Reviewerly/exHarmony huggingface dataset:
Hydrological classification by clustering approach of time-integrated samples at the outlet of the Rhône River: Application to Δ14C-POC

Within the framework of the Rhône Sediment Observatory, monthly time-integrated samples have been collected by Particle Traps in the last decade to monitor particulate contaminants in the Rhône River and its main tributaries. In this watershed with a contrasted hydrology, a clustering approach is used to classify the samples according to the main hydrological events. This approach has been applied to riverine particulate organic radiocarbon signatures (Δ14C-POC) that are strongly affected by the origin of the material and the occurrence of nuclear power plant releases. Suspended Particulate Matter (SPM) samples were collected near the outlet of the Rhône River and analysed for 14C along with particulate organic carbon (POC), chlorophyll a and tritium contents to confirm Δ14C-POC origins. Cluster Analysis, coupled to Principal Component Analysis, was performed based on monthly average water discharges of the Upper Rhône River and the five main tributaries. The classification obtained by fuzzy C-mean logic of the Rhône River hydrology into 5 clusters is similar to that already observed in the literature with Mediterranean/Cevenol flood, oceanic pluvial flood, nival flood, low-water level and baseflow clusters. The contributions of each cluster among the Δ14C-POC values demonstrate the complexity of hydrological classification of time-integrated samples. First, the samples with a unique and significantly dominant cluster are easily explained with negative Δ14C-POC values observed in the flood clusters due to input of 14C-depleted material from soil or rock weathering, and positive values observed in the low-water level and baseflow clusters due to anthropogenic input by nuclear industry. Second, samples that present a homogeneous mixture between several clusters demonstrate the occurrence of different hydrological events during the sampling periods. This tool appears as a solution to estimate the contribution of each hydrological event in time-integrated samples.
An efficient geometric approach to quantum-inspired classifications

Optimal measurements for the discrimination of quantum states are useful tools for classification problems. In order to exploit the potential of quantum computers, feature vectors have to be encoded into quantum states represented by density operators. However, quantum-inspired classifiers based on nearest mean and on Helstrom state discrimination are implemented on classical computers. We show a geometric approach that improves the efficiency of quantum-inspired classification in terms of space and time acting on quantum encoding and allows one to compare classifiers correctly in the presence of multiple preparations of the same quantum state as input. We also introduce the nearest mean classification based on Bures distance, Hellinger distance and Jensen-Shannon distance comparing the performance with respect to well-known classifiers applied to benchmark datasets.
Bayesian network-based over-sampling method (BOSME) with application to indirect cost-sensitive learning

Traditional supervised learning algorithms do not satisfactorily solve the classification problem on imbalanced data sets, since they tend to assign the majority class, to the detriment of the minority class classification. In this paper, we introduce the Bayesian network-based over-sampling method (BOSME), which is a new over-sampling methodology based on Bayesian networks. Over-sampling methods handle imbalanced data by generating synthetic minority instances, with the benefit that classifiers learned from a more balanced data set have a better ability to predict the minority class. What makes BOSME different is that it relies on a new approach, generating artificial instances of the minority class following the probability distribution of a Bayesian network that is learned from the original minority classes by likelihood maximization. We compare BOSME with the benchmark synthetic minority over-sampling technique (SMOTE) through a series of experiments in the context of indirect cost-sensitive learning, with some state-of-the-art classifiers and various data sets, showing statistical evidence in favor of BOSME, with respect to the expected (misclassification) cost.
Identifying Outliers in Astronomical Images with Unsupervised Machine Learning

Abstract Astronomical outliers, such as unusual, rare or unknown types of astronomical objects or phenomena, constantly lead to the discovery of genuinely unforeseen knowledge in astronomy. More unpredictable outliers will be uncovered in principle with the increment of the coverage and quality of upcoming survey data. However, it is a severe challenge to mine rare and unexpected targets from enormous data with human inspection due to a significant workload. Supervised learning is also unsuitable for this purpose because designing proper training sets for unanticipated signals is unworkable. Motivated by these challenges, we adopt unsupervised machine learning approaches to identify outliers in the data of galaxy images to explore the paths for detecting astronomical outliers. For comparison, we construct three methods, which are built upon the k-nearest neighbors (KNN), Convolutional Auto-Encoder (CAE) + KNN, and CAE + KNN + Attention Mechanism (attCAE_KNN) separately. Testing sets are created based on the Galaxy Zoo image data published online to evaluate the performance of the above methods. Results show that attCAE_KNN achieves the best recall (78%), which is 53% higher than the classical KNN method and 22% higher than CAE+KNN. The efficiency of attCAE_KNN (10 minutes) is also superior to KNN (4 h) and equal to CAE+KNN (10 minutes) for accomplishing the same task. Thus, we believe that it is feasible to detect astronomical outliers in the data of galaxy images in an unsupervised manner. Next, we will apply attCAE_KNN to available survey data sets to assess its applicability and reliability.
Deep Learning-Based Text Sentiment Analysis in Chinese International Promotion

