Reviewerly/exHarmony huggingface dataset:
Line-of-Sight-Based Guidance and Adaptive Neural Path-Following Control for Sailboats

This paper addresses the issue of waypoints-based path-following control for sailboats. Based on the parameterized line-of-sight framework, a novel guidance principle for the sailboat is developed, which comprises the mechanisms to observe the crab angle and generate the reference heading angle. For the crab angle observation, the approach with double reduced-order extended state observers and a saturation operator is developed, which involves the consideration of the intense sway. For the heading angle generation, three modes are devised, i.e., the path-following mode, the characteristic tacking mode, and the gybing mode. Sign functions are employed to describe tacking and gybing maneuvers of the sailboat such that rational switching of the reference heading angle is guaranteed, and no linguistic command is required. Matching with the proposed guidance principle, an adaptive neural control law is thereafter developed with the outputs landing in rudder angles. The echo state networks (ESNs) are introduced to offset model uncertainties and environmental disturbances with minimum parameters learning. By introducing two time-varying systems, all the errors in the closed-loop system are proved to be semiglobal uniformly ultimate bounded. Finally, the effectiveness of the proposed scheme is testified in MATLAB simulation.
QFlow

Wireless Internet access has brought legions of heterogeneous applications all sharing the same resources. However, current wireless edge networks that cater to worst or average case performance lack the agility to best serve these diverse sessions. Simultaneously, software reconfigurable infrastructure has become increasingly mainstream to the point that dynamic per packet and per flow decisions are possible at multiple layers of the communications stack. Exploiting such reconfigurability requires the design of a system that can enable a configuration, measure the impact on the application performance (Quality of Experience), and adaptively select a new configuration. Effectively, this feedback loop is a Markov Decision Process whose parameters are unknown. The goal of this work is to design, develop and demonstrate QFlow that instantiates this feedback loop as an application of reinforcement learning (RL). Our context is that of reconfigurable (priority) queueing, and we use the popular application of video streaming as our use case. We develop both model-free and model-based RL approaches that are tailored to the problem of determining which clients should be assigned to which queue at each decision period. Through experimental validation, we show how the RL-based control policies on QFlow are able to schedule the right clients for prioritization in a high-load scenario to outperform the status quo, as well as the best known solutions with over 25% improvement in QoE, and a perfect QoE score of 5 over 85% of the time.
LSwarm: Efficient Collision Avoidance for Large Swarms With Coverage Constraints in Complex Urban Scenes

In this letter, we address the problem of collision avoidance for a swarm of UAVs used for continuous surveillance of an urban environment. Our method, LSwarm, efficiently avoids collisions with static obstacles, dynamic obstacles and other agents in three-dimensional urban environments while considering coverage constraints. LSwarm calculates collision avoiding velocities that maximize the conformity of an agent to an optimal path given by a global coverage strategy and ensure sufficient resolution of the coverage data collected by each agent. Our algorithm is formulated based on optimal reciprocal collision avoidance and is scalable with respect to the size of the swarm. We evaluate the coverage performance of LSwarm in realistic simulations of a swarm of quadrotors in complex urban models. In practice, our approach can compute collision avoiding velocities for a swarm composed of tens to hundreds of agents in a few milliseconds on dense urban scenes consisting of tens of buildings.
Solving Quantum Chemistry Problems with a D-Wave Quantum Annealer

Quantum annealing devices have been subject to various analyses in order to classify their usefulness for practical applications. While it has been successfully proven that such systems can in general be used for solving combinatorial optimization problems, they have not been used to solve chemistry applications. In this paper we apply a mapping, put forward by Xia et al. [25], from a quantum chemistry Hamiltonian to an Ising spin glass formulation and find the ground state energy with a quantum annealer. Additionally we investigate the scaling in terms of needed physical qubits on a quantum annealer with limited connectivity. To the best of our knowledge, this is the first experimental study of quantum chemistry problems on quantum annealing devices. We find that current quantum annealing technologies result in an exponential scaling for such inherently quantum problems and that new couplers are necessary to make quantum annealers attractive for quantum chemistry.
Fast Methods for Eikonal Equations: An Experimental Survey

Fast methods are very popular algorithms to compute time-of-arrival maps (distance maps measured in time units) solving the Eikonal equation.Since fast marching was proposed in 1995, it has been applied to many different applications, such as robotics, medical computer vision, fluid simulation, and so on.From then on, many alternatives to the original method have been proposed with two main objectives: reducing its computational time and improving its accuracy.In this paper, we collect the main single-threaded approaches, which improve the computational time of the standard fast marching method and study them within a common mathematical framework.Then, they are evaluated using isotropic environments, which are representative of their possible applications.The studied methods are the fast marching method with the binary heap, the fast marching method with Fibonacci heap, the simplified fast marching method, the untidy fast marching method, the fast iterative method, the group marching method, the fast sweeping method, the locking sweeping method, and the double dynamic queue method.
Multilingual neural machine translation with soft decoupled encoding

