Reviewerly/exHarmony huggingface dataset:
Solar Radiation Forecasting Using Ensemble-Based Hybrid LGBM-GB-MLP Model: A Novel Stacked Generalization Method

Predicting solar radiance is an essential requirement for the authentic planning and efficient design of solar power systems. Machine learning models have been used to improve forecasting accuracy through the bias-variance trade-off technique. However, the accuracy of solar radiation cannot often fulfill our desired result due to natural weather intermittent affecting solar energy sources. This paper proposes a novel computational framework based on a stacked generalization approach to enhance the prediction performance of solar radiation. The proposed framework unifies three models, specifically, light gradient boosting machine (LGBM), gradient boosting (GB), and multilayer perceptron (MLP). The working principle of the stacked LGBM-GB-MLP model consists of aggregating the prediction of LGBM and GB to generate the final predictions using the MLP network. Several experiments to validate the performance of this model are carried out using the dataset provided by the National Aeronautics and Space Administration. The results prove the potential of the proposed approach compared with the existing benchmarks such as LGBM, GB, and MLP under the same conditions. This paper contributes mainly to the development of effective stacking ensemble-based algorithm to minimize the negative impacts of inaccurate solar radiation forecasting. Additionally, the utilization of solar energy under proper forecasting conditions is maximized which leads to the global warming issue handling.
The Optimum Angle for Solar Collectors in Road (Tabriz-Urmia)

This research aims is to calculate the optimum tilt angle with the horizon concurrently and also appropriate orientation toward north in the flat plate panels in different periods in the Tabriz-Uremia road. For doing so, at first using the average monthly total radiation on the horizontal plate, the diffuse radiation on the horizontal plate was determined. Then the level of radiation on the sloped plate is calculated via a mathematical model in various slopes and the slope and monthly optimal orientation are determined according to the maximum radiation. In the case study of Tabriz-Uremia road, 14 routes to distance of 10 km from each other were classified, optimal slope and appropriate orientation for fourteen roads routes are determined in different intervals as optimal quarterly, semiannual and annual tilt angles. Coding in software Excel was done based on existing relations .The results of calculations showed that the optimum tilt angle with optimal horizon and appropriate orientation toward north in the Tabriz-Uremia road route is 22 and 250 degrees. The results showed that if the tilt angel with horizon and orientation toward north is set monthly, the level of radiation is increased 38 % compared to radiation on the horizontal plate and this value for seasonal, six months and annually increases 45, 35 and 28 percent respectively. For increase of the level of received radiation, it is recommended to change the angle and orientation minimally twice in a year.
Bridge Relation Extraction: New Chinese Dataset and Model

As a transportation infrastructure, bridge plays an important role in the region connectivity and urban prosperity, and brings tremendous convenience to people's daily life and travel. The operation and management systems of bridges are more convenient owing to the popularization of artificial intelligence. Specifically, knowledge bases in bridge field not only contribute to the intelligent management and maintenance of bridges but are conducive to enhancing the efficiency of operation decisions. One of the predominant methods to construct knowledge bases automatically is relation extraction which aims to extract structured information from unstructured texts. There are, however, two major challenges in bridge relation extraction study: 1) no bridge-specific dataset for this task; 2) no specific relation extraction models for bridges. In this paper, we introduce a manually-crafted Chinese dataset called Bridge-RE for bridge relation extraction and propose an excellent model called BERT-Bridge that can extract the relations effectively from the sentences in bridge field. Specifically, we carefully select 2500 sentences from the PDF format articles which are strongly related to bridges. The entities and relations in sentences are annotated manually and the subjects of Bridge-RE are components, defects and maintenance methods about bridges. Furthermore, we intensively investigate BERT-based models and make a considerable improvement on bridge relation extraction task by appending entity information to the model's input. Experimental results have demonstrated that our model obtains better performance than baseline model on Bridge-RE dataset.
Research on Anti-noise Activation Function Based on LIF Biological Neuron Model

In the environment of Internet of vehicles ranging, the image sensor works for a long time, and the brightness is not enough or uneven when shooting, which will produce noise, resulting in poor image quality. In this paper, based on the LIF (Leaky Integrate-And-Fire) biological neuron model with noise immunity, NST (Noisy Softplus and Tanh) activation function is proposed and applied to PSM-Net to effectively overcome the influence of noise on image quality. The NST function is used to fit the response mechanism of LIF model, and the unknown $${\upalpha }$$ are added to make the NST function curve change with the change of input noise, so it has anti-noise performance. On this basis, the non monotonic property is added to the negative half axis to improve the processing accuracy of the function in the deep network. Experiments show that NST function is stable in the presence of noise input, and the error value is 13.504 lower than ReLU function in the presence of large noise input with variance value of 0.2, which effectively improves the processing accuracy of binocular stereo disparity prediction network.
Cognitive and Autonomic IoT System Design

