Reviewerly/exHarmony huggingface dataset:
A Deep Motion Deblurring Network Based on Per-Pixel Adaptive Kernels With Residual Down-Up and Up-Down Modules

Due to the object motion during the camera exposure time, latent pixel information appears scattered in a blurred image. A large dataset of dynamic motion blur and blur-free frame pairs enables deep neural networks to learn deblurring operations directly in end-to-end manners. In this paper, we propose a novel motion deblurring kernel learning network that predicts the per-pixel deblur kernel and a residual image. The learned deblur kernel filters and linearly combines neighboring pixels to restore the clean pixels in its corresponding location. The per-pixel adaptive convolution with the learned deblur kernel can effectively handle non-uniform blur. At the same time, the generated residual image is added to the adaptive convolution result to compensate for the limited receptive field of the learned deblur kernel. That is, the adaptive convolution and the residual image play different but complementary roles each other to reconstruct the latent clean images in a collaborative manner. We also propose residual down-up (RDU) and residual up-down (RUD) blocks that help improve the motion deblurring performance. The RDU and RUD blocks are designed to adjust the spatial size and the number of channels of the intermediate feature within the blocks. We demonstrate the effectiveness of our motion deblurring kernel learning network by showing intensive experimental results compared to those of the state-of-the-art methods.
Skeleton-Based Action Recognition with Synchronous Local and Non-Local Spatio-Temporal Learning and Frequency Attention

Benefiting from its succinctness and robustness, skeleton-based action recognition has recently attracted much attention. Most existing methods utilize local networks (e.g recurrent, convolutional, and graph convolutional networks) to extract spatio-temporal dynamics hierarchically. As a consequence, the local and non-local dependencies, which contain more details and semantics respectively, are asynchronously captured in different level of layers. Moreover, existing methods are limited to the spatio-temporal domain and ignore information in the frequency domain. To better extract synchronous detailed and semantic information from multi-domains, we propose a residual frequency attention (rFA) block to focus on discriminative patterns in the frequency domain, and a synchronous local and non-local (SLnL) block to simultaneously capture the details and semantics in the spatio-temporal domain. Besides, a soft-margin focal loss (SMFL) is proposed to optimize the learning whole process, which automatically conducts data selection and encourages intrinsic margins in classifiers. Our approach significantly outperforms other state-of-the-art methods on several large-scale datasets.
Action Recognition From Thermal Videos

Human action recognition using a camera-based surveillance system remains a challenging task. In particular, action recognition is difficult when a human is not visible in an image captured in a dark environment. The existing studies have utilized near-infrared (NIR) and thermal cameras to solve this problem. Compared to NIR cameras, thermal cameras enable long- and short-distance objects to be visible without an additional illuminator. However, thermal cameras have two major disadvantages: a halo effect and a temperature similarity. A halo effect occurs around an object with a high temperature. In a human object, such a halo effect is similar to a shadow under the body area. It is more difficult to segment a human area from an image with a halo effect. Moreover, if the background and human object have similar temperatures, it becomes more difficult to segment the human area. These disadvantages influence not only the accuracy of the segmentation of the human area but also the performance of human action recognition. Unfortunately, no studies have considered these issues. To address these problems, this study proposes the cycle-consistent generative adversarial network (CycleGAN)-based methods for removing halo effects from thermal images and restoring the areas of the human bodies. In addition, this study also considered a method for creating a skeleton image from a thermal image to analyze body movements. To extract more spatial and temporal features from skeleton image sequences thus created, a method for human action recognition that combines a convolutional neural network (CNN) and long short-term memory (LSTM) was proposed. In an experiment using an open database (Dongguk activities & actions database (DA&A-DB2)), the proposed method demonstrated a better performance than the existing methods.
Bug report severity level prediction in open source software: A survey and research opportunities

Context: The severity level attribute of a bug report is considered one of the most critical variables for planning evolution and maintenance in Free/Libre Open Source Software. This variable measures the impact the bug has on the successful execution of the software system and how soon a bug needs to be addressed by the development team. Both business and academic community have made an extensive investigation towards the proposal methods to automate the bug report severity prediction. Objective: This paper aims to provide a comprehensive mapping study review of recent research efforts on automatically bug report severity prediction. To the best of our knowledge, this is the first review to categorize quantitatively more than ten aspects of the experiments reported in several papers on bug report severity prediction. Method: The mapping study review was performed by searching four electronic databases. Studies published until December 2017 were considered. The initial resulting comprised of 54 papers. From this set, a total of 18 papers were selected. After performing snowballing, more nine papers were selected. Results: From the mapping study, we identified 27 studies addressing bug report severity prediction on Free/Libre Open Source Software. The gathered data confirm the relevance of this topic, reflects the scientific maturity of the research area, as well as, identify gaps, which can motivate new research initiatives. Conclusion: The message drawn from this review is that unstructured text features along with traditional machine learning algorithms and text mining methods have been playing a central role in the most proposed methods in literature to predict bug severity level. This scenario suggests that there is room for improving prediction results using state-of-the-art machine learning and text mining algorithms and techniques.
AIR<sub>5</sub>: Five Pillars of Artificial Intelligence Research

