Reviewerly/exHarmony huggingface dataset:
Generation of a Typical Meteorological Year for Global Solar Radiation in Taiwan

Solar energy technology is now a mature and environmentally friendly solution. Long-term and credible solar radiation data are required for energy assessments of solar applications. Due to the lack of a typical year and accurate, long-term global solar radiation data for Taiwan, data for the typical meteorological year (TMY) of global solar radiation from 30 weather stations across Taiwan of the Central Weather Bureau were gathered for this study. The database for solar radiation contains data for the 15 years between 2004 and 2018, except for one (Chigu) weather station which provides data for the 12 years between 2004 and 2015, which possesses credible data quality and meets the requirements of the TMY method. The minimum and maximum TMY global radiation observed from the 30 weather stations are 3421.8 and 5479.9 MJ/m2 at the Zhuzihu (Station 2) and Tainan (Station 16) weather stations, respectively. The effects of topography, geography, and latitude on the global radiation distribution in Taiwan are discussed. A trend of increasing annual global radiation from the northeast to the southwest in the Taiwanese mainland, which is attributed to the combined effects of topography and latitude, is observed. This credible, long-term database for global solar radiation is a prerequisite reference for solar information for use in determining the performance of solar energy applications in Taiwan.
Lessons learned: Development of COVID-19 clinical staging models at a large urban research institution

The University of Illinois at Chicago (UIC), along with many academic institutions worldwide, made significant efforts to address the many challenges presented during the COVID-19 pandemic by developing clinical staging and predictive models. Data from patients with a clinical encounter at UIC from July 1, 2019 to March 30, 2022 were abstracted from the electronic health record and stored in the UIC Center for Clinical and Translational Science Clinical Research Data Warehouse, prior to data analysis. While we saw some success, there were many failures along the way. For this paper, we wanted to discuss some of these obstacles and many of the lessons learned from the journey.Principle investigators, research staff, and other project team members were invited to complete an anonymous Qualtrics survey to reflect on the project. The survey included open-ended questions centering on participants' opinions about the project, including whether project goals were met, project successes, project failures, and areas that could have been improved. We then identified themes among the results.Nine project team members (out of 30 members contacted) completed the survey. The responders were anonymous. The survey responses were grouped into four key themes: Collaboration, Infrastructure, Data Acquisition/Validation, and Model Building.Through our COVID-19 research efforts, the team learned about our strengths and deficiencies. We continue to work to improve our research and data translation capabilities.
Loanword identification based on web resources: A case study on wikipedia

To alleviate the resource scarcity and improve the robustness in loanword identification, the current study proposes a novel loanword identification method based on Wikipedia. In this paper, we first present how to obtain loanword candidate datasets and comparable corpora from Wikipedia. On the basis of these corpora, we develop a pseudo-data generation model for loanword identification tasks. And then we put forward a loanword identification model, i.e. the PK-SM-Bi-LSTM-CRF framework, which is based on a bidirectional LSTM-CRF framework and further enhanced by prior knowledge and self-matching attention. The advantages of the proposed method mainly lie in two aspects. For one thing, besides the commonly used word embedding and character embedding features, several other features, including subword embedding, lexical similarity, word alignment and semantic similarity, are incorporated into our method. For another, geographic distance is set as a primary principle in the selection of the best matched donor word from several candidates. Moreover, in order to evaluate the effectiveness of the proposed method, we have conducted a series of experiments in different languages. Experimental results show that the proposed method achieves the best performance among all baseline systems.
THE CASE STUDY ON VEX ROBOTICS CURRICULUM SYSTEM OF VEX ACADEMY BASED ON STEAM EDUCATION

Appears in: INTED2023 Proceedings Publication year: 2023Pages: 3610-3615ISBN: 978-84-09-49026-4ISSN: 2340-1079doi: 10.21125/inted.2023.0974Conference name: 17th International Technology, Education and Development ConferenceDates: 6-8 March, 2023Location: Valencia, Spain
A storytelling framework based on multimedia knowledge graph using linked open data and deep neural networks

Abstract Automatic storytelling is a broad challenge in research contexts such as Natural Language Processing and Contend Based Image Analysis. Despite the considerable achievements of machine learning techniques in these research fields, combining different approaches to fill the gap between an automatic generated story and human handwriting is hard. This work proposes a novel storytelling framework in the Cultural Heritage domain. We developed our framework based on a Multimedia Knowledge Graph (MKG), a crucial point of our work. Furthermore, we populated our Multimedia Knowledge Graph with a focused crawler that employs deep learning techniques to recognise a multimedia object from web resources. Furthermore, we used a combined approach of deep learning techniques and Linked Open Data (LOD) to retrieve information about images and depicted figures using Instance Segmentation. The system has a dynamic, user-friendly interface that guides the user during the storytelling process. Finally, we evaluated the system from a qualitative and quantitative point of view.
EDUCATIONAL APPLICATION FOR THE DEVELOPMENT OF COMPUTATIONAL THINKING SKILLS IN STUDENTS WITH SPECIAL NEEDS