Currently, the Internet of Things (IoT) usage observes a drastic growth in several areas and participates in the rapid universe digitalization. Henceforward, the IoT systems next generation will be more difficult to develop and monitor. Gathering real-time data created by IoT triggers some novel opportunities for businesses to take at the right time more accurate and precise decisions. However, several challenges (such as IoT systems complexity and heterogeneous data management, and IoT system scalability) restrain the elaboration of IoT systems that are smart and impel business decision-making. This paper proposes to automatize IoT systems management using an autonomic computing approach. Nevertheless, autonomic computing is insufficient for smart IoT systems development. Actually, a smart IoT system implements cognitive abilities that allow its learning and decision-making in real-time. Therefore, this study proposes a model for designing smart IoT systems. It defines a set of cognitive design patterns that delineate the dynamiccooperation between management processes (MPs) (to handle the requirements evolvement and the system's environment unpredictability) and add cognitive capabilities to IoT systems (to generate new insights, perceive big data, and communicate with users). The study's primary goal is to support the developer in designing smart IoT systems that are flexible by choosing an appropriate pattern (or a set of patterns) to meet complex system requirements.
A Generalized Net Model of the Process of Obtaining and Diagnosing Convalescent Plasma from Patients with COVID-19

In the present study we propose a GN-model of the process of obtaining and diagnosing convalescent plasma from patients with COVID-19. The presented GN-model describes in details the different steps during the evaluation of the convalescent plasma, which will find expression in process optimization.
A Fast and Scalable Frequent Itemset Mining (Fsfim) Algorithm for Big Data Based on Mapreduce Programming Paradigm

Due to rapid growth of data from the different sources in organizations, the traditional tools and techniques that cannot handle such huge data is known as big data which is in scalable fashion. Similarly, many existing frequent itemset mining algorithms that are good in performance but have scalability problem as they cannot exploit parallel processing power available either locally or in cloud infrastructure. Since big data and cloud ecosystem overcomes the barriers or limitations in computing resources, it is a natural choice to use distributed programming paradigms such as Map Reduce. In this paper, we propose a novel algorithm known as Fast and Scalable Frequent Itemset Mining (FSFIM) to extract frequent itemsets from big data. Here, Pre-Order Coding (POC) tree is used to represent data and improve speed in processing. Nodeset is the underlying data structure that is efficient in discovering frequent itemsets. When compared with its predecessor such as Node-lists and N-lists, the Nodeset saves half of the memory as it needs only either pre-order or post-order code. Cloudera’s Distribution of Hadoop (CDH), a MapReduce framework, is used for empirical study. A prototype application is built to evaluate performance of the FSFIM. Experimental results revealed that FSFIM outperforms existing algorithms such as HBPFP, Mlib PFP and Big FIM. FSFIM is more scalable and found to be an ideal candidate for real time applications that mine frequent itemsets from big data.
A Talent Training System Integrating Teaching and Information Technology under the Background of the Internet

Education is the eternal theme of human society. With the rapid development of Internet mobile technology, mobile terminals have become an indispensable part of people's lives. With the help of mobile Internet technology, students are centered, students' needs and environment are identified, and the required content is delivered in real time. The emergence of mobile learning has become a new way of learning. This article uses the mobile Internet to carry out innovative training through the Internet-based innovative talent training platform. The use of the mobile Internet-based innovative talent training platform will change the activities of learners, educators and managers, improve the effect of education and teaching; provide new conditions and methods for cultivating innovative talents; enrich the content of mobile learning research and applications. By studying the basic knowledge and theories of mobile learning, this paper compares with other learning methods, gives out the characteristics of mobile learning, and conducts research in the form of questionnaires. The results of the study show that in the Internet environment, information technology students use the course website browsing and learning observations, in the observation that boys learn information technology online courses effectively, 375 people, while those who learn information technology online courses ineffectively are 893, and girls learn information technology online effectively. There are 587 people in the class, and 995 people who are ineffective in learning information technology online courses. Judging from this, the online course of information technology can have such a high proportion of effective learning for a website course, which shows that the Internet for information technology learning and work has brought great convenience to users.
Fully Unsupervised Person Re-Identification by Enhancing Cluster Samples

