Reviewerly/exHarmony huggingface dataset:
Probabilistic modelling of wind turbine power curves with application of heteroscedastic Gaussian Process regression

There exists continued interest in building accurate models of wind turbine power curves for better understanding of performance or assessment of the condition of the turbine or both. Better predictions of the power curve allow increased insight into the operation of the turbine, aid operational decision making, and can be a key feature of online monitoring and fault detection strategies. This work proposes the use of a heteroscedastic Gaussian Process model for this task. The model has a number of attractive properties when modelling power curves. These include, removing the need to specify a parametric functional form for the power curve and automatic quantification of the variance in the prediction. The model exists within a Bayesian framework which exhibits built-in protection against over-fitting and robustness to noisy measurements. The model is shown to be effective on data collected from an operational wind turbine, returning accurate mean predictions (<1% normalised mean-squared error) and higher likelihoods than a corresponding homoscedastic model.
A Case for Multi-Programming Quantum Computers

Existing and near-term quantum computers face significant reliability challenges because of high error rates caused by noise. Such machines are operated in the Noisy Intermediate Scale Quantum (NISQ) model of computing. As NISQ machines exhibit high error-rates, only programs that require a few qubits can be executed reliably. Therefore, NISQ machines tend to underutilize its resources. In this paper, we propose to improve the throughput and utilization of NISQ machines by using multi-programming and enabling the NISQ machine to concurrently execute multiple workloads.
Alleviating Irregularity in Graph Analytics Acceleration

Graph analytics is an emerging application which extracts insights by processing large volumes of highly connected data, namely graphs. The parallel processing of graphs has been exploited at the algorithm level, which in turn incurs three irregularities onto computing and memory patterns that significantly hinder an efficient architecture design. Certain irregularities can be partially tackled by the prior domain-specific accelerator designs with well-designed scheduling of data access, while others remain unsolved.
Data stream mining: methods and challenges for handling concept drift

Abstract Mining and analysing streaming data is crucial for many applications, and this area of research has gained extensive attention over the past decade. However, there are several inherent problems that continue to challenge the hardware and the state-of-the art algorithmic solutions. Examples of such problems include the unbound size, varying speed and unknown data characteristics of arriving instances from a data stream. The aim of this research is to portray key challenges faced by algorithmic solutions for stream mining, particularly focusing on the prevalent issue of concept drift. A comprehensive discussion of concept drift and its inherent data challenges in the context of stream mining is presented, as is a critical, in-depth review of relevant literature. Current issues with the evaluative procedure for concept drift detectors is also explored, highlighting problems such as a lack of established base datasets and the impact of temporal dependence on concept drift detection. By exposing gaps in the current literature, this study suggests recommendations for future research which should aid in the progression of stream mining and concept drift detection algorithms.
Edge Coordinated Query Configuration for Low-Latency and Accurate Video Analytics

To develop smart city and intelligent manufacturing, video cameras are being increasingly deployed. In order to achieve fast and accurate response to live video queries (e.g., license plate recording and object tracking), the real-time high-volume video streams should be delivered and analyzed efficiently. In this article, we introduce an end-edge-cloud coordination framework for low-latency and accurate live video analytics. Considering the locality of video queries, edge platform is designated as the system coordinator. It accepts live video queries and configures the related end cameras to generate video frames that meet quality requirements. By taking into account the latency constraint, edge computing resources are subtly distributed to process the live video frames from different sources such that the analytic accuracy of the accepted video queries can be maximized. Since the amount of required edge computing resource and video quality to accurately address different video queries are unknown in advance, we propose an online video quality and computing resource configuration algorithm to gradually learn the optimal configuration strategy. Extensive simulation results show that as compared to other benchmarks, the proposed configuration algorithm can effectively improve the analytic accuracy, while providing low-latency response.
Neuromorphic Photonics With Coherent Linear Neurons Using Dual-IQ Modulation Cells

