Reviewerly/exHarmony huggingface dataset:
Restoration of Video Frames from a Single Blurred Image with Motion Understanding

We propose a novel framework to generate clean video frames from a single motion-blurred image. While a broad range of literature focuses on recovering a single image from a blurred image, in this work, we tackle a more challenging task i.e. video restoration from a blurred image. We formulate video restoration from a single blurred image as an inverse problem by setting clean image sequence and their respective motion as latent factors, and the blurred image as an observation. Our framework is based on an encoder-decoder structure with spatial transformer network modules to restore a video sequence and its underlying motion in an end-to-end manner. We design a loss function and regularizers with complementary properties to stabilize the training and analyze variant models of the proposed network. The effectiveness and transferability of our network are highlighted through a large set of experiments on two different types of datasets: camera rotation blurs generated from panorama scenes and dynamic motion blurs in high speed videos.
Research of Construction and Practice of Blended "Golden Course" Teaching Based on C language programming

With the development of programming language, there are many software development tools, which fully show the characteristics of programming language. C language programming is a professional basic course, which focuses on cultivating students' programming ability and algorithm analysis ability, and lays a solid foundation for the subsequent courses. The blended "golden course" teaching mode integrates the advantages of online and offline teaching. The design of blended teaching mode consists of four parts: curriculum analysis, curriculum design, curriculum implementation and curriculum evaluation. The online platform provides MOOCS videos, online tests, development materials, etc. it can also release assignments, in class exercises, rush answers, group tasks, etc. through the platform and app, which can not only make students more flexible to master their own learning time, but also make students more actively participate in the course learning. Teachers can urge students to study by themselves, and the feedback homework can generate dynamic charts to help teachers understand students' learning effect more intuitively. After a semester of mixed teaching experience, we can make better use of online teaching platform, develop strengths and avoid weaknesses, make online and offline complement each other, and improve the quality of teaching and learning.
Fisher’s z Distribution-Based Mixture Autoregressive Model

We generalize the Gaussian Mixture Autoregressive (GMAR) model to the Fisher’s z Mixture Autoregressive (ZMAR) model for modeling nonlinear time series. The model consists of a mixture of K-component Fisher’s z autoregressive models with the mixing proportions changing over time. This model can capture time series with both heteroskedasticity and multimodal conditional distribution, using Fisher’s z distribution as an innovation in the MAR model. The ZMAR model is classified as nonlinearity in the level (or mode) model because the mode of the Fisher’s z distribution is stable in its location parameter, whether symmetric or asymmetric. Using the Markov Chain Monte Carlo (MCMC) algorithm, e.g., the No-U-Turn Sampler (NUTS), we conducted a simulation study to investigate the model performance compared to the GMAR model and Student t Mixture Autoregressive (TMAR) model. The models are applied to the daily IBM stock prices and the monthly Brent crude oil prices. The results show that the proposed model outperforms the existing ones, as indicated by the Pareto-Smoothed Important Sampling Leave-One-Out cross-validation (PSIS-LOO) minimum criterion.
Pareidolia Face Reenactment

We present a new application direction named Pareidolia Face Reenactment, which is defined as animating a static illusory face to move in tandem with a human face in the video. For the large differences between pareidolia face reenactment and traditional human face reenactment, two main challenges are introduced, i.e., shape variance and texture variance. In this work, we propose a novel Parametric Unsupervised Reenactment Algorithm to tackle these two challenges. Specifically, we propose to decompose the reenactment into three catenate processes: shape modeling, motion transfer and texture synthesis. With the decomposition, we introduce three crucial components, i.e., Parametric Shape Modeling, Expansionary Motion Transfer and Unsupervised Texture Synthesizer, to overcome the problems brought by the remarkably variances on pareidolia faces. Extensive experiments show the superior performance of our method both qualitatively and quantitatively. Code, model and data are available on our project page <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Energy-efficient Collaborative Sensing: Learning the Latent Correlations of Heterogeneous Sensors

With the proliferation of Internet of Things (IoT) devices in the consumer market, the unprecedented sensing capability of IoT devices makes it possible to develop advanced sensing and complex inference tasks by leveraging heterogeneous sensors embedded in IoT devices. However, the limited power supply and the restricted computation capability make it challenging to conduct seamless sensing and continuous inference tasks on resource-constrained devices. How to conduct energy-efficient sensing and perform rich-sensor inference tasks on IoT devices is crucial for the success of IoT applications. Therefore, we propose a novel energy-efficient collaborative sensing framework to optimize the energy consumption of IoT devices. Specifically, we explore the latent correlations among heterogeneous sensors via an attention mechanism in temporal convolutional network to quantify the dependency among sensors, and characterize the heterogeneous sensors in terms of energy consumption to categorize them into low-power sensors and energy-intensive sensors . Finally, to decrease the sampling frequency of energy-intensive sensors , we propose a multi-task learning strategy to predict the statuses of energy-intensive sensors based on the low-power sensors . To evaluate the performance of the proposed collaborative sensing framework, we develop a mobile application to collect concurrent heterogeneous data streams from all sensors embedded in Huawei Mate 8. The experimental results show that latent correlation learning is greatly helpful to understand the latent correlations among heterogeneous streams, and it is feasible to predict the statuses of energy-intensive sensors by low-power sensors with high accuracy and fast convergence. In terms of energy consumption, the proposed collaborative sensing framework is able to preserve the energy consumption of IoT devices by nearly 50% for continuous data acquisition tasks.
The terrorism-finance nexus contingent on globalisation and governance dynamics in Africa

