Reviewerly/exHarmony huggingface dataset:
Comparative study of machine learning approaches for predicting short-term photovoltaic power output based on weather type classification

To combat the worsening global energy shortage, global photovoltaic (PV) installation capacity has been increasing rapidly every year. Since the instability and intermittence of PV power output have great impacts on utility grids, accurate PV power output prediction is crucial. This paper proposes the use of machine learning approaches, combined with a weather type classification method, to predict short-term PV power output. The datasets are collected from a commercial PV power station located in Yangjiang, Guangdong province of China (latitude 21.56 °N, longitude 112.09 °E). Firstly, daytime meteorological data from 07:30 to 18:00 are divided into six 2-h intervals, and then the meteorological conditions of each interval are divided into four categories using an Extremely randomized Trees Classification model according to the PV power generation in each period. Secondly, nine machine learning models are established based on the weather type classification to predict the PV power output. The results show that weather type classification is vital to the selection of appropriate machine learning models and the accurate prediction of PV power output because the characteristic correlation between the meteorological data and PV power output always changes. In general, the Lasso Regressor, Random Forest Regressor, Gradient Boosting Regressor, and Support Vector Regressor models show better performances than the other models. Furthermore, all the models’ accuracy is relatively high when the local meteorological conditions are relatively stable, such as in October, November, and December, during which time the Mean Relative Error values are 2.07, 1.07, and 1.73, respectively. During the period when the weather is unstable, the performance of the SVR model is better than that of the other models. The prediction accuracy can be significantly improved with integrating the accurate weather classification into the model. With regards to each daytime period, the prediction accuracy in the morning and evening is relatively high and the MREs for these times are small. This study provides a theoretical basis for selecting appropriate machine learning models to predict photovoltaic power generation under different weather conditions.
Improving random forest predictions in small datasets from two-phase sampling designs

While random forests are one of the most successful machine learning methods, it is necessary to optimize their performance for use with datasets resulting from a two-phase sampling design with a small number of cases-a common situation in biomedical studies, which often have rare outcomes and covariates whose measurement is resource-intensive.Using an immunologic marker dataset from a phase III HIV vaccine efficacy trial, we seek to optimize random forest prediction performance using combinations of variable screening, class balancing, weighting, and hyperparameter tuning.Our experiments show that while class balancing helps improve random forest prediction performance when variable screening is not applied, class balancing has a negative impact on performance in the presence of variable screening. The impact of the weighting similarly depends on whether variable screening is applied. Hyperparameter tuning is ineffective in situations with small sample sizes. We further show that random forests under-perform generalized linear models for some subsets of markers, and prediction performance on this dataset can be improved by stacking random forests and generalized linear models trained on different subsets of predictors, and that the extent of improvement depends critically on the dissimilarities between candidate learner predictions.In small datasets from two-phase sampling design, variable screening and inverse sampling probability weighting are important for achieving good prediction performance of random forests. In addition, stacking random forests and simple linear models can offer improvements over random forests.
Multi-Content Complementation Network for Salient Object Detection in Optical Remote Sensing Images

In the computer vision community, great progresses have been achieved in salient object detection from natural scene images (NSI-SOD); by contrast, salient object detection in optical remote sensing images (RSI-SOD) remains to be a challenging emerging topic. The unique characteristics of optical RSIs, such as scales, illuminations and imaging orientations, bring significant differences between NSI-SOD and RSI-SOD. In this paper, we propose a novel Multi-Content Complementation Network (MCCNet) to explore the complementarity of multiple content for RSI-SOD. Specifically, MCCNet is based on the general encoder-decoder architecture, and contains a novel key component named Multi-Content Complementation Module (MCCM), which bridges the encoder and the decoder. In MCCM, we consider multiple types of features that are critical to RSI-SOD, including foreground features, edge features, background features, and global image-level features, and exploit the content complementarity between them to highlight salient regions over various scales in RSI features through the attention mechanism. Besides, we comprehensively introduce pixel-level, map-level and metric-aware losses in the training phase. Extensive experiments on two popular datasets demonstrate that the proposed MCCNet outperforms 23 state-of-the-art methods, including both NSI-SOD and RSI-SOD methods. The code and results of our method are available at https://github.com/MathLee/MCCNet.
Radar Deception Jamming Recognition Based on Weighted Ensemble CNN With Transfer Learning

