Reviewerly/exHarmony huggingface dataset:
An Enhanced Neural Word Embedding Model for Transfer Learning

Due to the expansion of data generation, more and more natural language processing (NLP) tasks are needing to be solved. For this, word representation plays a vital role. Computation-based word embedding in various high languages is very useful. However, until now, low-resource languages such as Bangla have had very limited resources available in terms of models, toolkits, and datasets. Considering this fact, in this paper, an enhanced BanglaFastText word embedding model is developed using Python and two large pre-trained Bangla models of FastText (Skip-gram and cbow). These pre-trained models were trained on a collected large Bangla corpus (around 20 million points of text data, in which every paragraph of text is considered as a data point). BanglaFastText outperformed Facebook’s FastText by a significant margin. To evaluate and analyze the performance of these pre-trained models, the proposed work accomplished text classification based on three popular textual Bangla datasets, and developed models using various machine learning classical approaches, as well as a deep neural network. The evaluations showed a superior performance over existing word embedding techniques and the Facebook Bangla FastText pre-trained model for Bangla NLP. In addition, the performance in the original work concerning these textual datasets provides excellent results. A Python toolkit is proposed, which is convenient for accessing the models and using the models for word embedding, obtaining semantic relationships word-by-word or sentence-by-sentence; sentence embedding for classical machine learning approaches; and also the unsupervised finetuning of any Bangla linguistic dataset.
Coverage Optimization of Wireless Sensor Networks Using Combinations of PSO and Chaos Optimization

The coverage rate is the most crucial index in wireless sensor networks (WSNs) design; it involves making the sensors with a reasonable distribution, which closely relates to the quality of service (QoS) and survival period of the entire network. This article proposes to use particle swarm optimization (PSO) and chaos optimization in conjunction for the coverage optimization. All sensor locations are encoded together as a particle position. PSO was used first to make sensors move close to their optimal positions; furthermore, a variable domain chaos optimization algorithm (VDCOA) was employed to reach a higher coverage rate, along with improved evenness and average moving distance. Six versions of VDCOA, taking circle, logistic, Gaussian, Chebyshev, sinusoidal and cubic maps, respectively, were investigated. The simulation experiment tested three cases: square, rectangular and circular regions using nine algorithms: six versions of PSO plus VDCOA, PSO and other two PSO variants. All six versions showed better performance than PSO and CPSO, with coverage all exceeding 90% for the first two cases. Moreover, one version, PSO plus circle map (PSO-Circle), increased the coverage rate by 3.17%, 2.41% and 12.94% compared with PSO in three cases, respectively, and outperformed the other eight algorithms.
Efficient time series anomaly detection by multiresolution self-supervised discriminative network

Time series anomaly detection aims to identify abnormal subsequences in time series that are markedly different from the temporal behaviors of the entire sequence. Although previous density-based or proximity-based anomaly detection methods are usually used for anomaly detection, they are still suffering from high computational costs due to the need of traversing the whole training dataset during testing. Recently, reconstruction-based deep learning methods are popular for time series anomaly detection. However, they may not work well because their objective is to recover all information appeared in time series, including high-frequency noises. In this paper, we propose a simple yet efficient method called Multiresolution Self-Supervised Discriminative Network (MS2D-Net) for efficient time series anomaly detection. Specifically, the MS2D-Net includes a multiresolution downsampling module, a feature extraction module, and a self-supervised discrimination module. The multiresolution downsampling module generates some multiresolution samples by downsampling the original time series with different sampling rates and creates different pseudo-labels representing multi-scale behaviors in time series. Then, in the feature extraction module, a shallow convolution network is used to extract temporal dynamics in time series at multiple resolutions. Finally, the self-supervised discrimination module uses the pseudo-labels obtained from the multiresolution downsampling module as the self-supervised information to help separate anomalies from the normal time series samples. Experimental results show that the proposed MS2D-Net can outperform recent strong deep learning baselines on 18 benchmarks for time series anomaly detection with a much lower computational cost.
Crossover based technique for data augmentation

