Reviewerly/exHarmony huggingface dataset:
An Open-Source Testbed for Outdoor Navigation With the AR.Drone Quadrotor

The development and the experimental validation of a workbench capable of supporting outdoor flights using basically the affordable commercial quadrotor AR.Drone 2.0 and a personal computer is considered in this article. The text discusses some technical details of the developed system, focusing on how to achieve real-time navigation adopting a simplified dynamic model as a high-level representation of the vehicle in flight, a general-purpose nonlinear controller, suitable for different types of control applications, and a sensor fusion algorithm, designed to compensate for delayed measurements in the available sensory data. To check the effectiveness of such testbed, results associated to some experimental flights are presented, with the vehicle accomplishing positioning tasks, considering a fixed target point and a sequence of waypoints, and trajectory tracking tasks. Such results, associated to the theoretical analysis provided, allow claiming that the proposed system is an efficient way to guide the vehicle to accomplish different tasks. Additionally, the source code used is available for free download and modification, resulting in a valuable open-source workbench for researchers just starting working with aerial robotics.
Structured and Consistent Multi-Layer Multi-Kernel Subtask Correction Filter Tracker

Some multi-task correlation filter trackers achieve the top-ranked performance in terms of accuracy and robustness. However, they directly fuse multiple types of features into a single kernel space. This operation fails to fully explore the discriminative strength and diversity of different features, and also ignores the structured correspondence of different tasks. To solve these issues, we propose a structured multi-kernel subtask correlation filter tracker with temporal-spatial consistency, which enjoys the merits of both layered multi-kernel subtask learning and structured correlation filter. Specifically, we firstly assign one kernel space to each channel feature. Multi-channel features correspond to multi-kernel spaces to boost their powerful discriminability. And then, we divide the target into multi-layer patches with different sizes, and regard the correlation filter trace of each patch with one channel feature as a subtask. In the following, we incorporate globally and locally structured correlation filters into a unified multi-kernel subtask particle tracking framework. The global and local subtasks complement and enhance each other with similar motion model. The proposed tracker not only exploits the cooperation and complementarity of layered multi-kernel subtask correlation filters, but also mines the underlying geometric structure of global subtasks, and the inner spatial locality correspondences of local subtasks inside the target. This operation is achieved by dual group sparsity regularized terms with mixed-norm l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p,q</sub> , which decomposes the multi-kernel subtask filter matrix into two collaborative components. They correspond to the adaptive filter feature selection and outlier subtask detection, respectively. Besides, the developed tracking model maintains the temporal coherence and spatial consistency of multi-layer subtask filters via the smooth regularizer. Finally, the tracking formulation is optimized by the accelerated proximal gradient approach (APG). Encouraging analyses on six benchmark datasets, verify the favorable effectiveness and robustness of our method against state-of-the-art trackers.
GNSS-Free Maritime Navigation using Radar and Digital Elevation Models

Modern maritime navigation is heavily dependent on satellite systems. Availability of an accurate position is critical for safe operations, but satellite-based navigation systems are vulnerable to interference, jamming, and spoofing. In this work, we propose a method for maritime navigation independent of GNSS, able to provide absolute positioning of the vessel based on marine radar scans. A measurement model is presented where a Digital Elevation Model is used to predict the output of a marine radar, given a hypothetical position. The model, as used by an on-line particle filter, is used to track the movements of a ship from real recorded data. This demonstrates the feasibility of this method for robust positioning, without the need of external positioning signals, in a maritime environment. The tracking only uses sensors commonly available on maritime vessels, and demonstrates its application using freely available elevation data.
Cross-domain recommender system using generalized canonical correlation analysis

Recommender systems provide personalized recommendations to the users from a large number of possible options in online stores. Matrix factorization is a well-known and accurate collaborative filtering approach for recommender system, which suffers from cold-start problem for new users and items. When new users join the system, it will take some time before they enter some ratings in the system, until that time, there are not enough ratings to learn the matrix factorization model. Using auxiliary data such as user’s demographic, ratings and reviews in relevant domains, is an effective solution to reduce the new user problem. In this paper, we used the data of users activity from auxiliary domains to build domain-independent users representation that could be used to predict users ratings in the target domains. We proposed an iterative method which applied MAX-VAR generalized canonical correlation analysis (GCCA) on user’s latent factors learned from matrix factorization on each domain. Also, to improve the capability of GCCA to learn latent factors for new users, we propose a generalized canonical correlation analysis by inverse sum of selection matrices (GCCA-ISSM) approach, which provides better recommendations in cold-start scenarios. The proposed approach is extended using content-based features like topic models extracted from user’s reviews. We demonstrate the accuracy and effectiveness of the proposed approaches on cross-domain rating predictions using comprehensive experiments on Amazon and MovieLens datasets.
Dynamic Play via Suit Factorization Search in Skat

