Reviewerly/exHarmony huggingface dataset:
Principal Component Analysis and Machine Learning Approaches for Photovoltaic Power Prediction: A Comparative Study

Nowadays, in the context of the industrial revolution 4.0, considerable volumes of data are being generated continuously from intelligent sensors and connected objects. The proper understanding and use of these amounts of data are crucial levers of performance and innovation. Machine learning is the technology that allows the full potential of big datasets to be exploited. As a branch of artificial intelligence, it enables us to discover patterns and make predictions from data based on statistics, data mining, and predictive analysis. The key goal of this study was to use machine learning approaches to forecast the hourly power produced by photovoltaic panels. A comparison analysis of various predictive models including elastic net, support vector regression, random forest, and Bayesian regularized neural networks was carried out to identify the models providing the best predicting results. The principal components analysis used to reduce the dimensionality of the input data revealed six main factor components that could explain up to 91.95% of the variation in all variables. Finally, performance metrics demonstrated that Bayesian regularized neural networks achieved the best results, giving an accuracy of R2 = 99.99% and RMSE = 0.002 kW.
Sensor network data denoising via recursive graph median filters

In wireless sensor networks (WSN) for environmental monitoring, the sensor nodes typically have limited computational and communication resources. Furthermore, the sensors often operate in a harsh environment, and the measurements can be subjected to significant levels of noise. In this work, with these considerations in mind, we propose an efficient method to denoise the sensor data. Using concepts from graph signal processing, the WSN is first modelled using an extended graph. The time-vertex graph jointly models the correlations between neighbouring sensor nodes and across the time dimension. A recursive graph median filter is developed that can be highly localized, and can be implemented with distributed processing. The filter is applied to the denoising of data that is subjected, simultaneously, to Gaussian noise and impulsive noise. Extensive experimental results, using three real-world measurement datasets, will demonstrate that the recursive filter significantly outperforms the equivalent linear filter and nonrecursive median filter, at high noise levels.
An efficient motion planning based on grid map: Predicted Trajectory Approach with global path guiding

This paper presents a Predicted Trajectory Approach (PTA) for global motion planning of an underactuated unmanned surface vehicle (USV). Different from the conventional path planning algorithms which mostly focus on the shortest, safest or smoothest paths, PTA takes all the dynamic constraints of the USV into account, and finds the global trajectory that can be traced precisely via its own maneuvering system. In present strategy, the predicted trajectories produced by the mathematical model of the USV are decomposed into a series of waypoints on grid map. Then an efficient trajectory can be found for the USV by performing the A* search method. To improve the search efficiency and accuracy, the PTA is enhanced with global path guiding (GPTA). When searching for feasible trajectories, GPTA can employ the global path obtained by Theta* to accurately estimate the heuristic cost of the objective function, rather than using the Euclidean distance directly as PTA does. Numerical simulations in various scenarios are conducted to compare the performance of the PTA, GPTA, Trajectory Units (TUs) and hybrid path planning algorithm combining global and local path planning (HGL). The results demonstrate that GPTA has higher safety and navigation efficiency for motion planning of the underactuated USV.
Towards Facet-Driven Generation of Clarifying Questions for Conversational Search

Clarifying an underlying user information need is an important aspect of a modern-day IR system. The importance of clarification is even higher in limited-bandwidth scenarios, such as conversational or mobile search, where a user is unable to easily browse through a long list of retrieved results. Thus, asking clarifying questions about user's potentially ambiguous queries arises as one of the main tasks of conversational search. Recent approaches have, while making significant progress in the field, remained limited to selecting a clarifying question from a predefined set or prompting the user with vague or template-based questions. However, with the recent advances in text generation through large-scale language models, an ideal system should generate the next clarifying question. The challenge of generating an appropriate clarifying question is twofold: 1) to produce the question in coherent natural language; 2) to ask a question that is relevant to the user query. In this paper, we propose a model that generates clarifying questions with respect to the user query and query facets. We fine-tune the GPT-2 language model to generate questions related to the query and one of the extracted query facets. Compared to competitive baselines, results show that our proposed method is both natural and useful, as judged by human annotators. Moreover, we discuss the potential theoretical framework this approach would fit in. We release the code for future work and reproducibility purposes.
DBDnet: A Deep Boosting Strategy for Image Denoising