Medical image classification problems are frequently constrained by the availability of datasets. "Data augmentation" has come as a data enhancement and data enrichment solution to the challenge of limited data. Traditionally data augmentation techniques are based on linear and label preserving transformations; however, recent works have demonstrated that even non-linear, non-label preserving techniques can be unexpectedly effective. This paper proposes a non-linear data augmentation technique for the medical domain and explores its results.This paper introduces "Crossover technique", a new data augmentation technique for Convolutional Neural Networks in Medical Image Classification problems. Our technique synthesizes a pair of samples by applying two-point crossover on the already available training dataset. By this technique, we create N new samples from N training samples. The proposed crossover based data augmentation technique, although non-label preserving, has performed significantly better in terms of increased accuracy and reduced loss for all the tested datasets over varied architectures.The proposed method was tested on three publicly available medical datasets with various network architectures. For the mini-MIAS database of mammograms, our method improved the accuracy by 1.47%, achieving 80.15% using VGG-16 architecture. Our method works fine for both gray-scale as well as RGB images, as on the PH2 database for Skin Cancer, it improved the accuracy by 3.57%, achieving 85.71% using VGG-19 architecture. In addition, our technique improved accuracy on the brain tumor dataset by 0.40%, achieving 97.97% using VGG-16 architecture.The proposed novel crossover technique for training the Convolutional Neural Network (CNN) is painless to implement by applying two-point crossover on two images to form new images. The method would go a long way in tackling the challenges of limited datasets and problems of class imbalances in medical image analysis. Our code is available at https://github.com/rishiraj-cs/Crossover-augmentation.
Optimal Deep Learning Enabled Prostate Cancer Detection Using Microarray Gene Expression

Prostate cancer is the main cause of death over the globe. Earlier detection and classification of cancer is highly important to improve patient health. Previous studies utilized statistical and machine learning (ML) techniques for prostate cancer detection. However, several challenges that exist in the investigation process are the existence of high dimensionality data and less number of training samples. Metaheuristic algorithms can be used to resolve the curse of dimensionality and improve the detection rate of artificial intelligence (AI) techniques. With this motivation, this article develops an artificial intelligence based feature selection with deep learning model for prostate cancer detection (AIFSDL-PCD) using microarray gene expression data. The AIFSDL-PCD technique involves preprocessing to enhance the input data quality. In addition, a chaotic invasive weed optimization (CIWO) based feature selection (FS) technique for choosing an optimal subset of features shows the novelty of the work. Moreover, the deep neural network (DNN) model can be applied as a classification model to detect the existence of prostate cancer in the microarray gene expression data. Furthermore, the hyperparameters of the DNN model can be effectively adjusted by the use of RMSprop optimizer. The design of CIWO based FS technique helps for reducing the computational complexity and improve the classification accuracy. The experimental results highlighted the betterment of the AIFSDL-PCD approach on the other techniques with respect to distinct measures.
An Effective Learning Evaluation Method Based on Text Data with Real-time Attribution - A Case Study for Mathematical Class with Students of Junior Middle School in China

In today's intelligent age, the vigorous development of education-based information analysis technology has had a profound impact on the education and teaching process. The use of computational linguistics technology to extract teaching data for learning evaluation is an important hot domain in this research field. Therefore, the study of student learning assessment methods based on text data has become a key issue. The text data extracted from the education process has attributes related to time and operational attributes, which are important indicators to measure the effect of student learning effect. However, these attributes are not focused by the traditional educational effect evaluation method, which make the learning effect of students difficult to measure comprehensively and effectively. In response to this problem, this article first uses perception technology to extract learning text data based on time and operational attributes. Secondly, according to the real-time attributes of text data, such as time and operation attributes, a learning evaluation method based on real-time text data is proposed. Finally, this article compares the traditional evaluation method with the proposed method. The results show that using real-time attribute text data is more effective in students’ learning measure.
Empirical evaluation of nodes and channels of the lightning network

Off-chain networks provide an attractive solution to the scalability challenges faced by cryptocurrencies such as Bitcoin. While first interesting networks are emerging, we currently have relatively limited insights into the structure and distribution of these networks. Such knowledge, however, is useful, when reasoning about possible performance improvements or the security of the network. For example, information about the different node types and implementations in the network can help when planning the distribution of critical software updates. This paper reports on a large measurement study of Lightning, a leading off-chain network, considering recorded network messages over a period of more than two years. In particular, we present an approach to classify the node types (LND, C-Lightning and Eclair) in the network, and find that we can determine the implementation of 99.9% of nodes correctly in our data set. We then report on geographical aspects of the Lightning Network, showing that proximity is less relevant, and that the Lightning Network is particularly predominant in metropolitan areas. Furthermore, we address various aspects of channels in the Lightning Network combined with the data we classified. We also demonstrate that channel endpoints behave very fairly and rarely cheat, that the same channel endpoints tend not to reconnect after the channel connection has closed and that there are more inactive than active channels in the Lightning Network. As a contribution to the research community, we will release our experimental data together with this paper.
Semi-asynchronous personalized federated learning for short-term photovoltaic power forecasting

