Miracle-dz/UniFAR-FTUs huggingface dataset:
Keep it simple and sparse: real-time action recognition

Sparsity has been showed to be one of the most important properties for visual recognition purposes. In this paper we show that sparse representation plays a fundamental role in achieving one-shot learning and real-time recognition of actions. We start off from RGBD images, combine motion and appearance cues and extract state-of-the-art features in a computationally efficient way. The proposed method relies on descriptors based on 3D Histograms of Scene Flow (3DHOFs) and Global Histograms of Oriented Gradient (GHOGs); adaptive sparse coding is applied to capture high-level patterns from data. We then propose a simultaneous on-line video segmentation and recognition of actions using linear SVMs. The main contribution of the paper is an effective real-time system for one-shot action modeling and recognition; the paper highlights the effectiveness of sparse coding techniques to represent 3D actions. We obtain very good results on three different data sets: a benchmark data set for one-shot action learning (the ChaLearn Gesture Data Set), an in-house data set acquired by a Kinect sensor including complex actions and gestures differing by small details, and a data set created for human-robot interaction purposes. Finally we demonstrate that our system is effective also in a human-robot interaction setting and propose a memory game, "All Gestures You Can", to be played against a humanoid robot.
Locally Weighted Regression Pseudo-Rehearsal for Online Learning of Vehicle Dynamics

We consider the problem of online adaptation of a neural network designed to represent vehicle dynamics. The neural network model is intended to be used by an MPC control law to autonomously control the vehicle. This problem is challenging because both the input and target distributions are non-stationary, and naive approaches to online adaptation result in catastrophic forgetting, which can in turn lead to controller failures. We present a novel online learning method, which combines the pseudo-rehearsal method with locally weighted projection regression. We demonstrate the effectiveness of the resulting Locally Weighted Projection Regression Pseudo-Rehearsal (LW-PR$^2$) method in simulation and on a large real world dataset collected with a 1/5 scale autonomous vehicle.
Plan-driven vs. Agile Software Engineering and Documentation: A Comparison from the Perspectives of both Developer and Consumer

This paper examines two different software development methodologies, plan-driven and agile, with respect to the documentation they produce and use to transfer knowledge between developers. We begin by establishing what we mean by documentation as these methodologies have different perspectives on what is included in a document set. The paper takes a wide view of documentation, considering it as the set of external artifacts, tests, code, and other artifacts that successfully contribute to encapsulating sufficient knowledge of the problem and its solution for the purpose of extending or modifying the solution. After examining what documentation is, we then turn our eye to how documentation is developed. We compare the plan-driven and agile methodologies to see what constitutes a complete document set under each methodology. We also look at the philosophy used in each method to discover how much effort is expended and what part documentation plays in the development process used. Following this we look at how documents are used. We consider different aspects of documentation that are important to developers and how the different methodologies treat these aspects. We find strengths and weaknesses in each methodology with respect to these attributes and discover how the philosophical approaches may or may not enhance attributes of particular importance such as how up-to-date a document is and how it is organized. Finally, after considering how plan-driven and agile methods approach documentation, we outline an experiment that will allow us to understand knowledge transfer with respect to the artifacts of interest in each method. The experiment uses a project course taught at the University of Iowa to collect information on how developers view the documentation process, giving insight into how developers would prefer to document their work. Additionally, we examine performance on a small extension to the class project with various types of documentation available to compare the utility of that type of documentation in conveying enough knowledge to perform the extension.
Gaussian Lattice Boltzmann method and its applications to rarefied flows

A novel discretization approach for the Bhatnager-Gross-Krook (BGK) kinetic equation is proposed. An hierarchy of LB models starting from $D1Q3$ model with increasing number of velocities converging to BGK model is derived. The method inherits properties of the Lattice Boltzmann (LB) method like linear streaming step, conservation of moments. Similar to the finite-difference methods for the BGK model the presented approach describes high-order moments of the distribution function with controllable error. The Sod shock tube problem, the Poiseuille flow between parallel plates and the plane Couette flow are considered for wide range of Knudsen numbers. Good stability and significant increase in precision over the conventional LB models are observed.
Asymmetric tail dependence modeling, with application to cryptocurrency market data