Multilingual training of neural machine translation (NMT) systems has led to impressive accuracy improvements on low-resource languages. However, there are still significant challenges in efficiently learning word representations in the face of paucity of data. In this paper, we propose Soft Decoupled Encoding (SDE), a multilingual lexicon encoding framework specifically designed to share lexical-level information intelligently without requiring heuristic preprocessing such as pre-segmenting the data. SDE represents a word by its spelling through a character encoding, and its semantic meaning through a latent embedding space shared by all languages. Experiments on a standard dataset of four low-resource languages show consistent improvements over strong multilingual NMT baselines, with gains of up to 2 BLEU on one of the tested languages, achieving the new state-of-the-art on all four language pairs.
Localized Multiple Kernel learning for Anomaly Detection: One-class Classification

Multi-kernel learning has been well explored in the recent past and has exhibited promising outcomes for multi-class classification and regression tasks. In this paper, we present a multiple kernel learning approach for the One-class Classification (OCC) task and employ it for anomaly detection. Recently, the basic multi-kernel approach has been proposed to solve the OCC problem, which is simply a convex combination of different kernels with equal weights. This paper proposes a Localized Multiple Kernel learning approach for Anomaly Detection (LMKAD) using OCC, where the weight for each kernel is assigned locally. Proposed LMKAD approach adapts the weight for each kernel using a gating function. The parameters of the gating function and one-class classifier are optimized simultaneously through a two-step optimization process. We present the empirical results of the performance of LMKAD on 25 benchmark datasets from various disciplines. This performance is evaluated against existing Multi Kernel Anomaly Detection (MKAD) algorithm, and four other existing kernel-based one-class classifiers to showcase the credibility of our approach. LMKAD achieves significantly better Gmean scores while using a lesser number of support vectors compared to MKAD. Friedman test is also performed to verify the statistical significance of the results claimed in this paper.
Cloth Manipulation Using Random-Forest-Based Imitation Learning

We present a novel approach for manipulating high-DOE deformable objects such as cloth. Our approach uses a random-forest-based controller that maps the observed visual features of the cloth to an optimal control action of the manipulator. The topological structure of this random-forest is determined automatically based on the training data, which consists of visual features and control signals. The training data is constructed online using an imitation learning algorithm. We have evaluated our approach on different cloth manipulation benchmarks such as flattening, folding, and twisting. In all these tasks, we have observed convergent behavior for the random-forest. On convergence, the random-forest-based controller exhibits superior robustness to observation noise compared with other techniques such as convolutional neural networks and nearest neighbor searches. Videos and supplemental material are available at http://gamma.cs.unc.edu/ClothM/.
How to make more from exposure data? An integrated machine learning pipeline to predict pathogen exposure

Predicting infectious disease dynamics is a central challenge in disease ecology. Models that can assess which individuals are most at risk of being exposed to a pathogen not only provide valuable insights into disease transmission and dynamics but can also guide management interventions. Constructing such models for wild animal populations, however, is particularly challenging; often only serological data are available on a subset of individuals and nonlinear relationships between variables are common. Here we provide a guide to the latest advances in statistical machine learning to construct pathogen-risk models that automatically incorporate complex nonlinear relationships with minimal statistical assumptions from ecological data with missing data. Our approach compares multiple machine learning algorithms in a unified environment to find the model with the best predictive performance and uses game theory to better interpret results. We apply this framework on two major pathogens that infect African lions: canine distemper virus (CDV) and feline parvovirus. Our modelling approach provided enhanced predictive performance compared to more traditional approaches, as well as new insights into disease risks in a wild population. We were able to efficiently capture and visualize strong nonlinear patterns, as well as model complex interactions between variables in shaping exposure risk from CDV and feline parvovirus. For example, we found that lions were more likely to be exposed to CDV at a young age but only in low rainfall years. When combined with our data calibration approach, our framework helped us to answer questions about risk of pathogen exposure that are difficult to address with previous methods. Our framework not only has the potential to aid in predicting disease risk in animal populations, but also can be used to build robust predictive models suitable for other ecological applications such as modelling species distribution or diversity patterns.
Recognition times for 62 thousand English words: Data from the English Crowdsourcing Project