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A Flexible Research-Oriented Framework for Distributed Training of Deep Neural Networks

We present PyDTNN, a framework for training deep neural networks (DNNs) on clusters of computers that has been designed as a research-oriented tool with a low learning curve. Our parallel training framework offers a set of functionalities that cover several must-have features for advanced deep learning (DL) software: 1) it is developed in Python in order to expose an accessible entry point for the newcomer; 2) it is extensible, allowing users to prototype new research ideas without requiring them to deal with complex software-stacks; and 3) it delivers high parallel performance, exploiting MPI via mpi4py/NCCL for communication; and NumPy, cuDNN, and cuBLAS for computation.This paper provides practical evidence that PyDTNN attains similar accuracy and parallel performance to those exhibited by Google&#x2019;s TensorFlow (TF), though we recognize that PyDTNN cannot compete with a production-level framework such as TF or PyTorch in terms of maturity and functionality. Instead, PyDTNN is designed as an accessible and customizable tool for prototyping ideas related to distributed training of DNN models on clusters.
WedgeChain: A Trusted Edge-Cloud Store With Asynchronous (Lazy) Trust

We propose WedgeChain, a data store that spans both edge and cloud nodes (an edge-cloud system). WedgeChain consists of a logging layer and a data indexing layer. In this study, we encounter two challenges: (1) edge nodes are untrusted and potentially malicious, and (2) edge-cloud coordination is expensive. WedgeChain tackles these challenges by the following proposals: (1) Lazy (asynchronous) certification: where data is committed at the untrusted edge and then lazily certified at the cloud node. This lazy certification method takes advantage of the observation that an untrusted edge node is unlikely to act maliciously if it knows it will be detected (and punished) eventually. Our lazy certification method guarantees that malicious acts (i.e., lying) are eventually detected. (2) Data-free certification: our lazy certification method only needs to send digests of data to the cloud-instead of sending all data to the cloud-which enables saving network and cloud resources and reduce costs. (3) LSMerkle: we extend a trusted index (mLSM [32]) to enable indexing data at the edge while utilizing lazy and data-free certification.
Comparing different metrics on an anisotropic depth completion model

This paper discussed an anisotropic interpolation model that filling in-depth data in a largely empty region of a depth map. We consider an image with an anisotropic metric gi⁢j that incorporates spatial and photometric data. We propose a numerical implementation of our model based on the “eikonal” operator, which compute the solution of a degenerated partial differential equation (the biased Infinity Laplacian or biased Absolutely Minimizing Lipschitz Extension). This equation’s solution creates exponential cones based on the available data, extending the available depth data and completing the depth map image. Because of this, this operator is better suited to interpolate smooth surfaces. To perform this task, we assume we have at our disposal a reference color image and a depth map. We carried out an experimental comparison of the AMLE and bAMLE using various metrics with square root, absolute value, and quadratic terms. In these experiments, considered color spaces were sRGB, XYZ, CIE-L*⁢a*⁢b*, and CMY. In this document, we also presented a proposal to extend the AMLE and bAMLE to the time domain. Finally, in the parameter estimation of the model, we compared EHO and PSO. The combination of sRGB and square root metric produces the best results, demonstrating that our bAMLE model outperforms the AMLE model and other contemporary models in the KITTI depth completion suite dataset. This type of model, such as AMLE and bAMLE, is simple to implement and represents a low-cost implementation option for similar applications.
Machine Learning Algorithms for Regression Analysis and Predictions of Numerical Data

Machine learning has become extremely popular in recent years due to its ability to train models to deal with complex task. Machine learning (ML) algorithms are one of the fundamentals behind Artificial Intelligence (AI), which is now widely spread among different areas of our lives. The success of the machine-learning algorithm very depends on the training datasets. In order to achieve good accuracy ML algorithms must be trained with well-prepared input datasets. Data preparation is a set of procedures that helps make the dataset more suitable for machine learning. The goal of the paper is to summarize different techniques for data preparation and to make analysis which of them directly affect the accuracy of the final model. Different ML algorithms are considers and tested for training a model to predict numerical variables which is not based on neural networks.
An L0-regularized global anisotropic gradient prior for single-image de-raining

