Reviewerly/exHarmony huggingface dataset:
Compositing light field video using multiplane images

We present a variety of new compositing techniques using Multi-plane Images (MPI's) [Zhou et al. 2018] derived from footage shot with an inexpensive and portable light field video camera array. The effects include camera stabilization, foreground object removal, synthetic depth of field, and deep compositing. Traditional compositing is based around layering RGBA images to visually integrate elements into the same scene, and often requires manual 2D and/or 3D artist intervention to achieve realism in the presence of volumetric effects such as smoke or splashing water. We leverage the newly introduced DeepView solver [Flynn et al. 2019] and a light field camera array to generate MPIs stored in the DeepEXR format for compositing with realistic spatial integration and a simple workflow which offers new creative capabilities. We demonstrate using this technique by combining footage that would otherwise be very challenging and time intensive to achieve when using traditional techniques, with minimal artist intervention.
Multistart with early termination of descents

Multistart is a celebrated global optimization technique frequently applied in practice. In its pure form, multistart has low efficiency. However, the simplicity of multistart and multitude of possibilities of its generalization make it very attractive especially in high-dimensional problems where e.g. Lipschitzian and Bayesian algorithms are not applicable. We propose a version of multistart where most of the local descents are terminated very early; we will call it METOD as an abbreviation for multistart with early termination of descents. The performance of the proposed algorithm is demonstrated on randomly generated test functions with 100 variables and a modest number of local minimizers.
Remote sensing-based geological mapping and petrogenesis of Wadi Khuda Precambrian rocks, South Eastern Desert of Egypt with emphasis on leucogranite

The exposed Precambrian rocks along Wadi Khuda area, South Eastern Desert of Egypt, comprise gneisses, amphibolites and migmatites associations (infrastructural rocks) which are intruded by diorite, tonalite, syenogranite and leucogranite (LG). LG forms homogeneous boss like bodies, devoid to xenoliths, except along the outer margins. Chemically and mineralogically they are dissimilar to the surrounding gneisses. In the current study, band ratio and Decorrelation Stretch image processing algorithms were proposed and applied on Landsat-8, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Sentinel-2 remote sensing data to discriminate the widely exposed lithological units in the mapped area. Geochemically, I-type classification and volcanic arc environment were revealed. LG is peraluminous (ASI average of 1.09) with normative corundum up to 1.38 for LG. The LG displays limited major and trace element variations (SiO2 = 74.5–76.2%, AI203 = 13.4–14.4%), MgO = 0.03–0.19%, Na2O = 3.6–4.6%), and Rb = 55–85 ppm) probably due to textural and mineralogical homogeneity. The contents of REE in the leucogranite are low (∑REE = 21–37), but the REE patterns are [(La/Yb)n = 2.45–9.03], [(La/Sm)n = 1.09–2.6], with negative Eu anomaly, (Eu/Eu* = 0.19–0.78). The negative Eu anomaly and the low Sr content (16–92 ppm) may be attributed to the plagioclase feldspars fractionation. The leucogranites represent residual melt of syenogranite which forms Um Itly pluton at the southern flank of Wadi Khuda entrance.
A Platform and Methodology Enabling Real-Time Motion Pattern Recognition on Low-Power Smart Devices

This article presents a low-power platform, i.e., Neblina <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TM</sup> system-on-module, with extensive hardware variants targeting Internet of Things applications. The base hardware utilizes a sensor fusion algorithm for 3D orientation tracking alongside a novel configurable framework for real-time Motion Pattern Recognition (MPR) on ARM Cortex M4F using inertial sensors. The MPR engine consists of configurable blocks performing shock-aware segmentation, histogram feature extraction, and classification using a single-hidden-layer Feedforward Neural Network. The framework can be used for human fitness/daily activity tracking or shock pattern recognition, e.g., in sports, such as tennis, golf, hockey, etc. Our platform can deliver multimodal user feedback as well. Experimental results have evaluated Neblina's MPR framework in terms of A) accuracy in human fitness activity recognition using some existing datasets, B) memory usage and latency for real-time execution on Cortex M4F and C) low power consumption for a longer lasting battery.
Advances in Machine Learning for the Behavioral Sciences

The areas of machine learning and knowledge discovery in databases have considerably matured in recent years. In this article, we briefly review recent developments as well as classical algorithms that stood the test of time. Our goal is to provide a general introduction into different tasks such as learning from tabular data, behavioral data, or textual data, with a particular focus on actual and potential applications in behavioral sciences. The supplemental appendix to the article also provides practical guidance for using the methods by pointing the reader to proven software implementations. The focus is on R, but we also cover some libraries in other programming languages as well as systems with easy-to-use graphical interfaces.
Low-Cost IoT Surveillance System Using Hardware-Acceleration and Convolutional Neural Networks