We present a new dataset of English word recognition times for a total of 62 thousand words, called the English Crowdsourcing Project. The data were collected via an internet vocabulary test in which more than one million people participated. The present dataset is limited to native English speakers. Participants were asked to indicate which words they knew. Their response times were registered, although at no point were the participants asked to respond as quickly as possible. Still, the response times correlate around .75 with the response times of the English Lexicon Project for the shared words. Also, the results of virtual experiments indicate that the new response times are a valid addition to the English Lexicon Project. This not only means that we have useful response times for some 35 thousand extra words, but we now also have data on differences in response latencies as a function of education and age.
Optimizing Fixation Prediction Using Recurrent Neural Networks for 360$^{\circ }$ Video Streaming in Head-Mounted Virtual Reality

We study the problem of predicting the viewing probability of different parts of 3600 videos when streaming them to head-mounted displays. We propose a fixation prediction network based on recurrent neural network, which leverages sensor and content features. The content features are derived by computer vision (CV) algorithms, which may suffer from inferior performance due to various types of distortion caused by diverse 3600 video projection models. We propose a unified approach with overlapping virtual viewports to eliminate such negative effects, and we evaluate our proposed solution using several CV algorithms, such as saliency detection, face detection, and object detection. We find that overlapping virtual viewports increase the performance of these existing CV algorithms that were not trained for 3600 videos. We next fine-tune our fixation prediction network with diverse design options, including: 1) with or without overlapping virtual viewports, 2) with or without future content features, and 3) different feature sampling rates. We empirically choose the best fixation prediction network and use it in a 3600 video streaming system. We conduct extensive trace-driven simulations with a large-scale dataset to quantify the performance of the 3600 video streaming system with different fixation prediction algorithms. The results show that our proposed fixation prediction network outperforms other algorithms in several aspects, such as: 1) achieving comparable video quality (average gaps between -0.05 and 0.92 dB), 2) consuming much less bandwidth (average bandwidth reduction by up to 8 Mb/s), 3) reducing the rebuffering time (on average 40 s in bandwidth-limited 4G cellular networks), and 4) running in real-time (at most 124 ms).
Benchmarking of six cloud segmentation algorithms for ground-based all-sky imagers

The detection and segmentation of clouds in images taken by ground based cameras is of utmost importance for a large number of applications including all-sky imager based nowcasting systems which optimize solar power plant operation, calculation of the global irradiance, estimation of the cloud base height and support of optical satellite downlink operations. Many approaches to segment clouds in camera images are published. However, comparisons of different approaches are not frequently conducted. Here, we address this question by benchmarking six different cloud segmentation algorithms on images taken by an off-the-shelf surveillance camera. The six different algorithms include (1) a color-channel threshold-based algorithm, (2) a Clear Sky Library (CSL) based approach, (3) a region growing algorithm, (4) the Hybrid thresholding algorithm (HYTA), and a (5) novel, HYTA-based development named HYTA+. Furthermore, (6) a deep convolutional neural network (FCN) is adapted via transfer learning to this problem. The segmentation results of algorithms (1) to (5) are compared to 829 manually segmented reference images. The segmentation algorithms are benchmarked on a test dataset which is divided into 16 meteorological categories. These categories cover different Linke turbidity values, solar positions and cloud cover situations. Results show that three out of the six presented segmentation methods (CSL, HYTA+ and FCN) achieve overall accuracy values above 90%. These approaches outperform the other methods and correctly segment images with a higher consistency. Fixed threshold based methods, as the multicolor criterion, HYTA or the region growing algorithm fail under certain meteorological conditions. The FCN based segmentation (6) is tested on 160 images where it delivers the best overall pixel-by-pixel accuracy of 97.0%.
Blockchain Based Transparent Vehicle Insurance Management

The automotive industry is re-blooming with recent enhances in technology. Electric vehicles and autonomous vehicles are already attracting attention to the industry and providing momentum for adoption of other emerging technologies. This has its impact on a diverse range of stakeholders from manufacturers to consumers.There is a new frontier that can lend its abilities to the experiences built around vehicles and it is the blockchain technology. Blockchain technology is an enabler. It can act as a transaction medium between interacting parties. It can also be used as a tamper-free ledger to store a history of transactions. With these two simple abilities, blockchains can enable several applications to make vehicle-related experiences better.In this paper, we propose a tamper-free ledger of events as an insurance record of motor vehicles. This insurance record system can include all aspects of insurance transactions. It not only would improve the experience around proving insurance, but also act as evidence in the event of a dispute. This ledger can have extended services around providing a clean driving record. Individual drivers, dealers, insurance companies, lawyers, law enforcement agencies and motor vehicle agencies are all stakeholders of this blockchain based solution.
Anonymization in Online Social Networks Based on Enhanced Equi-Cardinal Clustering

