Reviewerly/exHarmony huggingface dataset:
$p$ -Laplacian Regularization for Scene Recognition

The explosive growth of multimedia data on the Internet makes it essential to develop innovative machine learning algorithms for practical applications especially where only a small number of labeled samples are available. Manifold regularized semi-supervised learning (MRSSL) thus received intensive attention recently because it successfully exploits the local structure of data distribution including both labeled and unlabeled samples to leverage the generalization ability of a learning model. Although there are many representative works in MRSSL, including Laplacian regularization (LapR) and Hessian regularization, how to explore and exploit the local geometry of data manifold is still a challenging problem. In this paper, we introduce a fully efficient approximation algorithm of graph p -Laplacian, which significantly saving the computing cost. And then we propose p -LapR (pLapR) to preserve the local geometry. Specifically, p -Laplacian is a natural generalization of the standard graph Laplacian and provides convincing theoretical evidence to better preserve the local structure. We apply pLapR to support vector machines and kernel least squares and conduct the implementations for scene recognition. Extensive experiments on the Scene 67 dataset, Scene 15 dataset, and UC-Merced dataset validate the effectiveness of pLapR in comparison to the conventional manifold regularization methods.
Zeroing Neural Dynamics for Control Design: Comprehensive Analysis on Stability, Robustness, and Convergence Speed

Zeroing neural dynamics (ZND) can be seen as an effective controller to solve various challenging scientific and engineering problems. Computing Lyapunov equation is a kind of important issue in nonlinear systems for stability analysis in control. This paper presents a systematic and constructive procedure on using ZND to design control laws based on the efficient solution of dynamic Lyapunov equation. We particularly address three important aspects in the design: 1) the global stability of ZND, to guarantee the effectiveness of the solution; 2) the robustness against additive noises, to ensure the capability of ZND for using in harsh environments; and 3) the finite-time convergence of ZND, to endow ZND for real-time solution of dynamical problems. To do so, a novel formula is first designed in a unified manner of ZND. Differing from the conventional formula appearing in ZND, the proposed formula simultaneously has finite-time convergence and noise robustness property. According to this novel formula, a novel control law (termed nonlinear neural dynamics, NND) is established to compute dynamic Lyapunov equation in the presence of various additive noises. Both theoretical and simulative results ensure the finite-time convergence and noise robustness property of the NND model for computing dynamic Lyapunov equation in front of various additive noises. As compared to the conventional ZND model for computing dynamic Lyapunov, the superior property of the NND model is further demonstrated.
Mapping Color to Meaning in Colormap Data Visualizations

To interpret data visualizations, people must determine how visual features map onto concepts. For example, to interpret colormaps, people must determine how dimensions of color (e.g., lightness, hue) map onto quantities of a given measure (e.g., brain activity, correlation magnitude). This process is easier when the encoded mappings in the visualization match people's predictions of how visual features will map onto concepts, their inferred mappings. To harness this principle in visualization design, it is necessary to understand what factors determine people's inferred mappings. In this study, we investigated how inferred color-quantity mappings for colormap data visualizations were influenced by the background color. Prior literature presents seemingly conflicting accounts of how the background color affects inferred color-quantity mappings. The present results help resolve those conflicts, demonstrating that sometimes the background has an effect and sometimes it does not, depending on whether the colormap appears to vary in opacity. When there is no apparent variation in opacity, participants infer that darker colors map to larger quantities (dark-is-more bias). As apparent variation in opacity increases, participants become biased toward inferring that more opaque colors map to larger quantities (opaque-is-more bias). These biases work together on light backgrounds and conflict on dark backgrounds. Under such conflicts, the opaque-is-more bias can negate, or even supersede the dark-is-more bias. The results suggest that if a design goal is to produce colormaps that match people's inferred mappings and are robust to changes in background color, it is beneficial to use colormaps that will not appear to vary in opacity on any background color, and to encode larger quantities in darker colors.
A survey of fault tolerance in cloud computing