Since the inception of Bitcoin in 2008, cryptocurrencies have played an increasing role in the world of e-commerce, but the recent turbulence in the cryptocurrency market in 2018 has raised some concerns about their stability and associated risks. For investors, it is crucial to uncover the dependence relationships between cryptocurrencies for a more resilient portfolio diversification. Moreover, the stochastic behavior in both tails is important, as long positions are sensitive to a decrease in prices (lower tail), while short positions are sensitive to an increase in prices (upper tail). In order to assess both risk types, we develop in this paper a flexible copula model which is able to distinctively capture asymptotic dependence or independence in its lower and upper tails. Our proposed model is parsimonious and smoothly bridges (in each tail) both extremal dependence classes in the interior of the parameter space. Inference is performed using a full or censored likelihood approach, and we investigate by simulation the estimators' efficiency under three different censoring schemes which reduce the impact of non-extreme observations. We also develop a local likelihood approach to capture the temporal dynamics of extremal dependence among two leading cryptocurrencies. We here apply our model to historical closing prices of Bitcoin and Ethereum, which share most of the cryptocurrency market capitalizations. The results show that our proposed copula model outperforms alternative copula models and that the lower tail dependence level between Bitcoin and Ethereum has become stronger over time, smoothly transitioning from an asymptotic independence regime to an asymptotic dependence regime in recent years, whilst the upper tail has been more stable at a moderate dependence level.
DeepIlluminance: Contextual Illuminance Estimation via Deep Neural Networks

Computational color constancy refers to the estimation of the scene illumination and makes the perceived color relatively stable under varying illumination. In the past few years, deep Convolutional Neural Networks (CNNs) have delivered superior performance in illuminant estimation. Several representative methods formulate it as a multi-label prediction problem by learning the local appearance of image patches using CNNs. However, these approaches inevitably make incorrect estimations for the ambiguous patches affected by their neighborhood contexts. Inaccurate local estimates are likely to bring in degraded performance when combining into a global prediction. To address the above issues, we propose a contextual deep network for patch-based illuminant estimation equipped with refinement. First, the contextual net with a center-surround architecture extracts local contextual features from image patches, and generates initial illuminant estimates and the corresponding color corrected patches. The patches are sampled based on the observation that pixels with large color differences describe the illumination well. Then, the refinement net integrates the input patches with the corrected patches in conjunction with the use of intermediate features to improve the performance. To train such a network with numerous parameters, we propose a stage-wise training strategy, in which the features and the predicted illuminant from previous stages are provided to the next learning stage with more finer estimates recovered. Experiments show that our approach obtains competitive performance on two illuminant estimation benchmarks.
AFSCR: Annotation of Functional Satisfaction Conditions and their Reconciliation within i* models

Context: Researchers, both in industry and academia, are facing the challenge of leveraging the benefits of goal oriented requirements engineering (GORE) techniques to business compliance management. This requires analyzing goal models along with their semantics. However, most prominent goal modeling frameworks have no means of capturing the semantics of goals (except what is trivially conveyed by their nomenclature). Objective: In this paper, we propose the Annotation of Functional Satisfaction Conditions and their Reconciliation (AFSCR) framework for doing the same. The entire framework is presented with respect to i* modeling constructs. Method: This is a semi-automated framework that requires analysts to annotate individual goals with their immediate goal satisfaction conditions. The AFSCR framework can then reconcile these satisfaction conditions for every goal and verify whether the derived set of cumulative satisfaction conditions is in harmony with the intended set of goal satisfaction conditions. Result: If the derived and intended sets of satisfaction conditions are in conflict, the framework raises entailment and/or consistency flags. Whenever a conflict is flagged, the framework also provides alternate solutions and possible workaround strategies to the analysts by refactoring the given i* model. Conclusion: In this paper we present a new framework that uses satisfaction conditions for going beyond the nomenclature and capturing the functional semantics of the goals within i* models. The analysis performed during the reconciliation process is generic enough and can be adapted to any goal modeling framework if required.
NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding

