Reviewerly/exHarmony huggingface dataset:
Light Field Reconstruction Using Convolutional Network on EPI and Extended Applications

In this paper, a novel convolutional neural network (CNN)-based framework is developed for light field reconstruction from a sparse set of views. We indicate that the reconstruction can be efficiently modeled as angular restoration on an epipolar plane image (EPI). The main problem in direct reconstruction on the EPI involves an information asymmetry between the spatial and angular dimensions, where the detailed portion in the angular dimensions is damaged by undersampling. Directly upsampling or super-resolving the light field in the angular dimensions causes ghosting effects. To suppress these ghosting effects, we contribute a novel "blur-restoration-deblur" framework. First, the "blur" step is applied to extract the low-frequency components of the light field in the spatial dimensions by convolving each EPI slice with a selected blur kernel. Then, the "restoration" step is implemented by a CNN, which is trained to restore the angular details of the EPI. Finally, we use a non-blind "deblur" operation to recover the spatial high frequencies suppressed by the EPI blur. We evaluate our approach on several datasets, including synthetic scenes, real-world scenes and challenging microscope light field data. We demonstrate the high performance and robustness of the proposed framework compared with state-of-the-art algorithms. We further show extended applications, including depth enhancement and interpolation for unstructured input. More importantly, a novel rendering approach is presented by combining the proposed framework and depth information to handle large disparities.
Delay and Communication Tradeoffs for Blockchain Systems With Lightweight IoT Clients

The emerging blockchain protocols provide a decentralized architecture that is suitable of supporting Internet of Things (IoT) interactions. However, keeping a local copy of the blockchain ledger is infeasible for low-power and memory-constrained devices. For this reason, they are equipped with lightweight software implementations that only download the useful data structures, e.g., state of accounts, from the blockchain network, when they are updated. In this paper, we consider and analyze a novel scheme, implemented by the nodes of the blockchain network, which aggregates the blockchain data in periodic updates and further reduces the communication cost of the connected IoT devices. We show that the aggregation period should be selected based on the channel quality, the offered rate, and the statistics of updates of the useful data structures. The results, obtained for the Ethereum protocol, illustrate the benefits of the aggregation scheme in terms of a reduced duty cycle of the device, particularly for low signal-to-noise ratios, and the overall reduction of the amount of information transmitted in downlink from the wireless base station to the IoT device. A potential application of the proposed scheme is to let the IoT device request more information than actually needed, hence increasing its privacy, while keeping the communication cost constant. In the conclusion, this paper is the first to provide rigorous guidelines for the design of lightweight blockchain protocols with wireless connectivity.
An Economic Analysis of Privacy Protection and Statistical Accuracy as Social Choices

Statistical agencies face a dual mandate to publish accurate statistics while protecting respondent privacy. Increasing privacy protection requires decreased accuracy. Recognizing this as a resource allocation problem, we propose an economic solution: operate where the marginal cost of increasing privacy equals the marginal benefit. Our model of production, from computer science, assumes data are published using an efficient differentially private algorithm. Optimal choice weighs the demand for accurate statistics against the demand for privacy. Examples from U.S.\ statistical programs show how our framework can guide decision-making. Further progress requires a better understanding of willingness-to-pay for privacy and statistical accuracy.
An Ant Colony System for energy-efficient dynamic Virtual Machine Placement in data centers

Data centers are fundamental infrastructure for information technology and cloud services; however, their very high rates of energy consumption are a problem. The placement of Virtual Machines (VMs) to Physical Machines (PMs) in virtualized environments has a significant impact on the energy consumption of a data center. This is an NP-hard problem, for which an optimal solution is not practicable even for a small-scale data center. In this paper, we formulate placement of VMs to PMs in a data center as a constrained combinatorial optimization problem and make use of the information from PM and VM profiles to minimize the total energy consumption of all active PMs. An Ant Colony System (ACS) embedded with new heuristics is presented for an energy-efficient solution to the optimization problem. To demonstrate the effectiveness of the ACS, simulation experiments are conducted on small-, medium- and large-scale data centers. The results from our ACS are compared with two existing ACS methods as well as the widely used First-Fit-Decreasing (FFD) algorithm. Our ACS is shown to outperform the two existing ACS methods and FFD in energy performance for all small-, medium- and large-scale test problems. Our ACS also exhibits good scalability with the increase in the problem size.
High-capacity reversible data hiding in encrypted images based on extended run-length coding and block-based MSB plane rearrangement

