Reviewerly/exHarmony huggingface dataset:
Estimation of future changes in photovoltaic potential in Australia due to climate change

Abstract Solar photovoltaic (PV) energy is one of the fastest growing renewable energy sources globally. However, the dependency of PV generation on climatological factors such as the intensity of radiation, temperature, wind speed, cloud cover, etc can impact future power generation capacity. Considering the future large-scale deployment of PV systems, accurate climate information is essential for PV site selection, stable grid regulation, planning and energy output projections. In this study, the long-term changes in the future PV potential are estimated over Australia using regional climate projections for the near-future (2020–2039) and far-future (2060–2079) periods under a high emission scenario that projects 3.4 °C warming by 2100. The effects of projected changes in shortwave downwelling radiation, temperature and wind speed on the future performance of PV systems over Australia is also examined. Results indicate decline in the future PV potential over most of the continent due to reduced insolation and increased temperature. Northern coastal Australia experiences negligible increase in PV potential during the far future period due to increase in radiation and wind speed in that region. On further investigation, we find that the cell temperatures are projected to increase in the future under a high emission scenario (2.5 °C by 2079), resulting in increased degradation and risks of failure. The elevated cell temperatures significantly contribute to cell efficiency losses, that are expected to increase in the future (6–13 d yr −1 for multi-crystalline silicon cells) mostly around Western and central Australia indicating further reductions in PV power generation. Therefore, long-term PV power projections can help understand the variations in future power generation and identify regions where PV systems will be highly susceptible to losses in Australia.
Reference-guided structure-aware deep sketch colorization for cartoons

Abstract Digital cartoon production requires extensive manual labor to colorize sketches with visually pleasant color composition and color shading. During colorization, the artist usually takes an existing cartoon image as color guidance, particularly when colorizing related characters or an animation sequence. Reference-guided colorization is more intuitive than colorization with other hints, such as color points or scribbles, or text-based hints. Unfortunately, reference-guided colorization is challenging since the style of the colorized image should match the style of the reference image in terms of both global color composition and local color shading. In this paper, we propose a novel learning-based framework which colorizes a sketch based on a color style feature extracted from a reference color image. Our framework contains a color style extractor to extract the color feature from a color image, a colorization network to generate multi-scale output images by combining a sketch and a color feature, and a multi-scale discriminator to improve the reality of the output image. Extensive qualitative and quantitative evaluations show that our method outperforms existing methods, providing both superior visual quality and style reference consistency in the task of reference-based colorization.
Photon Limited Non-Blind Deblurring Using Algorithm Unrolling

Image deblurring in photon-limited conditions is ubiquitous in a variety of low-light applications such as photography, microscopy and astronomy. However, the presence of the photon shot noise due to the low illumination and/or short exposure makes the deblurring task substantially more challenging than the conventional deblurring problems. In this paper, we present an algorithm unrolling approach for the photon-limited deblurring problem by unrolling a Plug-and-Play algorithm for a fixed number of iterations. By introducing a three-operator splitting formation of the Plug-and-Play framework, we obtain a series of differentiable steps which allows the fixed iteration unrolled network to be trained end-to-end. The proposed algorithm demonstrates significantly better image recovery compared to existing state-of-the-art deblurring approaches. We also present a new photon-limited deblurring dataset for evaluating the performance of algorithms.
Bayesian Filtering for Dynamic Anomaly Detection and Tracking

This article presents a Bayesian approach for sequential detection of anomalies in the motion of a target and joint tracking. The anomaly is modeled as a binary (on/off) switching unknown control input that goes into action (begins to exist, or “switches on”) thereby modifying the object dynamics; and by ceasing its activity (becoming nonexistent, or “switching off”) returns the dynamics to nominal. The developed Bayesian framework brings together random finite set (RFS) theory to represent the switching nature of the anomaly, and optimal joint input and state estimation to sequentially update a hybrid state that incorporates a random vector for the kinematic state and a Bernoulli RFS for the unknown control input. In addition, a closed-form solution, the Gaussian-mixture hybrid Bernoulli filter (GM-HBF), has been developed to provide a customized solution for dynamic anomaly detection in the maritime domain characterized by linear Gaussian target dynamics. Based on the Ornstein–Uhlenbeck dynamic model for vessels, where the evolution of the object velocity is governed by a piecewise mean-reverting stochastic process, the anomaly can be represented by a change in the long-run mean velocity (i.e., the unknown control input) from the nominal condition that forces the vessel to deviate from its standard route. We demonstrate the effectiveness of the proposed GM-HBF in both simulated and real-world maritime surveillance applications and test its performance in the face of false measurements, detection uncertainty, and sensor data gaps.
Multi-surface Phase-shifting Interferometry Using Harmonic Frequency Solution Based on the Total Least Squares

