Reviewerly/exHarmony huggingface dataset:
VRoamer: Generating On-The-Fly VR Experiences While Walking inside Large, Unknown Real-World Building Environments

Procedural generation in virtual reality (VR) has been used to adapt the virtual world to various indoor environments, fitting different geometries and interiors with virtual environments. However, such applications require that the physical environment be known or pre-scanned prior to use to then generate the corresponding virtual scene, thus restricting the virtual experience to a controlled space. In this paper, we present VRoamer, which enables users to walk unseen physical spaces for which VRoamer procedurally generates a virtual scene on-the-fly. Scaling to the size of office buildings, VRoamer extracts walkable areas and detects physical obstacles in real time, instantiates pre-authored virtual rooms if their sizes fit physically walkable areas or otherwise generates virtual corridors and doors that lead to undiscovered physical areas. The use of these virtual structures allows VRoamer to (1) temporarily block users' passage, thus slowing them down while increasing VRoamer's insight into newly discovered physical areas, (2) prevent users from seeing changes beyond the current virtual scene, and (3) obfuscate the appearance of physical environments. VRoamer animates virtual objects to reflect dynamically discovered changes of the physical environment, such as people walking by or obstacles that become apparent. In our proof-of-concept study, participants were able to walk long distances through a procedurally generated dungeon experience and reported high levels of immersion.
Blockchain-Based Privacy-Preserving Healthcare Architecture

Social cues, such as eye gaze and pointing fingers, can increase the prioritisation of specific locations for cognitive processing.A previous study using a manual reaching task showed that, although both gaze and pointing cues altered target prioritisation (reaction times [RTs]), only pointing cues affected action execution (trajectory deviations).These differential effects of gaze and pointing cues on action execution could be because the gaze cue was conveyed through a disembodied head; hence, the model lacked the potential for a body part (i.e., hands) to interact with the target.In the present study, the image of a male gaze model, whose gaze direction coincided with two potential target locations, was centrally presented.The model either had his arms and hands extended underneath the potential target locations, indicating the potential to act on the targets (Experiment 1), or had his arms crossed in front of his chest, indicating the absence of potential to act (Experiment 2).Participants reached to a target that followed a nonpredictive gaze cue at one of three stimulus onset asynchronies.RTs and reach trajectories of the movements to cued and uncued targets were analysed.RTs showed a facilitation effect for both experiments, whereas trajectory analysis revealed facilitatory and inhibitory effects, but only in Experiment 1 when the model could potentially act on the targets.The results of this study suggested that when the gaze model had the potential to interact with the cued target location, the model's gaze affected not only target prioritisation but also movement execution.
Semantic Segmentation with Extended DeepLabv3 Architecture

In this work, semantic segmentation has been dealt with convolutional neural networks (CNN) which is a widely used recent approach in the field of computer vision. In the experiments using Cityscapes dataset, the images are scaled by various rates and the CNN architecture named DeepLabv3 is trained with different hyperparameters using these images. After the training phase, the success rates of the trained models were compared. The most successful DeepLabv3 model has achieved a success rate of 78.83% on Cityscapes test set. Afterwards, an ensemble of two different DeepLabv3 models and the Extended DeepLabv3 model is tested. In test results, while the success rate remains nearly the same, an increase in classes such as road and sidewalk is observed.
Learning to Perform Local Rewriting for Combinatorial Optimization

Search-based methods for hard combinatorial optimization are often guided by heuristics. Tuning heuristics in various conditions and situations is often time-consuming. In this paper, we propose NeuRewriter that learns a policy to pick heuristics and rewrite the local components of the current solution to iteratively improve it until convergence. The policy factorizes into a region-picking and a rule-picking component, each parameterized by a neural network trained with actor-critic methods in reinforcement learning. NeuRewriter captures the general structure of combinatorial problems and shows strong performance in three versatile tasks: expression simplification, online job scheduling and vehicle routing problems. NeuRewriter outperforms the expression simplification component in Z3; outperforms DeepRM and Google OR-tools in online job scheduling; and outperforms recent neural baselines and Google OR-tools in vehicle routing problems.
Single-Model Uncertainties for Deep Learning