The importance of teaching Chinese as a foreign language has grown in tandem with the rapid advancement of China’s politics and economy. The Chinese language’s international promotion strategy has taken shape, and it has become a major cause for our country and nation. Under such a new situation, the content and form of Chinese cultural communication have taken on new characteristics. Foreign friends will use a variety of ways to express their emotions and attitudes toward the Chinese on the Internet, most of which are mainly text comments. Sentiment analysis of text comments can provide valuable emotional information for our Chinese international communication and promotion activities. Text sentiment analysis using deep learning algorithms has advanced considerably in recent years. For that purpose, this study proposes a bidirectional long- and short-term memory network, as well as a text sentiment analysis model with a self-attentive mechanism, and applies it to comments on Chinese language promotion. Experiments show that our model achieves a good recognition situation and has application value.
Universality of the fully connected vertex in Laplacian continuous-time quantum walk problems

A fully connected vertex $w$ in a simple graph $G$ of order $N$ is a vertex connected to all the other $N-1$ vertices. Upon denoting by $L$ the Laplacian matrix of the graph, we prove that the continuous-time quantum walk (CTQW) -- with Hamiltonian $H=\gamma L$ -- of a walker initially localized at $\vert w \rangle$ does not depend on the graph $G$. We also prove that for any Grover-like CTQW -- with Hamiltonian $H=\gamma L +\sum_w \lambda_w \vert w \rangle\langle w \vert$ -- the probability amplitude at the fully connected marked vertices $w$ does not depend on $G$. The result does not hold for CTQW with Hamiltonian $H=\gamma A$ (adjacency matrix). We apply our results to spatial search and quantum transport for single and multiple fully connected marked vertices, proving that CTQWs on any graph $G$ inherit the properties already known for the complete graph of the same order, including the optimality of the spatial search. Our results provide a unified framework for several partial results already reported in literature for fully connected vertices, such as the equivalence of CTQW and of spatial search for the central vertex of the star and wheel graph, and any vertex of the complete graph.
A Comprehensive Survey for Deep-Learning-Based Abnormality Detection in Smart Grids with Multimodal Image Data

In this paper, we provide a comprehensive survey of the recent advances in abnormality detection in smart grids using multimodal image data, which include visible light, infrared, and optical satellite images. The applications in visible light and infrared images, enabling abnormality detection at short range, further include several typical applications in intelligent sensors deployed in smart grids, while optical satellite image data focus on abnormality detection from a large distance. Moreover, the literature in each aspect is organized according to the considered techniques. In addition, several key methodologies and conditions for applying these techniques to abnormality detection are identified to help determine whether to use deep learning and which kind of learning techniques to use. Traditional approaches are also summarized together with their performance comparison with deep-learning-based approaches, based on which the necessity, seen in the surveyed literature, of adopting image-data-based abnormality detection is clarified. Overall, this comprehensive survey categorizes and carefully summarizes insights from representative papers in this field, which will widely benefit practitioners and academic researchers.
Rating prediction of recommended item based on review deep learning and rating probability matrix factorization

With a sharp improvement in E-commerce and data, the precise rating prediction of recommended items under user preferences has been a hot research topic in the EC intelligence domain. The rating data Matrix Factorization based methods have been widely used in item rating predictions in e-commerce recommendation systems. However, "cold start" and "data sparsity" have seriously restricted the accuracy of such methods. In addition to the rating data, as side information, the massive reviews posted by users rich in semantic and emotional information express user preferences and item characteristics, and will certainly improve the accuracy of the rating prediction. Accordingly, this paper combines the deep learning for the review text and the matrix factorization method for rating data to predict the rating of the recommended items accurately. Firstly, based on the Deep Learning methods, self-attention mechanism and bi-directional RNN (Recurrent Neural Network) with the core of GRU (Gated Recurrent Unit), the deep nonlinear features of users and items are learned from review texts. Then, these features are introduced as a prior mean into the classical rating-based probability matrix factorization model to obtain the latent factor vectors of users and items with the rating of the recommended item accurately predicted. Finally, adopting MSE and MAE as the indicators, the extensive experiments conducted on four real datasets verify that the proposed model TFRMF (Topical Features Regularized Matrix Factorization) performs better than other classical counterparts. The achievements of this work will provide powerful methods and decision supports for accurate and personalized e-commerce recommendation practices.
Precision Measurement Method of Large Shaft Diameter Based on Dual Camera System