Wavelength-tuning phase-shifting interferometry plays a significant role in the measurements of transparent parallel plates. The difficulty of multi-surface measurements is that the harmonics with different frequencies and phases make the captured interference signal mixed. In multi-surface measurements, the demodulation of phases builds on the determination of the frequency of each signal, which is traditionally estimated from the prior information including the cavity length and the optical thickness of the measured plate. However, the estimation accuracy of this method is not satisfactory. In order to accurately obtain the signal frequencies and demodulate the initial phases of the measured surfaces simultaneously, this paper proposes a total least squares wavelength-tuning phase-shifting algorithm (TLSWPA). The developed TLSWPA includes two steps: Firstly, considering the influence of the Gaussian noise on the captured interferograms, the signal frequencies are obtained based on the total least squares principle with high accuracy; Secondly, introducing the Householder transformation, the least squares error based on the 2-Norm is used to demodulate the initial phase distribution for each signal. Comparison of the obtained results of the frequency solution and the accuracy of wavefront reconstruction shows that the developed TLSWPA allows obtaining results with an accuracy 2 orders of magnitude higher than the algorithm based on the fast Fourier transform and 1 order of magnitude higher than the advanced iterative algorithm. Experimental study on a borosilicate crown glass using a Fizeau wavelength-tuning interferometer further verifies its effectiveness.
Integrating Variable Reduction Strategy With Evolutionary Algorithms for Solving Nonlinear Equations Systems

Nonlinear equations systems (NESs) are widely used in real-world problems and they are difficult to solve due to their nonlinearity and multiple roots. Evolutionary algorithms (EAs) are one of the methods for solving NESs, given their global search capabilities and ability to locate multiple roots of a NES simultaneously within one run. Currently, the majority of research on using EAs to solve NESs focuses on transformation techniques and improving the performance of the used EAs. By contrast, problem domain knowledge of NESs is investigated in this study, where we propose the incorporation of a variable reduction strategy (VRS) into EAs to solve NESs. The VRS makes full use of the systems of expressing a NES and uses some variables (i.e., core variable) to represent other variables (i.e., reduced variables) through variable relationships that exist in the equation systems. It enables the reduction of partial variables and equations and shrinks the decision space, thereby reducing the complexity of the problem and improving the search efficiency of the EAs. To test the effectiveness of VRS in dealing with NESs, this paper mainly integrates the VRS into two existing state-of-the-art EA methods (i.e., MONES and DR-JADE) according to the integration framework of the VRS and EA, respectively. Experimental results show that, with the assistance of the VRS, the EA methods can produce better results than the original methods and other compared methods. Furthermore, extensive experiments regarding the influence of different reduction schemes and EAs substantiate that a better EA for solving a NES with more reduced variables tends to provide better performance.
A New SVDD Approach to Reliable and Explainable AI

Safety engineering and artificial intelligence are two fields that still need investigation on their reciprocal interactions. Safety should be guaranteed when autonomous decision may lead to risk for the environment and the human. The present work addresses how support vector data description (SVDD) can be redesigned to detect safety regions in a cyber-physical system with zero statistical error. Rule-based knowledge extraction is also presented, to let the SVDD be understandable. Two applications are considered for performance evaluation: domain name server tunneling detection and region of attraction estimation of dynamic systems. Results demonstrate how the new SVDD and its intelligible representation are both suitable in designing safety regions, still maximizing the space of the working conditions.
Hierarchical and smoothed topographic path planning for large-scale virtual simulation environments

Virtual simulation systems ought to make the most of agents' realistic behaviors so that users feel immersed in virtual scenarios that mimic the real-world environment. Usually used for training and instruction purposes, agent-based simulations have additional requirements to be considered by the Artificial Intelligence solutions often explored in the development of computer games. To provide a valuable impression of how the simulated agents navigate in the real-world terrain, one of the key requirements indicates that topographic terrain characteristics need to be considered in the path planning tasks. It means that important aspects of the agents' navigation capabilities have to be properly handled by the used pathfinding algorithms. This problem is even more challenging when large-scale terrain scenarios are considered in state-of-the-art simulation systems as the path planning may be too time-consuming when handling terrain representations with large search spaces. To approach the challenge of computing safe and realistic path solutions for agents inserted in simulation-based learning environments, this work discusses a hierarchical pathfinding algorithm named HPATheta*, showing how to compute terrain relief-aware smoothed paths in reasonable computing time when dealing with path planning in large-scale real-world virtual terrains. Using different simulated real-world virtual terrains, the proposal is validated by performing an extensive experimental campaign with the use of large-scale virtual terrain representations. The experimental results indicate that the proposed approach is able to efficiently address the targeted problem, showing better results compared to other similar path planning algorithms.
Drivable Dirt Road Region Identification Using Image and Point Cloud Semantic Segmentation Fusion