Research on depth-based human activity analysis achieved outstanding performance and demonstrated the effectiveness of 3D representation for action recognition. The existing depth-based and RGB+D-based action recognition benchmarks have a number of limitations, including the lack of large-scale training samples, realistic number of distinct class categories, diversity in camera views, varied environmental conditions, and variety of human subjects. In this work, we introduce a large-scale dataset for RGB+D human action recognition, which is collected from 106 distinct subjects and contains more than 114 thousand video samples and 8 million frames. This dataset contains 120 different action classes including daily, mutual, and health-related activities. We evaluate the performance of a series of existing 3D activity analysis methods on this dataset, and show the advantage of applying deep learning methods for 3D-based human action recognition. Furthermore, we investigate a novel one-shot 3D activity recognition problem on our dataset, and a simple yet effective Action-Part Semantic Relevance-aware (APSR) framework is proposed for this task, which yields promising results for recognition of the novel action classes. We believe the introduction of this large-scale dataset will enable the community to apply, adapt, and develop various data-hungry learning techniques for depth-based and RGB+D-based human activity understanding.
Segregation Network for Multi-Class Novelty Detection

The problem of multiple class novelty detection is gaining increasing importance due to the large availability of multimedia data and the increasing requirement of the classification models to work in an open set scenario. To this end, novelty detection tries to answer this important question: given a test example should we even try to classify it? In this work, we design a novel deep learning framework, termed Segregation Network, which is trained using the mixup technique. We construct interpolated points using convex combinations of pairs of training data and use our novel loss function for prediction of its constituent classes. During testing, for each input query, mixed samples with the known class prototypes are generated and passed through the proposed network. The output of the network reveals the constituent classes which can be used to determine whether the incoming data is from the known class set or not. Our algorithm is trained using just the data from the known classes and does not require any auxiliary dataset or attributes. Extensive evaluation on two benchmark datasets namely Caltech-256 and Stanford Dogs and comparison with the state-of-the-art justifies the effectiveness of the proposed framework.
Differentially Private Empirical Risk Minimization with Sparsity-Inducing Norms

Differential privacy is concerned about the prediction quality while measuring the privacy impact on individuals whose information is contained in the data. We consider differentially private risk minimization problems with regularizers that induce structured sparsity. These regularizers are known to be convex but they are often non-differentiable. We analyze the standard differentially private algorithms, such as output perturbation, Frank-Wolfe and objective perturbation. Output perturbation is a differentially private algorithm that is known to perform well for minimizing risks that are strongly convex. Previous works have derived excess risk bounds that are independent of the dimensionality. In this paper, we assume a particular class of convex but non-smooth regularizers that induce structured sparsity and loss functions for generalized linear models. We also consider differentially private Frank-Wolfe algorithms to optimize the dual of the risk minimization problem. We derive excess risk bounds for both these algorithms. Both the bounds depend on the Gaussian width of the unit ball of the dual norm. We also show that objective perturbation of the risk minimization problems is equivalent to the output perturbation of a dual optimization problem. This is the first work that analyzes the dual optimization problems of risk minimization problems in the context of differential privacy.
A clustering patch hierarchical routing protocol for wireless sensor networks

Due to the limited power of the wireless sensor networks(WSNs), the network lifetime becomes a critical index in WSNs. However, network lifetime can not depict network effectively, there exist deficiency in currently evaluative standard. To solve the problem, two new indexes (network coverage rate and effective network lifetime) are introduced for evaluating WSNs. Newly evaluative standard can depict the performance of WSNs more effectively. Meanwhile, a Clustering Patch Hierarchical Routing Protocol(CPHRP) is proposed with purposes of improving network coverage rate and effective network lifetime in WSNs. It improves network coverage rate through clustering patch, and it has hierarchical multi-path tree routing property. What's more, the nodes are partitioned into three classes: cluster node, sense node and non-sense node, which improves the energy conservation. Simulation results show that the CPHRP can guarantee more than 90% network coverage rate within most of network lifetime with comparison to HEED when the number of inner-cluster sense nodes is above 6. With the multiple growth of network nodes, the effective network lifetime of the CPHRP rises by more than 60%. When the number of inner-cluster nodes increases in multiple of 6, the growth of its network lifecycle is more than 50% in contrast of the less than 7% of HEED.
Hybrid Genetic Algorithms and Clustering

This paper introduces a hybrid genetic algorithm that uses fuzzy c-means clustering technique as a mechanism to reduce fitness evaluations and to preserve solution quality. Population clustering provides a means to evaluate only the representative individual of each cluster instead of the whole population. The remaining individuals are indirectly evaluated. The aim is to maintain reasonable population size and to obtain near-optimal solutions. This is an important issue especially in large-scale, complex optimization and decision-making problems.
Wavelets for computer graphics: a primer. 2

