Reviewerly/exHarmony huggingface dataset:
A Novel Progressive Gaussian Approximate Filter for Tightly Coupled GNSS/INS Integration

In this article, we focus on addressing the nonlinear filtering problem with large prior uncertainty but high measurement accuracy, which may be encountered in the application of tightly coupled global navigation satellite system (GNSS)/inertial navigation system (INS) integration. Although the existing methods, such as progressive Gaussian approximate filter (PGAF), can address this problem, it has poor estimation accuracy. To improve the estimation accuracy of PGAF, the step sizes as well as the measurement noise covariance matrix (MNCM) are jointly estimated based on the variational Bayesian approach, from which a novel PGAF with variable step size is developed. Tightly coupled GNSS/INS integration simulations illustrate that the proposed filter outperforms the existing methods both in estimation accuracy and rate of convergence.
Distributed Joint Sensor Registration and Multitarget Tracking via Sensor Network

This paper addresses distributed registration of a sensor network for multitarget tracking. Each sensor gets measurements of the target position in a local coordinate frame, having no knowledge about the relative positions (referred to as drift parameters) and azimuths (referred to as orientation parameters) of its neighboring nodes. The multitarget set is modeled as an independent and identically distributed cluster random finite set (RFS), and a consensus cardinality probability hypothesis density (CPHD) filter is run over the network to recursively compute in each node the posterior RFS density. Then, a suitable cost function, expressing the discrepancy between the local posteriors in terms of averaged Kullback-Leibler divergence, is minimized with respect to the drift and orientation parameters for sensor registration purposes. In this way, a computationally feasible optimization approach for joint sensor registraton and multitarget tracking is devised. Finally, the effectiveness of the proposed approach is demonstrated through simulation experiments.
Optical Scanning of a Laser Triangulation Sensor for 3-D Imaging

In scan-based 3-D systems, the achievable measurement time is strongly restrained by the moving mass. This limitation can be relaxed, by scanning the optical path instead of moving the entire sensor, such that a higher measurement speed can be achieved. This article deals with the design, the control, and the measurement results of a scanning triangulation sensor, in which the illumination and reflection paths of the sensor are scanned by a fast steering mirror (FSM). The system architecture is determined using ray-tracing simulations, such that the performance of the scanning system can be determined in advance. To scan the area of interest with the FSM, conventional raster trajectories and Lissajous trajectories are employed, which provide an early overview of the scan area. For tracking these trajectories, PID and dual-tone controllers are used, respectively. The experimental results demonstrate that for both scan trajectories, the sample can be captured correctly, with a frame rate of 1 frame/s, a maximum spatial resolution of 130 μm, and a field of view of 11.5 × 18.5 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> .
ATC-NLSP: Prediction of the Classes of Anatomical Therapeutic Chemicals Using a Network-Based Label Space Partition Method

Anatomical Therapeutic Chemical (ATC) classification system proposed by the World Health Organization is a widely accepted drug classification scheme in both academic and industrial realm. It is a multilabeling system which categorizes drugs into multiple classes according to their therapeutic, pharmacological, and chemical attributes. In this study, we adopted a data-driven network-based label space partition (NLSP) method for prediction of ATC classes of a given compound within the multilabel learning framework. The proposed method ATC-NLSP is trained on the similarity-based features such as chemical-chemical interaction and structural and fingerprint similarities of a compound to other compounds belonging to the different ATC categories. The NLSP method trains predictors for each label cluster (possibly intersecting) detected by community detection algorithms and takes the ensemble labels for a compound as final prediction. Experimental evaluation based on the jackknife test on the benchmark dataset demonstrated that our method has boosted the absolute true rate, which is the most stringent evaluation metrics in this study, from 0.6330 to 0.7497, in comparison to the state-of-the-art approaches. Moreover, the community structures of the label relation graph were detected through the label propagation method. The advantage of multilabel learning over the single-label models was shown by label-wise analysis. Our study indicated that the proposed method ATC-NLSP, which adopts ideas from network research community and captures the correlation of labels in a data driven manner, is the top-performing model in the ATC prediction task. We believed that the power of NLSP remains to be unleashed for the multilabel learning tasks in drug discovery. The source codes are freely available at https://github.com/dqwei-lab/ATC.
Multi-robot Task Allocation Strategy based on Particle Swarm Optimization and Greedy Algorithm