Recent trends show that the popularity of online social networks (OSNs) has been increasing rapidly. From daily communication sites to online communities, an average person's daily life has become dependent on these online networks. Hence, it has become evident that protection should be provided to these networks from unwanted intruders. In this paper, we consider the data privacy on OSNs at the network level rather than the user level. This network-level privacy helps us to prevent information leakage to third-party users, such as advertisers. We propose a novel scheme that combines the privacy of all the elements of a social network: node, edge, and attribute privacy by clustering the users based on their attribute similarity. We use an enhanced equi-cardinal clustering (ECC) as a way to achieve k-anonymity. We further improve k-anonymity with l-diversity. Our proposed enhanced ECC ensures that there are at least “k” users in any given network as well as the attributes in each cluster has at least l-distinct values. We further provide proofs on how the proposed ECC ensures k-anonymity and the maximum information loss. We consider a weighted directed social network graph as an input to our method to consider the existing complexities in a social network. With the help of two real-world data sets, we evaluate this method in terms of privacy and efficiency.
Simultaneous Representation Learning and Clustering for Incomplete Multi-view Data

Incomplete multi-view clustering has attracted various attentions from diverse fields. Most existing methods factorize data to learn a unified representation linearly. Their performance may degrade when the relations between the unified representation and data of different views are nonlinear. Moreover, they need post-processing on the unified representations to extract the clustering indicators, which separates the consensus learning and subsequent clustering. To address these issues, in this paper, we propose a Simultaneous Representation Learning and Clustering (SRLC) method. Concretely, SRLC constructs similarity matrices to measure the relations between pair of instances, and learns low-dimensional representations of present instances on each view and a common probability label matrix simultaneously. Thus, the nonlinear information can be reflected by these representations and the clustering results can obtained from label matrix directly. An efficient iterative algorithm with guaranteed convergence is presented for optimization. Experiments on several datasets demonstrate the advantages of the proposed approach.
Regulation spillovers across cryptocurrency markets

Several countries have already introduced restrictions on trading of cryptocurrencies, and many more are evaluating whether to follow suit. We document an unprecedented drop in trading volume on the Chinese cryptocurrency market after a recent regulatory change by the Chinese authorities that severely restricted bitcoin trading. This paper shows how changes in domestic regulation not only have large effects on the domestic cryptocurrency market but also produce large international spillovers. Specifically, we observe a large increase in trading volume and relative bitcoin prices in exchange for Korean won, Japanese yen, and U.S. dollars, and on Chinese peer-to-peer exchanges.
Multi-Granularity Reasoning for Social Relation Recognition From Images

Discovering social relations in images can make machines better interpret the behavior of human beings. However, automatically recognizing social relations in images is a challenging task due to the significant gap between the domains of visual content and social relation. Existing studies separately process various features such as faces expressions, body appearance, and contextual objects, thus they cannot comprehensively capture the multi-granularity semantics, such as scenes, regional cues of persons, and interactions among persons and objects. To bridge the domain gap, we propose a Multi-Granularity Reasoning framework for social relation recognition from images. The global knowledge and mid-level details are learned from the whole scene and the regions of persons and objects, respectively. Most importantly, we explore the fine-granularity pose keypoints of persons to discover the interactions among persons and objects. Specifically, the pose-guided Person-Object Graph and Person-Pose Graph are proposed to model the actions from persons to object and the interactions between paired persons, respectively. Based on the graphs, social relation reasoning is performed by graph convolutional networks. Finally, the global features and reasoned knowledge are integrated as a comprehensive representation for social relation recognition. Extensive experiments on two public datasets show the effectiveness of the proposed framework.
An automatic short-answer grading model for semi-open-ended questions

Automatic short-answer grading has been studied for more than a decade. The technique has been used for implementing auto assessment as well as building the assessor module for intelligent tutoring systems. Many early works automatically grade mainly based on the similarity between a student answer and the reference answer to the question. This method performs well for closed-ended questions that have single or very limited numbers of correct answers. However, some short-answer questions ask students to express their own thoughts based on various facts; hence, they have no reference answers. Such questions are called semi-open-ended short-answer questions. Questions of this type often appear in reading comprehension assessments. In this paper, we developed an automatic semi-open-ended short-answer grading model that integrates both domain-general and domain-specific information. The model also utilizes a long-short-term-memory recurrent neural network to learn the representation in the classifier so that word sequence information is considered. In experiments on 7 reading comprehension questions and over 16,000 short-answer samples, our proposed automatic grading model demonstrates its advantage over existing models.