Accurate forecasting for photovoltaic power generation is one of the key enablers for the integration of solar photovoltaic systems into power grids. Existing deep-learning-based methods can perform well if there are sufficient training data and enough computational resources. However, there are challenges in building models through centralized shared data due to data privacy concerns and industry competition. Federated learning is a new distributed machine learning approach which enables training models across edge devices while data reside locally. In this paper, we propose an efficient semi-asynchronous federated learning framework for short-term solar power forecasting and evaluate the framework performance using a CNN-LSTM model. We design a personalization technique and a sem78i-asynchronous aggregation strategy to improve the efficiency of the proposed federated forecasting approach. Thorough evaluations using a real-world dataset demonstrate that the federated models can achieve significantly higher forecasting performance than fully local models while protecting data privacy, and the proposed semi-asynchronous aggregation and the personalization technique can make the forecasting framework more robust in real-world scenarios.
The possibility of improving automated calculation of measures of lexical richness for EFL writing: A comparison of the LCA, NLTK and SpaCy tools

Automatically calculating measures of lexical richness is important for L2 learning because they can be used for assessment of productive abilities and general linguistic ability. One popular tool for doing so is the Lexical Complexity Analyzer (LCA), but more advanced tools for parsing have become available since its creation. This paper compares a modified version of the LCA code run with NLTK and SpaCy, two popular natural language processing toolkits, and the online version of the LCA to calculate 26 measures of lexical richness. We show how similarly they calculate the measures and how well each of the three tools' calculations correlate with EFL writer's human-rated scores and TOEFL® ITP scores. We found that six of the measures suggested to be associated with higher oral proficiency by Lu (2012) were also highly correlated with higher human-rated scores and TOEFL® ITP scores in our data set. However, the modifications to our code that utilize a different list to determine word sophistication and allow be and have verbs to be treated as lexical verbs caused four measures which Lu (2012) found to be unassociated with proficiency to be correlated with both human-rated scores and TOEFL® ITP scores, particularly when run with SpaCy.
A parallel compact firefly algorithm for the control of variable pitch wind turbine

Firefly algorithm (FA) is a meta-heuristic optimization algorithm inspired by the flickering behavior of fireflies. Due to its excellent performance, it has been widely used in engineering field.​ However, FA is a population-based algorithm, which needs to occupy a lot of running memory. It is adverse for some small wind turbines or scenarios with limited memory Therefore, this paper proposes an improved firefly algorithm, called parallel compact firefly algorithm (PCFA). The compact technique helps FA save operation memory, which is advantageous for some usage scenarios limited memory. And the parallel technique helps compact FA achieve better solutions and faster convergence. The proposed PCFA was tested on 28 benchmark functions and applied to the proportional–integral–derivative (PID) parameter tuning of the variable pitch wind turbine. Results demonstrate that (1) PCFA is superior to common compact optimization algorithms not only on less memory consumption, but also on more competitive solutions and faster convergence. (2) PCFA shows the better applicability in the PID parameter tuning of variable pitch wind turbine. It can availably smooth the power output of wind turbine under less memory consumption.
Ensemble feature selection using distance-based supervised and unsupervised methods in binary classification

Feature selection refers to the problem of finding the optimal subset of features by removing irrelevant and redundant features to improve classification accuracy. The determination of the most effective distance measures to evaluate the relevance and redundancy of features has not been investigated precisely to date. Moreover, the relation between relevancy and redundancy is still uncertain. This paper presents a novel relevancy-redundancy measurement based on distance applying the idea of the mRMR criteria to an unsupervised method. In addition, a supervised method is proposed, in which the features are ranked in terms of the distance between each pair of samples in different classes of the feature vector. Then an ensemble of the proposed supervised and unsupervised methods is applied to choose the most relevant features subset. This study investigates and compares the effects of 24 distance measures selected from five major families of distance functions on the performance of the proposed feature selection methods. The highest-ranked features are selected using an empirically achieved threshold. To evaluate the selected features, three classifiers, i.e., Decision Tree, Support Vector Machine and Naive Bayes were applied to biomedical datasets representing binary problems from the UCI data repository. The experimental results demonstrate the superiority of the proposed methods over the state-of-the-art and also classical feature selection ones in terms of improving stability, classification accuracy, Recall (Sensitivity), Precision, F-measure, and Specificity.
Vis-NIR-spectroscopy- and loss-on-ignition-based functions to estimate organic matter content of calcareous soils