In order to improve the resource utilization efficiency of heterogeneous multi-robots, minimize the execution time of multi-type tasks, effectively maintain the load balancing of robot resources, solve the problem of multiple resources and difficult to find a near-optimal solution for multi-robot collaborative planning, a multi-robot task allocation strategy combining improved particle swarm optimization and greedy (IPSO-G) algorithm is proposed. The strategy is divided into two steps: First, the improved particle swarm optimization algorithm is used to search for the combination of tasks and robots; after that, the greedy algorithm is used to sort the task execution order in the task combination, and generate the overall cost of task execution plan. Through continuous iteration of the above process, the strategy finally finds the optimal solution. In the computer simulation environment, one TSP example is used to verify the feasibility and effectiveness of the proposed strategy.
A data hiding scheme based on multidirectional line encoding and integer wavelet transform

An effective image steganography scheme can make a good compromise among embedding capacity, imperceptibility, security and robustness. This paper presents a novel data hiding method based on multidirectional line encoding (MDLE) and integer wavelet transform (IWT). It can get a good result in those indicators simultaneously. The method firstly uses the IWT instead of the DWT to achieve accurate extraction of secret bits. The four wavelet sub-bands are divided into non-overlapping 3 ×3 coefficient blocks which are exploited as embedding regions. Secondly, the MDLE method based on the 3×3 block model is proposed. The center coefficient of the block is paired with surrounding coefficients in eight directions to embed n-bit secret data respectively, which improves the utilization of coefficients and ensures the change value of coefficient in one direction not to exceed 2n−1, thereby embedding capacity is increased and good imperceptibility is obtained. Finally, a simple edge detection method is proposed and applied to the low frequency approximation sub-band to embed more secret data in edge blocks in order to further enhance the embedding capacity. The experimental results show that the proposed method is not only superior in terms of embedding capacity and image quality than previous methods, but also secure to steganalysis and robust to attacks.
Movie collaborative filtering with multiplex implicit feedbacks

Movie recommender systems have been widely used in a variety of online networking platforms to give users reasonable advice from a large number of choices. As a representative method of movie recommendation, collaborative filtering uses explicit and implicit feedbacks to mine users’ preferences. The use of implicit features can help to improve the accuracy of movie collaborative filtering. However, multiplex implicit feedbacks have not been investigated and utilized comprehensively. In this paper, we analyze different kinds of implicit feedbacks in movie recommendation, including user similarities for movie tastes, rated records of each movie and positive attitude of users, and incorporate these feedbacks for collaborative filtering. User relationships are extracted according to user similarities. We propose a recommendation method with multiplex implicit feedbacks (RMIF), which factorizes both the explicit rating matrix and implicit attitude matrix. To demonstrate the effectiveness of our method, we conduct extensive experiments on two real datasets. Experiment results prove that RMIF significantly outperforms state-of-the-art models in terms of accuracy. Among different kinds of implicit feedbacks, positive attitude has the most important role in movie collaborative filtering.
(Short Paper) A Faster Constant-Time Algorithm of CSIDH Keeping Two Points

At ASIACRYPT 2018, Castryck, Lange, Martindale, Panny and Renes proposed CSIDH, which is a key-exchange protocol based on isogenies between elliptic curves, and a candidate for post-quantum cryptography. However, the implementation by Castryck et al. is not constant-time. Specifically, a part of the secret key could be recovered by the side-channel attacks. Recently, Meyer, Campos, and Reith proposed a constant-time implementation of CSIDH by introducing dummy isogenies and taking secret exponents only from intervals of non-negative integers. Their non-negative intervals make the calculation cost of their implementation of CSIDH twice that of the worst case of the standard (variable-time) implementation of CSIDH. In this paper, we propose a more efficient constant-time algorithm that takes secret exponents from intervals symmetric with respect to the zero. For using these intervals, we need to keep two torsion points on an elliptic curve and calculation for these points. We implemented our algorithm by extending the implementation in C of Meyer et al. (originally from Castryck et al.). Then our implementation achieved 152.8 million clock cycles, which is about 29.03% faster than that of Meyer et al.
A conceptual replication of continuous integration pain points in the context of Travis CI

Continuous integration (CI) is an established software quality assurance practice, and the focus of much prior research with a diverse range of methods and populations. In this paper, we first conduct a literature review of 37 papers on CI pain points. We then conduct a conceptual replication study on results from these papers using a triangulation design consisting of a survey with 132 responses, 12 interviews, and two logistic regressions predicting Travis CI abandonment and switching on a dataset of 6,239 GitHub projects. We report and discuss which past results we were able to replicate, those for which we found conflicting evidence, those for which we did not find evidence, and the implications of these findings.
LVIS: Learning from Value Function Intervals for Contact-Aware Robot Controllers