In this paper, we propose a new deep network architecture named deep boosting denoising net (DBDnet) for image denoising. It is a residual learning network that can generate a noise map from a noisy observation. In detail, it first generates a coarse noise map via a simple structure, and then updates the noise map gradually via a boosting function. The motivation of our DBDnet stems from the observation that the noise map recovered by any algorithm cannot ideally equal the ground-truth noise map, which typically contains noise. We call this noise NoN, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , noise of noise map. Based on this observation, we formulate the denoising as a process of reducing NoN, and the role of DBDnet is to eliminate the NoN from the coarse noise map. In particular, we analyze the process of reducing NoN theoretically, and propose an NoN eliminating module to simulate it accordingly. We evaluate the proposed DBDnet on images polluted by different levels of additive white Gaussian noise and real noise. Experiment results demonstrate that our DBDnet can attain better denoising performance compared with state-of-the-art methods on several kinds of image denoising tasks. In particular, for the Gaussian denoising and real image denoising tasks, the average improvements of the PSNR values brought by our DBDnet are about 0.25 dB and 1.01 dB, respectively. In addition, we find and verify that the deep boosting insight can be easily introduced into the state-of-the-art image denoising network, and promotes its denoising performance. Our code is publicly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/jiayi-ma/DBDNet</uri> .
Hyperspectral Denoising Using Unsupervised Disentangled Spatiospectral Deep Priors

Image denoising is often empowered by accurate prior information. In recent years, data-driven neural network priors have shown promising performance for RGB natural image denoising. Compared to classic handcrafted priors (e.g., sparsity and total variation), the "deep priors" are learned using a large number of training samples -- which can accurately model the complex image generating process. However, data-driven priors are hard to acquire for hyperspectral images (HSIs) due to the lack of training data. A remedy is to use the so-called unsupervised deep image prior (DIP). Under the unsupervised DIP framework, it is hypothesized and empirically demonstrated that proper neural network structures are reasonable priors of certain types of images, and the network weights can be learned without training data. Nonetheless, the most effective unsupervised DIP structures were proposed for natural images instead of HSIs. The performance of unsupervised DIP-based HSI denoising is limited by a couple of serious challenges, namely, network structure design and network complexity. This work puts forth an unsupervised DIP framework that is based on the classic spatio-spectral decomposition of HSIs. Utilizing the so-called linear mixture model of HSIs, two types of unsupervised DIPs, i.e., U-Net-like network and fully-connected networks, are employed to model the abundance maps and endmembers contained in the HSIs, respectively. This way, empirically validated unsupervised DIP structures for natural images can be easily incorporated for HSI denoising. Besides, the decomposition also substantially reduces network complexity. An efficient alternating optimization algorithm is proposed to handle the formulated denoising problem. Semi-real and real data experiments are employed to showcase the effectiveness of the proposed approach.
Minimum-Distortion Embedding

We consider the vector embedding problem. We are given a finite set of items, with the goal of assigning a representative vector to each one, possibly under some constraints (such as the collection of vectors being standardized, i.e., have zero mean and unit covariance). We are given data indicating that some pairs of items are similar, and optionally, some other pairs are dissimilar. For pairs of similar items, we want the corresponding vectors to be near each other, and for dissimilar pairs, we want the corresponding vectors to not be near each other, measured in Euclidean distance. We formalize this by introducing distortion functions, defined for some pairs of the items. Our goal is to choose an embedding that minimizes the total distortion, subject to the constraints. We call this the minimum-distortion embedding (MDE) problem. The MDE framework is simple but general. It includes a wide variety of embedding methods, such as spectral embedding, principal component analysis, multidimensional scaling, dimensionality reduction methods (like Isomap and UMAP), force-directed layout, and others. It also includes new embeddings, and provides principled ways of validating historical and new embeddings alike. We develop a projected quasi-Newton method that approximately solves MDE problems and scales to large data sets. We implement this method in PyMDE, an open-source Python package. In PyMDE, users can select from a library of distortion functions and constraints or specify custom ones, making it easy to rapidly experiment with different embeddings. Our software scales to data sets with millions of items and tens of millions of distortion functions. To demonstrate our method, we compute embeddings for several real-world data sets, including images, an academic co-author network, US county demographic data, and single-cell mRNA transcriptomes.
A wearable-based posture recognition system with AI-assisted approach for healthcare IoT

Human posture recognition is a challenging task in the medical healthcare industry, when pursuing intelligence, accuracy, security, privacy, and efficiency, etc. Currently, the main posture recognition methods are captured-behaviors-based visual image analysis and wearable devices-based signal analysis. However, these methods suffer from issues such as high misjudgment rate, high-cost and low-efficiency. To address these issues, we propose a collaborative AI-IoT-based solution (namely, WMHPR) that embeds with advanced AI-assisted approach. In WMHPR, we propose the multi-posture recognition (MPR), an offline algorithm is implemented on wearable hardware, to identify posture based on multi-dimensions data. Meanwhile, an AI-based algorithm running on the cloud server (online), named Cascade-AdaBoosting-CART (CACT), is proposed to further enhance the reliability and accuracy of MPR. We recruit 20 volunteers for real-life experiments to evaluate the effectiveness, and the results show our solution is significantly outstanding in terms of accuracy and reliability while comparing with other typical algorithms.
Daily Gesture Recognition During Human-Robot Interaction Combining Vision and Wearable Systems