Cloud computing has brought about a transformation in the delivery model of information technology from a product to a service. It has enabled the availability of various software, platforms and infrastructural resources as scalable services on demand over the internet. However, the performance of cloud computing services is hampered due to their inherent vulnerability to failures owing to the scale at which they operate. It is possible to utilize cloud computing services to their maximum potential only if the performance related issues of reliability, availability, and throughput are handled effectively by cloud service providers. Therefore, fault tolerance becomes a critical requirement for achieving high performance in cloud computing. This paper presents a comprehensive overview of fault tolerance-related issues in cloud computing; emphasizing upon the significant concepts, architectural details, and the state-of-art techniques and methods. The objective is to provide insights into the existing fault tolerance approaches as well as challenges yet required to be overcome. The survey enumerates a few promising techniques that may be used for efficient solutions and also, identifies important research directions in this area.
Less is more: multiparty session types revisited

Multiparty Session Types (MPST) are a typing discipline ensuring that a message-passing process implements a multiparty session protocol , without errors. In this paper, we propose a new, generalised MPST theory. Our contribution is fourfold. (1) We demonstrate that a revision of the theoretical foundations of MPST is necessary : classic MPST have a limited subject reduction property, with inherent restrictions that are easily overlooked, and in previous work have led to flawed type safety proofs; our new theory removes such restrictions and fixes such flaws. (2) We contribute a new MPST theory that is less complicated, and yet more general, than the classic one: it does not require global multiparty session types nor binary session type duality — instead, it is grounded on general behavioural type-level properties, and proves type safety of many more protocols and processes. (3) We produce a detailed analysis of type-level properties, showing how, in our new theory, they allow to ensure decidability of type checking, and statically guarantee that processes enjoy, , deadlock-freedom and liveness at run-time. (4) We show how our new theory can integrate type and model checking: type-level properties can be expressed in modal µ-calculus, and verified with well-established tools.
An Ensemble Framework for Day-Ahead Forecast of PV Output Power in Smart Grids

The uncertainty associated with solar photo-voltaic (PV) power output (PO) is a big challenge to design, manage and implement effective demand response, and management strategies. Therefore, an accurate PV power output forecast is an utmost importance to allow seamless integration and a higher level of penetration. In this research, a neural network ensemble (NNE) scheme is proposed, which is based on particle swarm optimization trained feedforward neural network (FNN). Five different FFN structures with varying network complexities are used to achieve the diverse and accurate forecast results. These results are combined using trim aggregation after removing the upper and lower forecast error extremes. Correlated variables namely wavelet transformed historical PO of PV, solar irradiance, wind speed, temperature, and humidity are applied as inputs to the multivariate NNE. Clearness index is used to classify days into clear, cloudy, and partial cloudy days. Test case studies are designed to predict the solar output for these days selected from all seasons. The performance of the proposed framework is analyzed by applying training dataset of different resolution, length, and quality from seven solar PV sites of the University of Queensland, Australia. The forecast results demonstrate that the proposed framework improves the forecast accuracy significantly in comparison with individual and benchmark models.
Differential evolution with neighborhood-based adaptive evolution mechanism for numerical optimization

This paper presents a novel differential evolution algorithm for numerical optimization by designing the neighborhood-based mutation strategy and adaptive evolution mechanism. In the proposed strategy, two novel neighborhood-based mutation operators and an individual-based selection probability are developed to adjust the search performance of each individual suitably. Meanwhile, the evolutionary dilemmas of the neighborhood are identified by tracking its performance and diversity, and alleviated by designing a dynamic neighborhood model and two exchanging operations in the proposed mechanism. Furthermore, the population size is adaptively adjusted by a simple reduction method. Differing from differential evolution variants based on neighborhood and evolutionary state, the proposed algorithm makes full use of the characteristics of individuals, identifies and alleviates the neighborhood evolutionary dilemmas of each individual. Compared with 21 typical algorithms, the numerical results on 30 benchmark functions from CEC2014 show that the proposed algorithm is reliable and has better performance.
Hierarchical Tracking by Reinforcement Learning-Based Searching and Coarse-to-Fine Verifying

