Reviewerly/exHarmony huggingface dataset:
Stereo vision based obstacle collision avoidance for a quadrotor using ellipsoidal bounding box and hierarchical clustering

A collision avoidance algorithm for multiple unknown static obstacles is proposed for a quadrotor system. A stereo vision system with a limited field of view and sensing range is assumed to be mounted on the quadrotor to obtain obstacle information. An ellipsoid is chosen as a circumscribed bounding box containing the obtained obstacle data points, which can be determined by solving a convex optimization problem. An affine transformation is used to form a collision cone consisting of straight lines tangent to the ellipsoid. A collision condition is examined using the collision cone and velocity vector of the quadrotor. The hierarchical clustering method is proposed to address multiple obstacles, and the bounding boxes are updated using the clusters. Numerical simulations are performed to demonstrate the performance of the proposed collision avoidance algorithm.
IoT Data in the Home: Observing Entanglements and Drawing New Encounters

Internet of Things (IoT) technologies for the home are gaining in popularity, generating exponential data byproducts. Yet, everyday relationships between home dwellers and domestic IoT data often remain secondary interactions, preventing deeper understanding and awareness of data tracked in the home. Our paper offers a design ethnography and design inquiry which examine these human-data entanglements. Findings from working with 10 inhabitants who interact with their IoT data illustrate five characteristics of current data encounters: manifesting, inquiring, exposing, repositioning, and broadening. In response, we used speculative sketches to refine, refract and complicate these encounters. We argue that data do not have to be laborious, tidy or the byproduct of a service, but rather lively and affecting. We further suggest new modes of engagement with data which expand or step away from self-improvement and reflection: through diverse acts of noticing, by allowing data to remain invisible, and by embracing imaginative practices.
Pricing cloud IaaS computing services

Abstract The economics of cloud computing has recently attracted increasing attention. In particular, a topic which is still under debate is how prices charged to customers for cloud resources are formed, since alternative pricing rules could be considered. Based on three pricing schemes inspired by those used by Amazon EC2, the main global cloud service provider, in the paper we address two main issues. First we present a methodology for the relevant parameters of the pricing rules to be determined in an optimal way, that is to maximise the provider’s revenue. Moreover, we discuss reasons for co-existence of three pricing rules, rather than fewer, to access the cloud. Our findings suggest that this may be due to a larger coverage of the potential demand, since customers applying for cloud services vary in their willingness to pay for the job, the time length of the service, the computational power requested etc. Furthermore, the pricing rule in the so-called, spot market, can provide the platform with useful information on the customers willingness to pay for cloud services. This is because in the spot market users offer a price for service, but pay less than that if their request is satisfied.
Resolvable Designs for Speeding Up Distributed Computing

Distributed computing frameworks such as MapReduce are often used to process large computational jobs. They operate by partitioning each job into smaller tasks executed on different servers. The servers also need to exchange intermediate values to complete the computation. Experimental evidence suggests that this so-called Shuffle phase can be a significant part of the overall execution time for several classes of jobs. Prior work has demonstrated a natural tradeoff between computation and communication whereby running redundant copies of jobs can reduce the Shuffle traffic load, thereby leading to reduced overall execution times. For a single job, the main drawback of this approach is that it requires the original job to be split into a number of files that grows exponentially in the system parameters. When extended to multiple jobs (with specific function types), these techniques suffer from a limitation of a similar flavor, i.e., they require an exponentially large number of jobs to be executed. In practical scenarios, these requirements can significantly reduce the promised gains of the method. In this work, we show that a class of combinatorial structures called resolvable designs can be used to develop efficient coded distributed computing schemes for both the single and multiple job scenarios considered in prior work. We present both theoretical analysis and exhaustive experimental results (on Amazon EC2 clusters) that demonstrate the performance advantages of our method. For the single and multiple job cases, we obtain speed-ups of 4.69x (and 2.6x over prior work) and 4.31x over the baseline approach, respectively.
On the computational aspects of Charlier polynomials

Charlier polynomials (CHPs) and their moments are commonly used in image processing due to their salient performance in the analysis of signals and their capability in signal representation. The major issue of CHPs is the numerical instability of coefficients for high-order polynomials. In this study, a new recurrence algorithm is proposed to generate CHPs for high-order polynomials. First, sufficient initial values are obtained mathematically. Second, the reduced form of the recurrence algorithm is determined. Finally, a new symmetry relation for CHPs is realized to reduce the number of recurrence times. The symmetry relation is applied to calculate ∼50% of the polynomial coefficients. The performance of the proposed recurrence algorithm is evaluated in terms of computational cost and reconstruction error. The evaluation involves a comparison with existing recurrence algorithms. Moreover, the maximum size that can be generated using the proposed recurrence algorithm is investigated and compared with those of existing recurrence algorithms. Comparison results; indicate that the proposed algorithm exhibits better performance because it can generate a polynomial 44 times faster than existing recurrence algorithms. In addition, the improvement of the proposed algorithm over the traditional recurrence algorithms in terms of maximum-generated size is between 19.25 and 42.85.
Spatial evaluation of land-use dynamics in gold mining area using remote sensing and GIS technology