Fully unsupervised person re-identification aims to train a discriminative model with unlabeled person images. Most existing methods first generate pseudo labels by clustering image features (convolutional features) and then fine-tune the convolutional neural network (CNN) with pseudo labels. However, these methods are greatly limited by the quality of the pseudo labels. In this paper, a cluster sample enhancement method is introduced to increase the reliability of pseudo-label samples to facilitate the CNN training. Specifically, when generating pseudo labels, only the samples with high-confidence pseudo-label predictions are selected. In addition, to enhance the selected samples for training, two different image transformations are adopted and coupled with specific-design loss functions to boost the model performance. Experiments demonstrate the effectiveness of the proposed method. Concretely, the proposed method achieves 87.1% rank-1 and 70.2% mAP accuracy on Market-1501.
Internet memes as multilayered re‑contextualization vehicles in lay-political online discourse

It is well established that the internet meme has come to represent a highly creative discursive device used to “facilitate the […] communication of one’s own political beliefs, attitudes and orientations” (Ross and Rivers 2017: 1). Although internet memes and political internet memes in particular have been addressed to many communicative situations such as participatory culture (e.g., Jenkins 2006; Shifman 2014; Theocharis 2015), one aspect that has not been paid enough attention to concerns the forms in which users refer to individual political figures and events in political memes. This being said, the present paper focuses on referring strategies (see Kirner-Ludwig and Zimmermann 2015; Kirner-Ludwig 2020) as employed in political internet memes on Reddit, including direct and indirect quotes, citations and allusions. A specific focus is going to be on such political internet memes that employ pop-cultural and telecinematic reference points and recontextualize them from their original into new target contexts (see Bublitz 2015; Gruber 2019). As shall be shown, practices such as combining constructed speech elements into recontextualized elements in political internet memes create multiple intertextual references that may enhance visibility, saliency and, thus, the ‘lifetime’ of a political meme.
Exploring Contrast Multi-Attribute Representation With Deep Network for No-Reference Perceptual Quality Assessment

Aiming at the effectiveness of contrast feature design, we proposed a promising novel non-reference quality assessment approach in exploring Attribute-Based representation. The method generates three perceptual attribute categories tailored to contrast. The first is semantic attribute derived from deep convolutional neural network, which implements adaptive contrast prediction relevant to scenario content. Second, for perceiving Spatial channel attribute, the global and local features generated by dark channel map through the designed dual convolution structures. Third, for statistical attribute, we assume the enhanced image as &#x201C;reference&#x201D; and calculate the structural similarity with pristine image, and the entropy and histogram metrics are also employed to assist learning. After that, for maximizing utilization, the features are embedded and integrated hierarchically to translate into objective score. In addition, a medium-scale contrast distortion database is established to support further research, which is more challenging than existing datasets because of the sufficient content and sophisticated changes. We demonstrate the availability of structures quantitatively and verify the rationality of hypothesis. Extensive experiments reveal that the proposed method outperforms advanced methods and achieves the state-of-the-art on the created database and CSIQ, TID2013, CCID2014.
Central Bank Digital Currencies and Cryptocurrencies: a Game Theoretic Analysis

We explore the interaction between central bank digital currencies and cryptocurrencies using a game-theoretic model. The solution of this model is characterized by a multiplicity of equilibria, delimited by the regulatory capability of the central bank and its interaction with the financial intermediaries. In addition, the informational problems faced by the central bank can generate asymmetries and unstable situations that are difficult to solve. We also explore the conditioning role of private centralized digital issuers, central banks of emerging countries and financial intermediaries by means of specific game-theoretic models.
Improving Robustness of Logistic Regression under Label Noise

Administered classification of remotely detected pictures could be a classical strategy to overhaul topographic geospatial databases. The assignment requires preparing information within the shape of picture information with known course names, whose era is time-consuming. To maintain a strategic distance from this issue one can utilize the names from the obsolete database for preparing. As a few of these labels may be off-base due to changes in arrival cover, one should utilize preparing procedures that can adapt with off-base course names within the preparing information. In this paper, the class labels in the training data are commonly assumed to be perfectly accurate. This is not always a justified assumption. Annotators may have made mistakes for example, because the analysis is not obvious or because the annotator is not an expert on the task. It is possible to reduce the impact of labeling mistakes by changing the assumptions the classifier makes. This we explore one way to extend a multiclass logistic regression classifier to account for class-dependent label noise. The vigorous relapse show is assessed on the iris dataset with included manufactured name commotion.
PCRLB for Multiple Target Tracking Based on Symmetric Measurement Equation with Pd&lt; 1