We propose a method based on a global anisotropic gradient prior (GAGP) for addressing the problem of rain streak removal. We observe that both images and rain streaks have anisotropy at edges, and that the rain distribution in images is sparse. We therefore propose an L0-regularized sparse term with an L0-regularized GAGP term to efficiently detect the rain streaks. We applied the L0-regularized GAGP as the image regularization term to protect the image structure and details. We also developed an alternating half-quadratic algorithm to solve the proposed L0 optimization model by introducing the variable splitting method and analyzing the convergence of the algorithm. Experimental results show that the proposed method outperforms state-of-the-art methods in rain streak removal and preserving image structure on both synthesized and real-world images with different levels and types of rain.
Bit-Complexity of Solving Systems of Linear Evolutionary Partial Differential Equations

Finite Elements are a common method for solving differential equations via discretization. Under suitable hypotheses, the solution $$\mathbf {u}=\mathbf {u}(t,\vec x)$$ of a well-posed initial/boundary-value problem for a linear evolutionary system of PDEs is approximated up to absolute error $$1/2^n$$ by repeatedly (exponentially often in n) multiplying a matrix $$\mathbf {A}_n$$ to the vector from the previous time step, starting with the initial condition $$\mathbf {u}(0)$$ , approximated by the spatial grid vector $$\mathbf {u}(0)_n$$ . The dimension of the matrix $$A_n$$ is exponential in n, which is the number of the bits of the output. We investigate the bit-cost of computing exponential powers and inner products $$\mathbf {A}_n^K\cdot \mathbf {u}(0)_n$$ , $$K\sim 2^{\mathcal {O}(n)}$$ , of matrices and vectors of exponential dimension for various classes of such difference schemes $$\mathbf {A}_n$$ . Non-uniformly fixing any polynomial-time computable initial condition and focusing on single but arbitrary entries (instead of the entire vector/matrix) allows to improve naïve exponential sequential runtime EXP: Closer inspection shows that, given any time $$0\le t\le 1$$ and space $$\vec x\in [0;1]^d$$ , the computational cost of evaluating the solution $$\mathbf {u}(t,\vec x)$$ corresponds to the discrete class PSPACE. Many partial differential equations, including the Heat Equation, admit difference schemes that are (tensor products of constantly many) circulant matrices of constant bandwidth; and for these we show exponential matrix powering, and PDE solution computable in #P. This is achieved by calculating individual coefficients of the matrix’ multivariate companion polynomial’s powers using Cauchy’s Differentiation Theorem; and shown optimal for the Heat Equation. Exponentially powering twoband circulant matrices is established even feasible in P; and under additional conditions, also the solution to certain linear PDEs becomes computable in P.
Joint Sparse Locality-Aware Regression for Robust Discriminative Learning

With the dramatic increase of dimensions in the data representation, extracting latent low-dimensional features becomes of the utmost importance for efficient classification. Aiming at the problems of weakly discriminating marginal representation and difficulty in revealing the data manifold structure in most of the existing linear discriminant methods, we propose a more powerful discriminant feature extraction framework, namely, joint sparse locality-aware regression (JSLAR). In our model, we formulate a new strategy induced by the nonsquared L2 norm for enhancing the local intraclass compactness of the data manifold, which can achieve the joint learning of the locality-aware graph structure and the desirable projection matrix. Besides, we formulate a weighted retargeted regression to perform the marginal representation learning adaptively instead of using the general average interclass margin. To alleviate the disturbance of outliers and prevent overfitting, we measure the regression term and locality-aware term together with the regularization term by forcing the row sparsity with the joint L2,1 norms. Then, we derive an effective iterative algorithm for solving the proposed model. The experimental results over a range of benchmark databases demonstrate that the proposed JSLAR outperforms some state-of-the-art approaches.
Factor Graphs for Heterogeneous Bayesian Decentralized Data Fusion

This paper explores the use of factor graphs as an inference and analysis tool for Bayesian peer-to-peer decentralized data fusion. We propose a framework by which agents can each use local factor graphs to represent relevant partitions of a complex global joint probability distribution, thus allowing them to avoid reasoning over the entirety of a more complex model and saving communication as well as computation cost. This allows heterogeneous multi-robot systems to cooperate on a variety of real world, task oriented missions, where scalability and modularity are key. To develop the initial theory and analyze the limits of this approach, we focus our attention on static linear Gaussian systems in tree-structured networks and use Channel Filters (also represented by factor graphs) to explicitly track common information. We discuss how this representation can be used to describe various multi-robot applications and to design and analyze new heterogeneous data fusion algorithms. We validate our method in simulations of a multi-agent multi-target tracking and cooperative multi-agent mapping problems, and discuss the computation and communication gains of this approach.
Page-Level Main Content Extraction From Heterogeneous Webpages