In this paper, we provide an overview of what we consider to be some of the most pressing research questions currently facing the fields of artificial and computational intelligence (AI and CI). While AI spans a range of methods that enable machines to learn from data and operate autonomously, CI serves as a means to this end by finding its niche in algorithms that are inspired by complex natural phenomena (including the working of the brain). In this paper, we demarcate the key issues surrounding these fields using five unique Rs, namely, rationalizability, resilience, reproducibility, realism, and responsibility. Notably, just as air serves as the basic element of biological life, the term AIR5-cumulatively referring to the five aforementioned Rs-is introduced herein to mark some of the basic elements of artificial life, for sustainable AI and CI. A brief summary of each of the Rs is presented, highlighting their relevance as pillars of future research in this arena.
Effects of dominant wolves in grey wolf optimization algorithm

Bio-inspired computation is one of the emerging soft computing techniques of the past decade. Although they do not guarantee optimality, the underlying reasons that make such algorithms become popular are indeed simplicity in implementation and being open to various improvements. Grey Wolf Optimizer (GWO), which derives inspiration from the hierarchical order and hunting behaviours of grey wolves in nature, is one of the new generation bio-inspired metaheuristics. GWO is first introduced to solve global optimization and mechanical design problems. Next, it has been applied to a variety of problems. As reported in numerous publications, GWO is shown to be a promising algorithm, however, the effects of characteristic mechanisms of GWO on solution quality has not been sufficiently discussed in the related literature. Accordingly, the present study analyses the effects of dominant wolves, which clearly have crucial effects on search capability of GWO and introduces new extensions, which are based on the variations of dominant wolves. In the first extension, three dominant wolves in GWO are evaluated first. Thus, an implicit local search without an additional computational cost is conducted at the beginning of each iteration. Only after repositioning of wolf council of higher-ranks, the rest of the pack is allowed to reposition. Secondarily, dominant wolves are exposed to learning curves so that the hierarchy amongst the leading wolves is established throughout generations. In the final modification, the procedures of the previous extensions are adopted simultaneously. The performances of all developed algorithms are tested on both constrained and unconstrained optimization problems including combinatorial problems such as uncapacitated facility location problem and 0-1 knapsack problem, which have numerous possible real-life applications. The proposed modifications are compared to the standard GWO, some other metaheuristic algorithms taken from the literature and Particle Swarm Optimization, which can be considered as a fundamental algorithm commonly employed in comparative studies. Finally, proposed algorithms are implemented on real-life cases of which the data are taken from the related publications. Statistically verified results point out significant improvements achieved by proposed modifications. In this regard, the results of the present study demonstrate that the dominant wolves have crucial effects on the performance of GWO.
Quantum correlations and quantum-memory-assisted entropic uncertainty relation in two kinds of spin squeezing models

We investigate thermal quantum correlations and the entropic uncertainty relation in the presence of quantum memory under two kinds of two-qubit spin squeezing models, namely the one-axis twisting model and two-axis countertwisting model. We provide the analytical expressions of quantum correlations and the entropic uncertainty relation in the ground state for these two models and we find a simple relationship between quantum discord and entropic uncertainty. It is found that there is a fully anti-correlated relationship between quantum correlations and entropic uncertainty. Also, the maximal quantum correlation and minimal uncertainty can be achieved when the spin squeezing parameters are large enough. Accordingly, these results may be significant in practical goals in which the maximum quantum correlation and minimum uncertainty are required such as in quantum key distribution and quantum computing.
A dual CNN–RNN for multiple people tracking