EDUCATIONAL APPLICATION FOR THE DEVELOPMENT OF COMPUTATIONAL THINKING SKILLS IN STUDENTS WITH SPECIAL NEEDS
Performance Analysis of a Hybrid Algorithm for Lost Target Recovery in Wireless Sensor Network

Constructing a successful framework for recovering a lost target is a well-researched problem in wireless sensor networks. Better target prediction algorithms can reduce the overall energy consumption of the network. These can be accomplished by carefully choosing the best nodes for continuous target monitoring situations. Traditional prediction algorithms, on the other hand, were limited to constant motion models and typically failed in fast target movements. Designing prediction algorithms for resource-constrained micro sensor nodes is typically a difficult issue, but neural networks’ capacity to understand all non-linearity between input and output can be used. Recovery of the target after it has been lost in the network is one of the main problems with tracking applications. An efficient recovery method for applications involving linear and nonlinear target tracking is presented in this paper. The suggested method learns the temporal correlation between subsequent samples of a target trajectory using Kalman and time delay neural network (TDNN) models. Hybrid method that further improvements in terms of energy efficiency are realized taking into consideration geometrical shapes (circle, contour) and velocity of target while executing recovery algorithms. A hybridization of the present framework is suggested using a direct broadcast-based technique for targets that have been lost for longer periods of time.
Constraint Based Clustering Technique for Web Services

Web services are loosely coupled methods which need to be located and used according to the requirement. Loose coupling nature of Web services made the system to integrate or combine constituent Web services of a standalone business. A Web service application that can compose the Web service from different organizations will have transactions that span the multiple services. It is difficult for an application to create a software component and use it if it is unlikely to be reused. Therefore, this system provides reusability feature that allowed composing the Web services and these composable services are integrated into an application. Service composing is an ability to combine Web Services into complex applications to get the coarser grained behavior. The Web services may contain more than one operation. These operations are required to be clustered according to the required constraints. This paper is focused on constraint-based clustering to group the Web services according to user request for faster access. Constraint based clustering is a technique to integrate Web services according to required constraints. The system performance is checked with real time available Web Services during dynamic composition. This is a novel approach of clustering due to dynamic technique of collecting data during run-time.
Responding to the coronavirus disease-2019 pandemic with innovative data use: The role of data challenges

Abstract Innovative, responsible data use is a critical need in the global response to the coronavirus disease-2019 (COVID-19) pandemic. Yet potentially impactful data are often unavailable to those who could utilize it, particularly in data-poor settings, posing a serious barrier to effective pandemic mitigation. Data challenges, a public call-to-action for innovative data use projects, can identify and address these specific barriers. To understand gaps and progress relevant to effective data use in this context, this study thematically analyses three sets of qualitative data focused on/based in low/middle-income countries: (a) a survey of innovators responding to a data challenge, (b) a survey of organizers of data challenges, and (c) a focus group discussion with professionals using COVID-19 data for evidence-based decision-making. Data quality and accessibility and human resources/institutional capacity were frequently reported limitations to effective data use among innovators. New fit-for-purpose tools and the expansion of partnerships were the most frequently noted areas of progress. Discussion participants identified building capacity for external/national actors to understand the needs of local communities can address a lack of partnerships while de-siloing information. A synthesis of themes demonstrated that gaps, progress, and needs commonly identified by these groups are relevant beyond COVID-19, highlighting the importance of a healthy data ecosystem to address emerging threats. This is supported by data holders prioritizing the availability and accessibility of their data without causing harm; funders and policymakers committed to integrating innovations with existing physical, data, and policy infrastructure; and innovators designing sustainable, multi-use solutions based on principles of good data governance.
Calculation model of non-nuclear steam rush in steam turbine of nuclear power unit Construction and application

According to the technical characteristics of steam turbines and their thermodynamic systems of various types of nuclear power units, a calculation software suitable for evaluating the feasibility of nonnuclear steam rushing of steam turbines of various types of nuclear power units is developed The software can accurately evaluate and check the non-nuclear steam rushing parameters by comparing the steam intake and heat consumption required by the steam turbine with the effective steam quantity and heat storage provided by the primary loop, and solve the quantitative evaluation problem of the feasibility of non-nuclear steam rushing for steam turbines of various types of nuclear power units.
Study on the method of identifying fractures by ant tracking technique