Guided policy search is a popular approach for training controllers for high-dimensional systems, but it has a number of pitfalls. Non-convex trajectory optimization has local minima, and non-uniqueness in the optimal policy itself can mean that independently-optimized samples do not describe a coherent policy from which to train. We introduce LVIS, which circumvents the issue of local minima through global mixed-integer optimization and the issue of non-uniqueness through learning the optimal value function rather than the optimal policy. To avoid the expense of solving the mixed-integer programs to full global optimality, we instead solve them only partially, extracting intervals containing the true cost-to-go from early termination of the branch-and-bound algorithm. These interval samples are used to weakly supervise the training of a neural net which approximates the true cost-to-go. Online, we use that learned cost-to-go as the terminal cost of a one-step model-predictive controller, which we solve via a small mixed-integer optimization. We demonstrate LVIS on piecewise affine models of a cart-pole system with walls and a planar humanoid robot and show that it can be applied to a fundamentally hard problem in feedback control-control through contact.
Adversarial Semantic Alignment for Improved Image Captions

In this paper, we study image captioning as a conditional GAN training, proposing both a context-aware LSTM captioner and co-attentive discriminator, which enforces semantic alignment between images and captions. We empirically focus on the viability of two training methods: Self-critical Sequence Training (SCST) and Gumbel Straight-Through (ST) and demonstrate that SCST shows more stable gradient behavior and improved results over Gumbel ST, even without accessing discriminator gradients directly. We also address the problem of automatic evaluation for captioning models and introduce a new semantic score, and show its correlation to human judgement. As an evaluation paradigm, we argue that an important criterion for a captioner is the ability to generalize to compositions of objects that do not usually co-occur together. To this end, we introduce a small captioned Out of Context (OOC) test set. The OOC set, combined with our semantic score, are the proposed new diagnosis tools for the captioning community. When evaluated on OOC and MS-COCO benchmarks, we show that SCST-based training has a strong performance in both semantic score and human evaluation, promising to be a valuable new approach for efficient discrete GAN training.
NTIRE 2019 Image Dehazing Challenge Report

This paper reviews the second NTIRE challenge on image dehazing (restoration of rich details in hazy image) with focus on proposed solutions and results. The training data consists from 55 hazy images (with dense haze generated in an indoor or outdoor environment) and their corresponding ground truth (haze-free) images of the same scene. The dense haze has been produced using a professional haze/fog generator that imitates the real conditions of haze scenes. The evaluation consists from the comparison of the dehazed images with the ground truth images. The dehazing process was learnable through provided pairs of haze-free and hazy train images. There were ~ 270 registered participants and 23 teams competed in the final testing phase. They gauge the state-of-the-art in image dehazing.
The benefits and limitations of voting mechanisms in evolutionary optimisation

We study the use of voting mechanisms in populations, and introduce a new Voting algorithm which can solve ONEMAX and JUMP in O(n log n), even for gaps as large as O(n). More significantly, the algorithm solves ONEMAX with added posterior noise in O(n log n), when the variance of the noise distribution is σ2 = O(n) and in O(σ2 log n) when the noise variance is greater than this. We assume only that the noise distribution has finite mean and variance and (for the larger noise case) that it is unimodal. We also examine the performance on arbitrary linear and monotonic functions. The Voting algorithm fails on LEADINGSONES but we give a variant which can solve the problem in O(n log n). We empirically study the use of voting in population based algorithms (UMDA, PCEA and cGA) and show that this can be effective for large population sizes.
SEG-VoxelNet for 3D Vehicle Detection from RGB and LiDAR Data

This paper proposes a SEG-VoxelNet that takes RGB images and LiDAR point clouds as inputs for accurately detecting 3D vehicles in autonomous driving scenarios, which for the first time introduces semantic segmentation technique to assist the 3D LiDAR point cloud based detection. Specifically, SEG-VoxelNet is composed of two sub-networks: an image semantic segmentation network (SEG-Net) and an improved-VoxelNet. The SEG-Net generates the semantic segmentation map which represents the probability of the category for each pixel. The improved-VoxelNet is capable of effectively fusing point cloud data with image semantic feature and generating accurate 3D bounding boxes of vehicles. Experiments on the KITTI 3D vehicle detection benchmark show that our approach outperforms the methods of state-of-the-art.
Feature Forwarding for Efficient Single Image Dehazing

Haze degrades content and obscures information of images, which can negatively impact vision-based decision-making in real-time systems. In this paper, we propose an efficient fully convolutional neural network (CNN) image dehazing method designed to run on edge graphical processing units (GPUs). We utilize three variants of our architecture to explore the dependency of dehazed image quality on parameter count and model design. The first two variants presented, a small and big version, make use of a single efficient encoder-decoder convolutional feature extractor. The final variant utilizes a pair of encoder-decoders for atmospheric light and transmission map estimation. Each variant ends with an image refinement pyramid pooling network to form the final dehazed image. For the big variant of the single-encoder network, we demonstrate state-of-the-art performance on the NYU Depth dataset. For the small variant, we maintain competitive performance on the super-resolution O/I-HAZE datasets without the need for image cropping. Finally, we examine some challenges presented by the Dense-Haze dataset when leveraging CNN architectures for dehazing of dense haze imagery and examine the impact of loss function selection on image quality. Benchmarks are included to show the feasibility of introducing this approach into real-time systems.
Automatic Data Clustering based on Hybrid Atom Search Optimization and Sine-Cosine Algorithm