The study was carried out to derive pedotransfer (PTFs) and spectrotransfer (STF) functions to estimate soil organic matter (SOM) content measured by time-consuming and expensive Walkley–Black wet combustion (W-B), using SOM content obtained from loss on ignition (LOI) and spectra reflectance bands at visible (Vis) and near infrared (NIR) regions. In total, 171 soil samples were collected from calcareous soils of southern Iran. The SOM content of the samples was determined using W–B wet combustion (SOMW-B) and LOI at temperatures of 360 °C during 2 h (LOI360-2); 550 °C during 3 h (LOI550-3); and 375°C during 16 h (LOI375-16). For the spectroscopy method, partial least squares regression (SpectPLSR) and forward stepwise multiple linear regression (SpectSMLR) approaches were used for function development. The LOI360-2 procedure with R2val of 0.9 for the validation dataset provided the best match with SOMW-B content. Furthermore, the SpectSMLR provided a STF using spectral reflectance bands at 495, 730, 970, 1906, 2262 and 2342 nm which predicted SOMW-B with R2val of 0.87. Results indicated both the SpectSMLR and LOI360-2, as easy and inexpensive approaches, could be recommended to accurately assess SOM content of calcareous soils.
Enhancing low-resource neural machine translation with syntax-graph guided self-attention

Most neural machine translation (NMT) models only rely on parallel sentence pairs, while the performance drops sharply in low-resource cases, as the models fail to mine the linguistry of the corpus. Incorporating prior monolingual knowledge explicitly, such as syntax, has been shown to be effective for NMT, particularly in low-resource scenarios. However, existing approaches have not exploited the full potential of the NMT architectures. In this paper, we present syntax-graph guided self-attention (SGSA): a neural network model that combines the source-side syntactic knowledge with multi-head self-attention. We introduce an additional syntax-aware localness modeling as a bias, which indicates that the syntactically relevant parts need to be paid more attention to. The bias is then incorporated into the original attention distribution to form a revised distribution. Moreover, to maintain the strength of capturing the meaningful semantic representations of source-sentence, we adopt a node random dropping strategy in multi-head self-attention subnetworks. Extensive experiments on several standard small-scale datasets demonstrate that SGSA can significantly improve the performance of Transformer-based NMT, and is also superior to the previous syntax-dependent state-of-the-art.
Identification of Soil Arsenic Contamination in Rice Paddy Field Based on Hyperspectral Reflectance Approach

Toxic heavy metals in soil negatively impact soil’s physical, biological, and chemical characteristics, and also human wellbeing. The traditional approach of chemical analysis procedures for assessing soil toxicant element concentration is time-consuming and expensive. Due to accessibility, reliability, and rapidity at a high temporal and spatial resolution, hyperspectral remote sensing within the Vis-NIR region is an indispensable and widely used approach in today’s world for monitoring broad regions and controlling soil arsenic (As) pollution in agricultural land. This study investigates the effectiveness of hyperspectral reflectance approaches in different regions for assessing soil As pollutants, as well as a basic review of space-borne earth observation hyperspectral sensors. Multivariate and various regression models were developed to avoid collinearity and improve prediction capabilities using spectral bands with the perfect correlation coefficients to access the soil As contamination in previous studies. This review highlights some of the most significant factors to consider when developing a remote sensing approach for soil As contamination in the future, as well as the potential limits of employing spectroscopy data.
Artificial Intelligence in Classical and Quantum Photonics

Abstract The last decades saw a huge rise of artificial intelligence (AI) as a powerful tool to boost industrial and scientific research in a broad range of fields. AI and photonics are developing a promising two‐way synergy: on the one hand, AI approaches can be used to control a number of complex linear and nonlinear photonic processes, both in the classical and quantum regimes; on the other hand, photonics can pave the way for a new class of platforms to accelerate AI‐tasks. This review provides the reader with the fundamental notions of machine learning (ML) and neural networks (NNs) and presents the main AI applications in the fields of spectroscopy and chemometrics, computational imaging (CI), wavefront shaping and quantum optics. The review concludes with an overview of future developments of the promising synergy between AI and photonics.
Broad and deep neural network for high-dimensional data representation learning

Limited by the shallow structure, broad learning system (BLS) suffers from the high-dimensional data classification difficulty. To this end, the cascade of the convolutional feature mappings and enhancement mappings broad learning system (CCFEBLS) framework is proposed from the perspective of representation learning in this article. Firstly, convolution kernels are exploited to construct the convolutional feature nodes and enhancement nodes instead of using sparse auto-enocder or linear combination in the BLS. Secondly, we design a novel broad and deep architecture which cascades the feature mappings and enhancement mappings as the broad and deep representations to connect the output directly for the CCFEBLS framework. This architecture utilizes all representations thoroughly and improves the representation learning capability. Moreover, to boost the robustness of the CCFEBLS, the weighted hyper-parameters and the group regularization are developed to adjust the broad and deep representations and require the group output directly approximate the label, respectively. And the experimental results on several synthetic and real world datasets have demonstrated that CCFEBLS models outperform the baselines with better performance, less parameters and training time, which are validated to be consistent with the model design and analysis.