Wavelets are a mathematical tool for hierarchically decomposing functions. Using wavelets, a function can be described in terms of a coarse overall shape, plus details that range from broad to narrow. Regardless of whether the function of interest is an image, a curve, or a surface, wavelets provide an elegant technique for representing the levels of detail present. This primer is intended to provide those working in computer graphics with some intuition for what wavelets are, as well as to present the mathematical foundations necessary for studying and using them. In Part I, we discussed the simple case of Haar wavelets in one and two dimensions, and showed how they can be used for image compression. Part II presents the mathematical theory of multiresolution analysis, develops bounded-interval spline wavelets, and describes their use in multiresolution curve and surface editing.
Adaptive Confidence Smoothing for Generalized Zero-Shot Learning

Generalized zero-shot learning (GZSL) is the problem of learning a classifier where some classes have samples and others are learned from side information, like semantic attributes or text description, in a zero-shot learning fashion (ZSL). Training a single model that operates in these two regimes simultaneously is challenging. Here we describe a probabilistic approach that breaks the model into three modular components, and then combines them in a consistent way. Specifically, our model consists of three classifiers: A "gating" model that makes soft decisions if a sample is from a "seen" class, and two experts: a ZSL expert, and an expert model for seen classes. We address two main difficulties in this approach: How to provide an accurate estimate of the gating probability without any training samples for unseen classes; and how to use expert predictions when it observes samples outside of its domain. The key insight to our approach is to pass information between the three models to improve each one's accuracy, while maintaining the modular structure. We test our approach, adaptive confidence smoothing (COSMO), on four standard GZSL benchmark datasets and find that it largely outperforms state-of-the-art GZSL models. COSMO is also the first model that closes the gap and surpasses the performance of generative models for GZSL, even-though it is a light-weight model that is much easier to train and tune.
Massive MIMO Extensions to the COST 2100 Channel Model: Modeling and Validation

To enable realistic studies of massive multiple-input multiple-output systems, the COST 2100 channel model is extended based on measurements. First, the concept of a base station-side visibility region (BS-VR) is proposed to model the appearance and disappearance of clusters when using a physically-large array. We find that BS-VR lifetimes are exponentially distributed, and that the number of BS-VRs is Poisson distributed with mean proportional to the sum of the array length and the mean lifetime. Simulations suggest that under certain conditions longer lifetimes can help decorrelating closely-located users. Second, the concept of a multipath component visibility region (MPC-VR) is proposed to model birth-death processes of individual MPCs at the mobile station side. We find that both MPC lifetimes and MPC-VR radii are lognormally distributed. Simulations suggest that unless MPC-VRs are applied the channel condition number is overestimated. Key statistical properties of the proposed extensions, e.g., autocorrelation functions, maximum likelihood estimators, and Cramer-Rao bounds, are derived and analyzed.
Competition, Collaboration, and Optimization in Multiple Interacting Spreading Processes

Competition and collaboration are at the heart of multi-agent probabilistic spreading processes. The battle on public opinion and competitive marketing campaigns are typical examples of the former, while the joint spread of multiple diseases such as HIV and tuberculosis demonstrates the latter. These spreads are influenced by the underlying network topology, the infection rates between network constituents, recovery rates and, equally importantly, the interactions between the spreading processes themselves. Here, for the first time we derive dynamic message-passing equations that provide an exact description of the dynamics of two interacting spreading processes on tree graphs, and develop systematic low-complexity models that predict the spread on general graphs. We also develop a theoretical framework for an optimal control of interacting spreading processes through an optimized resource allocation under budget constraints and within a finite time window. Derived algorithms can be used to maximize the desired spread in the presence of a rival competitive process, and to limit the spread through vaccination in the case of coupled infectious diseases. We demonstrate the efficacy of the framework and optimization method on both synthetic and real-world networks.
Physical Layer Security for NOMA: Requirements, Merits, Challenges, and Recommendations

Non-Orthogonal Multiple Access (NOMA) has been recognized as one of the most significant enabling technologies for future wireless systems due to its eminent spectral efficiency, ability to provide an additional degree of freedom for Ultra Reliable Low Latency Communications (URLLC) and grant free random access. Meanwhile, Physical Layer Security (PLS) has got much attention for future wireless communication systems due to its capability to provide security without relying on traditional cryptography based algorithms. In this article, security design requirements for NOMA and solutions provided by PLS to fulfill these requirements are discussed. The merits and challenges arising from employing PLS to NOMA are identified. Finally, future recommendations and prospective solutions are also presented.