Automatic clustering based hybrid metaheuristic algorithms has attracted the center of interest of scientists and engineers which become a hot topic for different data analysis applications. For example, image clustering, bioinformatics, image segmentation, and natural language processing. Where the process of determining the number and position of centroids is an NP-hard problem. So, this paper presents an alternative automatic clustering algorithm based on the hybrid between the atom search optimization (ASO) and the sine-cosine algorithm (SCA). The main objective of the proposed clustering method, called ASOSCA, is to find automatically the optimal number of centroids and their positions in order to minimize the CS-index (which refers to Compact-separated index). To achieve this goal, the ASOSCA uses SCA as a local search operator to improve the quality of ASO. The performance of the proposed hybrid method is compared with other metaheuristic methods; in which all of them are tested on sixteen clustering datasets and using different cluster validity indexes as Dunn, Silihouette, Davies Bouldin, and Calinski Harabasz. The experimental results show that the ASOSCA depict high superiority in comparison with other types of hybrid metaheuristic in terms of clustering measures.
The review linkage graph for code review analytics: a recovery approach and empirical study

Modern Code Review (MCR) is a pillar of contemporary quality assurance approaches, where developers discuss and improve code changes prior to integration. Since review interactions (e.g., comments, revisions) are archived, analytics approaches like reviewer recommendation and review outcome prediction have been proposed to support the MCR process. These approaches assume that reviews evolve and are adjudicated independently; yet in practice, reviews can be interdependent.
VASABI: Hierarchical User Profiles for Interactive Visual User Behaviour Analytics

User behaviour analytics (UBA) systems offer sophisticated models that capture users' behaviour over time with an aim to identify fraudulent activities that do not match their profiles. Motivated by the challenges in the interpretation of UBA models, this paper presents a visual analytics approach to help analysts gain a comprehensive understanding of user behaviour at multiple levels, namely individual and group level. We take a user-centred approach to design a visual analytics framework supporting the analysis of collections of users and the numerous sessions of activities they conduct within digital applications. The framework is centred around the concept of hierarchical user profiles that are built based on features derived from sessions, as well as on user tasks extracted using a topic modelling approach to summarise and stratify user behaviour. We externalise a series of analysis goals and tasks, and evaluate our methods through use cases conducted with experts. We observe that with the aid of interactive visual hierarchical user profiles, analysts are able to conduct exploratory and investigative analysis effectively, and able to understand the characteristics of user behaviour to make informed decisions whilst evaluating suspicious users and activities.
Multi-robot Informative Path Planning with Continuous Connectivity Constraints

We consider the problem of information collection from a polygonal environment using a multi-robot system, subject to continuous connectivity constraints. In particular, the robots, having a common radius of communication range, must remain connected throughout the exploration maximizing the information collection. The information gained through the exploration of the terrain is wirelessly transmitted to a base station. The base station performs the centralized planning of informative paths for the robots based on the information collected by them and thereafter, the robots follow these paths. This paper formulates the problem of multi-robot informative path planning under continuous connectivity constraints as an integer program leveraging the ideas of bipartite graph matching and minimal node separators. Theoretical analysis of the proposed solution proves that the informative paths will be collision-free and will be free of both livelock and deadlock. Experimental results demonstrate the low computational requirements of our algorithm for planning the informative paths, taking only about 0.75 sec. for planning a joint set of collision-free informative locations for 10 robots.
Fine-Grained Classification via Hierarchical Bilinear Pooling With Aggregated Slack Mask

Extracting discriminative fine-grained features is essential for fine-grained image recognition tasks.Many researchers utilize expensive human annotations to learn discriminative part models, which may be impossible for real-world applications.Recently, bilinear pooling has been frequently adopted and has shown its effectiveness owing to its learning discriminative regions automatically.However, most bilinear pooling models still utilize the all convolutional part/region features for recognition, including those noisy or even harmful feature elements.In this paper, we devise a novel fine-grained image classification approach by the Hierarchical Bilinear Pooling with Aggregated Slack Mask (HBPASM) model.The proposed model generates a RoI-aware image feature representation for better performance.We conduct experiments on three frequently used fine-grained image classification datasets.The experimental results demonstrate that HBPASM achieves competitive performance or even match the state-of-the-art methods on CUB-200-2011, Stanford Cars, and FGVC-Aircraft, respectively.