A class-agnostic tracker typically consists of three key components, i.e., its motion model, its target appearance model, and its updating strategy. However, most recent topperforming trackers mainly focus on constructing complicated appearance models and updating strategies, while using comparatively simple and heuristic motion models that may result in an inefficient search and degrade the tracking performance. To address this issue, we propose a hierarchical tracker that learns to move and track based on the combination of data-driven search at the coarse level, and coarse-to-fine verification at the fine level. At the coarse level, a data-driven motion model learned from deep recurrent reinforcement learning provides our tracker with coarse localization of an object. By formulating motion search as an action-decision problem in reinforcement learning, our tracker utilizes a recurrent convolutional neural network based deep Q-network to effectively learn data-driven searching policies. The learned motion model cannot only significantly reduce the search space, but also provide more reliable interested regions for further verifying. At the fine level, a kernelized correlation filter (KCF) based appearance model is adopted to densely yet efficiently verify a local region centered on the predicted location from the motion model. Through using of circulant matrices and fast Fourier transformation, a large number of candidate samples in the local region can be efficiently and effectively evaluated by the KCF based appearance model. Finally, a simple yet robust estimator is designed to analyze possible tracking failure. The experiments on OTB50 and OTB100 illustrate that our tracker achieves better performance than the state-of-the-art trackers.
Synthesis of High-Quality Visible Faces from Polarimetric Thermal Faces using Generative Adversarial Networks

The large domain discrepancy between faces captured in polarimetric (or conventional) thermal and visible domains makes cross-domain face verification a highly challenging problem for human examiners as well as computer vision algorithms. Previous approaches utilize either a two-step procedure (visible feature estimation and visible image reconstruction) or an input-level fusion technique, where different Stokes images are concatenated and used as a multi-channel input to synthesize the visible image given the corresponding polarimetric signatures. Although these methods have yielded improvements, we argue that input-level fusion alone may not be sufficient to realize the full potential of the available Stokes images. We propose a generative adversarial networks based multi-stream feature-level fusion technique to synthesize high-quality visible images from polarimetric thermal images. The proposed network consists of a generator sub-network, constructed using an encoder–decoder network based on dense residual blocks, and a multi-scale discriminator sub-network. The generator network is trained by optimizing an adversarial loss in addition to a perceptual loss and an identity preserving loss to enable photo realistic generation of visible images while preserving discriminative characteristics. An extended dataset consisting of polarimetric thermal facial signatures of 111 subjects is also introduced. Multiple experiments evaluated on different experimental protocols demonstrate that the proposed method achieves state-of-the-art performance. Code will be made available at https://github.com/hezhangsprinter .
Globally optimal sequential and distributed fusion state estimation for multi-sensor systems with cross-correlated noises

This paper is concerned with globally optimal sequential and distributed fusion estimation algorithms in the linear minimum variance (LMV) sense for multi-sensor systems with cross-correlated noises, where the measurement noises from different sensors are cross-correlated with each other at the same time step and correlated with the system noise at the previous time step. First, a globally optimal sequential fusion filter is proposed by considering the estimators for measurement noises. The equivalence on estimation accuracy of the proposed sequential fusion filter and centralized fusion filter is proven. It has reduced computational cost since it avoids the measurement augmentation. Then, a distributed fusion filter is also proposed by considering the prior fusion estimator and feedback from the fusion center. Under the condition that local gain matrices are full column rank, the proposed distributed fusion filter has the same estimation accuracy as the centralized fusion filter, that is, it also has global optimality. Their equivalence on estimation accuracy is proven. Stability and steady-state properties of the proposed algorithms are analyzed. A sufficient condition for the existence of steady-state filters is given. Finally, simulation results for a target tracking system show the effectiveness of the proposed algorithms.
Action Quality Assessment Across Multiple Actions

Can learning to measure the quality of an action help in measuring the quality of other actions? If so, can consolidated samples from multiple actions help improve the performance of current approaches? In this paper, we carry out experiments to see if knowledge transfer is possible in the action quality assessment (AQA) setting. Experiments are carried out on our newly released AQA dataset (http://rtis.oit.unlv.edu/datasets.html) consisting of 1106 action samples from seven actions with quality as measured by expert human judges. Our experimental results show that there is utility in learning a single model across multiple actions.
Empirical Research in Mid-Air Interaction: A Systematic Review

Mid-air interaction is a distinct style of natural HCI (Human-Computer Interaction). In mid-air interaction, users make use of their whole body—with a strong focus on hands—and apply gestures, postures, and movements to interact with digital content on distant displays or remote devices. Although the idea of exploiting body movements and gestures in HCI is not new, mid-air interaction is now possible due to the availability of reasonably priced depth cameras and sensors. This article presents a systematic review of empirical research in mid-air interaction, based on a corpus of 104 publications from 2011 to 2018. The review includes: (a) a retrospective on mid-air interaction, which outlines its historical development and clarifications on important concepts; (b) current application domains of mid-air interaction; (c) user requirements methods, focusing on gesture elicitation studies; (d) dimensions of prototyping and design; (e) empirical evaluation methods and issues; (f) a discussion on several trends and challenges for further research and development.
Minimum distance calculation using laser scanner and IMUs for safe human-robot interaction