At present, there is a problem that the measurement accuracy and measurement range cannot be balanced in the measurement of shaft diameter by the machine vision method. In this paper, we propose a large-scale shaft diameter precision measurement method based on a dual camera measurement system. The unified world coordinate system of the two cameras is established by analyzing the dual camera imaging model and obtaining the measurement formula. In order to verify the validity of the proposed method, two black blocks in the calibration plate with a known center distance of 100 mm were measured. The mean value was 100.001 mm and the standard deviation was 0.00039 in 10 measurements. Finally, the proposed system was applied to the diameter measurement of a complexed crankshaft. The mean μ95 values of CMM and the proposed method were ±1.02 μm and ±1.07 μm, respectively, indicating that the measurement accuracy of the proposed method is roughly equal to the CMM.
GraphAD

In recent years, the emergence and development of third-party platforms have greatly facilitated the growth of the Online to Offline (O2O) business. However, the large amount of transaction data raises new challenges for retailers, especially anomaly detection in operating conditions. Thus, platforms begin to develop intelligent business assistants with embedded anomaly detection methods to reduce the management burden on retailers. Traditional time-series anomaly detection methods capture underlying patterns from the perspectives of time and attributes, ignoring the difference between retailers in this scenario. Besides, similar transaction patterns extracted by the platforms can also provide guidance to individual retailers and enrich their available information without privacy issues. In this paper, we pose an entity-wise multivariate time-series anomaly detection problem that considers the time-series of each unique entity. To address this challenge, we propose GraphAD, a novel multivariate time-series anomaly detection model based on the graph neural network. GraphAD decomposes the Key Performance Indicator (KPI) into stable and volatility components and extracts their patterns in terms of attributes, entities and temporal perspectives via graph neural networks. We also construct a real-world entity-wise multivariate time-series dataset from the business data of Ele.me. The experimental results on this dataset show that GraphAD significantly outperforms existing anomaly detection methods.
Prediction of Rooftop Photovoltaic Solar Potential Using Machine Learning

Solar energy forecasting accuracy is essential for increasing the quantity of renewable energy that can be integrated into the existing electrical grid control systems. The availability of data at unprecedented levels of granularity allows for the development of data-driven algorithms to improve the estimation of solar energy generation and production. In this paper, we develop a prediction of solar potential across large photovoltaic panels from the roof tops using a machine learning method. The Restricted Boltzmann Machine (RBM) is the machine learning method used in the study to predict or forecast the solar potential in rooftops. The machine learning model is supplied with training dataset to get trained with the dataset for conversion into the model and then tested with the test dataset for validating the model. The results of simulation are conducted on R-package over various libraries to predict the rooftop solar potential. The results of simulation shows that the proposed method achieves higher rate of prediction accuracy than the other methods. The results of the simulation show that the proposed method achieves a higher rate of prediction accuracy of 99% than the other methods.
Super-resolving and composing building dataset using a momentum spatial-channel attention residual feature aggregation network

Model generalizability is crucial in the deployment of deep learning (DL) techniques. When trained on specific datasets, generalizability problems arise across many applications of DL including building extractions. Apart from regularizing the training process, collecting data with distinctive characteristics or distributions can be a promising solution. Over the past decade, several open building datasets have been released. However, in practice, a single dataset cannot overcome the generalization error. By unifying the spatial resolution and spectral bands of different datasets, those datasets could be integrated to relieve the generalization error in building footprint extraction. In this work, we focused on the difference in the spatial resolution between different building datasets. We first examined state-of-the-art super-resolution methods and proposed our own method based on Residual Feature Aggregation Network (RFANet), which we named Momentum and Spatial-Channel Attention RFANet (MSCA-RFANet). We then benchmarked our MSCA-RFANet in a comparative study; our new method achieved higher performance on spatial resolution enhancement. Specifically, in the four times spatial resolution enhancement on the SWOOP 2010 Dataset, our MSCA-RFANet result's peak signal-to-noise ratio (PSNR) of 30.72 dB exceeded that of RFANet (30.66 dB). Likewise, we achieved a lower mean squared error (MSE) of 36.64 compared to RFANet's 36.94. With detailed benchmarks against Second-order Attention Network (SAN) and Residual Channel Attention Network (RCAN), we confirmed the superior performance of our method in enhancing the spatial resolution of high-spatial-resolution images. Then, we explored the impact of super-resolution resolution and data composition on building footprint extraction. Our building footprint extraction experiments demonstrated the positive impact of super-resolution and data composition. These promising results showed that our method is suitable to integrating existing public building dataset to overcome generalization error in DL-based building footprint extraction.
Progressive Denoising of Monte Carlo Rendered Images