In this paper, we propose a novel reversible data hiding method in encrypted images. The proposed method takes full advantage of the spatial correlation in the original images to vacate room for embedding data before image encryption. By jointly using an extended run-length coding and a block-based most significant bit (MSB) plane rearrangement mechanism, the MSB planes of images can be compressed efficiently to generate room for high-capacity embedding. The receiver can extract data directly from encrypted images with only the data hiding key, and the original image or the high-quality plain image that contains secret data can be recovered with only the encryption key. The experimental results prove that the proposed method can reach a high embedding rate and a high PSNR.
A survey of variational and CNN-based optical flow techniques

Dense motion estimations obtained from optical flow techniques play a significant role in many image processing and computer vision tasks. Remarkable progress has been made in both theory and its application in practice. In this paper, we provide a systematic review of recent optical flow techniques with a focus on the variational method and approaches based on Convolutional Neural Networks (CNNs). These two categories have led to state-of-the-art performance. We discuss recent modifications and extensions of the original model, and highlight remaining challenges. For the first time, we provide an overview of recent CNN-based optical flow methods and discuss their potential and current limitations.
A novel intelligent classification model for breast cancer diagnosis

Breast cancer is one of the leading causes of death among women worldwide. Accurate and early detection of breast cancer can ensure long-term surviving for the patients. However, traditional classification algorithms usually aim only to maximize the classification accuracy, failing to take into consideration the misclassification costs between different categories. Furthermore, the costs associated with missing a cancer case (false negative) are clearly much higher than those of mislabeling a benign one (false positive). To overcome this drawback and further improving the classification accuracy of the breast cancer diagnosis, in this work, a novel breast cancer intelligent diagnosis approach has been proposed, which employed information gain directed simulated annealing genetic algorithm wrapper (IGSAGAW) for feature selection, in this process, we performs the ranking of features according to IG algorithm, and extracting the top m optimal feature utilized the cost sensitive support vector machine (CSSVM) learning algorithm. Our proposed feature selection approach which can not only help to reduce the complexity of SAGASW algorithm and effectively extracting the optimal feature subset to a certain extent, but it can also obtain the maximum classification accuracy and minimum misclassification cost. The efficacy of our proposed approach is tested on Wisconsin Original Breast Cancer (WBC) and Wisconsin Diagnostic Breast Cancer (WDBC) breast cancer data sets, and the results demonstrate that our proposed hybrid algorithm outperforms other comparison methods. The main objective of this study was to apply our research in real clinical diagnostic system and thereby assist clinical physicians in making correct and effective decisions in the future. Moreover our proposed method could also be applied to other illness diagnosis.
Demographic Effects in Facial Recognition and Their Dependence on Image Acquisition: An Evaluation of Eleven Commercial Systems

We examined the effect of demographic factors on the performance of 11 commercial face biometric systems tested as part of the 2018 U.S. Department of Homeland Security, Science and Technology Directorate biometric technology rally. Each system that participated in this evaluation was tasked with acquiring face images from a diverse population of 363 subjects in a controlled environment. Biometric performance was assessed using a systematic, repeatable test process measuring both efficiency (transaction times), and accuracy (mated similarity scores) using a leading commercial algorithm. Prior works have documented differences in biometric algorithm performance across demographic categories and proposed that skin phenotypes offer a superior explanation for these differences. To test this concept, we developed an automatic method for measuring relative facial skin reflectance using subjects' enrollment images and quantified the effect of this metric and other demographic covariates on performance using linear modeling. Both the efficiency and accuracy of the tested acquisition systems were significantly affected by multiple demographic covariates, including skin reflectance, gender, age, eyewear, and height. Skin reflectance had the strongest net linear effect on performance. Linear modeling showed that lower (darker) skin reflectance was associated with lower efficiency (higher transaction times) and accuracy (lower mated similarity scores). Skin reflectance was also a statistically better predictor of these effects than self-identified race labels. Unlike other covariates, the degree to which skin reflectance altered accuracy varied between systems. We show that the size of this skin reflectance effect was inversely related to the overall accuracy of the system such that the effect was almost negligible for the system with the highest overall accuracy. These results suggest that, in evaluations of biometric accuracy, the magnitude of measured demographic effects depends on image acquisition.
Market-sensing capability, knowledge creation and innovation: The moderating role of entrepreneurial-orientation

