Reviewerly/exHarmony huggingface dataset:
Multi-resolution, multi-horizon distributed solar PV power forecasting with forecast combinations

Distributed, small-scale solar photovoltaic (PV) systems are being installed at a rapidly increasing rate. This can cause major impacts on distribution networks and energy markets. As a result, there is a significant need for improved forecasting of the power generation of these systems at different time resolutions and horizons. However, the performance of forecasting models depends on the resolution and horizon. Forecast combinations (ensembles), that combine the forecasts of multiple models into a single forecast may be robust in such cases. Therefore, in this paper, we provide comparisons and insights into the performance of five state-of-the-art forecast models and existing forecast combinations at multiple resolutions and horizons. We propose a forecast combination approach based on particle swarm optimization (PSO) that will enable a forecaster to produce accurate forecasts for the task at hand by weighting the forecasts produced by individual models. Furthermore, we compare the performance of the proposed combination approach with existing forecast combination approaches. A comprehensive evaluation is conducted using a real-world residential PV power data set measured at 25 houses located in three locations in the United States. The results across four different resolutions and four different horizons show that the PSO-based forecast combination approach outperforms the use of any individual forecast model and other forecast combination counterparts, with an average Mean Absolute Scaled Error reduction by 3.81% compared to the best performing individual model. Our approach enables a solar forecaster to produce accurate forecasts for their application regardless of the forecast resolution or horizon.
WatMIF: Multimodal Medical Image Fusion-Based Watermarking for Telehealth Applications

Over recent years, the volume of big data has drastically increased for medical applications. Such data are shared by cloud providers for storage and further processing. Medical images contain sensitive information, and these images are shared with healthcare workers, patients, and, in some scenarios, researchers for diagnostic and study purposes. However, the security of these images in the transfer process is extremely important, especially after the COVID-19 pandemic. This paper proposes a secure watermarking algorithm, termed WatMIF, based on multimodal medical image fusion. The proposed algorithm consists of three major parts: the encryption of the host media, the fusion of multimodal medical images, and the embedding and extraction of the fused mark. We encrypt the host media with a key-based encryption scheme. Then, a nonsubsampled contourlet transform (NSCT)-based fusion scheme is employed to fuse the magnetic resonance imaging (MRI) and computed tomography (CT) scan images to generate the fused mark image. Furthermore, the encrypted host media conceals the fused watermark using redundant discrete wavelet transform (RDWT) and randomised singular value decomposition (RSVD). Finally, denoising convolutional neural network (DnCNN) is used to improve the robustness of the WatMIF algorithm. The simulation experiments on two standard datasets were used to evaluate the algorithm in terms of invisibility, robustness, and security. When compared with the existing algorithms, the robustness is improved by 20.14%. Overall, the implementation of proposed watermarking for hiding fused marks and efficient encryption improved the identity verification, invisibility, robustness and security criteria in our WatMIF algorithm.
Storage and analysis of light-matter entanglement in a fiber-integrated system

The deployment of a full-fledged quantum internet poses the challenge of finding adequate building blocks for entanglement distribution between remote quantum nodes. A practical system would combine propagation in optical fibers with quantum memories for light, leveraging on the existing communication network while featuring the scalability required to extend to network sizes. Here, we demonstrate a fiber-integrated quantum memory entangled with a photon at telecommunication wavelength. The storage device is based on a fiber-pigtailed laser-written waveguide in a rare earth–doped solid and allows an all-fiber stable addressing of the memory. The analysis of the entanglement is performed using fiber-based interferometers. Our results feature orders-of-magnitude advances in terms of storage time and efficiency for integrated storage of light-matter entanglement and constitute a substantial step forward toward quantum networks using integrated devices.
Ancilla-free implementation of generalized measurements for qubits embedded in a qudit space

Informationally complete (IC) positive operator-valued measures (POVMs) are generalized quantum measurements that offer advantages over the standard computational basis readout of qubits. For instance, IC-POVMs enable efficient extraction of operator expectation values, a crucial step in many quantum algorithms. POVM measurements are typically implemented by coupling one additional ancilla qubit to each logical qubit, thus imposing high demands on the device size and connectivity. Here, we show how to implement a general class of IC-POVMs without ancilla qubits. We exploit the higher-dimensional Hilbert space of a qudit in which qubits are often encoded. POVMs can then be realized by coupling each qubit to two of the available qudit states, followed by a projective measurement. We develop the required control pulse sequences and numerically establish their feasibility for superconducting transmon qubits through pulse-level simulations. Finally, we present an experimental demonstration of a qudit-space POVM measurement on IBM Quantum hardware. This paves the way to making POVM measurements broadly available to quantum computing applications.
<i>C C I B A</i>*: An Improved <i>BA</i>* Based Collaborative Coverage Path Planning Method for Multiple Unmanned Surface Mapping Vehicles

