Reviewerly/exHarmony huggingface dataset:
Novel short-term solar radiation hybrid model: Long short-term memory network integrated with robust local mean decomposition

• LSTM integrated with Robust Local Mean Decomposition for half-hour solar prediction. • RLMD-LSTM benchmarked against RLMD-MARS, RLMD-SVR, RLMD- Persistence. • RLMD-LSTM evaluated at four solar-rich cities in Vietnam. • RLMD-LSTM outperforms comparative models at Vietnam’s studied sites. Data-intelligent algorithms tailored for short-term energy forecasting can generate meaningful information on the future variability of solar energy developments. Traditional forecasting methods find it relatively difficult to obtain a reliable solar energy monitoring system because of the inherent nonlinearities in solar radiation and the related atmospheric input variables to any forecasting system. This paper proposes a new artificial intelligence-based hybrid model by employing the robust version of local mean decomposition (RLMD) and Long Short-term Memory (LSTM) network denoted as RLMD-LSTM. The objective model ( i.e ., RLMD-LSTM) is built near real-time, half-hourly ground-based solar radiation dataset for the solar rich, metropolitan study sites in Vietnam with all of the forecasting results being benchmarked through classical modelling approaches ( i.e ., Support Vector Regression SVR, Long Short-term Memory LSTM, Multivariate Adaptive Regression Spline MARS, Persistence) as well as the other alternative hybrid methods ( i.e ., RLMD-MARS, RLMD-Persistence and RLMD-SVR). Verified by statistical metrics and visual infographics, the present results demonstrate that the proposed model can generate satisfactory predictions, outperforming several counterpart methods. The predictive performance is stable for all study sites that the root-mean-square error remained profoundly lower for RLMD-LSTM (19–20%) compared with RLMD-MARS (20–21%), RLMD-SVR (29–35%), RLMD- Persistence (29–51%), LSTM (25–48%), MARS (21–51%) and SVR (23–85%), Persistence (29–51%). The Legates and McCabe’s Index, yielding a value of approximately 0.7988–0.9256 for RLMD-LSTM compared with 0.765–0.8142, 0.4917–0.5711, 0.6900–0.7482, 0.6914–0.7646, 0.4349–0.7170 respectively, for the RLMD-MARS, RLMD-SVR, RLMD-Persistence, LSTM, MARS, SVR, Persistence models, also confirms the outstanding performance of RLMD-LSTM model. Accordingly, the study ascertains that the newly designed approach can be a potential candidate for real-time energy management, renewable energy integration into a power grid and other decisions to optimise the overall system's scheduling and performance.
Wavelength-based Attributed Deep Neural Network for Underwater Image Restoration

Background: Underwater images, in general, suffer from low contrast and high color distortions due to the non-uniform attenuation of the light as it propagates through the water. In addition, the degree of attenuation varies with the wavelength, resulting in the asymmetric traversing of colors. Despite the prolific works for underwater image restoration (UIR) using deep learning, the above asymmetricity has not been addressed in the respective network engineering. Contributions: As the first novelty, this article shows that attributing the right receptive field size ( context ) based on the traversing range of the color channel may lead to a substantial performance gain for the task of UIR. Further, it is important to suppress the irrelevant multi-contextual features and increase the representational power of the model. Therefore, as a second novelty, we have incorporated an attentive skip mechanism to adaptively refine the learned multi-contextual features. The proposed framework, called Deep WaveNet , is optimized using the traditional pixel-wise and feature-based cost functions. An extensive set of experiments have been carried out to show the efficacy of the proposed scheme over existing best-published literature on benchmark datasets. More importantly, we have demonstrated a comprehensive validation of enhanced images across various high-level vision tasks, e.g., underwater image semantic segmentation and diver’s 2D pose estimation. A sample video to exhibit our real-world performance is available at https://tinyurl.com/yzcrup9n . Also, we have open-sourced our framework at https://github.com/pksvision/Deep-WaveNet-Underwater-Image-Restoration .
Bargainnet: Background-Guided Domain Translation for Image Harmonization

Given a composite image with inharmonious foreground and background, image harmonization aims to adjust the foreground to make it compatible with the background. Previous image harmonization methods mainly focus on learning the mapping from composite image to real image, while ignoring the crucial guidance role that background plays. In this work, we formulate image harmonization task as background-guided domain translation. Specifically, we use a domain code extractor to capture the background domain information to guide the foreground harmonization, which is regulated by well-tailored triplet losses. Extensive experiments on the benchmark dataset demonstrate the effectiveness of our proposed method. Code is available at https://github.com/bcmi/BargainNet.
Learning to Predict Visual Attributes in the Wild