The recognition of human gestures is crucial for improving the quality of human-robot cooperation. This article presents a system composed of a Pepper robot that mounts an RGB-D camera and an inertial device called SensHand. The system acquired data from twenty people who performed five daily living activities (i.e., Having Lunch, Personal Hygiene, Working, House Cleaning, Relax). The activities were composed of at least two “basic” gestures for a total of 10 gestures. The data acquisition was performed by two cameras positioned laterally and frontally to mimic the real conditions. The acquired data were off-line classified considering different combinations of sensors to evaluate how the sensor fusion approach improves the recognition abilities. Specifically, the article presents an experimental study that evaluated four algorithms often used in computer vision, i.e., three classical machine learning and one belonging to the field of deep learning, namely Support Vector Machine, Random Forest, K-Nearest Neighbor and Long Short-Term Memory Recurrent Neural Network. The comparative analysis of the results shows a significant improvement of the accuracy when fusing camera and sensors data, i.e., 0.81 for the whole system configuration when the robot is in a frontal position with respect to the user (0.79 if we consider only the index finger sensors) and equal to 0.75 when the robot is in a lateral position. Interestingly, the system performs well in recognising the transitions between gestures when these are presented one after the other, a common event in the real-life that was often neglected in the previous studies.
MRDDANet: A Multiscale Residual Dense Dual Attention Network for SAR Image Denoising

Synthetic aperture radar (SAR), due to its inherent characteristics, will produce speckle noise, which results in the deterioration of image quality, so the removal of speckle in SAR image is very important for the subsequent high-level image processing. In order to balance the relationship between denoising and texture preservation, we propose a multiscale residual dense dual attention network (MRDDANet) for SAR image denoising. This algorithm can effectively suppress the speckle while fully retaining the texture details of the image. In MRDDANet, shallow features are extracted from the noisy images by multiscale modules with different kernel sizes, and then, the extracted shallow features are mapped to the residual dense dual-attention network to obtain the deep features of SAR image. Finally, the final denoising image is generated through global residual learning. MRDDANet has advantages of both multiscale blocks and residual dense dual attention networks. The dense connection can fully extract features in the image, and the dual-channel attention enables MRDDANet to pay more attention to noise information, which is beneficial to remove noise and keep the details of the original image at the same time. Compared with state-of-the-art algorithms, the results of the experiment indicate that our method not only improves various objective indicators but also shows great advantages in visual effects.
Robust Semantic Segmentation With Multi-Teacher Knowledge Distillation

Recent studies have recently exploited knowledge distillation (KD) technique to address timeconsuming annotation task in semantic segmentation, through which one teacher trained on a single dataset could be leveraged for annotating unlabeled data.However, in this context, knowledge capacity is restricted, and knowledge variety is rare in different conditions, such as cross-model KD, in which the single teacher KD prohibits the student model from distilling information using cross-domain context.To fix this concern, we have looked into learning a lightweight student from a group of teachers.To be more specific, we train five distinct lightweight convolutional neural networks (CNNs) for semantic segmentation on several datasets.Several state-of-the-art augmentation transformations have also been utilized in our training phase.The impacts of such training scenarios are then assessed in terms of student robustness and accuracy.As the main contribution of this paper, our proposed multi-teacher KD paradigm endows the student with the ability to amalgamate and capture a variety of knowledge illustrations from different sources.Results demonstrated that our method outperforms the existing studies on both clean and corrupted data in the semantic segmentation task while benefiting from our proposed score weight system.Experiments validate that our multi-teacher framework results in an improvement of 9% up to 32.18% compared to the singleteacher paradigm.Moreover, it is demonstrated that our paradigm surpasses previous supervised real-time studies in the semantic segmentation challenge.
DHACS: Smart Contract-Based Decentralized Hybrid Access Control for Industrial Internet-of-Things