The main emphasis of this work is placed on the problem of collaborative coverage path planning for unmanned surface mapping vehicles (USMVs). As a result, the collaborative coverage improved <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$BA^{*}$ </tex-math></inline-formula> algorithm ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$C C I B A^{*}$ </tex-math></inline-formula> ) is proposed. In the algorithm, coverage path planning for a single vehicle is achieved by task decomposition and level map updating. Then a multiple USMV collaborative behavior strategy is designed, which is composed of area division, recall and transfer, area exchange and recognizing obstacles. Moverover, multiple USMV collaborative coverage path planning can be achieved. Consequently, a high-efficiency and high-quality coverage path for USMVs can be implemented. Water area simulation results indicate that our <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$CCIBA^{*}$ </tex-math></inline-formula> brings about a substantial increase in the performances of path length, number of turning, number of units and coverage rate.
Diversity policy gradient for sample efficient quality-diversity optimization

A fascinating aspect of nature lies in its ability to produce a large and diverse collection of organisms that are all high-performing in their niche. By contrast, most AI algorithms focus on finding a single efficient solution to a given problem. Aiming for diversity in addition to performance is a convenient way to deal with the exploration-exploitation trade-off that plays a central role in learning. It also allows for increased robustness when the returned collection contains several working solutions to the considered problem, making it well-suited for real applications such as robotics. Quality-Diversity (QD) methods are evolutionary algorithms designed for this purpose. This paper proposes a novel algorithm, QDPG, which combines the strength of Policy Gradient algorithms and Quality Diversity approaches to produce a collection of diverse and high-performing neural policies in continuous control environments. The main contribution of this work is the introduction of a Diversity Policy Gradient (DPG) that exploits information at the time-step level to drive policies towards more diversity in a sample-efficient manner. Specifically, QDPG selects neural controllers from a MAP-Elites grid and uses two gradient-based mutation operators to improve both quality and diversity. Our results demonstrate that QDPG is significantly more sample-efficient than its evolutionary competitors.
Reviews and syntheses: The promise of big diverse soil data, moving current practices towards future potential

Abstract. In the age of big data, soil data are more available and richer than ever, but – outside of a few large soil survey resources – they remain largely unusable for informing soil management and understanding Earth system processes beyond the original study. Data science has promised a fully reusable research pipeline where data from past studies are used to contextualize new findings and reanalyzed for new insight. Yet synthesis projects encounter challenges at all steps of the data reuse pipeline, including unavailable data, labor-intensive transcription of datasets, incomplete metadata, and a lack of communication between collaborators. Here, using insights from a diversity of soil, data, and climate scientists, we summarize current practices in soil data synthesis across all stages of database creation: availability, input, harmonization, curation, and publication. We then suggest new soil-focused semantic tools to improve existing data pipelines, such as ontologies, vocabulary lists, and community practices. Our goal is to provide the soil data community with an overview of current practices in soil data and where we need to go to fully leverage big data to solve soil problems in the next century.
A Local and Global Context Focus Multilingual Learning Model for Aspect-Based Sentiment Analysis

Aspect-Based Sentiment Analysis (ABSA) aims to predict the sentiment polarity of different aspects in a sentence or document, which is a fine-grained task of natural language processing. Most of the existing work focuses on the correlation between aspect sentiment polarity and local context. The important deep correlations between global context and aspect sentiment polarity have not received enough attention. Besides, there are few studies on Chinese ABSA tasks and multilingual ABSA tasks. Based on the local context focus mechanism, we propose a multilingual learning model based on the interactive learning of local and global context focus, namely LGCF. Compared with the existing models, this model can effectively learn the correlation between local context and aspect words and the correlation between global context and aspect words simultaneously. In addition, the model can effectively analyze comments in Chinese and English simultaneously. Experiments conducted on three Chinese benchmark datasets(Camera, Phone and Car) and six English benchmark datasets(Laptop, Restaurant14, Restaurant16, Twitter, Tshirt and Television) demonstrate that LGCF has achieved compelling performance and efficiency improvements compared with several existing state-of-the-art models. Moreover, the ablation experiment results also verify the effectiveness of each part in LGCF.
Secure human action recognition by encrypted neural network inference