In this paper we look at multi-player trick-taking card games that rely on obeying suits, which include Bridge, Hearts, Tarot, Skat, and many more. We propose mini-game solving in the suit factors of the game, and exemplify its application as a single-dummy or double-dummy analysis tool that restricts game play to either trump or non-trump suit cards. Such factored solvers are applicable to improve card selections of the declarer and the opponents, mainly in the middle game, and can be adjusted for optimizing the number of points or tricks to be made. While on the first glance projecting the game to one suit is an over-simplification, the partitioning approach into suit factors is a flexible and strong weapon, as it solves apparent problems arising in the phase transition of accessing static table information to dynamic play. Experimental results show that by using mini-game play, the strength of trick-taking Skat AIs can be improved.
Learning While Tracking: A Practical System Based on Variational Gaussian Process State-Space Model and Smartphone Sensory Data

We implement a wireless indoor tracking system based on the variational Gaussian process state-space model (GPSSM) with smartphone-collected WiFi received signal strength and inertial measurement unit readings. We adapt the existing variational GPSSM framework to wireless tracking scenarios, and provide a practical learning procedure for the variational GPSSM. The proposed system explores both the expressive power of the non-parametric Gaussian process model and its natural mechanism for integrating the state-of-the-art tracking techniques designed upon state-space model. Experimental results obtained from a real office environment validate the outstanding performance of the variational GPSSM in comparison with the traditional parametric state-space model in terms of tracking accuracy.
Second-Order Learning and Inference using Incomplete Data for Uncertain Bayesian Networks: A Two Node Example

Efficient second-order probabilistic inference in uncertain Bayesian networks was recently introduced. However, such second -order inference methods presume training over complete training data. While the expectation-maximization framework is well-established for learning Bayesian network parameters for incomplete training data, the framework does not determine the covariance of the parameters. This paper introduces two methods to compute the covariances for the parameters of Bayesian networks or Markov random fields due to incomplete data for two-node networks. The first method computes the covariances directly from the posterior distribution of parameters, and the second method more efficiently estimates the covariances from the Fisher information matrix. Finally, the implications and effectiveness of these covariances is theoretically and empirically evaluated.
Neuro-Symbolic Neurodegenerative Disease Modeling as Probabilistic Programmed Deep Kernels

We present a probabilistic programmed deep kernel learning approach to personalized, predictive modeling of neurodegenerative diseases. Our analysis considers a spectrum of neural and symbolic machine learning approaches, which we assess for predictive performance and important medical AI properties such as interpretability, uncertainty reasoning, data-efficiency, and leveraging domain knowledge. Our Bayesian approach combines the flexibility of Gaussian processes with the structural power of neural networks to model biomarker progressions, without needing clinical labels for training. We run evaluations on the problem of Alzheimer’s disease prediction, yielding results that surpass deep learning in both accuracy and timeliness of predicting neurodegeneration, and with the practical advantages of Bayesian non-parametrics and probabilistic programming.
Self-Attention and Dynamic Convolution Hybrid Model for Neural Machine Translation

In sequence-to-sequence learning, models based on the self-attention mechanism dominate the network structures used for neural machine translation. Recently, convolutional networks have been demonstrated to perform excellently on various translation tasks. Despite the fact that self-attention and convolution have different strengths in modeling sequences, few efforts have been devoted to combining them. In this work, we propose a hybrid model that benefits from both mechanisms. We combine a self-attention module and a dynamic convolution module by taking a weighted sum of their outputs where the weights can be dynamically learned by the model during training. Experimental results show that our hybrid model outperforms baseline models built solely on either of these two mechanisms. And we produce new state-of-the-art results on IWSLT'15 English-German dataset.
Nanofibrous paper for fingerprint mapping and the inspiration in microimprinting assisted precious patterning