The integration between blockchains, Internet-of-Thing (IoT), and smart contracts is an emerging and promising technology. The advantages of this technology have raised the importance of Industrial Internet-of-Thing (IIoT) and have paved the pathway for “Industry 4.0.” Surprisingly, access control has received less attention in IIoTs. Though there are some solutions coming forward to use blockchains for IIoT to enable secure and resilient access control management, the challenge is to satisfy the low-latency requirements of IIoTs for validating and adding the blocks to the chain. Besides, role-based and rule-based access controls in the existing systems can be forged without organizational access controls and compliance. Therefore, we address these problems in this article. In the present work, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DHACS</i> , a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Decentralized Hybrid Access Control for Smart contract</i> , for IIoTs. DHACS aims to provide transparency, reliability, and robustness to the existing access control mechanism in IIoTs. The framework is based on blockchain feasibilities that contribute to an interconnected hybrid access control through smart contract provision. It is a novel idea in the domain of IIoTs. We use three access control strategies, role-based, rule-based, and organization-based, to develop a hybrid approach for smart contract in DHACS. The operational transactions along with their access controls are accounted and blocks are made by the transaction pooler and block creator. We use a private blockchain environment; however, it can be extended to a public blockchain or consortium blockchain for geographical distributed dependency. We compare DHACS with three existing approaches in recent time. We measure the performance in terms of computational costs, storage complexity, and energy consumption. DHACS outperforms the others approaches and is considered to be efficient for IIoT applications with more than 30% better efficiency in access control management. To the best of our knowledge, DHACS is the first attempt to use decentralized blockchains with smart contract for hybrid access control in IIoTs.
FedSAE: A Novel Self-Adaptive Federated Learning Framework in Heterogeneous Systems

Federated Learning (FL) is a novel distributed machine learning which allows thousands of edge devices to train model locally without uploading data concentrically to the server. But since real federated settings are resource-constrained, FL is encountered with systems heterogeneity which causes a lot of stragglers directly and then leads to significantly accuracy reduction indirectly. To solve the problems caused by systems heterogeneity, we introduce a novel self-adaptive federated framework FedSAE which adjusts the training task of devices automatically and selects participants actively to alleviate the performance degradation. In this work, we 1) propose FedSAE which leverages the complete information of devices' historical training tasks to predict the affordable training workloads for each device. In this way, FedSAE can estimate the reliability of each device and self-adaptively adjust the amount of training load per client in each round. 2)combine our framework with Active Learning to self-adaptively select participants. Then the framework accelerates the convergence of the global model. In our framework, the server evaluates devices' value of training based on their training loss. Then the server selects those clients with bigger value for the global model to reduce communication overhead. The experimental result indicates that in a highly heterogeneous system, FedSAE converges faster than FedAvg, the vanilla FL framework. Furthermore, FedSAE outperforms than FedAvg on several federated datasets - FedSAE improves test accuracy by 26.7% and reduces stragglers by 90.3% on average.
Emoji for the Medical Community—Challenges and Opportunities

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End-to-End Federated Learning for Autonomous Driving Vehicles

In recent years, with the development of computation capability in devices, companies are eager to investigate and utilize suitable ML/DL methods to improve their service quality. However, with the traditional learning strategy, companies need to first build up a powerful data center to collect and analyze data from the edge and then perform centralized model training, which turns out to be inefficient. Federated Learning has been introduced to solve this challenge. Because of its characteristics such as model-only exchange and parallel training, the technique can not only preserve user data privacy but also accelerate model training speed. The method can easily handle real-time data generated from the edge without taking up a lot of valuable network transmission resources. In this paper, we introduce an approach to end-to-end on-device Machine Learning by utilizing Federated Learning. We validate our approach with an important industrial use case in the field of autonomous driving vehicles, the wheel steering angle prediction. Our results show that Federated Learning can significantly improve the quality of local edge models and also reach the same accuracy level as compared to the traditional centralized Machine Learning approach without its negative effects. Furthermore, Federated Learning can accelerate model training speed and reduce the communication overhead, which proves that this approach has great strength when deploying ML/DL components to various real-world embedded systems.
Unraveling Two-Photon Entanglement via the Squeezing Spectrum of Light Traveling through Nanofiber-Coupled Atoms

We observe that a weak guided light field transmitted through an ensemble of atoms coupled to an optical nanofiber exhibits quadrature squeezing. From the measured squeezing spectrum we gain direct access to the phase and amplitude of the energy-time entangled part of the two-photon wavefunction which arises from the strongly correlated transport of photons through the ensemble. For small atomic ensembles we observe a spectrum close to the lineshape of the atomic transition, while sidebands are observed for sufficiently large ensembles, in agreement with our theoretical predictions. Furthermore, we vary the detuning of the probe light with respect to the atomic resonance and infer the phase of the entangled two-photon wavefunction. From the amplitude and the phase of the spectrum, we reconstruct the real- and imaginary part of the time-domain wavefunction. Our characterization of the entangled two-photon component constitutes a diagnostic tool for quantum optics devices.