This article examines the nexus between market-sensing capability, knowledge creation, strategic entrepreneurial-orientation and innovation in small and medium sized enterprises (SME). Data was garnered from (n = 255) SMEs operating in Jordan and a covariance-based structural equation modelling (CB-SEM) was applied to analyze the data. SEM result illustrates a positive effect of market-sensing capability on (1) knowledge creation, and (2) firm innovation. Knowledge creation process has positive effect on (3) firm innovation. Knowledge creation process (4) mediated the link between market-sensing capability and firm innovation. Strategic entrepreneurial-orientation moderates the link between (5) knowledge creation and firm innovation, such that the positive relationship became weaker when strategic entrepreneurial-orientation is high. Theoretically and empirically this study has contributed to the existing inconsistent findings, and also offer useful managerial insights.
Improved Symmetric and Nonnegative Matrix Factorization Models for Undirected, Sparse and Large-Scaled Networks: A Triple Factorization-Based Approach

Undirected, sparse and large-scaled networks existing ubiquitously in practical engineering are vitally important since they usually contain rich information in various patterns. Matrix factorization (MF) technique is an efficient method to extract the useful latent factors (LFs) from the LF model, which directly gives rise to the so-called MF model. However, most MF models cannot maintain some frequently encountered constraints such as nonnegativity of LFs and the symmetry of the target network. In addition, in spite of its potential capability of obtaining the effectiveness of both the computation and the storage, the currently developed double factorization (DF)-based model still suffers from the problem of the low prediction accuracy due to the limited amount of LFs. To address the above problems, a novel MF model is proposed in terms of the triple-factorization (TF) technique, thereby leading to TF-based symmetric and nonnegative latent factor (SNLF) models. Compared with the traditional DF-based SNLF model, the proposed TF-based SNLF model is equipped with: 1) constraints on symmetry and nonnegativity; 2) desirable performance with high accuracy; 3) the convergence of the algorithm; and 4) fairly low storage and computational complexity. Furthermore, in order to reduce overfitting so as to further improve the model performance, regularization is precisely considered into the proposed TF-based SNLF model. Experiments on real datasets show that the proposed TF-based SNLF model has a whelming ability of improving the estimation accuracy for the missing data as well as guaranteeing the symmetry of the target network and the nonnegativity of LFs at a little expense of the computation and storage burden. Moreover, it is easy to be implemented for the data analysis.
An execution model for serverless functions at the edge

Serverless computing platforms allow developers to host single-purpose applications that automatically scale with demand. In contrast to traditional long-running applications on dedicated, virtualized, or container-based platforms, serverless applications are intended to be instantiated when called, execute a single function, and shut down when finished. State-of-the-art serverless platforms achieve these goals by creating a new container instance to host a function when it is called and destroying the container when it completes. This design allows for cost and resource savings when hosting simple applications, such as those supporting IoT devices at the edge of the network. However, the use of containers introduces some overhead which may be unsuitable for applications requiring low-latency response or hardware platforms with limited resources, such as those served by edge computing environments. In this paper, we present a nomenclature for characterizing server-less function access patterns which allows us to derive the basic requirements of a serverless computing runtime. We then propose the use of WebAssembly as an alternative method for running serverless applications while meeting these requirements. Finally, we demonstrate how a WebAssembly-based serverless platform provides many of the same isolation and performance guarantees of container-based platforms while reducing average application start times and the resources needed to host them.
A User-Centric Data Protection Method for Cloud Storage Based on Invertible DWT

Protection on end users’ data stored in Cloud servers becomes an important issue in today’s Cloud environments. In this paper, we present a novel data protection method combining Selective Encryption (SE) concept with fragmentation and dispersion on storage. Our method is based on the invertible Discrete Wavelet Transform (DWT) to divide agnostic data into three fragments with three different levels of protection. Then, these three fragments can be dispersed over different storage areas with different levels of trustworthiness to protect end users’ data by resisting possible leaks in Clouds. Thus, our method optimizes the storage cost by saving expensive, private, and secure storage spaces and utilizing cheap but low trustworthy storage space. We have intensive security analysis performed to verify the high protection level of our method. Additionally, the efficiency is proved by implementation of deploying tasks between CPU and General Purpose Graphic Processing Unit (GPGPU) in an optimized manner.
Content based image retrieval using image features information fusion