Calculation of Cramer-Rao Lower Bound conditioned by missed detection is a basic problem in multiple target tracking. The Multiple Target Tracking problem is transformed into a nonlinear state estimation one using Symmetric Measurement Equation firstly. Then the calculation of PCRLB for single target based on Information Reduction Factor is introduced and modified. The PCRLB with <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{Pd} &lt; 1$</tex> in MTT is then proposed by combing SME and two PCRLB above. At last, an application of tracking multiple targets in two dimensions in presented to verify the new PCRLB.
Handling Imbalance in Fraudulent Reviewer Detection based on Expectation Maximization and KL Divergence

Online review fraud and review manipulation hurt the profits of stakeholders and undermine the value of online reviews. For this reason, it is critical to detect online review fraud and fraudulent reviewers effectively for the development of e-commerce. Extent studies propose various fraud detection techniques to detect fraudulent reviewers. However, most of these studies do not handle the data imbalance problem in fraudulent reviewer detection. To fill this research gap, this paper proposes a novel approach to detect fraudulent reviewers in handling the data imbalance based on Expectation Maximization (EM) and Kullback–Leibler (KL) divergence (called EMKL). We first use the expectation maximization algorithm to model the latent topic distributions of reviewers on the review features. Then, we adopt the Kullback–Leibler divergence to measure the similarities of reviewers based on their topic distributions to detect fraudulent reviewers. The experiment on Yelp dataset shows that the EMKL approach has a good performance in detecting fraudulent reviewers. In addition, the proposed EMKL method performs better than the performance of state-of-the-art techniques.
Utility of Deep Learning Model to Prioritize the A&amp;E Patients Admission Criteria

Overcrowding in hospital emergency departments is a rudimentary issue due to patients who are presenting for treatment, but do not require admission or could be treated by their own general practitioner or over-the-counter remedies. This research work analyses the existing process of patient triage admission in accident and emergency departments and attempts to apply deep learning techniques to automate, improve and evaluate the triage process. This research proposed to utilize a deep learning model for efficiency and reducing the requirement for specialized triage professionals when evaluating and determining admission, treatment in accident and emergency departments. Automating the triage process could potentially be developed into an online application which a patient or less specialized medical practitioner could potentially perform prior to presenting at emergency departments, reducing the overall inflow to emergency departments and freeing up resources to better treat those who do require admission or treatment. The core areas to be considered are the use of emergency health records (EHR), as a suitable data source for performing the triage process in emergency departments and the application of deep learning methods using the said EHR dataset(s).
Fan-ring interpolation method applied to the panorama unwrapping of the deep-hole parts

Image interpolation is a critical step in panoramic image unwrapping studies. Information calculated in the Cartesian coordinates, although broadly applied, applies to operation between rectangles that will destroy the compressed depth information. The polar coordinates, in contrast, can store depth information by handing between rectangle and circle to obtain more true images. A fan-ring interpolation based on the polar coordinates is proposed for unwrapping panoramic images in this study through replanning the pixel search path in the panorama, and is then supported by redefining third-order interpolation. We validate our method on synthetic and practical images. Compared with competitor models, the unwrapping image obtained from the fan-ring interpolation can provide better quality in subjective and objective evaluation with guaranteed accuracy.
Discriminant Feature Selection via Minimizing the Minority-emphasis Regularization to Within-class Matrix for Imbalanced Data

In real-world applications, imbalanced data is frequently found, such as medical diagnosis and intrusion detection. Due to the hypothesis that samples are evenly distributed among classes, the classification performance obtained by traditional classifiers is easily influenced by class-imbalance issue. Feature selection is an important way to retain a more discriminative feature subset with less redundancy. The performance on minority class needs to be focused in feature selection for imbalanced data. In this study, to address class-imbalance issue, a new form of regularization to LDA (IR-LDA) is embedded into a feature selection framework, in which $\ell_{2}-$ norm regularization is adopted. A feature selection algorithm (IR-DFS) is presented. An efficient optimization algorithm is applied to achieve the optimal solution. The effectiveness of IR-DFS is demonstrated by experiments on public imbalanced data sets as well.
Covid-19 Tweets Analysis with Topic Modeling

Social media has become an important data resource for knowledge discovery and data mining in multiple disciplines. With the exploding amount of social media data, how to efficiently and effectively exploit values and insights from such overwhelming amount of data has become an emerging area. Recently, various natural language processing techniques have been developed, e.g., word embedding, deep neural network and Latent Dirichlet Allocation (LDA), for studies such as sentiment analysis, traffic event detection, nature disaster assessment and COVID-19 tweet analysis. In this paper, topic modeling through LDA was used to conduct text mining on a large real-world COVID-19 tweet dataset, which contains more than 524 million multilingual tweets and covers 218 countries over a period of 3 months. We conducted extensive experiments and visualise insights discovered through this unsupervised process.