Reliable object detection is crucial for intelligent surveillance systems. Despite the abundance of available systems, these are often expensive or require cloud connectivity which restricts their usage. In this paper, we present a complete low-cost solution using embedded devices. Person detection is performed locally, using an efficient implementation of a state-of-the-art convolutional Single Shot Detector. The proposed model is fine-tuned to the surveillance task at hand and tested using a Raspberry Pi and the Up Squared Board devices. Additionally, the benefits of hardware-acceleration are evaluated using an Intel <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> Movidius™ Neural Compute Stick (NCS). The final model performs person detection with an average precision of 0.48. The Raspberry Pi is capable of acquiring and processing images at a rate of 0.4 to 5.3 FPS without/with NCS, whereas the Up Board achieves 2.1 to 7.3 FPS. Further tests demonstrate how the system performs when multiple cameras and multiple NCS devices are deployed. In conclusion, the proposed architecture is suitable for real-time, affordable surveillance systems in home and commercial settings.
Sprague–Grundy function of symmetric hypergraphs

We consider a generalization of the classical game of Nim called hypergraph Nim. Given a hypergraph H on the ground set V={1,…,n} of n piles of stones, two players alternate in choosing a hyperedge H∈H and strictly decreasing all piles i∈H. The player who makes the last move is the winner. In 1980 Jenkyns and Mayberry obtained an explicit formula for the Sprague–Grundy function of the hypergraph Nim whose hypergraph contains as the hyperedges all proper subsets of V (that is, all except ∅ and V itself). Somewhat surprisingly, the same formula works for a very wide family of hypergraphs. In this paper we characterize symmetric hypergraphs in this family.
Permutation $p$-value approximation via generalized Stolarsky invariance

It is common for genomic data analysis to use p-values from a large number of permutation tests.The multiplicity of tests may require very tiny p-values in order to reject any null hypotheses and the common practice of using randomly sampled permutations then becomes very expensive.We propose an inexpensive approximation to p-values for two sample linear test statistics, derived from Stolarsky's invariance principle.The method creates a geometrically derived reference set of approximate p-values for each hypothesis.The average of that set is used as a point estimate p and our generalization of the invariance principle allows us to compute the variance of the p-values in that set.We find that in cases where the point estimate is small, the variance is a modest multiple of the square of that point estimate, yielding a relative error property similar to that of saddlepoint approximations.On a Parkinson's disease data set, the new approximation is faster and more accurate than the saddlepoint approximation.We also obtain a simple probabilistic explanation of Stolarsky's invariance principle.
Stability and optimization error of stochastic gradient descent for pairwise learning

In this paper, we study the stability and its trade-off with optimization error for stochastic gradient descent (SGD) algorithms in the pairwise learning setting. Pairwise learning refers to a learning task which involves a loss function depending on pairs of instances among which notable examples are bipartite ranking, metric learning, area under ROC curve (AUC) maximization and minimum error entropy (MEE) principle. Our contribution is twofolded. Firstly, we establish the stability results for SGD for pairwise learning in the convex, strongly convex and non-convex settings, from which generalization errors can be naturally derived. Secondly, we establish the trade-off between stability and optimization error of SGD algorithms for pairwise learning. This is achieved by lower-bounding the sum of stability and optimization error by the minimax statistical error over a prescribed class of pairwise loss functions. From this fundamental trade-off, we obtain lower bounds for the optimization error of SGD algorithms and the excess expected risk over a class of pairwise losses. In addition, we illustrate our stability results by giving some specific examples of AUC maximization, metric learning and MEE.
Adaptive MTD Security using Markov Game Modeling

Large scale cloud networks consist of distributed networking and computing elements that process critical information and thus security is a key requirement for any environment. Unfortunately, assessing the security state of such networks is a challenging task and the tools used in the past by security experts such as packet filtering, firewall, Intrusion Detection Systems (IDS) etc., provide a reactive security mechanism. In this paper, we introduce a Moving Target Defense (MTD) based proactive security framework for monitoring attacks which lets us identify and reason about multi-stage attacks that target software vulnerabilities present in a cloud network. We formulate the multi-stage attack scenario as a two-player zero-sum Markov Game (between the attacker and the network administrator) on attack graphs. The rewards and transition probabilities are obtained by leveraging the expert knowledge present in the Common Vulnerability Scoring System (CVSS). Our framework identifies an attacker's optimal policy and places countermeasures to ensure that this attack policy is always detected, thus forcing the attacker to use a sub-optimal policy with higher cost.
Neural network architecture search with differentiable cartesian genetic programming for regression