We provide single-model estimates of aleatoric and epistemic uncertainty for deep neural networks. To estimate aleatoric uncertainty, we propose Simultaneous Quantile Regression (SQR), a loss function to learn all the conditional quantiles of a given target variable. These quantiles can be used to compute well-calibrated prediction intervals. To estimate epistemic uncertainty, we propose Orthonormal Certificates (OCs), a collection of diverse non-constant functions that map all training samples to zero. These certificates map out-of-distribution examples to non-zero values, signaling epistemic uncertainty. Our uncertainty estimators are computationally attractive, as they do not require ensembling or retraining deep models, and achieve state-of-the-art performance.
Mobile web augmented reality in 5G and beyond: Challenges, opportunities, and future directions

The popularity of wearable devices and smartphones has fueled the development of Mobile Augmented Reality (MAR), which provides immersive experiences over the real world using techniques, such as computer vision and deep learning. However, the hardware-specific MAR is costly and heavy, and the App-based MAR requires an additional download and installation and it also lacks cross-platform ability. These limitations hamper the pervasive promotion of MAR. This paper argues that mobile Web AR (MWAR) holds the potential to become a practical and pervasive solution that can effectively scale to millions of end-users because MWAR can be developed as a lightweight, cross-platform, and low-cost solution for end-to-end delivery of MAR. The main challenges for making MWAR a reality lie in the low efficiency for dense computing in Web browsers, a large delay for real-time interactions over mobile networks, and the lack of standardization. The good news is that the newly emerging 5G and Beyond 5G (B5G) cellular networks can mitigate these issues to some extent via techniques such as network slicing, device-to-device communication, and mobile edge computing. In this paper, we first give an overview of the challenges and opportunities of MWAR in the 5G era. Then we describe our design and development of a generic service-oriented framework (called MWAR5) to provide a scalable, flexible, and easy to deploy MWAR solution. We evaluate the performance of our MWAR5 system in an actually deployed 5G trial network under the collaborative configurations, which shows encouraging results. Moreover, we also share the experiences and insights from our development and deployment, including some exciting future directions of MWAR over 5G and B5G networks.
A Divide-and-Conquer Approach to the Summarization of Long Documents

We present a novel divide-and-conquer method for the neural summarization of long documents. Our method exploits the discourse structure of the document and uses sentence similarity to split the problem into an ensemble of smaller summarization problems. In particular, we break a long document and its summary into multiple source-target pairs, which are used for training a model that learns to summarize each part of the document separately. These partial summaries are then combined in order to produce a final complete summary. With this approach we can decompose the problem of long document summarization into smaller and simpler problems, reducing computational complexity and creating more training examples, which at the same time contain less noise in the target summaries compared to the standard approach. We demonstrate that this approach paired with different summarization models, including sequence-to-sequence RNNs and Transformers, can lead to improved summarization performance. Our best models achieve results that are on par with the state-of-the-art in two two publicly available datasets of academic articles.
A novel UV sensor-based dual-axis solar tracking system: Implementation and performance analysis

The application of photovoltaic (PV) systems has been increasing rapidly worldwide in the field of sustainable electricity generation. However, the efficiency of PV systems depends significantly on the orientation of solar PV modules. To enhance the efficiency of PV systems, previous studies focused mainly on the development of sensor-based solar tracking systems using sun position sensors. In particular, they used light-dependent resistors (LDRs), which detect the visible light spectrum. However, LDRs have significant limitations (e.g., saturation of light intensity and ineffectiveness under low-visibility conditions) that can adversely affect the solar tracking performance. Utilization of ultraviolet (UV) spectrum captured by UV sensors displays the potential for overcoming the limitations of LDRs. This is owing to the enhancement of UV radiation by the cloud effect during overcast conditions and the capability of UV sensors. Considering this, we propose a novel UV sensor-based dual-axis solar tracking system to improve tracking movements and PV energy generation by utilizing the advantages of UV radiation enhancement and UV sensor capability. Four intensity signals of UV radiation obtained by UV sensors are compared and employed as inputs to the solar tracking system after it is implemented on a pseudo-azimuthal mounting structure, which is capable of following the sun trajectory through daily and elevation angles. A comparative analysis is performed to evaluate the solar tracking performance of the proposed solar tracking system with respect to that of a fixed flat-plate system and conventional LDR-based solar tracking system. The experimental results reveal that our tracking system increases energy generation (after accounting for the operational energy consumption) by 19.97% and 11.00% compared with the fixed flat-plate system and LDR-based solar tracking system, respectively. Moreover, the proposed solar tracking system performs effectively in terms of movement tracking under the tested conditions. Finally, the economic performance evaluation demonstrates that the proposed tracking system is profitable.
Causality for Machine Learning

chapter Share on Causality for Machine Learning Author: Bernhard Schölkopf Max Planck Institute for Intelligent Systems Max Planck Institute for Intelligent SystemsSearch about this author Authors Info & Claims Probabilistic and Causal Inference: The Works of Judea PearlFebruary 2022 Pages 765–804https://doi.org/10.1145/3501714.3501755Online:04 March 2022Publication History 0citation111DownloadsMetricsTotal Citations0Total Downloads111Last 12 Months111Last 6 weeks18 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Extreme Learning Machine With Affine Transformation Inputs in an Activation Function