Abstract Advanced computer vision technology can provide near real-time home monitoring to support “aging in place” by detecting falls and symptoms related to seizures and stroke. Affordable webcams, together with cloud computing services (to run machine learning algorithms), can potentially bring significant social benefits. However, it has not been deployed in practice because of privacy concerns. In this paper, we propose a strategy that uses homomorphic encryption to resolve this dilemma, which guarantees information confidentiality while retaining action detection. Our protocol for secure inference can distinguish falls from activities of daily living with 86.21% sensitivity and 99.14% specificity, with an average inference latency of 1.2 seconds and 2.4 seconds on real-world test datasets using small and large neural nets, respectively. We show that our method enables a 613x speedup over the latency-optimized LoLa and achieves an average of 3.1x throughput increase in secure inference compared to the throughput-optimized nGraph-HE2.
NTIRE 2022 Burst Super-Resolution Challenge

Burst super-resolution has received increased attention in recent years due to its applications in mobile photography. By merging information from multiple shifted images of a scene, burst super-resolution aims to recover details which otherwise cannot be obtained using a simple input image. This paper reviews the NTIRE 2022 challenge on burst super-resolution. In the challenge, the participants were tasked with generating a clean RGB image with 4× higher resolution, given a RAW noisy burst as input. That is, the methods need to perform joint denoising, demosaicking, and super-resolution. The challenge consisted of 2 tracks. Track 1 employed synthetic data, where pixel-accurate high-resolution ground truths are available. Track 2 on the other hand used real-world bursts captured from a handheld camera, along with approximately aligned reference images captured using a DSLR. 14 teams participated in the final testing phase. The top performing methods establish a new state-of-the-art on the burst super-resolution task.
Blockchain-based vehicular ad-hoc networks: A comprehensive survey

Vehicular ad-hoc networks (VANETs) are increasingly commonplace, partly due to the popularity of electric vehicles and the digitalization of cities. Data collected and shared in VANETs include traffic-related information, such as those relating to real-time traffic situations and road works. In recent times, there has been a trend of moving away from a centralized approach to a decentralized approach, for example, using Blockchain to facilitate secure data sharing and traceability of critical information. Hence, in this paper, we comprehensively survey the existing literature on blockchain-based VANET systems, focusing on the application of different blockchain technologies in different contexts, as well as the associated challenges and research opportunities.
A Survey of Deep Learning Architectures for Privacy-Preserving Machine Learning With Fully Homomorphic Encryption

Outsourced computation for neural networks allows users access to state-of-the-art models without investing in specialized hardware and know-how.The problem is that the users lose control over potentially privacy-sensitive data.With homomorphic encryption (HE), a third party can perform computation on encrypted data without revealing its content.In this paper, we reviewed scientific articles and publications in the particular area of Deep Learning Architectures for Privacy-Preserving Machine Learning (PPML) with Fully HE.We analyzed the changes to neural network models and architectures to make them compatible with HE and how these changes impact performance.Next, we find numerous challenges to HEbased privacy-preserving deep learning, such as computational overhead, usability, and limitations posed by the encryption schemes.Furthermore, we discuss potential solutions to the HE PPML challenges.Finally, we propose evaluation metrics that allow for a better and more meaningful comparison of PPML solutions.
When to Prune? A Policy towards Early Structural Pruning

Pruning enables appealing reductions in network memory footprint and time complexity. Conventional post-training pruning techniques lean towards efficient inference while overlooking the heavy computation for training. Recent exploration of pre-training pruning at initialization hints on training cost reduction via pruning, but suffers noticeable performance degradation. We attempt to combine the benefits of both directions and propose a policy that prunes as early as possible during training without hurting performance. Instead of pruning at initialization, our method exploits initial dense training for few epochs to quickly guide the architecture, while constantly evaluating dominant sub-networks via neuron importance ranking. This unveils dominant sub-networks whose structures turn stable, allowing conventional pruning to be pushed earlier into the training. To do this early, we further introduce an Early Pruning Indicator (EPI) that relies on sub-network architectural similarity and quickly triggers pruning when the sub-network's architecture stabilizes. Through extensive experiments on ImageNet, we show that EPI empowers a quick tracking of early training epochs suitable for pruning, offering same efficacy as an otherwise “oracle” grid-search that scans through epochs and requires orders of magnitude more compute. Our method yields 1.4% top-l accuracy boost over state-of-the-art pruning counterparts, cuts down training cost on GPU by 2.4x, hence offers a new efficiency-accuracy boundary for network pruning during training.
Semi-supervised Vision Transformers