While optimized neural network architectures are essential for effective training with gradient descent, their development remains a challenging and resource-intensive process full of trial-and-error iterations. We propose to encode neural networks with a differentiable variant of Cartesian Genetic Programming (dCGPANN) and present a memetic algorithm for architecture design: local searches with gradient descent learn the network parameters while evolutionary operators act on the dCGPANN genes shaping the network architecture towards faster learning. Studying a particular instance of such a learning scheme, we are able to improve the starting feed forward topology by learning how to rewire and prune links, adapt activation functions and introduce skip connections for chosen regression tasks. The evolved network architectures require less space for network parameters and reach, given the same amount of time, a significantly lower error on average.
Ontology-Based Intelligent Agent for Determination of Sufficiency of Metric Information in the Software Requirements

This research is devoted to implementation of the ontology-based intelligent agent (OBIA) for the determination of the sufficiency of metric information in the software requirements. The method of actions of such an OBIA is developed. The intelligent agent, who works on the basis of the developed method, determines the sufficiency of metric information in the software requirements, performs a numerical assessment of the sufficiency level of metric information, and offers a visual list of missing indicators necessary for the calculation of metrics. Functioning of the realized agent allows increasing the sufficiency level of metric information in the software requirements. The developed intelligent agent allows to partially eliminate the person from the processes of processing the information, to avoid the losses of important information and to increase the amount of metric information at the phase of requirements gathering, which in the complex provides increasing the software quality. During the experiments, the intelligent agent investigated the requirements for software of the system of improving the safety of computer systems’ software, which resulted in the sufficiency of metric information in requirements increased by 44%.
Partial Person Re-identification with Alignment and Hallucination

Partial person re-identification involves matching pedestrian views where only a part of a body is visible in corresponding images. This reflects practical CCTV surveillance scenario, where full person views are often unavailable. Missing body parts make the comparison very challenging due to significant misalignment and varying scale of the views. We propose Partial Matching Net (PMN) that detects body joints, aligns partial views and hallucinates the missing parts based on the information present in the frame and a learned model of a person. The aligned and reconstructed views are then combined into a joint representation and used for matching images. We evaluate our approach and compare to other methods on three different datasets, demonstrating significant improvements.
Fast dynamic routing based on weighted kernel density estimation

Summary Capsules as well as dynamic routing between them are most recently proposed structures for deep neural networks. A capsule groups data into vectors or matrices as poses rather than conventional scalars to represent specific properties of target instance. Based on pose, a capsule should be attached to a probability (often denoted as activation) for its presence. The dynamic routing helps capsule network achieve more generalization capacity with fewer model parameters. However, the bottleneck, which prevents widespread applications of capsule, is the expense of computation during routing. To address this problem, we generalize existing routing methods within the framework of weighted kernel density estimation, proposing two fast routing methods with different optimization strategies. Our methods prompt the time efficiency of routing by nearly 40% with negligible performance degradation. By stacking a hybrid of convolutional layers and capsule layers, we construct a network architecture to handle inputs at a resolution of 64 × 64 pixels. The proposed models achieve a parallel performance with other leading methods in multiple benchmarks.
Learning Autonomous Exploration and Mapping with Semantic Vision

We address the problem of autonomous exploration and mapping for a mobile robot using visual inputs. Exploration and mapping is a well-known and key problem in robotics, the goal of which is to enable a robot to explore a new environment autonomously and create a map for future usage. Different to classical methods, we propose a learning-based approach this work based on semantic interpretation of visual scenes. Our method is based on a deep network consisting of three modules: semantic segmentation network, mapping using camera geometry and exploration action network. All modules are differentiable, so the whole pipeline is trained end-to- end based on actor-critic framework. Our network makes action decision step by step and generates the free space map simultaneously. To our best knowledge, this is the first algorithm that formulate exploration and mapping into learning framework. We validate our approach in simulated real world environments and demonstrate performance gains over competitive baseline approaches.
Cloud Security Issues Based on People, Process and Technology Model: A Survey

Combination of utility computing and World Wide Web is base of the cloud computing. The intrinsic features of cloud computing have produced many competitive and computing benefits. The development of cloud computing and the expansion of service providers in this area has led to increase in investment in cloud computing. Large number of users on the one hand and increasing the number of sensitive data on cloud environments on the other hand, caused a dramatic growth in the motivation of malicious activities and as a result of security challenges. Solving the cloud computing security challenges need to proper knowledge of the security issues and the scope of their resolution. Security is a long-term product of interaction between people, process and technology. This categorization is based on possible solutions to security issues. Thus, the division of threats in these three areas can help the managers and security sectors to solve the security problems. Hence, in this paper, along with the comprehensive identification of cyber security challenges, we try to address these threats to categories of people, process, and technologies, in order to find cost effective, efficient and feasible security solutions based on this basis.
Safe Policy Improvement with Soft Baseline Bootstrapping