In this study we investigate the use of a laser scanner/range-finder and inertial measurement units (IMUs) for the application of human-robot interaction in a dynamic environment with moving obstacles/humans. Humans and robots are represented by capsules, allowing to calculate the human-robot minimum distance on-the-fly. A major challenge is to capture the capsules pose. Data from a laser scanner and IMUs attached to the human body are fused to define the torso relative position and the upper body (arms and chest) configuration, respectively. Collision avoidance is achieved with a customized potential field’s method that allows to adjust the pre-defined robot paths established off-line while keeping the task target. The proposed framework is validated in real environment using a SICK laser scanner, IMUs and a KUKA iiwa robot. Experiments demonstrated the robustness of the proposed approach in capturing human motion, calculating the human-robot minimum distance and the robot behavior that smoothly avoids collisions with the human.
A survey on data storage and placement methodologies for Cloud-Big Data ecosystem

Currently, the data to be explored and exploited by computing systems increases at an exponential rate. The massive amount of data or so-called "Big Data" put pressure on existing technologies for providing scalable, fast and efficient support. Recent applications and the current user support from multi-domain computing, assisted in migrating from data-centric to knowledge-centric computing. However, it remains a challenge to optimally store and place or migrate such huge data sets across data centers (DCs). In particular, due to the frequent change of application and DC behaviour (i.e., resources or latencies), data access or usage patterns need to be analyzed as well. Primarily, the main objective is to find a better data storage location that improves the overall data placement cost as well as the application performance (such as throughput). In this survey paper, we are providing a state of the art overview of Cloud-centric Big Data placement together with the data storage methodologies. It is an attempt to highlight the actual correlation between these two in terms of better supporting Big Data management. Our focus is on management aspects which are seen under the prism of non-functional properties. In the end, the readers can appreciate the deep analysis of respective technologies related to the management of Big Data and be guided towards their selection in the context of satisfying their non-functional application requirements. Furthermore, challenges are supplied highlighting the current gaps in Big Data management marking down the way it needs to evolve in the near future.
Generative Adversarial Networks for Unsupervised Monocular Depth Prediction

Estimating depth from a single image is a very challenging and exciting topic in computer vision with implications in several application domains. Recently proposed deep learning approaches achieve outstanding results by tackling it as an image reconstruction task and exploiting geometry constraints (e.g., epipolar geometry) to obtain supervisory signals for training. Inspired by these works and compelling results achieved by Generative Adversarial Network (GAN) on image reconstruction and generation tasks, in this paper we propose to cast unsupervised monocular depth estimation within a GAN paradigm. The generator network learns to infer depth from the reference image to generate a warped target image. At training time, the discriminator network learns to distinguish between fake images generated by the generator and target frames acquired with a stereo rig. To the best of our knowledge, our proposal is the first successful attempt to tackle monocular depth estimation with a GAN paradigm and the extensive evaluation on CityScapes and KITTI datasets confirm that it enables to improve traditional approaches. Additionally, we highlight a major issue with data deployed by a standard evaluation protocol widely used in this field and fix this problem using a more reliable dataset recently made available by the KITTI evaluation benchmark.
Grayscale-based block scrambling image encryption using YCbCr color space for encryption-then-compression systems

A novel grayscale-based block scrambling image encryption scheme is presented not only to enhance security, but also to improve the compression performance for Encryption-then-Compression (EtC) systems with JPEG compression, which are used to securely transmit images through an untrusted channel provider. The proposed scheme enables the use of a smaller block size and a larger number of blocks than the color-based image encryption scheme. Images encrypted using the proposed scheme include less color information due to the use of grayscale images even when the original image has three color channels. These features enhance security against various attacks, such as jigsaw puzzle solver and brute-force attacks. Moreover, generating the grayscale-based images from a full-color image in YCbCr color space allows the use of color sub-sampling operation, which can provide the higher compression performance than the conventional grayscale-based encryption scheme, although the encrypted images have no color information. In an experiment, encrypted images were uploaded to and then downloaded from Twitter and Facebook, and the results demonstrated that the proposed scheme is effective for EtC systems and enhances the compression performance, while maintaining the security against brute-force and jigsaw puzzle solver attacks.