In this paper, we present a deep learning-based approach, namely a dual CNN–RNN for multiple people tracking. We follow tracking-by-detection paradigm by first training a CNN to measure the similarity of two detection boxes. To solve the data association (DA) problem, we build a graph with nodes as detections and edge costs that are the outputs of a CNN. The general minimum cost lifted multi-cut problem (LMP) and corresponding optimization algorithms are utilized to solve the DA problem. To tackle occlusion and ID-switch problems, an RNN network is proposed to predict the nonlinear motion of people. Moreover, we utilize target motion information to stitch tracklets and build long trajectories. The results of our experiments conducted on Multiple Object Tracking Benchmark 2016 (MOT2016) confirm the efficiency of the proposed algorithm.
Uncertainty-Aware Occupancy Map Prediction Using Generative Networks for Robot Navigation

Efficient exploration through unknown environments remains a challenging problem for robotic systems. In these situations, the robot's ability to reason about its future motion is often severely limited by sensor field of view (FOV). By contrast, biological systems routinely make decisions by taking into consideration what might exist beyond their FOV based on prior experience. We present an approach for predicting occupancy map representations of sensor data for future robot motions using deep neural networks. We develop a custom loss function used to make accurate prediction while emphasizing physical boundaries. We further study extensions to our neural network architecture to account for uncertainty and ambiguity inherent in mapping and exploration. Finally, we demonstrate a combined map prediction and information-theoretic exploration strategy using the variance of the generated hypotheses as the heuristic for efficient exploration of unknown environments.
Dynamic Channel: A Planning Framework for Crowd Navigation

Real-time navigation in dense human environments is a challenging problem in robotics. Most existing path planners fail to account for the dynamics of pedestrians because introducing time as an additional dimension in search space is computationally prohibitive. Alternatively, most local motion planners only address imminent collision avoidance and fail to offer long-term optimality. In this work, we present an approach, called Dynamic Channels, to solve this global to local quandary. Our method combines the high-level topological path planning with low-level motion planning into a complete pipeline. By formulating the path planning problem as graph-searching in the triangulation space, our planner is able to explicitly reason about the obstacle dynamics and capture the environmental change efficiently. We evaluate efficiency and performance of our approach on public pedestrian datasets and compare it to a state-of-the-art planning algorithm for dynamic obstacle avoidance. Completeness proofs are provided in the supplement at http://caochao.me/files/proof.pdf. An extended version of the paper is available on arXiv.
A novel data condition and performance hybrid imputation method for energy efficient operations of marine systems

Datasets with missing values can adversely affect the accuracy of any subsequent decision making, for instance in condition- and performance-monitoring for energy efficient operations of ship systems. Missing data imputation is therefore, a necessary step as it ensures that the data can reach their full knowledge-extracting potential. This paper aims at developing a novel hybrid imputation method, which can be employed to condition data acquired from marine machinery systems, thus increasing the quality of the original dataset and improving the decision making for ship efficient operations. The paper includes of all necessary imputation preparatory steps and further post-imputation processes. The developed method employs a hybrid knnk-NN and miceMICE imputation algorithm which combines data mining with first-principle knowledge. The proposed hybrid approach is compared with the individual performance of knn and mice algorithms and is implemented in a dataset acquired from the main engine system of an oceangoing vessel. It is shown that the hybrid approach performs best, exhibiting an average error of 2.2% compared to the knn and mice algorithms with errors 5.6% and 3.3%, respectively, highlighting that the small error of the proposed novel method improves the quality of data used in condition- and performance-monitoring.
Quantum Circuit Transformation Based on Simulated Annealing and Heuristic Search

Quantum algorithm design usually assumes access to a perfect quantum computer with ideal properties like full connectivity, noise-freedom and arbitrarily long coherence time. In Noisy Intermediate-Scale Quantum (NISQ) devices, however, the number of qubits is highly limited and quantum operation error and qubit coherence are not negligible. Besides, the connectivity of physical qubits in a quantum processing unit (QPU) is also strictly constrained. Thereby, additional operations like SWAP gates have to be inserted to satisfy this constraint while preserving the functionality of the original circuit. This process is known as quantum circuit transformation. Adding additional gates will increase both the size and depth of a quantum circuit and therefore cause further decay of the performance of a quantum circuit. Thus it is crucial to minimize the number of added gates. In this paper, we propose an efficient method to solve this problem. We first choose by using simulated annealing an initial mapping which fits well with the input circuit and then, with the help of a heuristic cost function, stepwise apply the best selected SWAP gates until all quantum gates in the circuit can be executed. Our algorithm runs in time polynomial in all parameters including the size and the qubit number of the input circuit, and the qubit number in the QPU. Its space complexity is quadratic to the number of edges in the QPU. Experimental results on extensive realistic circuits confirm that the proposed method is efficient and can reduce by 57% on average the size of the output circuits when compared with the state-of-the-art algorithm on the most recent IBM quantum device viz. IBM Q20 (Tokyo).
ParallelEye-CS: A New Dataset of Synthetic Images for Testing the Visual Intelligence of Intelligent Vehicles