Driving scene understanding is an essential technique for realizing autonomous driving. Although many large-scale datasets for autonomous driving have enabled studies in terms of driving scene understanding, unpaved dirt roads have not been considered as their target driving environment. Drivable region identification is important in dirt roads to prevent damage to the vehicle and passengers. Semantic segmentation of camera images and lidar point clouds have been used to recognize the environment around an autonomous vehicle. The road and objects are recognized by classifying image pixels and point clouds into semantic classes. In this study, we introduce a perception method for drivable region identification on dirt roads through fusion of image and point cloud semantic segmentation. Our approach includes an image semantic segmentation algorithm and a point cloud semantic segmentation algorithm, which we combine into a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">bird’s-eye-view grid map</i> format. To transform the point-wise drivable region identification results into the area-wise information, we adopt the alphashape algorithm. Speed improvements are made by using small proportion of drivable points, and the accuracy degradation is compensated by accumulating the perception results along the time.
Scrooge

Advances in deep learning (DL) have prompted the development of cloud-hosted DL-based media applications that process video and audio streams in real-time. Such applications must satisfy throughput and latency objectives and adapt to novel types of dynamics, while incurring minimal cost. Scrooge, a system that provides media applications as a service, achieves these objectives by packing computations efficiently into GPU-equipped cloud VMs, using an optimization formulation to find the lowest cost VM allocations that meet the performance objectives, and rapidly reacting to variations in input complexity (e.g., changes in participants in a video). Experiments show that Scrooge can save serving cost by 16-32% (which translate to tens of thousands of dollars per year) relative to the state-of-the-art while achieving latency objectives for over 98% under dynamic workloads.
Fast Haptic Terrain Classification for Legged Robots Using Transformer

The haptic terrain classification is an essential component of a mobile walking robot control system, ensuring proper gait adaptation to the changing environmental conditions. In this work, we further tackle this problem with force and torque measurements from feet while focusing on real-life applicability defined as low computational demand and rapid inference time. To meet these requirements, we propose two classical machine learning algorithms (DTW-KNN and ROCKET) and two deep-learning solutions – a typical feed-forward solution based on temporal convolution network (TCN) and the currently prevailing transformer architecture. The experiments conducted on the publicly available haptic classification dataset revealed that we could reach classification results marginally lower than state of the art with networks containing up to 50 times fewer parameters within an improved inference time of several milliseconds on a CPU.
Characterizing commodity serverless computing platforms

Abstract Serverless computing has become a new trending paradigm in cloud computing, allowing developers to focus on the development of core application logic and rapidly construct the prototype via the composition of independent functions. With the development and prosperity of serverless computing, major cloud vendors have successively rolled out their commodity serverless computing platforms. However, the characteristics of these platforms have not been systematically studied. Measuring these characteristics can help developers to select the most adequate serverless computing platform and develop their serverless‐based applications in the right way. To fill this knowledge gap, we present a comprehensive study on characterizing mainstream commodity serverless computing platforms, including AWS Lambda, Google Cloud Functions, Azure Functions, and Alibaba Cloud Function Compute. Specifically, we conduct both qualitative analysis and quantitative analysis. In qualitative analysis, we compare these platforms from three aspects (i.e., development, deployment, and runtime) based on their official documentation to construct a taxonomy of characteristics. In quantitative analysis, we analyze the runtime performance of these platforms from multiple dimensions with well‐designed benchmarks. First, we analyze three key factors that can influence the startup latency of serverless‐based applications. Second, we compare the resource efficiency of different platforms with 16 representative benchmarks. Finally, we measure their performance difference when dealing with different concurrent requests and explore the potential causes in a black‐box fashion. Based on the results of both qualitative and quantitative analysis, we derive a series of findings and provide insightful implications for both developers and cloud vendors.
Measuring DNS-over-HTTPS performance around the world