The main content of a webpage is often surrounded by other boilerplate elements related to the template, such as menus, advertisements, copyright notices, and comments. For crawlers and indexers, isolating the main content from the template and other noisy information is an essential task, because processing and storing noisy information produce a waste of resources such as bandwidth, storage space, and computing time. Besides, the detection and extraction of the main content is useful in different areas, such as data mining, web summarization, and content adaptation to low resolutions. This work introduces a new technique for main content extraction. In contrast to most techniques, this technique not only extracts text, but also other types of content, such as images, and animations. It is a Document Object Model-based page-level technique, thus it only needs to load one single webpage to extract the main content. As a consequence, it is efficient enough as to be used online (in real-time). We have empirically evaluated the technique using a suite of real heterogeneous benchmarks producing very good results compared with other well-known content extraction techniques.
DebtHunter: A Machine Learning-based Approach for Detecting Self-Admitted Technical Debt

Due to limited time, budget or resources, a team is prone to introduce code that does not follow the best software development practices. This code that introduces instability in the software projects is known as Technical Debt (TD). Often, TD intentionally manifests in source code, which is known as Self-Admitted Technical Debt (SATD). This paper presents DebtHunter, a natural language processing (NLP)- and machine learning (ML)- based approach for identifying and classifying SATD in source code comments. The proposed classification approach combines two classification phases for differentiating between the multiple debt types. Evaluations over 10 open source systems, containing more than 259k comments, showed that the approach was able to improve the performance of others in the literature. The presented approach is supported by a tool that can help developers to effectively manage SATD. The tool complements the analysis over Java source code by allowing developers to also examine the associated issue tracker. DebtHunter can be used in a continuous evolution environment to monitor the development process and make developers aware of how and where SATD is introduced, thus helping them to manage and resolve it.
Data Cooperatives for Neighborhood Watch

The increasing proliferation of user data is moving the world from the era of 'big data' to a new era of shared data, and some are considering data as a factor of production that is paving the way for a new business economy. In this paper, we propose a solution that uses blockchain technology as a platform for online neighborhood watch using a form of data cooperative among individuals or organizations in the sharing of data through a peer-to-peer mechanism. We prove the concept by implementing a distributed phishing data sharing system that will maintain a community ledger of reported phishing activities with a consensus-based approval of the phishing transaction and a novel reputation scoring system thereby adding reliability to the system and effectively tackling the phishing problem. The data cooperative provides a way for timely multi-party sharing of phishing data among anti-phishing organizations and users of the internet eliminating the current approach of each organization maintaining its database. Our results show that blockchain is effective in complementing the existing methods of phishing detection and serves as a platform for sharing phishing data with respect to scalability, cost, and memory consumption. Also, our results further show that transaction times on the Ropsten test net follow a Gamma distribution. Our approach can be extrapolated to other data sharing systems like medical data, spam calls, discussion forums, etc.
Applying Deep Learning to Autonomous Vehicles: A Survey

Autonomous vehicles will revolutionize the future automotive industry by reducing or eliminating human errors and increasing vehicle safety. The design of autonomous vehicles will bring together many modern technologies such as control, computer vision, path planning, sensor fusion and fault diagnosis. Although traditional algorithms can be used to implement these technologies, in the last decade, deep learning has been proposed to challenge traditional algorithms and excels in many applications and research areas. Deep learning has shown its significant use in the design and operation of autonomous vehicles. In this paper, we will review the application of deep learning in five major research areas of autonomous vehicles i.e. path planning, control, computer vision, sensor fusion and fault diagnosis. Recent advances of the deep learning in autonomous vehicles will be reviewed and discussed.
An Influence-Based Approach for Root Cause Alarm Discovery in Telecom Networks

Alarm root cause analysis is a significant component in the day-to-day telecommunication network maintenance, and it is critical for efficient and accurate fault localization and failure recovery. In practice, accurate and self-adjustable alarm root cause analysis is a great challenge due to network complexity and vast amounts of alarms. A popular approach for failure root cause identification is to construct a graph with approximate edges, commonly based on either event co-occurrences or conditional independence tests. However, considerable expert knowledge is typically required for edge pruning. We propose a novel data-driven framework for root cause alarm localization, combining both causal inference and network embedding techniques. In this framework, we design a hybrid causal graph learning method (HPCI), which combines Hawkes Process with Conditional Independence tests, as well as propose a novel Causal Propagation-Based Embedding algorithm (CPBE) to infer edge weights. We subsequently discover root cause alarms in a real-time data stream by applying an influence maximization algorithm on the weighted graph. We evaluate our method on artificial data and real-world telecom data, showing a significant improvement over the best baselines.