Virtual simulation testing is becoming indispensable for the intelligence testing of intelligent vehicles. However, even the most advanced simulation software provides rather limited test conditions. In the long run, intelligent vehicles are expected to work at SAE (Society of Automotive Engineers) level 4 or level 5. Researchers should make full use of virtual simulation scenarios to test the visual intelligence algorithms of intelligent vehicles under various imaging conditions. In this paper, we create realistic artificial scenes to simulate the self-driving scenarios, and collect a dataset of synthetic images from the virtual driving scenes, named “ParallelEye-CS”. In the artificial scenes, we can flexibly change environmental conditions and automatically acquire accurate and diverse ground-truth labels. As a result, ParallelEye-CS has six ground-truth labels and includes twenty types of tests, which are divided into normal, environmental, and difficult tasks. Furthermore, we utilize ParallelEye-CS in combination with other publicly available datasets to conduct experiments for visual object detection. The experimental results indicate that: 1) object detection algorithms of intelligent vehicles can be tested under various scenario challenges; 2) mixed dataset can improve the accuracy of object detection algorithms, but domain shift is a serious issue worthy of attention.
Non-interactive zero knowledge proofs for the authentication of IoT devices in reduced connectivity environments

Current authentication protocols seek to establish authenticated sessions over insecure channels while maintaining a small footprint considering the energy consumption and computational overheads. Traditional authentication schemes must store a form of authentication data on the devices, putting this data at risk. Approaches based on purely public/private key infrastructure come with additional computation and maintenance costs. This work proposes a novel non-interactive zero-knowledge (NIZKP) authentication protocol that incorporates the limiting factors in IoT communication devices and sensors. Our protocol considers the inherent network instability and replaces the ZKP NP-hard problem using the Merkle tree structure for the creation of the authentication challenge. A series of simulations evaluate the performance of NIZKP against traditional ZKP approaches based on graph isomorphism. A set of performance metrics has been used, namely the channel rounds for client authentication, effects of the authentication processes, and the protocol interactions to determine areas of improvements. The simulation results indicate empirical evidence for the suitability of our NIKP approach for authentication purposes in resource-constrained IoT environments.
Face Detection Based on Viola-Jones Algorithm Applying Composite Features

Viola-Jones' face detection algorithm was jointly proposed by Paul Viola and Michael Jones. Although it realized face real-time detection to some extent, its false detection rate is not low. Because the block features in the Viola-Jones algorithm can't handle purely rigid objects, such as chopsticks and cups, so if there are rigid objects in the face image, Viola-Jones' face detection algorithm is prone to generate false detection of faces. In this paper, we propose to apply the composite features based on Viola-Jones algorithm to improve the above problems, and prove the feasibility of this method through experiments.
TextPlace: Visual Place Recognition and Topological Localization Through Reading Scene Texts

Visual place recognition is a fundamental problem for many vision based applications. Sparse feature and deep learning based methods have been successful and dominant over the decade. However, most of them do not explicitly leverage high-level semantic information to deal with challenging scenarios where they may fail. This paper proposes a novel visual place recognition algorithm, termed TextPlace, based on scene texts in the wild. Since scene texts are high-level information invariant to illumination changes and very distinct for different places when considering spatial correlation, it is beneficial for visual place recognition tasks under extreme appearance changes and perceptual aliasing. It also takes spatial-temporal dependence between scene texts into account for topological localization. Extensive experiments show that TextPlace achieves state-of-the-art performance, verifying the effectiveness of using high-level scene texts for robust visual place recognition in urban areas.
Research on trend prediction of internet user intention understanding and public intelligence mining based on fractional differential method

With the rapid development of Internet technology, network users and data are also rapidly accumulating, which leads to significant asymmetry between user needs and massive information. Therefore, how to perceive user intentions and provide users with personalized and accurate information becomes an important research topic; at the same time, the Internet has become another important source of Public Intelligence, widely used in political, economic, military and other fields, people need to explore a new Internet-based public intelligence collection and processing technology. Based on fractional order differential, a new model for predicting user intentions and Public Intelligence mining trends is constructed. The order of the fractional differential equation model is identified by the optimization algorithm. The second and the parameters give the analytical solution of the model, and the resulting prediction equation can be used as a new method for Internet user intention and public intelligence mining prediction. The verification results of real experimental data show that the method can identify the model parameters more accurately, and the obtained fractional differential equation model is more consistent with the actual data.