We study the training of Vision Transformers for semi-supervised image classification. Transformers have recently demonstrated impressive performance on a multitude of supervised learning tasks. Surprisingly, we show Vision Transformers perform significantly worse than Convolutional Neural Networks when only a small set of labeled data is available. Inspired by this observation, we introduce a joint semi-supervised learning framework, Semiformer, which contains a transformer stream, a convolutional stream and a carefully designed fusion module for knowledge sharing between these streams. The convolutional stream is trained on limited labeled data and further used to generate pseudo labels to supervise the training of the transformer stream on unlabeled data. Extensive experiments on ImageNet demonstrate that Semiformer achieves 75.5% top-1 accuracy, outperforming the state-of-the-art by a clear margin. In addition, we show, among other things, Semiformer is a general framework that is compatible with most modern transformer and convolutional neural architectures. Code is available at https://github.com/wengzejia1/Semiformer .
Reinforcement learning-based distant supervision relation extraction for fault diagnosis knowledge graph construction under industry 4.0

Fault diagnosis is the key concern in the operation and maintenance of industrial assets. A fault diagnosis knowledge graph (KG) can provide decision support to the engineers to efficiently conduct maintenance tasks. However, as a type of domain KG, it would be time-consuming to manually label the corpus collected from the multi-source including the maintenance log, handbook and article. Meanwhile, the existence of the noisy sentence in the multi-source corpus jeopardises the performance of relation extraction modelling. In order to address this issue, this paper proposes a distant supervision relation extraction (DSRE)-based approach to construct a fault diagnosis KG. In this approach, the ontology of the fault diagnosis KG is firstly designed. Subsequently, a DSRE algorithm named relation-aware-based sentence-level attention enhanced piecewise convolutional neural network with reinforcement learning strategy (PCNN-ATTRA-RL) is proposed. The algorithm can effectively lower the impact of noisy sentences and accurately label the relation of different entities when the labelled data is insufficient. In this algorithm, PCNN-ATTRA is designed as the DSRE classifier to effectively extract the relation between entity pairs. RL is conducted to remove the noisy sentence so as to further improve the performance. An experimental study based on the multi-source corpus collected from the real world reveals that the proposed approach shows merits in comparison with the state-of-the-art algorithms. Meanwhile, a fault diagnosis KG, which can greatly support the decision-making of the engineers in the fault diagnosis, is established via the proposed approach.
DBCNet: Dynamic Bilateral Cross-Fusion Network for RGB-T Urban Scene Understanding in Intelligent Vehicles

Understanding urban scenes is a fundamental capability required of intelligent vehicles. Depth cues provide useful geometric information for semantic segmentation, thus complementing RGB (color) data. Although single-modal RGB images are improved by depth information, semantic segmentation may be degraded in poor-visibility conditions. Thermal imaging can address some limitations of depth data. Therefore, we leverage the multimodal information in RGB-and-thermal (RGB-T) images by introducing a dynamic bilateral cross-fusion network (DBCNet) for RGB-T urban scene understanding. First, RGB-T features extracted by a given backbone are regrouped as high-or low-level features. Second, multimodal high-level features are sent to a dynamic bilateral cross-fusion module for further refinement. Third, a bounded high-level semantic-feature integration module is added to provide feature guidance, and a multitask supervision mechanism is used for fine-tuning. Extensive experiments on two RGB-T urban scene-understanding datasets indicate that DBCNet aggregates multilevel deep features effectively and outperforms state-of-the-art deep-learning scene-understanding methods.
SimpleNet: A Simple Network for Image Anomaly Detection and Localization

We propose a simple and application-friendly network (called SimpleNet) for detecting and localizing anoma-lies. SimpleNet consists of four components: (1) a pre-trained Feature Extractor that generates local features, (2) a shallow Feature Adapter that transfers local features to-wards target domain, (3) a simple Anomaly Feature Gener-ator that counterfeits anomaly features by adding Gaussian noise to normal features, and (4) a binary Anomaly Discriminator that distinguishes anomaly features from normal features. During inference, the Anomaly Feature Generator would be discarded. Our approach is based on three in-tuitions. First, transforming pre-trained features to target-oriented features helps avoid domain bias. Second, gen-erating synthetic anomalies in feature space is more effective, as defects may not have much commonality in the image space. Third, a simple discriminator is much efficient and practical. In spite of simplicity, SimpleNet outper-forms previous methods quantitatively and qualitatively. On The MVTec AD benchmark, SimpleNet achieves an anomaly detection AUROC of 99.6%, reducing the error by 55.5% compared to the next best performing model. Further-more, SimpleNet is faster than existing methods, with a high frame rate of 77 FPS on a 3080ti GPU. Additionally, SimpleNet demonstrates significant improvements in per-formance on the One-Class Novelty Detection task. Code: https://github.com/DonaldRR/SimpleNet.