Batch Reinforcement Learning (Batch RL) consists in training a policy using trajectories collected with another policy, called the behavioural policy. Safe policy improvement (SPI) provides guarantees with high probability that the trained policy performs better than the behavioural policy, also called baseline in this setting. Previous work shows that the SPI objective improves mean performance as compared to using the basic RL objective, which boils down to solving the MDP with maximum likelihood (Laroche et al. 2019). Here, we build on that work and improve more precisely the SPI with Baseline Bootstrapping algorithm (SPIBB) by allowing the policy search over a wider set of policies. Instead of binarily classifying the state-action pairs into two sets (the uncertain and the safe-to-train-on ones), we adopt a softer strategy that controls the error in the value estimates by constraining the policy change according to the local model uncertainty. The method can take more risks on uncertain actions all the while remaining provably-safe, and is therefore less conservative than the state-of-the-art methods. We propose two algorithms (one optimal and one approximate) to solve this constrained optimization problem and empirically show a significant improvement over existing SPI algorithms both on finite MDPS and on infinite MDPs with a neural network function approximation.
Generation of 8-Bit S-Boxes Having Almost Optimal Cryptographic Properties Using Smaller 4-Bit S-Boxes and Finite Field Multiplication

Substitution Boxes (S-Boxes) as the only component of nonlinearity in modern ciphers, play a crucial role in the protection against differential, linear and algebraic attacks. The construction of S-Boxes with cryptographic properties close to optimal is an open problem. In this article we propose a new construction for generating such 8-bit permutations with nonlinearity up to a value of 108.
Bell’s inequality, generalized concurrence and entanglement in qubits

It is well known that the maximal violation of the Bell’s inequality for a two-qubit system is related to the entanglement formation in terms of a concurrence. However, a generalization of this relation to an [Formula: see text]-qubit state has not been found. In this paper, we demonstrate some extensions of the relation between the upper bound of the Bell’s violation and a generalized concurrence in several [Formula: see text]-qubit states. In particular, we show the upper bound of the Bell’s violation can be expressed as a function of the generalized concurrence, if a state can be expressed in terms of two variables. We apply the relation to the Wen-Plaquette model and show that the topological entanglement entropy can be extracted from the maximal Bell’s violation.
A Novel Real-Time Match-Moving Method with HoloLens

With the advancement of media and computing technologies, video compositing techniques have improved to a great extent. These techniques have been used not only in the entertainment industry but also in advertisement and new media. Match-moving is a cinematic technology in virtual-real image synthesis that allows the insertion of computer graphics (virtual objects) into real world scenes. To make a realistic virtual-real image synthesis, it is important to obtain internal parameters (such as focal length) and external parameters (position and rotation) from an Red-Green-Blue(RGB) camera. Conventional methods recover these parameters by extracting feature points from recorded video frames to guide the virtual camera. These methods fail when there is occlusion or motion blur in the recorded scene. In this paper, we propose a novel method (system) for pre-visualization and virtual-real image synthesis that overcomes the limitations of conventional methods. This system uses the spatial understanding principle of Microsoft HoloLens to perform the match-moving of virtual-real video scenes. Experimental results demonstrate that our system is much more accurate and efficient than existing systems for video compositing.
Visualizations Out of Context: Addressing Pitfalls of Real-Time Realistic Hazard Visualizations

Realistic 3D hazard visualizations based on advanced Geographic Information Systems (GIS) may be directly driven by hydrodynamic and wind model outputs (e.g., ADCIRC, the ADvanced CIRCulation Model) and hazard impact modeling (e.g., predicting damage to structures and infrastructure). These methods create new possibilities for representing hazard impacts and support the development of near-real-time hazard forecasting and communication tools. This paper considers the wider implications of using these storm visualizations in light of current frameworks in the context of landscape and urban planning and cartography that have addressed the use of realistic 3D visualizations. Visualizations used outside of engagement processes organized by experts risk misleading the public and may have consequences in terms of feelings of individual self-efficacy or perception of scientists behind the visualizations. In addition to summarizing the implications of using these visualizations outside of recommended practices, a research agenda is proposed to guide the development of real-time realistic and semi-realistic visualizations for future use in hazard communication. Development of a clearer use-case for real-time visualization capabilities is an essential first step if such work is to continue.