Gold mining operations generate a range of ecological and environmental impacts that can be measured spatially using geographic information system and remote sensing methods. This study assessed land-use and land-cover dynamics in the gold mining area using geographic information system and remote sensing techniques with the aid of Landsat 5 for years 1984, 1994, 2004 and Landsat 8 for 2014 and 2019 obtained from the United States Geological Survey and Remote Pixel Databases using ArcGIS 10.4 and R programming. The result from the study revealed the current changes and how they have evolved over the years where tailings dam and built-up areas increased from 90.5 to 172.9 km2 between the year 1984 and 2019, while mine effluent (return water ponds) and water bodies increased from 14.7 to 18.8 km2 during the same period. The area also experienced increased vegetation from 342.5 to 371.1 km2 (though fluctuating during the study period) an indication that the area has witnessed revegetation in the area. Results from the study further revealed the vegetation health in the area utilises some vegetation indices such as the Global Environmental Monitoring Index, Normalised Difference Impervious Surface Index and Normalised Difference Moisture Index. Findings from the study show that areas with low index values are susceptible to the impact of mining and other anthropogenic activities, whereas high-index areas connote little or no impact. The outcome of this study provides a cost-effective tool for evaluating environmental impacts in mining areas that can drive policy interventions for remediation in affected areas.
Detecting causality from time series in a machine learning framework

Detecting causality from observational data is a challenging problem. Here, we propose a machine learning based causality approach, Reservoir Computing Causality (RCC), in order to systematically identify causal relationships between variables. We demonstrate that RCC is able to identify the causal direction, coupling delay, and causal chain relations from time series. Compared to a well-known phase space reconstruction based causality method, Extended Convergent Cross Mapping, RCC does not require the estimation of the embedding dimension and delay time. Moreover, RCC has three additional advantages: (i) robustness to noisy time series; (ii) computational efficiency; and (iii) seamless causal inference from high-dimensional data. We also illustrate the power of RCC in identifying remote causal interactions of high-dimensional systems and demonstrate its usability on a real-world example using atmospheric circulation data. Our results suggest that RCC can accurately detect causal relationships in complex systems.
Fast Learning of Spatially Regularized and Content Aware Correlation Filter for Visual Tracking

With a good balance between accuracy and speed, correlation filter (CF) has become a popular and dominant visual object tracking scheme. It implicitly extends the training samples by circular shifts of a given target patch, which serve as negative samples for fast online learning of the filters. Since all these shifted patches are not real negative samples of the target, CF tracking scheme suffers from the annoying boundary effects that can greatly harm the tracking performance, especially under challenging situations, like occlusion and fast temporal variation. Spatial regularization is known as a potent way to alleviate such boundary effects, but with the cost of highly increased time complexity, caused by complex optimization imported by spatial regularization. In this paper, we propose a new fast learning approach to content-aware spatial regularization, namely weighted sample based CF tracking (WSCF). In WSCF, specifically, we present a simple yet effective energy function that implicitly weighs different training samples by spatial deviations. With the energy function, the learning of correlation filters is composed of two subproblems with closed-form solution and can be efficiently solved in an alternate way. We further develop a content-aware updating strategy to dynamically refine the weight distribution to well adapt to the temporal variations of the target and background. Finally, the proposed WSCF is used to enhance two state-of-the-art CF trackers to significantly boost their tracking accuracy, with little sacrifice on the tracking speed. Extensive experiments on five benchmarks validate the effectiveness of the proposed approach.
Human urbanization algorithm: A novel metaheuristic approach

In the recent decade, the application of metaheuristic algorithms for optimization and solving different problems is increased and various nature-inspired algorithms are designed to improve the quality and convergence speed of getting the answer. In this essay, a novel metaheuristic search for optimization inspired by human behavior for urbanization and improving life situations is proposed. This algorithm uses different strategies including combined searching in an open and limited scope and population management and concentration of agents in the search process. The evaluation results using a wide range of different test functions show the prevalence of the proposed algorithm compared with other potent algorithms in most search spaces.
Novel View Synthesis of Dynamic Scenes With Globally Coherent Depths From a Monocular Camera