The extreme learning machine (ELM) has attracted much attention over the past decade due to its fast learning speed and convincing generalization performance. However, there still remains a practical issue to be approached when applying the ELM: the randomly generated hidden node parameters without tuning can lead to the hidden node outputs being nonuniformly distributed, thus giving rise to poor generalization performance. To address this deficiency, a novel activation function with an affine transformation (AT) on its input is introduced into the ELM, which leads to an improved ELM algorithm that is referred to as an AT-ELM in this paper. The scaling and translation parameters of the AT activation function are computed based on the maximum entropy principle in such a way that the hidden layer outputs approximately obey a uniform distribution. Application of the AT-ELM algorithm in nonlinear function regression shows its robustness to the range scaling of the network inputs. Experiments on nonlinear function regression, real-world data set classification, and benchmark image recognition demonstrate better performance for the AT-ELM compared with the original ELM, the regularized ELM, and the kernel ELM. Recognition results on benchmark image data sets also reveal that the AT-ELM outperforms several other state-of-the-art algorithms in general.
Satellite-augmented diffuse solar radiation separation models

Separation models predict diffuse horizontal irradiance from other meteorological parameters such as the global horizontal irradiance or zenith angle. From a mathematical point of view, the separation modeling problem is a regression, where the regressors are observed or computed variables and the regressand is the unobserved diffuse fraction. The most successful minute-resolution separation model prior to 2016 was proposed by Engerer, which is constructed using a trend component (cloud enhancement) and a main effect (logistic function). Subsequently, the Starke model published in 2018 further improved the Engerer model. It is herein shown that the logistic-function type of model, and many other separation models, can be transformed into a (condition-based) multiple linear regression problem. Under this transformation framework, two new models are proposed, which strictly dominate the performance of the Engerer model and the Starke model, at all 7 test sites across the continental United States, making them probably the most accurate separation models to date. The new models are also tested at 5 European sites with unseen data (i.e., not involved during model parameter fitting); their performance again dominates all benchmarking models. The new separation models leverage half-hourly satellite-derived diffuse fraction. Since satellite data are available globally, the satellite-augmented separation models have universal applicability. However, despite their good performance, empirical separation models suffer from a series of issues. Hence, models driven by atmospheric physics are the “true gems” that one should pursue.
Information Dashboards and Tailoring Capabilities - A Systematic Literature Review

The design and development of information dashboards are not trivial. Several factors must be accounted; from the data to be displayed to the audience that will use the dashboard. However, the increase in popularity of these tools has extended their use in several and very different contexts among very different user profiles. This popularization has increased the necessity of building tailored displays focused on specific requirements, goals, user roles, situations, domains, etc. Requirements are more sophisticated and varying; thus, dashboards need to match them to enhance knowledge generation and support more complex decision-making processes. This sophistication has led to the proposal of new approaches to address personal requirements and foster individualization regarding dashboards without involving high quantities of resources and long development processes. The goal of this work is to present a systematic review of the literature to analyze and classify the existing dashboard solutions that support tailoring capabilities and the methodologies used to achieve them. The methodology follows the guidelines proposed by Kitchenham and other authors in the field of software engineering. As results, 23 papers about tailored dashboards were retrieved. Three main approaches were identified regarding tailored solutions: customization, personalization, and adaptation. However, there is a wide variety of employed paradigms and features to develop tailored dashboards. The present systematic literature review analyzes challenges and issues regarding the existing solutions. It also identifies new research paths to enhance tailoring capabilities and thus, to improve user experience and insight delivery when it comes to visual analysis.
Deep Scale-spaces: Equivariance Over Scale