Visual attributes constitute a large portion of information contained in a scene. Objects can be described using a wide variety of attributes which portray their visual appearance (color, texture), geometry (shape, size, posture), and other intrinsic properties (state, action). Existing work is mostly limited to study of attribute prediction in specific domains. In this paper, we introduce a large-scale in-the-wild visual attribute prediction dataset consisting of over 927K attribute annotations for over 260K object instances. Formally, object attribute prediction is a multi-label classification problem where all attributes that apply to an object must be predicted. Our dataset poses significant challenges to existing methods due to large number of attributes, label sparsity, data imbalance, and object occlusion. To this end, we propose several techniques that systematically tackle these challenges, including a base model that utilizes both low- and high-level CNN features with multi-hop attention, reweighting and resampling techniques, a novel negative label expansion scheme, and a novel supervised attribute-aware contrastive learning algorithm. Using these techniques, we achieve near 3.7 mAP and 5.7 overall F1 points improvement over the current state of the art. Further details about the VAW dataset can be found at https://vawdataset.com/
Relevance-CAM: Your Model Already Knows Where to Look

With increasing fields of application for neural networks and the development of neural networks, the ability to explain deep learning models is also becoming increasingly important. Especially, prior to practical applications, it is crucial to analyze a model’s inference and the process of generating the results. A common explanation method is Class Activation Mapping(CAM) based method where it is often used to understand the last layer of the convolutional neural networks popular in the field of Computer Vision. In this paper, we propose a novel CAM method named Relevance-weighted Class Activation Mapping(Relevance-CAM) that utilizes Layer-wise Relevance Propagation to obtain the weighting components. This allows the explanation map to be faithful and robust to the shattered gradient problem, a shared problem of the gradient based CAM methods that causes noisy saliency maps for intermediate layers. Therefore, our proposed method can better explain a model by correctly analyzing the intermediate layers as well as the last convolutional layer. In this paper, we visualize how each layer of the popular image processing models extracts class specific features using Relevance-CAM, evaluate the localization ability, and show why the gradient based CAM cannot be used to explain the intermediate layers, proven by experimenting the weighting component. Relevance-CAM outperforms other CAM-based methods in recognition and localization evaluation in layers of any depth. The source code is available at: https://github.com/mongeoroo/Relevance-CAM
Clustering Documents based on Semantic Similarity using HAC and K-Mean Algorithms

The continuing success of the Internet has greatly increased the number of text documents in electronic formats. The techniques for grouping these documents into meaningful collections have become mission-critical. The traditional method of compiling documents based on statistical features and grouping did use syntactic rather than semantic. This article introduces a new method for grouping documents based on semantic similarity. This process is accomplished by identifying document summaries from Wikipedia and IMDB datasets, then deriving them using the NLTK dictionary. A vector space afterward is modeled with TFIDF, and the clustering is performed using the HAC and K-mean algorithms. The results are compared and visualized as an interactive webpage.
BoostGCN: A Framework for Optimizing GCN Inference on FPGA

Graph convolutional networks (GCNs) have revolutionized many big data applications, such as recommendation systems, traffic prediction, etc. However, accelerating GCN inference is challenging due to (1) massive external memory traffic and irregular memory access, (2) workload imbalance due to skewed degree distribution, and (3) intra-stage load imbalance caused by two heterogeneous computation phases of the algorithm. To address the above challenges, we propose a framework named BoostGCN to optimize GCN inference on FPGA. First, we develop a novel hardware-aware Partition-Centric Feature Aggregation (PCFA) scheme that leverages 3-D partitioning with the vertex-centric computing paradigm. This increases on-chip data reuse and reduces the total data communication volume with external memory. Second, we design a novel hardware architecture to enable pipelined execution of the two heterogeneous computation phases. We develop a low-overhead task scheduling strategy to reduce the pipeline stalls caused by the two computation phases. Third, we provide a complete GCN acceleration framework on FPGA with optimized RTL templates. It can generate hardware designs based on the customized configuration and is adaptable to various GCN models. Using our framework, we generate accelerators for various GCN models on a state-of-the-art FPGA platform and evaluate our designs using widely used datasets. Experimental results show that the accelerators produced by our framework achieve significant speedup compared with state-of-the-art implementations on CPU (≈ 100×), GPU (≈ 30×), prior FPGA accelerator (3-45)×.
Digital mapping of zinc in urban topsoil using multisource geospatial data and random forest