Neuromorphic photonics aims to transfer the high-bandwidth and low-energy credentials of optics into neuromorphic computing architectures. In this effort, photonic neurons are trying to combine the optical interconnect segments with optics that can realize all critical constituent neuromorphic functions, including the linear neuron stage and the activation function. However, aligning this new platform with well-established neural network training models in order to allow for the synergy of the photonic hardware with the best-in-class training algorithms, the following requirements should apply: i) the linear photonic neuron has to be able to handle both positive and negative weight values, ii) the activation function has to closely follow the widely used mathematical activation functions that have already shown an enormous performance in demonstrated neural networks so far. Herein, we demonstrate a coherent linear neuron architecture that relies on a dual-IQ modulation cell as its basic neuron element, introducing distinct optical elements for weight amplitude and weight sign representation and exploiting binary optical carrier phase-encoding for positive/negative number representation. We present experimental results of a typical IQ modulator performing as an elementary two-input linear neuron cell and successfully implementing all-optical linear algebraic operations with 104-ps long optical pulses. We also provide the theoretical proof and formulation of how to extend a dual-IQ modulation cell into a complete N-input coherent linear neuron stage that requires only a single-wavelength optical input and avoids the resource-consuming Wavelength Division Multiplexing (WDM) weighting schemes. An 8-input coherent linear neuron is then combined with an experimentally validated optical sigmoid activation function into a physical layer simulation environment, with respective training and physical layer simulation results for the MNIST dataset revealing an average accuracy of 97.24% and 94.37%, respectively.
Deep Fusion Network for Image Completion

Deep image completion usually fails to harmonically blend the restored image into existing content, especially in the boundary area. This paper handles this problem from a new perspective of creating a smooth transition and proposes a concise Deep Fusion Network (DFNet). Firstly, a fusion block is introduced to generate a flexible alpha composition map for combining known and unknown regions. The fusion block not only provides a smooth fusion between restored and existing content but also provides an attention map to make network focus more on the unknown pixels. In this way, it builds a bridge for structural and texture information, so that information can be naturally propagated from the known region into completion. Furthermore, fusion blocks are embedded into several decoder layers of the network. Accompanied by the adjustable loss constraints on each layer, more accurate structure information is achieved. We qualitatively and quantitatively compare our method with other state-of-the-art methods on Places2 and CelebA datasets. The results show the superior performance of DFNet, especially in the aspects of harmonious texture transition, texture detail and semantic structural consistency.
Confronting the scarcity of digital skills among the poor in developing countries

Abstract Motivation As part of the second digital divide”, the scarcity of digital skills among the “bottom billion” in poor countries has been noted in the literature. The typical policy response to this daunting situation is to directly increase the supply of these missing resources. The article does not dispute the need for such policy, but argues in favour of supplementary interventions that bypass this scarcity in a bid to bring the benefit of digital technologies to people living in poverty. Purpose To describe and compare the components of a package of policies to address the scarcity of digital skills in poor countries. The package comprises an increase in the supply of these skills, a choice of technologies that make few demands on them, and to find substitutes that can indirectly supply information from the Internet to those who need it. Approach and methods The approach is based on Albert Hirshman’s distinction between trait‐making , which seeks to increase the supply of resources in short supply, and trait‐taking , which accepts those resources as being given temporarily in the making of policy. The complementary approaches are explained and compared in detail with the use of numerous examples. Findings The brief answer of how best to respond to the lack of digital skills in poor countries is that all components of the policy package are needed, because they make different demands, deal with different aspects of the problem, use different methods and operate in different time periods. With respect to the trait‐taking alternatives, I show the need to make better use of local resources, institutions and cultures, because these are what are available. Policy implications Although most policies to deal with the scarcity of digital skills in poor countries seek directly to increase their supply in schools and other places, this will often be a long and difficult process. For this and other reasons, supplementary policies are also needed to bypass the shortage and yield the benefits of information technology to people living in poverty, within a relatively short period of time.
Blind image super-resolution with spatially variant degradations

Existing deep learning approaches to single image super-resolution have achieved impressive results but mostly assume a setting with fixed pairs of high resolution and low resolution images. However, to robustly address realistic upscaling scenarios where the relation between high resolution and low resolution images is unknown, blind image super-resolution is required. To this end, we propose a solution that relies on three components: First, we use a degradation aware SR network to synthesize the HR image given a low resolution image and the corresponding blur kernel. Second, we train a kernel discriminator to analyze the generated high resolution image in order to predict errors present due to providing an incorrect blur kernel to the generator. Finally, we present an optimization procedure that is able to recover both the degradation kernel and the high resolution image by minimizing the error predicted by our kernel discriminator. We also show how to extend our approach to spatially variant degradations that typically arise in visual effects pipelines when compositing content from different sources and how to enable both local and global user interaction in the upscaling process.
Is the Pen Mightier than the Controller? A Comparison of Input Devices for Selection in Virtual and Augmented Reality