Abstract Image denoising based on deep learning has become a powerful tool to accelerate Monte Carlo rendering. Deep learning techniques can produce smooth images using a low sample count. Unfortunately, existing deep learning methods are biased and do not converge to the correct solution as the number of samples increase. In this paper, we propose a progressive denoising technique that aims to use denoising only when it is beneficial and to reduce its impact at high sample counts. We use Stein's unbiased risk estimate (SURE) to estimate the error in the denoised image, and we combine this with a neural network to infer a per‐pixel mixing parameter. We further augment this network with confidence intervals based on classical statistics to ensure consistency and convergence of the final denoised image. Our results demonstrate that our method is consistent and that it improves existing denoising techniques. Furthermore, it can be used in combination with existing high quality denoisers to ensure consistency. In addition to being asymptotically unbiased, progressive denoising is particularly good at preserving fine details that would otherwise be lost with existing denoisers.
Credit Card Fraud Detection using Ensemble Methods in Machine Learning

Credit cards are being used vastly by many people nowadays. As the usage is increasing, the frauds are also increasing. Due to fraudulent transactions, many common people are suffering a lot. Hence, the need to find out whether a given transaction is legit or not is a must these days. The advancement of technology also brings with it an increase in risks. Hence, a good technique for detecting fraudulent transactions is a requisite. This paper proposes a predictive classification model which performs Weighted Average Ensemble on simple classifiers as well as classifier ensembles such as Logistic Regression (LR), Random Forest (RF), k-nearest neighbours (KNN), Adaboost, Bagging. The proposed model improved the performance up to 99 per cent whereas important base classifier ensembles like RF and Bagging achieved 98 per cent metrics. Other Classifiers like Adaboost and LR attained 97 per cent metrics while KNN went up to 95 per cent. Performance is monitored using various metrics like accuracy, precision and f-1 score.
FPGA-Based CNN for Real-Time UAV Tracking and Detection

Neural networks (NNs) are now being extensively utilized in various artificial intelligence platforms specifically in the area of image classification and real-time object tracking. We propose a novel design to address the problem of real-time unmanned aerial vehicle (UAV) monitoring and detection using a Zynq UltraScale FPGA-based convolutional neural network (CNN). The biggest challenge while implementing real-time algorithms on FPGAs is the limited DSP hardware resources available on FPGA platforms. Our proposed design overcomes the challenge of autonomous real-time UAV detection and tracking using a Xilinx’s Zynq UltraScale XCZU9EG system on a chip (SoC) platform. Our proposed design explores and provides a solution for overcoming the challenge of limited floating-point resources while maintaining real-time performance. The solution consists of two modules: UAV tracking module and neural network–based UAV detection module. The tracking module uses our novel background-differencing algorithm, while the UAV detection is based on a modified CNN algorithm, designed to give the maximum field-programmable gate array (FPGA) performance. These two modules are designed to complement each other and enabled simultaneously to provide an enhanced real-time UAV detection for any given video input. The proposed system has been tested on real-life flying UAVs, achieving an accuracy of 82%, running at the full frame rate of the input camera for both tracking and neural network (NN) detection, achieving similar performance than an equivalent deep learning processor unit (DPU) with UltraScale FPGA-based HD video and tracking implementation but with lower resource utilization as shown by our results.
Novel Perspectives for the Management of Multilingual and Multialphabetic Heritages through Automatic Knowledge Extraction: The DigitalMaktaba Approach

The linguistic and social impact of multiculturalism can no longer be neglected in any sector, creating the urgent need of creating systems and procedures for managing and sharing cultural heritages in both supranational and multi-literate contexts. In order to achieve this goal, text sensing appears to be one of the most crucial research areas. The long-term objective of the DigitalMaktaba project, born from interdisciplinary collaboration between computer scientists, historians, librarians, engineers and linguists, is to establish procedures for the creation, management and cataloguing of archival heritage in non-Latin alphabets. In this paper, we discuss the currently ongoing design of an innovative workflow and tool in the area of text sensing, for the automatic extraction of knowledge and cataloguing of documents written in non-Latin languages (Arabic, Persian and Azerbaijani). The current prototype leverages different OCR, text processing and information extraction techniques in order to provide both a highly accurate extracted text and rich metadata content (including automatically identified cataloguing metadata), overcoming typical limitations of current state of the art approaches. The initial tests provide promising results. The paper includes a discussion of future steps (e.g., AI-based techniques further leveraging the extracted data/metadata and making the system learn from user feedback) and of the many foreseen advantages of this research, both from a technical and a broader cultural-preservation and sharing point of view.