With the development of new active deception jamming, radar antijamming has become a major research hotspot, and the recognition of jamming type is one of its key steps. In recent years, deep learning has been successfully applied in the field of radar jamming recognition, such as convolutional neural networks (CNNs). However, it is difficult to effectively improve the accuracy of deep learning algorithms in the case of small sample. Furthermore, ensemble learning and transfer learning can effectively improve the model generalization performance. For the small sample problem, this article proposes a weighted ensemble CNN with transfer learning (WECNN-TL)-based radar active deception jamming recognition algorithm. The main idea of this method is to obtain the time–frequency distribution maps of jamming signals by the short-time Fourier transform (STFT), and then, their real parts, imaginary parts, moduli, and phases are combined differently to construct multiple datasets. Finally, an ensemble CNN (ECNN) model with weighted voting and transfer learning is constructed to realize jamming recognition. Experiments on the simulated and measured mixed datasets (including 12 types of samples) show that the proposed method can get better recognition performance than random forest (RF), support vector machine (SVM), and some CNN-based methods.
Construction site information decentralized management using blockchain and smart contracts

Abstract The digital management of engineering construction is one of the primary tasks of smart construction sites. In the current practice of construction site information management, there are a series of fraudulent behaviors such as engineering data being forged and modified and this seriously interferes with the safety supervision of engineering projects. In order to improve the security and traceability of multi‐type engineering data, this paper presents a framework of system for construction site information management using blockchain and smart contracts. In this method, multiple independent smart contracts are developed for different data types. It ensures that the system can dynamically deploy new smart contracts without interrupting service, facilitating the expansion of scenario services. More importantly, the data analysis function is extended to the terminal, which allows the integration of different data analysis algorithms such as deep learning into information management. The design breaks the restriction that only rule‐based data analysis can be performed. Meanwhile, the distributed federation architecture ensures that the data cannot be tampered with. Finally, the three most common types of data (namely text, time‐series, and image) were used to verify the security and stability of the management system.
Extension of interval-valued Pythagorean FDOSM for evaluating and benchmarking real-time SLRSs based on multidimensional criteria of hand gesture recognition and sensor glove perspectives

Several DataGlove wearable electronic devices based on real-time Sign Language Recognition Systems (SLRSs) have been developed recently to assist the deaf and dumb community in translating hand gestures to their spoken language equivalents. Multidimensional evaluation and benchmarking of these systems are critical for determining the most desirable approach for meeting all essential requirements. However, this process is considered a Multicriteria Decision-Making (MCDM) problem due to the presence of several issues, including multiple evaluation criteria, criteria importance and criteria confliction. Hence, the MCDM approach is required to solve these issues. In this study, a new extension of the Fuzzy Decision by Opinion Score Method (FDOSM) for evaluating and benchmarking SLRSs is developed under an Interval-Valued Pythagorean Fuzzy Set (IVPFS) named IVP-FDOSM. Fundamentally, the methodology includes two phases. In the first phase, a decision matrix is formulated on the basis of identified ‘multidimensional criteria of hand gesture recognition and sensor glove perspectives’ and ‘real-time SLRSs’. The second phase introduces the development of IVP-FDOSM in two stages. The decision matrix is transformed into an opinion matrix in the first stage (data transformation unit). Meanwhile, in the second stage (data-processing unit), the opinion matrix is converted into fuzzy opinion decision matrices with the assistance of three experts by transforming the opinion matrix’s linguistic terms to Interval-Value Pythagorean Fuzzy Numbers (IVPFNs). Results indicate the following: (1) individual benchmarking results of real-time SLRS showed high variation (90%) based on the preference of each Decision Maker (DM), with only 10% of preferences being identical. (2) The results of group benchmarking reveal that the 10th real-time SLRS was the optimal one and the 6th was the worst. In addition, the rates of ranking match between the group benchmarking and each DM (first DM, second DM and third DM) were 23%, 37% and 43%, respectively. (3) For the results evaluation, the statistical test-based objective assessment shows that the first group received the lowest mean value (2.80333), while the third group received the highest mean (3.8). These values indicate that the group benchmarked systems resulting from IVP-FDOSM are undergoing a systematic ranking. Furthermore, the comparative analysis reveals that IVP-FDOSM is superior to IVP-TOPSIS and IVP-AHP in terms of ranking and weighting.
GraphLily: Accelerating Graph Linear Algebra on HBM-Equipped FPGAs