A fingerprint is one of the unique and authoritative information for personal identification; however, the entire three levels of ridge patterns, especially clear sweat pore information, can not be facilely obtained by the current methodologies. Herein, after careful investigation on the physiological structure of a fingertip, we describe the use of a super-soft and water-sensitive poly (vinyl alcohol) (PVA) nanopaper for mapping the entire three levels of ridge patterns. Thanks to the unique softness, the detailed ridge/furrow patterns (level 1 and 2) can be copied onto the nanopaper, while the water-sensitive feature enables an in-situ fast and selective dissolution of nanopaper by a tiny amount of water secreted through sweat pores to map clear sweat pore patterns (level 3). Further, inspired by the fingerprint pressing process, we combine electrospinning with microimprinting approach to create nanofibrous materials with controllable patterns (single, dual, and complex patterns). The proposed method exhibits superior features than currently reported patterning approaches, such as high throughput, low cost, and high efficiency. Finally, we demonstrate the potential application of the patterned PVA nanopaper for ultrafast water-sensing anti-counterfeiting.
Intelligent chat robot in digital campus based on deep learning

Abstract In this paper, two different technologies are adopted to realize the digital campus intelligent chatbot. The robot has three core modules: chat module, campus information module and virtual teaching assistant module. For chat module, this paper uses Encoder-Decoder framework. For the campus information module and virtual teaching assistant module, this paper pursue high precision and adopt retrieval mode. In practice, the two modules implemented by different technical means complement each other, greatly improving the user experience of students.
Research on Robot Teaching for Complex Task

As the traditional teaching technology can not meet the requirements of complex teaching tasks, this paper studies the direct teaching technology of robots based on the force sensor's gravity compensation and admittance control. Through the gravity compensation of the force sensor, the influence of the gravity of the robot end tool, the installation tilt angle of the robot base and the zero point of the force sensor on the force sensor is reduced, and the teaching accuracy is improved. Using the admittance control principle, the robot can move according to the traction of the teaching staff, and regularly record the teaching points in the process to form the teaching trajectory. Finally, a direct teaching system was established, and the proposed gravity compensation algorithm and direct teaching algorithm were experimentally verified. The experimental results show that the proposed gravity compensation algorithm can achieve better compensation effect. The direct teaching algorithm based on admittance control can simplify the teaching process and complete complex teaching tasks.
Lekana - Blockchain Based Archive Storage for Large-Scale Cloud Systems

Blockchain is a form of a distributed storage system that stores a chronological sequence of transactions in a tamper-evident manner. Due to the decentralized trust ecosystem in blockchain, various industries have adopted blockchain to build their applications. This paper presents a novel approach to building a blockchain-based document archive storage platform, "Lekana". The Lekana platform can be adopted by service providers that require frequent document operations, such as "Pageroonline", a cloud-based e-invoicing provider in Europe. With Lekana we introduce a novel approach to store an immutable hash chain of archived data which are owned by the customers in the blockchain. The proposed Lekana platform is built on top of Mystiko which is a highly scalable blockchain storage platform targeted for big data. We have integrated real-time data analytics and machine learning techniques into the Lekana platform by using the Mystiko-Ml machine learning service on Mystiko blockchain. By integrating the Lekana platform with blockchain technology, we have addressed major issues in most cloud-based, centralized storage platforms (e.g. lack of data privacy, lack of data immutability, lack of traceability and lack of data provenance). As a case study in the paper, we present how "Pageroonline" cloud-based e-invoicing provider stores their archived document data and archived document hash chain in the blockchain-based Lekana platform.
The COVID-19 Contact Tracing App In England and ‘Experimental Proportionality’

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Bearing fault diagnosis of two-dimensional improved Att-CNN2D neural network based on Attention mechanism

Effective fault diagnosis of rolling bearings is essential to the reliability and safety of modern industry. Although traditional intelligent fault diagnosis technologies such as support vector machines, extreme learning machines and artificial neural networks can achieve satisfactory accuracy, they still rely heavily on expert knowledge and manual intervention in the process of feature extraction and selection. This paper proposes a new method for fault diagnosis of rolling bearings based on CNN deep learning based on attention mechanism. First, in order to make full use of the convolutional network to obtain a larger receptive field, a one-dimensional time series is transformed into a two-dimensional matrix as the input of the CNN network by mapping. The neural network is used to extract the advanced features of the input signal. The high-level features of the output are scored, and the results are finally output through a fully connected classifier. Finally, the model was verified by an experiment. The results show that the improved Att-CNN2D network with the attention mechanism has greatly improved the model generalization ability and obtained higher accuracy than the traditional CNN network.
Usage of Terms “Science” and “Scientific Knowledge” in Nature of Science (NOS): Do Their Lexicons in Different Accounts Indicate Shared Conceptions?