Controllers are currently the typical input device for commercial Virtual Reality (VR) systems. Yet, such controllers are not as efficient as other devices, including the mouse. This motivates us to investigate devices that substantially exceed the controller’s performance, for both VR and Augmented Reality (AR) systems. We performed a user study to compare several input devices, including a mouse, controller, and a 3D pen-like device on a VR and AR pointing task. Our results show that the 3D pen significantly outperforms modern VR controllers in all evaluated measures and that it is comparable to the mouse. Participants also liked the 3D pen more than the controller. Finally, we show how 3D pen devices could be integrated into today’s VR and AR systems.
Survey on Aerial Manipulator: System, Modeling, and Control

SUMMARY The aerial manipulator is a special and new type of flying robot composed of a rotorcraft unmanned aerial vehicle (UAV) and a/several manipulator/s. It has gained a lot of attention since its initial appearance in 2010. This is mainly because it enables traditional UAVs to conduct versatile manipulating tasks from air, considerably enriching their applications. In this survey, a complete and systematic review of related research on this topic is conducted. First, various types of structure designs of aerial manipulators are listed out. Subsequently, the modeling and control methods are introduced in detail from the perspective of two types of typical application cases: free-flight and motion-restricted operations. Finally, challenges for future research are presented.
Exploring Deep Learning Models for Overhead View Multiple Object Detection

The Internet of Things (IoT), with smart sensors, collects and generates big data streams for a wide range of applications. One of the important applications in this regard is video analytics which includes object detection. It has been considered as an important research area particularly after the development of deep neural networks. We demonstrate the applications, effectiveness, and efficiency of the convolutional neural network algorithms, i.e., Faster-RCNN and Mask-RCNN, to facilitate video analytics in the IoT domain, for overhead view multiple object detection and segmentation. We used the Faster-RCNN and Mask-RCNN models trained on the frontal view data set. To evaluate the performance of both algorithms, we used a newly recorded overhead view data set containing images of different objects having variation in field of view, background, illumination condition, poses, scales, sizes, angles, height, aspect ratio, and camera resolutions. Although the overhead view appearance of an object is significantly different as compared to a frontal view, even then the experimental results show the potential of the deep learning models by achieving the promising results. For Faster-RCNN, we achieved a true-positive rate (TPR) of 94% with a false-positive rate (FPR) of 0.4% for the overhead view images of persons, while for other objects the maximum obtained TPR is 92%. The Mask-RCNN model produced TPR of 93% with FPR of 0.5% for person images and maximum TPR of 92% for other objects. Furthermore, the detailed discussion is made on output results which highlights the challenges and possible future directions.
Quantifying the impact of dirty OCR on historical text analysis: Eighteenth Century Collections Online as a case study

Abstract This article aims to quantify the impact optical character recognition (OCR) has on the quantitative analysis of historical documents. Using Eighteenth Century Collections Online as a case study, we first explore and explain the differences between the OCR corpus and its keyed-in counterpart, created by the Text Creation Partnership. We then conduct a series of specific analyses common to the digital humanities: topic modelling, authorship attribution, collocation analysis, and vector space modelling. The article concludes by offering some preliminary thoughts on how these conclusions can be applied to other datasets, by reflecting on the potential for predicting the quality of OCR where no ground-truth exists.
FiNet: Compatible and Diverse Fashion Image Inpainting

Visual compatibility is critical for fashion analysis, yet is missing in existing fashion image synthesis systems. In this paper, we propose to explicitly model visual compatibility through fashion image inpainting. We present Fashion Inpainting Networks (FiNet), a two-stage image-to-image generation framework that is able to perform compatible and diverse inpainting. Disentangling the generation of shape and appearance to ensure photorealistic results, our framework consists of a shape generation network and an appearance generation network. More importantly, for each generation network, we introduce two encoders interacting with one another to learn latent codes in a shared compatibility space. The latent representations are jointly optimized with the corresponding generation network to condition the synthesis process, encouraging a diverse set of generated results that are visually compatible with existing fashion garments. In addition, our framework is readily extended to clothing reconstruction and fashion transfer. Extensive experiments on fashion synthesis quantitatively and qualitatively demonstrate the effectiveness of our method.
Mitigating Leakage in Secure Cloud-Hosted Data Structures