Graph processing is typically memory bound due to low compute to memory access ratio and irregular data access pattern. The emerging high-bandwidth memory (HBM) delivers exceptional bandwidth by providing multiple channels that can service memory requests concurrently, thus bringing the potential to significantly boost the performance of graph processing. This paper proposes GraphLily, a graph linear algebra overlay, to accelerate graph processing on HBM-equipped FPGAs. GraphLily supports a rich set of graph algorithms by adopting the GraphBLAS programming interface, which formulates graph algorithms as sparse linear algebra operations. GraphLily provides efficient, memory-optimized accelerators for the two widely-used kernels in GraphBLAS, namely, sparse-matrix dense-vector multiplication (SpMV) and sparse-matrix sparse-vector multiplication (SpMSpV). The SpMV accelerator uses a sparse matrix storage format tailored to HBM that enables streaming, vectorized accesses to each channel and concurrent accesses to multiple channels. Besides, the SpMV accelerator exploits data reuse in accesses of the dense vector by introducing a scalable on-chip buffer design. The SpMSpV accelerator complements the SpMV accelerator to handle cases where the input vector has a high sparsity. GraphLily further builds a middleware to provide runtime support. With this middleware, we can port existing GraphBLAS programs to FPGAs with slight modifications to the original code intended for CPU/GPU execution. Evaluation shows that compared with state-of-the-art graph processing frameworks on CPUs and GPUs, GraphLily achieves up to 2.5 x and 1.1 x higher throughput, while reducing the energy consumption by 8.1 x and 2.4 x; compared with prior single-purpose graph accelerators on FPGAs, GraphLily achieves 1.2 x -1.9 x higher throughput.
Multi-strategy fusion differential evolution algorithm for UAV path planning in complex environment

The path planning of Unmanned Aerial Vehicle (UAV) is a real-world optimization problem, and even develops into a hard optimization problem with many objectives and constraints when UAVs work in a complex environment. In a complex environment, the resulting constraints can lead to decrease the quantity of the feasible solutions, so that can bring difficulties to plan routes for UAVs. Therefore, it is necessary to design a high-quality planner for a UAV in a complex environment. In this work, we have proposed a Multi-Strategy Fusion Differential Evolution algorithm (MSFDE). The proposed algorithm integrates the multi-population strategy, the novel self-adaptive strategy and the ensemble of the interactive mutation strategy in order to balance the exploitation and exploration capabilities. The multi-population strategy is used to divide the whole population into the three indicator subpopulations and a reward subpopulation for maintaining the diversity of the whole population; the novel self-adaptive strategy is introduced to control the parameters F and CR based on the teaching-learning-based optimization method; the ensemble of the interactive mutation strategy is to exchange the information among the three indicator subpopulations on each generation for boosting the population diversity. The constraints in the UAV path planning are transformed into the objective functions by the linear weighted sum method. Scenario 1, 2, 3, and 4 are designed with different complex level, and other eight algorithms are introduced to be compared with MSFDE. The simulation results confirm that MSFDE has an outstanding performance for the UAV three-dimensional path planning in the complex environment.
Interaction-Aware Graph Neural Networks for Fault Diagnosis of Complex Industrial Processes

Fault diagnosis of complex industrial processes becomes a challenging task due to various fault patterns in sensor signals and complex interactions between different units. However, how to explore the interactions and integrate with sensor signals remains an open question. Considering that the sensor signals and their interactions in an industrial process with the form of nodes and edges can be represented as a graph, this article proposes a novel interaction-aware and data fusion method for fault diagnosis of complex industrial processes, named interaction-aware graph neural networks (IAGNNs). First, to describe the complex interactions in an industrial process, the sensor signals are transformed into a heterogeneous graph with multiple edge types, and the edge weights are learned by the attention mechanism, adaptively. Then, multiple independent graph neural network (GNN) blocks are employed to extract the fault feature for each subgraph with one edge type. Finally, each subgraph feature is concatenated or fused by a weighted summation function to generate the final graph embedding. Therefore, the proposed method can learn multiple interactions between sensor signals and extract the fault feature from each subgraph by message passing operation of GNNs. The final fault feature contains the information from raw data and implicit interactions between sensor signals. The experimental results on the three-phase flow facility and power system (PS) demonstrate the reliable and superior performance of the proposed method for fault diagnosis of complex industrial processes.
Employees' perceptions of chatbots in B2B marketing: Affordances vs. disaffordances

We investigate the impacts of chatbots' technical features on employees' perceptions (namely, chatbot effectiveness and discomfort with using chatbots) in the context of B2B marketing. In particular, to capture the technical features of chatbots, we identify three types of chatbot affordances (i.e., automatability, personalization, and availability) and three types of chatbot disaffordances (i.e., limited understanding, lack of emotion, and null decision-making). We show that these types of chatbot affordances and disaffordances are related to chatbot effectiveness and discomfort with using chatbots, which in turn can affect employees' attitudes toward chatbots. We conducted an empirical study via an online survey and collected data from 228 B2B marketing employees. While automatability and personalization enhance chatbot effectiveness, null decision-making increases discomfort with using chatbots. Further, chatbot effectiveness and discomfort with using chatbots both influence employees' attitudes toward chatbots. Theoretical and managerial implications are also discussed.
SVDD-based weighted oversampling technique for imbalanced and overlapped dataset learning