This study aimed to map the spatial patterns of Zn in urban topsoil by using multisource geospatial data and machine learning method. Geological map, digital elevation models, and Landsat images were used to extract data related to geology, relief, and land use types and a vegetation index. Urban functional types were derived from the fusion of Systeme Probatoire d'Observation de la Terre 5 images, points of interest, and real-time Tencent user data. A geodetector was adopted to select key environmental covariates. Random forest (RF) and geographically weighted regression (GWR) were employed to model and map Zn concentrations in urban topsoil. The results showed that urban functional type, geology, NDVI, elevation, slope, and aspect were key environmental covariates. Compared with land use types, urban functional types could better reflect the spatial variation in Zn. The RF and GWR models were established using the key environmental covariates, with leave-one-out cross-validated R values of 0.68 and 0.58 and root mean square errors of 0.51 and 0.57, respectively. The results indicated that digital mapping of Zn in urban topsoil by using multisource geospatial data and RF was feasible. RF might be more suitable to fit the stochastic characteristics of Zn in urban topsoils than GWR, which considers deterministic trends in modeling.
Blockchain for Increased Trust in Virtual Health Care: Proof-of-Concept Study

Health care systems are currently undergoing a digital transformation that has been primarily triggered by emerging technologies, such as artificial intelligence, the Internet of Things, 5G, blockchain, and the digital representation of patients using (mobile) sensor devices. One of the results of this transformation is the gradual virtualization of care. Irrespective of the care environment, trust between caregivers and patients is essential for achieving favorable health outcomes. Given the many breaches of information security and patient safety, today's health information system portfolios do not suffice as infrastructure for establishing and maintaining trust in virtual care environments.This study aims to establish a theoretical foundation for a complex health care system intervention that aims to exploit a cryptographically secured infrastructure for establishing and maintaining trust in virtualized care environments and, based on this theoretical foundation, present a proof of concept that fulfills the necessary requirements.This work applies the following framework for the design and evaluation of complex intervention research within health care: a review of the literature and expert consultation for technology forecasting. A proof of concept was developed by following the principles of design science and requirements engineering.This study determined and defined the crucial functional and nonfunctional requirements and principles for enhancing trust between caregivers and patients within a virtualized health care environment. The cornerstone of our architecture is an approach that uses blockchain technology. The proposed decentralized system offers an innovative governance structure for a novel trust model. The presented theoretical design principles are supported by a concrete implementation of an Ethereum-based platform called VerifyMed.A service for enhancing trust in a virtualized health care environment that is built on a public blockchain has a high fit for purpose in Healthcare 4.0.
Incremental Few-Shot Instance Segmentation

Few-shot instance segmentation methods are promising when labeled training data for novel classes is scarce. However, current approaches do not facilitate flexible addition of novel classes. They also require that examples of each class are provided at train and test time, which is memory intensive. In this paper, we address these limitations by presenting the first incremental approach to few-shot instance segmentation: iMTFA. We learn discriminative embeddings for object instances that are merged into class representatives. Storing embedding vectors rather than images effectively solves the memory overhead problem. We match these class embeddings at the RoI-level using cosine similarity. This allows us to add new classes without the need for further training or access to previous training data. In a series of experiments, we consistently outperform the current state-of-the-art. Moreover, the reduced memory requirements allow us to evaluate, for the first time, few-shot instance segmentation performance on all classes in COCO jointly <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
SEAR: Secure and Efficient Aggregation for Byzantine-Robust Federated Learning

Federated learning facilitates the collaborative training of a global model among distributed clients without sharing their training data. Secure aggregation, a new security primitive for federated learning, aims to preserve the confidentiality of both local models and training data. Unfortunately, existing secure aggregation solutions fail to defend against Byzantine failures that are common in distributed computing systems. In this work, we propose a new secure and efficient aggregation framework, SEAR, for Byzantine-robust federated learning. Relying on the trusted execution environment, i.e., Intel SGX, SEAR protects clients’ private models while enabling Byzantine resilience. Considering the limitation of the current Intel SGX's architecture (i.e., the limited trusted memory), we propose two data storage modes to efficiently implement aggregation algorithms efficiently in SGX. Moreover, to balance the efficiency and performance of aggregation, we propose a sampling-based method to efficiently detect Byzantine failures without degrading the global model's performance. We implement and evaluate SEAR in a LAN environment, and the experiment results show that SEAR is computationally efficient and robust to Byzantine adversaries. Compared to the previous practical secure aggregation framework, SEAR improves aggregation efficiency by 4-6 times while supporting Byzantine resilience at the same time.
An empirical study of emoji usage on Twitter in linguistic and national contexts

Emojis or ‘picture characters’ have become ubiquitous in modern-day digital communication, including social media sharing and smartphone texting. Despite this ubiquity, many questions remain about their usage, especially with respect to global variations in language and country. These questions are important, in part because they reveal how people communicate digitally on social platforms, but also because they provide a lens through which different regions and cultures can be studied. In this paper, we conduct a principled, quantitative study to understand emoji usage in terms of linguistic and country correlates. Our study involves 30 languages and countries each, and is conducted over tens of millions of tweets collected from the Twitter decahose over an entire month. Drawing on both statistical measures and information theory, our results reveal that, not only does emoji usage have strong dependencies at both the language and country level, but that some languages and countries are much more constrained in the diversity of their emoji usage. However, we also discover that the ‘popularity’ of emojis, both globally and within the context of a given language, follows a robust and invariant trend that emerges fairly quickly (over just a day’s worth of data) and cannot be explained either by a power-law or Heap’s law-like distribution.
Self-attentive deep learning method for online traffic classification and its interpretability