Recent image retrieval techniques are focusing on multiple image features for the efficient image retrieval. It has been an inevitable requirement to fetch the images from a variety of semantic groups and datasets. It is vital to retrieve the images based on their primitive features shape, texture, color and spatial information to cater the versatile image datasets. State-of-the-art detectors and descriptors are capable of finding the interest points based on their specialty. To encompass the strength of the image features for the information fusion purpose this contribution presents a novel technique to fuse the spatial color information with shaped extracted features and object recognition. For RGB channels L2 spatial color arrangements are applied and features are extracted, thereby fused with intensity ranged shapes formed by connecting the discovered edges and corners for the grey level image. Perifoveal receptive field estimation with 128-bit cascade matching with symmetric sampling on the detected interest points that discovers the potential information for the complex, overlay, foreground and background objects. Firstly the process is accomplished by reducing the massive features vectors, selecting high variance coefficient and secondly obtaining the indexing and retrieval by employing a Bag-of-Words approach. Extensive experiments are conducted on ten highly recognized image dataset benchmarks, specialized for texture, shapes, colors and objects including ImageNet, Caltech-256, Caltech-101, 102-Flower, Corel-10,000, 17-Flower, Corel-1000, COIL, ALOT and FTVL tropical fruits. To check the affectivity and robustness of the proposed method, it is compared with state-of-the-art detectors and descriptors SIFT, SURF, HOG, LBP, DoG, MSER and RGBLBP. Encouraging results reported that the proposed method has a remarkable performance in most of the image categories of versatile image datasets and can gain better precision to those of the state-of-the-art detectors and descriptors.
Fast Spatially-Varying Indoor Lighting Estimation

We propose a real-time method to estimate spatially-varying indoor lighting from a single RGB image. Given an image and a 2D location in that image, our CNN estimates a 5th order spherical harmonic representation of the lighting at the given location in less than 20ms on a laptop mobile graphics card. While existing approaches estimate a single, global lighting representation or require depth as input, our method reasons about local lighting without requiring any geometry information. We demonstrate, through quantitative experiments including a user study, that our results achieve lower lighting estimation errors and are preferred by users over the state-of-the-art. Our approach can be used directly for augmented reality applications, where a virtual object is relit realistically at any position in the scene in real-time.
Trajectory Planning for Autonomous High-Speed Overtaking in Structured Environments Using Robust MPC

Automated vehicles are increasingly getting main-streamed and this has pushed development of systems for autonomous manoeuvring (e.g., lane-change, merge, and overtake) to the forefront. A novel framework for situational awareness and trajectory planning to perform autonomous overtaking in high-speed structured environments (e.g., highway and motorway) is presented in this paper. A combination of a potential field like function and reachability sets of a vehicle are used to identify safe zones on a road that the vehicle can navigate towards. These safe zones are provided to a tube-based robust model predictive controller as reference to generate feasible trajectories for combined lateral and longitudinal motion of a vehicle. The strengths of the proposed framework are: 1) it is free from non-convex collision avoidance constraints; 2) it ensures feasibility of trajectory even if decelerating or accelerating while performing lateral motion; and 3) it is real-time implementable. The ability of the proposed framework to plan feasible trajectories for high-speed overtaking is validated in a high-fidelity IPG CarMaker and Simulink co-simulation environment.
Software Documentation Issues Unveiled

(Good) Software documentation provides developers and users with a description of what a software system does, how it operates, and how it should be used. For example, technical documentation (e.g., an API reference guide) aids developers during evolution/maintenance activities, while a user manual explains how users are to interact with a system. Despite its intrinsic value, the creation and the maintenance of documentation is often neglected, negatively impacting its quality and usefulness, ultimately leading to a generally unfavourable take on documentation. Previous studies investigating documentation issues have been based on surveying developers, which naturally leads to a somewhat biased view of problems affecting documentation. We present a large scale empirical study, where we mined, analyzed, and categorized 878 documentation-related artifacts stemming from four different sources, namely mailing lists, Stack Overflow discussions, issue repositories, and pull requests. The result is a detailed taxonomy of documentation issues from which we infer a series of actionable proposals both for researchers and practitioners.
Residual Regression With Semantic Prior for Crowd Counting

Crowd counting is a challenging task due to factors such as large variations in crowdedness and severe occlusions. Although recent deep learning based counting algorithms have achieved a great progress, the correlation knowledge among samples and the semantic prior have not yet been fully exploited. In this paper, a residual regression framework is proposed for crowd counting utilizing the correlation information among samples. By incorporating such information into our network, we discover that more intrinsic characteristics can be learned by the network which thus generalizes better to unseen scenarios. Besides, we show how to effectively leverage the semantic prior to improve the performance of crowd counting. We also observe that the adversarial loss can be used to improve the quality of predicted density maps, thus leading to an improvement in crowd counting. Experiments on public datasets demonstrate the effectiveness and generalization ability of the proposed method.