This paper presents a new method to synthesize an image from arbitrary views and times given a collection of images of a dynamic scene. A key challenge for the novel view synthesis arises from dynamic scene reconstruction where epipolar geometry does not apply to the local motion of dynamic contents. To address this challenge, we propose to combine the depth from single view (DSV) and the depth from multi-view stereo (DMV), where DSV is complete, i.e., a depth is assigned to every pixel, yet view-variant in its scale, while DMV is view-invariant yet incomplete. Our insight is that although its scale and quality are inconsistent with other views, the depth estimation from a single view can be used to reason about the globally coherent geometry of dynamic contents. We cast this problem as learning to correct the scale of DSV, and to refine each depth with locally consistent motions between views to form a coherent depth estimation. We integrate these tasks into a depth fusion network in a self-supervised fashion. Given the fused depth maps, we synthesize a photorealistic virtual view in a specific location and time with our deep blending network that completes the scene and renders the virtual view. We evaluate our method of depth estimation and view synthesis on a diverse real-world dynamic scenes and show the outstanding performance over existing methods.
CNN-FCM: System modeling promotes stability of deep learning in time series prediction

Time series data are usually non-stationary and evolve over time. Even if deep learning has been found effective in dealing with sequential data, the stability of deep neural networks in coping with the situations unseen during the training stage is also important. This paper deals with this problem based on a fuzzy cognitive block (FCB) which embeds the learning of high-order fuzzy cognitive maps into the deep learning architecture. Thereafter, computers can automatically model the complex system that produces the observation rather than simply regress the available data. Respectively, we design a deep neural network termed CNN-FCM which has combined the available convolution network with FCB. To validate the advantages of our design and verify the effectiveness of FCB, twelve benchmark datasets are employed and classic deep learning architectures are introduced as the comparison. The experimental results show that the performance of many current popular deep learning architectures declines when handling data deviated from the training set. FCB plays an important role in promoting the performance of CNN-FCM in the corresponding experiments. Thereafter, we conclude that system modeling can promote the stability of deep learning in time series prediction.
<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si17.svg"><mml:mrow><mml:msub><mml:mrow><mml:mi>H</mml:mi></mml:mrow><mml:mrow><mml:mi>∞</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math>state estimation for multi-rate artificial neural networks with integral measurements: A switched system approach

In this paper, the H∞ state estimation problem is studied for a class of multi-rate artificial neural networks with integral measurements. A novel method, rather than the widely used lifting technique, is proposed to transform the multi-rate artificial neural networks to single-rate switched ones. The purpose of the addressed H∞ state estimation problem is to design an estimator such that the estimation error dynamics is exponentially stable and the H∞ performance requirement is satisfied. First, with the help of the Lyapunov–Krasovskii functional and the switched system approach, sufficient conditions are derived under which the existence of the desired estimator is ensured. Then, the characterization of the estimator gains is realized by solving certain linear matrix inequalities. Finally, two illustrative examples are given that confirm the usefulness of the developed H∞ state estimation scheme and reveal the influence of the multi-rate sampling on the state estimation performance.
Multiple Site Intraday Solar Irradiance Forecasting by Machine Learning Algorithms: MGGP and MLP Neural Networks

The forecasting of solar irradiance in photovoltaic power generation is an important tool for the integration of intermittent renewable energy sources (RES) in electrical utility grids. This study evaluates two machine learning (ML) algorithms for intraday solar irradiance forecasting: multigene genetic programming (MGGP) and the multilayer perceptron (MLP) artificial neural network (ANN). MGGP is an evolutionary algorithm white-box method and is a novel approach in the field. Persistence, MGGP and MLP were compared to forecast irradiance at six locations, within horizons from 15 to 120 min, in order to compare these methods based on a wide range of reliable results. The assessment of exogenous inputs indicates that the use of additional weather variables improves irradiance forecastability, resulting in improvements of 5.68% for mean absolute error (MAE) and 3.41% for root mean square error (RMSE). It was also verified that iterative predictions improve MGGP accuracy. The obtained results show that location, forecast horizon and error metric definition affect model accuracy dominance. Both Haurwitz and Ineichen clear sky models have been implemented, and the results denoted a low influence of these models in the prediction accuracy of multivariate ML forecasting. In a broad perspective, MGGP presented more accurate and robust results in single prediction cases, providing faster solutions, while ANN presented more accurate results for ensemble forecasting, although it presented higher complexity and requires additional computational effort.
Generalizing Hand Segmentation in Egocentric Videos With Uncertainty-Guided Model Adaptation