Imbalanced dataset classification issue poses a major challenge on machine learning domain. Traditional supervised learning algorithms usually bias towards the majority class when handling imbalanced datasets, thus leading to poor classification results on the minority class. The learning task would become crucially difficult when there are overlapping and within-imbalance issues in imbalanced datasets, which are often the case and have been proven to severely deteriorate the classification performance relative to between-class imbalance. In this paper, we propose a novel SVDD boundary-based weighted oversampling approach (SVDDWSMOTE) for handling imbalanced and overlapped data. The proposed approach first applies support vector data description (SVDD) model with greater penalty constant for the minority class than the majority class to generate the class boundary, and then identifies those misclassified majority or few minority instances by the generated class boundary as potential overlapped or noisy ones and eliminates them. To address the within-balance issues, we propose a weight assignment strategy based on densities and the distances to the SVDD class boundary, which facilitates simultaneously combating between-class and within-class imbalance issues caused by complicated distribution. In addition, such a strategy also favors generating more synthetic minority instances for borderline and sparser instances which are usually informative to the later learning tasks. Finally, oversampling is performed by the weighed SMOTE scheme based on SVDD boundary to not only counteract the within imbalance but also avoid the generation of any noisy or overlapped synthetic instance. Extensive comparison results on various datasets show that the proposed approach achieves statistically significant improvements in terms of different classification performance metrics relative to state-of-the-art ones.
Data security storage mechanism based on blockchain industrial Internet of Things

In the age of Industry 5.0, the industrial Internet of Things (IIoT) system has changed from the original “cyber-physical” system to a complex “human-cyber-physical” system, data security issues become more important. Blockchain technology can be used to ensure the security of IIoT data. However, the traditional blockchain system uses Merkle trees to store data, in which the proof size is large when verifying the integrity and correctness of the data. And it is unable to perform batch verification of the data. Large size proof will bring great pressure to communication, causing end-to-end communication delays, which seriously affect the stability, efficiency, and security of IIoT system. To solve it, in the paper, the incremental aggregator subvector commitment (IASVC) is used to replace Merkle tree, which reduces the size of proof and communication consumption. Each block processes 1000 transactions, the proof size of a single data piece is 15% of the original scheme. Moreover, our scheme can realize the aggregation verification of the proof. In addition, the qualifications of data upload on nodes are set using IASVC, which can reduce the storage pressure of nodes by storing a single commitment instead of the entire qualification list.
NFStream

Network traffic analytics have increased in relevance as researchers promoted machine learning techniques to tackle several traffic management challenges. Over the past decade, the research community and the networking industry have investigated, proposed, and developed a growing number of solutions. However, a large subset of proposed approaches is based on unreliable measurement tools and methodologies. Additionally, some findings are reported on private datasets, which results in a lack of applicability and reproducibility. This paper covers the design and implementation of NFStream, a flexible network data analysis framework. Its key features are flexibility, real-time statistical analysis, and the ability to provide reliable ground truth for modern network usage. NFStream provides the community with a common research framework that can help stimulate research in this field and develop more efficient, reproducible solutions.
Joint-attention feature fusion network and dual-adaptive NMS for object detection

Attention mechanisms and Non-Maximum Suppression (NMS) have proven to be effective components in object detection. However, feature fusion of different scales and layers based on a single attention mechanism cannot always yield gratifying performance, and may introduce redundant information that makes the results worse than expected. NMS methods, on the other hand, generally face the single-constant threshold dilemma, namely, a lower threshold leads to the miss of highly overlapped instance objects while a higher one brings in more false positives. Therefore, how to optimize different dimensions of correlation in feature mapping and how to adaptively set the NMS threshold still hinder effective object detection. While independently addressing each will cause suboptimal detection, this paper proposes to feed the informative feature representation from a joint-attention feature fusion network into adaptive NMS for a comprehensive performance enhancement. Specifically, we embed two types of attention modules in a three-level Feature Pyramid Network (FPN): the channel-attention module is adopted for enhanced feature representation by re-evaluating relationships between channels from a global perspective; the position-attention module is used to exploit the correlation between features to discover rich contextual feature information. Furthermore, we develop dual-adaptive NMS to dynamically adjust the suppression thresholds according to instance objects density, namely, the threshold rises as instance objects gather and decays when objects appear sparsely. The proposed method is evaluated on the COCO dataset and extensive experimental results demonstrate its superior performance compared with existing methods.