Fracture is an effective reservoir space and important seepage channel for low permeability sandstone reservoirs, which is very important for reservoir exploration and development. Conventional fracture prediction methods have high cost, slow operation speed and low resolution, which limits the promotion of fracture identification methods. Therefore, this paper proposes a fracture prediction method based on ant tracking. This method is based on post-stack seismic data. Firstly, the seismic data is denoised and the fault continuity is enhanced by using structural smoothing and variance body attributes. On this basis, the active ant tracking is used to enhance the fault boundary of the data body. Then, the data body is subjected to multiple ant tracking calculations by optimizing parameters, and finally the suitable parameter values are determined to realize the prediction of natural fractures. The results show that the technology is used to predict natural fractures, and the prediction results have high resolution and reasonable fracture distribution.
A Transformer-Based Network with Character-Level Masks for CAPTCHA Recognition

Text-based CAPTCHA recognition remains a hot research topic in artificial intelligence and Internet security. The end-to-end deep learning model, which simultaneously localizes and recognizes the character, has recently become the mainstream framework to solve CAPTCHAs. However, prior works are still limited in generalization ability when the training set is relatively small, and the performance achieved on some CAPTCHA schemes remains to be improved. In this paper, we introduce a transformer-based model that uses a novel self-attention mechanism with character-level masks to solve text-based CAPTCHAs. This mechanism enables the encoder and decoder to learn the effective representation of the non-semantic character sequence. Experimental results show that our model has superior generalization ability, even when the training set contains only 600 samples. Meanwhile, our model significantly improves the robustness to different CAPTCHA schemes, achieving a high accuracy above 90% on five representative CAPTCHAs deployed on popular websites.
A New Approach to Image Classification Dataset Privacy-Preserving with Deep Neural Network

Deep learning technology has made great achievements in some fields such as computer vision, natural language understanding, speech processing, etc. However, a big challenge when using deep learning models is the require a large amount of data. Meanwhile, most of the data belong to organizations or personalities that cannot be public due to privacy. In this paper, we propose a new approach for data publishers to conceal the original dataset but still ensure the features for training deep learning models. Our framework has three advantages. First, we proposed a neural network as a encoder to hide a full-size color image within a noisy image of the same size. The noisy images make it impossible to guessable the contents of the original dataset inside. Second, that method is privacy-preserving as the encode method can’t be inverted without having an original dataset. Third, we demonstrate that our framework is accuracy-preserving because the encoded image still keeps features for the image classification task. All of our experiments based on a trade-off between two factors: the number of features retained (information loss), and the difficulty to invert the original dataset (privacy loss). Our code and pre-trained models are available at: https://github.com/sun-asterisk-research/privacy-dataset-research .
Nanophotonic and Optical Platforms for Deep Learning

The preceding chapters introduced two classes of applications of DL in nanophotonics. In both cases, DL-based solutions provide an alternative to existing techniques, like advanced optimization algorithmsAlgorithms for inverse designInverse design and electromagnetic solvers for forward modeling. These data-driven solutions, despite the pros and cons, are among the many advances ML has enabled across a wide spectrum of disciplines. From the standpoint of computer scientists, solving a problem from nanophotonics with DL may have nothing special in workflow compared with other tasks such as drug discovery or prediction of material properties; it is largely just a matter of feeding the ANNsArtificial neural networks (ANNs) data from another scientific domain. One of the most fascinating aspects of the interplay between DL and nanophotonics (or optics in general), which makes the latter a unique research field, is that the direction of application can be reversed. Owning the advantage in realizing massively parallel interconnections, nanophotonic systems as optical computing platforms have proved to be fast and power-efficient in implementing computational operations essential for ANNsArtificial neural networks (ANNs). In the ideal case, once trained, they could run at the speed of light with little to no power consumption, leading to promising alternatives to electronic AI acceleratorsAI accelerators like GPUGraphics processing unit (GPU). In this chapter, we discuss photonic implementations of DL modelsModels for inference tasks (Wetzstein et al., Nature 588(7836):39–47, 2020). Foci will be given to all-optical nanophotonic circuits and free-space diffractive optics, each starting with showing how analogues can be established between computational operations and behaviors of light waves, followed by a representative proof-of-concept demonstration. TrainingTraining of ANNsArtificial neural networks (ANNs) has different requirements on computation and thus will not be discussed in detail. Nonetheless, trainingTraining in optical hardware is still possible in several ways (Wetzstein et al., Nature 588(7836):39–47, 2020; Wagner and Psaltis, Appl Opt 26(23):5061–5076, 1987; Hughes et al., Optica 5(7):864–871, 2018; Wright et al., Nature 601(7894):549–555, 2022). In addition to AI applications, optical and photonic systems have also been used in other paradigms such as neuromorphic computing (Shastri et al., Nat Photonics 15(2):102–114, 2021). The reader interested in these topics or in hybrid optical-electronic systemsHybrid optical-electronic systems for AI is referred to some recent review articles (Lima et al., Nanophotonics 6(3):577–599, 2017; Miller, J Lightwave Technol 35(3):346–396, 2017; Sande et al., Nanophotonics 6(3):561–576, 2017; Brunner et al., J Appl Phys 124(15):152004, 2018; Nahmias et al., IEEE J Sel Top Quant Electron 26(1):1–18, 2020; Zangeneh-Nejad et al., Nat Rev Mater 6(3):207–225, 2021; Wu et al., Engineering 10:133–145, 2022; Huang et al., Adv Phys X 7(1):1981155, 2022).
Using NARX algorithm and Gaussian process to predict the losses data of GPS receiver