Nature of science (NOS) has been a central theme in science education and research on it for nearly three decades, but there is still debate on its proper focus and underpinnings. The focal points of these debates revolve around different ways of understanding the terms “science” and “scientific knowledge”. It is suggested here that the lack of agreement is at least partially related to and reflected as a lack of common vocabulary and terminology that would provide a shared basis for finding consensus. Consequently, the present study seeks motivation from the notions of centrality of lexicons in recognizing the identity of disciplinary communities and different schools of thought within NOS. Here, by using a network approach, we investigate how lexicons used by different authors to discuss NOS are confluent or divergent. The lexicons used in these texts are investigated on the basis of a network analysis. The results of the analysis reveal clear differences in the lexicons that are partially related to differences in views, as evident from the debates surrounding the consensus NOS. The most divergent views are related to epistemology, while regarding the practices and social embeddedness of science the lexicons overlap significantly. This suggests that, in consensus NOS, one can find much basis for converging views, with common understanding, where constructive communication may be possible. The basic vocabulary, in the form of a lexicon, can reveal much about the different stances and the differences and similarities between various disciplinary schools. The advantage of such an approach is its neutrality and how it keeps a distance from preferred epistemological positions and views of nature of knowledge.
System Description: Lang-n-Change - A Tool for Transforming Languages

$$\textsc {lang}\text {-}\textsc {n}\text {-}\textsc {change}$$ is a tool for transforming language definitions into other language definitions. It provides a declarative domain-specific language for expressing algorithms over languages. lang-n-change is implemented in OCaml and generates language definitions that can be compiled and executed in $$\lambda $$ Prolog. The tool provides a repository with a number of language definitions and a handful of transformations.
Enhancing Reliability of Compute Environments on Amazon EC2 Spot Instances

Amazon Elastic Compute Cloud (EC2) gives access to resources in the form of virtual servers, also known as instances. EC2 Spot Instances (SIs) offer spare compute capacity at steep discounts compared to reliable and fixed price on-demand instances. The drawback, however, is that waiting time until requested spots become fulfilled can be incredible high. In this paper, we propose a container migration-based solution to enhance the reliability of virtual cluster computing environments built on top of non-reserved EC2 pricing model instances. We compare the performance of our algorithm by executing different resource provisioning plans for running real-life workflow applications, constrained by user-defined deadline and budget Quality of Service (QoS) parameters. The results show that our solution is able to successfully conclude almost 98% of workflow applications and more than 99% of workflow tasks for on-demand- and spot block-based virtual compute environments. For SI-based virtual compute environments, our solution achieves similar results, completing more than 98% of workflow applications, and over 99% of workflow tasks, for a worse-case scenario.
Uncertainty measurements in neural network predictions for classification tasks

Explainability in Artificial Intelligence (AI) has become increasingly popular in order to understand model predictions, especially for noisy and uncertain observations close to the decision boundary. Bayesian Neural Networks (BNNs) infer posterior distributions over the weight parameters in order to express uncertainty in high dimensional spaces. As an alternative to exact calculation, we approximate a probabilistic model of the weight parameters in neural networks (NNs) using Automatic Differentiation Variational Inference (ADVI). The computational cost of this approximation is very high and the question is how we can utilize the resulting posterior distributions in the decision-making process. We propose a novel measurement in order to quantify classification uncertainty based on samples from the NN posterior predictive distribution. The purpose of this metric is to identify and describe boundary observations. We compare this novel uncertainty measure - Generative Class Counts (GCC) - with the posterior predictive standard deviation. We introdudce a novel metric which measures the uncertainty measure's ability to separate correctly classified test observations from incorrectly classified ones. To demonstrate the performance of the BNN as well as the GCC uncertainty measurement, we perform image classification on the MNIST handwritten digits data set using a Bayesian Convolutional Neural Network. We show that the GCC uncertainty scored the highest performance in its ability to separate correctly classified test observations from incorrectly classified ones.
Time-Optimal Trajectory Planning of Industrial Robot based on Improved Particle Swarm Optimization Algorithm

Industrial robot has been widely used since the development of manufacturing. A time-optimal trajectory planning for industrial robot can help to save energy and improve efficiency. This paper proposed a new trajectory planning method based on improved particle swarm optimization. Golden section method is used to find the maximum of velocity, acceleration and jerk. Compared to derivative method, way used Golden section method can find the maximum more quickly. Roll-back technology is introduced to ensure the diversity of particle swarm, which makes sure that the search ability will not be weaken by the reduction of number of particles. An improved particle swarm optimization with variable learning factor is used to search the global best solution. The results of simulation show that the proposed trajectory planning method can generate a time-optimal trajectory for industrial robot.