Although the performance of hand segmentation in egocentric videos has been significantly improved by using CNNs, it still remains a challenging issue to generalize the trained models to new domains, e.g., unseen environments. In this work, we solve the hand segmentation generalization problem without requiring segmentation labels in the target domain. To this end, we propose a Bayesian CNN-based model adaptation framework for hand segmentation, which introduces and considers two key factors: 1) prediction uncertainty when the model is applied in a new domain and 2) common information about hand shapes shared across domains. Consequently, we propose an iterative self-training method for hand segmentation in the new domain, which is guided by the model uncertainty estimated by a Bayesian CNN. We further use an adversarial component in our framework to utilize shared information about hand shapes to constrain the model adaptation process. Experiments on multiple egocentric datasets show that the proposed method significantly improves the generalization performance of hand segmentation.
Self-Supervised Viewpoint Learning From Image Collections

Training deep neural networks to estimate the viewpoint of objects requires large labeled training datasets. However, manually labeling viewpoints is notoriously hard, error-prone, and time-consuming. On the other hand, it is relatively easy to mine many unlabeled images of an object category from the internet, e.g., of cars or faces. We seek to answer the research question of whether such unlabeled collections of in-the-wild images can be successfully utilized to train viewpoint estimation networks for general object categories purely via self-supervision. Self-supervision here refers to the fact that the only true supervisory signal that the network has is the input image itself. We propose a novel learning framework which incorporates an analysis-by-synthesis paradigm to reconstruct images in a viewpoint aware manner with a generative network, along with symmetry and adversarial constraints to successfully supervise our viewpoint estimation network. We show that our approach performs competitively to fully-supervised approaches for several object categories like human faces, cars, buses, and trains. Our work opens up further research in self-supervised viewpoint learning and serves as a robust baseline for it. We open-source our code at https://github.com/NVlabs/SSV.
MVSNet++: Learning Depth-Based Attention Pyramid Features for Multi-View Stereo

The goal of Multi-View Stereo (MVS) is to reconstruct 3D point-cloud model from multiple views. On the basis of the considerable progress of deep learning, an increasing amount of research has moved from traditional MVS methods to learning-based ones. However, two issues remain unsolved in the existing state-of-the-art methods: (1) only high-level information is considered for depth estimation. This may reduce the localization accuracy of 3D points as the learned model lacks spatial information; and (2) most of the methods require additional post-processing or network refinement to generate a smooth 3D model. This significantly increases the number of model parameters or the computational complexity. To this end, we propose MVSNet++, an end-to-end trainable network for dense depth estimation. Such an estimated depth map can further be applied to 3D model reconstruction. Different from previous methods, in the proposed method, we first adopt feature pyramid structures for both feature extraction and cost volume regularization. This can lead to accurate 3D point localization by fusing multi-level information. To generate smooth depth map, we then carefully integrate instance normalization into MVSNet++ without increasing model parameters and computational burden. Furthermore, we additionally design three loss functions and integrate Curriculum Learning framework into the training process, which can lead to an accurate reconstruction of 3D model. MVSNet++ is evaluated on DTU and Tanks & Temples benchmarks with comprehensive ablation studies. Experimental results demonstrate that our proposed method performs favorably against previous state-of-the-art methods, showing the accuracy and effectiveness of the proposed MVSNet++.
High-Throughput Convolutional Neural Network on an FPGA by Customized JPEG Compression

The growing interest in using FPGAs to accelerate convolutional neural network (CNN) workloads is driving the deployment of FPGAs on cloud services such as Amazon AWS and Microsoft Azure. Such current cloud-based FPGAs have serious problems concerning data transfer bandwidth. In this paper, we compress a transfer image using customized JPEG coding and implement a customized image decoder architecture. We analyze the trade-off between data transfer speed-up and recognition accuracy drop. Based on this compression scheme, we design a high-throughput CNN inference engine. Almost all existing FPGA-based CNN accelerators are based with the same idea as their GPU counterparts, where operations from different network layers are mapped onto the same hardware units working in a multiplexed way. Our fully pipelined architecture maps all the network layers on-chip and transfers the computation from different layers to their unit with independent optimization. We apply two CNN optimization techniques to a residual network, one is a channel shift and point-wise approximation, and the other is a binary weight quantization. We implement the proposed CNN inference accelerator on the Xilinx Virtex UltraScale+ XCVU9P FPGA. Our system peak-performance achieves 2.41 TOPS. Our compressed JPEG image transfer only consumes 4% of the system resource, drops 0.3 points of accuracy and achieves 81,120 FPS which is 65.27 times faster than the conventional straightforward RGB data transfer. Thus, our proposed data transfer architecture is sufficient to increase system performance. As for the system throughput, our system is 3.84-34.41 times higher than existing FPGA implementations. Compared with the Xeon CPU, it achieves 138.38 times higher throughput, and it dissipates 1.2 times lower power, so its efficiency is 177.12 times better. Compared with the Tesla V100 GPU, it achieves 9.48 times higher throughput, dissipates 3.9 times lower power, and its efficiency is 37.52 times better. Thus, our parallel architecture on an FPGA provides superior throughput for the acceleration of a CNN.