In recent years, DNS-over-HTTPS (DoH) has gained significant traction as a privacy-preserving alternative to unencrypted DNS. While several studies have measured DoH performance relative to traditional DNS and other encrypted DNS schemes, they are often incomplete, either conducting measurements from single countries or are unable to compare encrypted DNS to default client behavior. To expand on existing research, we use the BrightData proxy network to gather a dataset consisting of 22,052 unique clients across 224 countries and territories. Our data shows that the performance impact of a switch to DoH is mixed, with a median slowdown of 65ms per query across a 10-query connection, but with 28% of clients receiving a speedup over that same interval. We compare four public DoH providers, noting that Cloudflare excels in both DoH resolution time (265ms) and global points-of-presence (146). Furthermore, we analyze geographic differences between DoH and Do53 resolution times, and provide analysis on possible causes, finding that clients from countries with low Internet infrastructure investment are almost twice as likely to experience a slowdown when switching to DoH as those with high Internet infrastructure investment. We conclude with possible improvements to the DoH ecosystem. We hope that our findings can help to inform continuing DoH deployments.
NFC: a deep and hybrid item-based model for item cold-start recommendation

Abstract New items, also called cold-start items, are introduced every day in the catalogs of numerous online systems. Due to the absence of previous preferences, recommending these items is difficult but important task for a recommender system. For this reason, the item cold-start recommendation problem still represents an interesting research topic for the community. In this work, we propose Neural Feature Combiner ( NFC ), a novel deep learning, item-based approach for cold-start item recommendation. The model learns to map the content features of the items into a low-dimensional hybrid embedding space. The features that compose the embeddings are then combined in order to reproduce collaborative item similarity values. We compare NFC with three variants of the same model that learn from user feedback, showing the advantages of learning from similarities in terms of accuracy and convergence time. With an extensive set of experiments on four datasets, we show that NFC outperforms several cutting edge approaches in the top-n recommendation task of cold-start items. Results in extremely cold (i.e., with a very low amount of interactions for training) and cold-warm hybrid scenarios prove that NFC effectively exploits collaborative information, leading to state-of-the-art accuracy. We finally conduct a qualitative analysis of the embeddings generated by different models, and we provide an analysis of the importance that different models assign to the input features, empirically demonstrating the robustness of the hybrid representations produced by our new model.
Single Morphing Attack Detection Using Feature Selection and Visualization Based on Mutual Information

Face morphing attack detection is a challenging task. Automatic classification methods and manual inspection are realised in automatic border control gates to detect morphing attacks. Understanding how a machine learning system can detect morphed faces and the most relevant facial areas is crucial. Those relevant areas contain texture signals that allow us to separate the bona fide and the morph images. Also, it helps in the manual examination to detect a passport generated with morphed images. This paper explores features extracted from intensity, shape, texture, and proposes a feature selection stage based on the Mutual Information filter to select the most relevant and less redundant features. This selection allows us to reduce the workload and know the exact localisation of such areas to understand the morphing impact and create a robust classifier. The best results were obtained for the method based on Conditional Mutual Information and Shape features using only 500 features for FERET images and 800 features for FRGCv2 images from 1,048 features available. The eyes and nose are identified as the most critical areas to be analysed.
The Relationship between Word Difficulty and Frequency: A Response to Hashimoto (2021)

Hashimoto (2021) reported a correlation of −.50 (r2 = .25) between word frequency rank and difficulty, concluding the construct of modern vocabulary size tests is questionable. In this response we show that the relationship between frequency and difficulty is clear albeit non-linear and demonstrate that if a wider range of frequencies is tested and log transformations are applied, the correlation can approach .80. Finally, while we acknowledge the great promise of knowledge-based word lists, we note that a strong correlation between difficulty and frequency is not, in fact, the primary reason size tests are organized by frequency.
Edge-Preserving Image Filtering Based on Soft Clustering

Edge-preserving image filtering is an essential task in computational photography and imaging. In this paper, we propose a simple yet effective global edge-preserving filter based on soft clustering, and we propose a novel soft clustering algorithm based on a restricted Gaussian mixture model. Given specified parameters, the soft clustering process is firstly performed on the image to derive the partition matrix, from which the affinity matrix is then constructed for filtering. The filtering output is calculated as the weighted average of the pixels in the local window, so the proposed filter could suppress the intensity shift artifacts that impede most global filters. Besides, the weights in the proposed filter are derived by clustering, which properly separates dissimilar pixels, so the proposed filter could handle the halo artifacts that haunt many local filters. Moreover, our filter provides flexible control over the amount of smoothing that is deficient in the deep learning-based filters. Besides the efficacy in smoothing, the proposed filter naturally has low computational complexity. Qualitative and quantitative results suggest that the proposed filter benefits various applications, including edge-preserving smoothing, image enhancing, flash/non-flash fusion, HDR tone mapping, and dehazing.