Traffic classification is one of the fundamental tasks in computer networking. This task aims to associate network traffic to a specific class according to the requirements (e.g., QoS provisioning). Online classification, which refers to the situations where flows need to be classified in real time, is an essential technique for this topic. In recent academic research, traffic classification methods based on machine learning (ML) or deep learning (DL) have been proposed. However, most of these methods take flow-level data as input, which requires observing the entire or large portion of a flow and violates the restrictions of online classification. Furthermore, the DL-based methods scarcely discuss the interpretability (e.g., which features are learned by DL, where is the discrimination power from). The lack of interpretability makes people question their reliability and may hinder their further applications. In this paper, we propose a self-attentive method (SAM) for traffic classification. We properly design a neural network whose input can be more fine-grained (i.e., packet-level). This neural network outputs classification results ( ∼ 2 m s per packet) and consequently satisfies the requirements of online classification. Furthermore, a new technique called self-attention mechanism is utilized for interpretability exploration. By assigning attentive weights to different parts of the input, the self-attention mechanism allows us to understand how the DL model learns discriminative features from the input. According to experimental results, SAM outperforms the current state-of-the-art schemes, improving classification accuracy by ∼ 8 % (protocol classification), ∼ 5 % (application classification), and ∼ 13 % (traffic type classification). The code is available at https://github.com/xgr19/SAM-for-Traffic-Classification .
Proposing two novel hybrid intelligence models for forecasting copper price based on extreme learning machine and meta-heuristic algorithms

The focus of this study aims at developing two novel hybrid intelligence models for forecasting copper prices in the future with high accuracy based on the extreme learning machine (ELM) and two meta-heuristic algorithms (i.e., particle swarm optimization (PSO) and genetic algorithm (GA)), named as PSO-ELM and GA-ELM models. Accordingly, the time series datasets of the copper price for thirty years were collected based on the influencing parameters, such as crude oil, iron ore, gold, silver, and natural gas prices. Furthermore, the exchange rate of the four largest countries in copper-producing, including Chile (USD/CLP), China (USD/CNY), Peru (USD/PEN), and Australia (USD/AUD), were also considered to evaluate the copper prices. The GA and PSO algorithms then optimized the weights and biases of the ELM model to reduce the error of the ELM model for forecasting copper price. The traditional ELM model (without optimization), and artificial neural networks (ANN) were also developed as the comparative models for resulting in convincing experimental results in the proposed PSO-ELM and GA-ELM models. The results indicated that the proposed hybrid PSO-ELM and GA-ELM models could forecast copper price with higher accuracy and reliability over the traditional ELM and ANN models. Of those, the PSO-ELM yielded the most dominant accuracy with a root-mean-squared error (RMSE) of 304.943, mean absolute error (MAE) of 241.946, mean absolute percentage error (MAPE) of 0.037, and mean absolute scaled error (MASE) of 0.933. The t-test and Wilcoxon test also demonstrated the statistical significance of the proposed models and the best 95% confident interval of the PSO-ELM model with the range of $177.046 to $67.054 with p-value = 2.589e-05. Whereas, the GA-ELM model provided the forecasted copper price higher $137.233 than the actual copper price, and the 95% confidence interval is from $189.672 to $84.793 with p-value = 1.027e-06.
Towards Accurate 3D Human Motion Prediction from Incomplete Observations

Predicting accurate and realistic future human poses from historically observed sequences is a fundamental task in the intersection of computer vision, graphics, and artificial intelligence. Recently, continuous efforts have been devoted to addressing this issue, which has achieved remarkable progress. However, the existing work is seriously limited by complete observation, that is, once the historical motion sequence is incomplete (with missing values), it can only produce unexpected predictions or even deformities. Furthermore, due to inevitable reasons such as occlusion and the lack of equipment precision, the incompleteness of motion data occurs frequently, which hinders the practical application of current algorithms.In this work, we first notice this challenging problem, i.e., how to generate high-fidelity human motion predictions from incomplete observations. To solve it, we propose a novel multi-task graph convolutional network (MTGCN). Specifically, the model involves two branches, in which the primary task is to focus on forecasting future 3D human actions accurately, while the auxiliary one is to repair the missing value of the incomplete observation. Both of them are integrated into a unified framework to share the spatio-temporal representation, which improves the final performance of each collaboratively. On three large-scale datasets, for various data missing scenarios in the real world, extensive experiments demonstrate that our approach is consistently superior to the state-of-the-art methods in which the missing values from incomplete observations are not explicitly analyzed.