We introduce deep scale-spaces, a generalization of convolutional neural networks, exploiting the scale symmetry structure of conventional image recognition tasks. Put plainly, the class of an image is invariant to the scale at which it is viewed. We construct scale equivariant cross-correlations based on a principled extension of convolutions, grounded in the theory of scale-spaces and semigroups. As a very basic operation, these cross-correlations can be used in almost any modern deep learning architecture in a plug-and-play manner. We demonstrate our networks on the Patch Camelyon and Cityscapes datasets, to prove their utility and perform introspective studies to further understand their properties.
Blockchain for smart grid

The demand for electricity increases rapidly along with the advancement of the industrial age. To ensure efficient distribution of the electricity, maintain low losses and high level of quality, and the security of electricity supply, the smart grid concept was proposed. The concept enables a small, individual scale to generate electricity and sell it to the grid. However, the concept adds complexity to the existing system, such as how a transaction between these generators and consumers are conducted, verified and recorded. This paper proposes the blockchain as a tool to manage transactions in the smart grid. Transactions are performed with smart contracts, and the network acts as a transaction verifier. The blockchain provides immutability of the transactions, which ensure every transaction between generators and consumers will always be executed. It also provides immutability to transaction history, which can be used for audit or solving a transaction dispute.
Unsupervised Discovery of Interpretable Directions in the GAN Latent Space

The latent spaces of GAN models often have semantically meaningful directions. Moving in these directions corresponds to human-interpretable image transformations, such as zooming or recoloring, enabling a more controllable generation process. However, the discovery of such directions is currently performed in a supervised manner, requiring human labels, pretrained models, or some form of self-supervision. These requirements severely restrict a range of directions existing approaches can discover. In this paper, we introduce an unsupervised method to identify interpretable directions in the latent space of a pretrained GAN model. By a simple model-agnostic procedure, we find directions corresponding to sensible semantic manipulations without any form of (self-)supervision. Furthermore, we reveal several non-trivial findings, which would be difficult to obtain by existing methods, e.g., a direction corresponding to background removal. As an immediate practical benefit of our work, we show how to exploit this finding to achieve competitive performance for weakly-supervised saliency detection.
Gradient Descent with Early Stopping is Provably Robust to Label Noise for Overparameterized Neural Networks

Modern neural networks are typically trained in an over-parameterized regime where the parameters of the model far exceed the size of the training data. Such neural networks in principle have the capacity to (over)fit any set of labels including pure noise. Despite this, somewhat paradoxically, neural network models trained via first-order methods continue to predict well on yet unseen test data. This paper takes a step towards demystifying this phenomena. Under a rich dataset model, we show that gradient descent is provably robust to noise/corruption on a constant fraction of the labels despite overparameterization. In particular, we prove that: (i) In the first few iterations where the updates are still in the vicinity of the initialization gradient descent only fits to the correct labels essentially ignoring the noisy labels. (ii) to start to overfit to the noisy labels network must stray rather far from from the initialization which can only occur after many more iterations. Together, these results show that gradient descent with early stopping is provably robust to label noise and shed light on the empirical robustness of deep networks as well as commonly adopted heuristics to prevent overfitting.
Shadow Tomography of Quantum States

Related DatabasesWeb of Science You must be logged in with an active subscription to view this.Article DataHistorySubmitted: 25 July 2018Accepted: 19 March 2020Published online: 30 June 2020Keywordsinformation theory, one-way communication, quantum advice, quantum money, quantum computingAMS Subject Headings68Q12, 81P15, 81P45, 81P50Publication DataISSN (print): 0097-5397ISSN (online): 1095-7111Publisher: Society for Industrial and Applied MathematicsCODEN: smjcat
Is Depth Really Necessary for Salient Object Detection?

Salient object detection (SOD) is a crucial and preliminary task for many computer vision applications, which have made progress with deep CNNs. Most of the existing methods mainly rely on the RGB information to distinguish the salient objects, which faces difficulties in some complex scenarios. To solve this, many recent RGBD-based networks are proposed by adopting the depth map as an independent input and fuse the features with RGB information. Taking the advantages of RGB and RGBD methods, we propose a novel depth-aware salient object detection framework, which has following superior designs: 1) It does not rely on depth data in the testing phase. 2) It comprehensively optimizes SOD features with multi-level depth-aware regularizations. 3) The depth information also serves as error-weighted map to correct the segmentation process. With these insightful designs combined, we make the first attempt in realizing an unified depth-aware framework with only RGB information as input for inference, which not only surpasses the state-of-the-art performance on five public RGB SOD benchmarks, but also surpasses the RGBD-based methods on five benchmarks by a large margin, while adopting less information and implementation light-weighted.