative Adversarial Networks, which include a human-operator as a trainer, an autonomous Pi-Mind clone as a trainee (a discriminator) and a smart digital adversary as a challenger (generator of sophisticated decision situations, emergencies and attacks, which supposedly catalyzes the cloning process).
Colon cancer prediction using 2DReCA segmentation and hybrid features on histopathology images

Since histopathological images exist in various forms, performing segmentation on these images is tedious. While in cancer-free colon tissue, epithelial cells generally have an elliptical shape; their structure alters in a malignant tissue. This study proposes a technique consisting of colon biopsy image segmentation and a hybrid set of features for classification, and is evaluated on multiple databases with various levels of magnifications. This study presents a novel image segmentation method with multi-level thresholding based on Rényi's two-dimensional entropy with a cultural algorithm (2DR e CA). Based on the entropy, elliptical epithelial cells, being the region of interest, are identified from the segmented background. After successful segmentation, shape descriptors are extracted with morphological operations. Two sets of texture features (grey-level co-occurrence matrix and block-wise elliptical local binary pattern) are calculated based on pre-processed grey-scale colon images. The proposed hybrid feature vector set, then concatenates the extracted features for training and testing with a random forest classifier. The proposed segmentation and classification model is evaluated by considering four data sets consisting of various colon images at different magnifications. In addition, it is evaluated by multiple performance measures and compared with existing techniques.
Neural networks design and training for safe human-robot cooperation

In the present paper, a neural network (NN) is proposed for detecting the human-manipulator collisions. Because of that purpose, three types of NNs are designed and trained named as; multilayer feedforward, cascaded forward, and recurrent NNs. The NN is designed based on the joints’ dynamics of the manipulator. In addition, the NN is trained using a dataset with and without the collisions using the algorithm of Levenberg-Marquardt to detect the collisions happened with the manipulator. During the design of the NN, only the intrinsic joint position sensor of the robot is used which enable the proposed method to be applied to any robot. The three designed neural networks are compared quantitatively and qualitatively to each other. Furthermore, the proposed method was evaluated with the KUKA LWR manipulator using one joint motion. The results of the designed systems achieve high effectiveness in detecting the collisions between human and robot.
Evaluation of augmented reality supported approaches for product design and production processes

Augmented Reality (AR) has been a focus of research in the field of production for many years with the goal to improve e.g. monitoring and maintenance processes. Although the benefits have been well researched, the AR technology has not been widespread yet in the industry. This article discusses reasons why existing approaches have not become established outside the research context. Following this, solutions are examined for three identified problematic areas. First, this article presents a theoretical method to create use cases for AR applications in the product design process based on the Double Diamond design process model. The second main part discusses which device properties are relevant for a production environment. A catalogue of criteria is presented, weighting properties according to their importance. Third, a discussion is described regarding data integration concepts, problems and solutions for AR design and production use cases.
Visual question answering based on local-scene-aware referring expression generation

Visual question answering requires a deep understanding of both images and natural language. However, most methods mainly focus on visual concept; such as the relationships between various objects. The limited use of object categories combined with their relationships or simple question embedding is insufficient for representing complex scenes and explaining decisions. To address this limitation, we propose the use of text expressions generated for images, because such expressions have few structural constraints and can provide richer descriptions of images. The generated expressions can be incorporated with visual features and question embedding to obtain the question-relevant answer. A joint-embedding multi-head attention network is also proposed to model three different information modalities with co-attention. We quantitatively and qualitatively evaluated the proposed method on the VQA v2 dataset and compared it with state-of-the-art methods in terms of answer prediction. The quality of the generated expressions was also evaluated on the RefCOCO, RefCOCO+, and RefCOCOg datasets. Experimental results demonstrate the effectiveness of the proposed method and reveal that it outperformed all of the competing methods in terms of both quantitative and qualitative results.