Volume leakage has recently been identified as a major threat to the security of cryptographic cloud-based data structures by Kellaris \em et al. [CCS'16] (see also the attacks in Grubbs \em et al. [CCS'18] and Lacharité \em et al. [S&P'18]). In this work, we focus on volume-hiding implementations of \em encrypted multi-maps as first considered by Kamara and Moataz [Eurocrypt'19]. Encrypted multi-maps consist of outsourcing the storage of a multi-map to an untrusted server, such as a cloud storage system, while maintaining the ability to perform private queries. Volume-hiding encrypted multi-maps ensure that the number of responses (volume) for any query remains hidden from the adversarial server. As a result, volume-hiding schemes can prevent leakage attacks that leverage the adversary's knowledge of the number of query responses to compromise privacy. We present both conceptual and algorithmic contributions towards volume-hiding encrypted multi-maps. We introduce the first formal definition of volume-hiding leakage functions. In terms of design, we present the first volume-hiding encrypted multi-map dprfMM whose storage and query complexity are both asymptotically optimal. Furthermore, we experimentally show that our construction is practically efficient. Our server storage is smaller than the best previous construction while we improve query complexity by a factor of 10-16x. In addition, we introduce the notion of differentially private volume-hiding leakage functions which strikes a better, tunable balance between privacy and efficiency. To accompany our new notion, we present a differentially private volume-hiding encrypted multi-map dpMM whose query complexity is the volume of the queried key plus an additional logarithmic factor. This is a significant improvement compared to all previous volume-hiding schemes whose query overhead was the maximum volume of any key. In natural settings, our construction improves the average query overhead by a factor of 150-240x over the previous best volume-hiding construction even when considering small privacy budget of ε=0.2.
Shape-Aware Human Pose and Shape Reconstruction Using Multi-View Images

We propose a scalable neural network framework to reconstruct the 3D mesh of a human body from multi-view images, in the subspace of the SMPL model. Use of multi-view images can significantly reduce the projection ambiguity of the problem, increasing the reconstruction accuracy of the 3D human body under clothing. Our experiments show that this method benefits from the synthetic dataset generated from our pipeline since it has good flexibility of variable control and can provide ground-truth for validation. Our method outperforms existing methods on real-world images, especially on shape estimations.
Towards Unsupervised Image Captioning With Shared Multimodal Embeddings

Understanding images without explicit supervision has become an important problem in computer vision. In this paper, we address image captioning by generating language descriptions of scenes without learning from annotated pairs of images and their captions. The core component of our approach is a shared latent space that is structured by visual concepts. In this space, the two modalities should be indistinguishable. A language model is first trained to encode sentences into semantically structured embeddings. Image features that are translated into this embedding space can be decoded into descriptions through the same language model, similarly to sentence embeddings. This translation is learned from weakly paired images and text using a loss robust to noisy assignments and a conditional adversarial component. Our approach allows to exploit large text corpora outside the annotated distributions of image/caption data. Our experiments show that the proposed domain alignment learns a semantically meaningful representation which outperforms previous work.
Multimodal Style Transfer via Graph Cuts

An assumption widely used in recent neural style transfer methods is that image styles can be described by global statics of deep features like Gram or covariance matrices. Alternative approaches have represented styles by decomposing them into local pixel or neural patches. Despite the recent progress, most existing methods treat the semantic patterns of style image uniformly, resulting unpleasing results on complex styles. In this paper, we introduce a more flexible and general universal style transfer technique: multimodal style transfer (MST). MST explicitly considers the matching of semantic patterns in content and style images. Specifically, the style image features are clustered into sub-style components, which are matched with local content features under a graph cut formulation. A reconstruction network is trained to transfer each sub-style and render the final stylized result. We also generalize MST to improve some existing methods. Extensive experiments demonstrate the superior effectiveness, robustness, and flexibility of MST.