GPS receiver signal can be lost in a few seconds for different reasons especially in high building reigns; hence in order to overcome on this problem, a predictor is proposal to predict GPS signal when its signal is lost for a few seconds. So, this work proposed two methods for predict GPS receiver signal, the first method is the Nonlinear Autoregressive with exogenous input (NARX), which deals with neural networks and their different methods, while the second one is the Gaussian process (GP), which is a Bayesian method. GPS receiver data are taken from an actual GPS receiver which is placed on a hand-made boat and tested in the water, and the readings obtained were treated in MATLAP software. As well as a comparison between these two methods has been carried out, and the results showed that the Gaussian Process method is better than NARX method, where the prediction values are close to the real GPS values.
Sentiment analysis in arabic language using machine learning: Iraqi dialect case study

People's perspectives about products, services, events and other persons are widely available on social media and they represent an important feedback for organizations and governments. In the last decade, with the increased amount of online posted opinions, sentiment analysis (SA) started gaining higher attention from researchers. The aim of SA, an application of natural language processing, is to analyze sentiments within text, and in the case of this work is to extract sentiments associated with positive or negative polarities. Arabic language is of high interest as it is one of the widely used languages. Unfortunately, Arabic SA has many challenges due the special characteristics of the Arabic language which limited the number of works in this area and those that concern the Iraqi dialect in particular. This paper proposes some enhanced classifiers based on hybrid machine learning algorithms combined with two proposed preprocessing steps. These preprocessing steps along with the proposed hybrid classifiers enhance the classification accuracy for the Iraqi dialect reviews which are collected from online movie reviews. The results show up to 12.5% enhancement in the classification performance as compared to the conventional preprocessing and classifiers.
Breast Cancer Identification Using Improved DarkNet53 Model

Breast cancer is a challenging problem in the field of Bio-Medical image processing. In this research, our focus is to improve the accuracy of multiple datasets. In this proposed framework multiple pre-trained networks have been used to predict cancer. We use Darknet53, SqueezeNet, and ResNet50 on two different datasets of pathological images. The goal is that experimental shape elucidation requires substantial effort and can be time-consuming. As a result, we seek an automatic technique for detecting cancer. The increased number of softmax classifiers, leakyrelu activation layer, and additional Batch normalization layer is implemented in darknet53 mod-el. As a result, changes aided in the improvement of the Darknet model's structure and parameters. These findings show that this technique can extract multi-layer characteristics from cancer pathological images efficiently and accurately, regardless of batch size. In our research, we added an additional batch normalization layer to all three used networks, which improve the learning rate, and validation accuracy and made learning easier. Among used networks, Darknet53 achieved the highest accuracy with 95%.
DeepPRS: A Deep Learning Integrated Pattern Recognition Methodology for Secure Data in Cloud Environment

The distributed data environment in the cloud poses many security and privacy challenges. The cloud environments and the Intrusion Detection System (IDS) for the same should work with low latency and high efficacy. This paper proposes a Deep learning integrated Pattern Recognition methodology for Secure (DeepPRS) data in cloud environment. The proposed methodology works in two phases, (i) the first phase runs a single pattern matching algorithm named as PRS algorithm that takes as input – data supplied by the centralized controller, attack history, feature data and detect patterns (ii) the second phase which runs a deep learning model to label the detected pattern as attack or non-attack type. The performance analysis of the proposed algorithm is compared with the existing pattern matching algorithms such as KMP (Knuth-Morris-Pratt Algorithm) and BM (Boyer-Moore).The experimental results shows that the performance of the PRS algorithm is highly appreciable in terms of latency. The detected pattern from the PRS algorithm is fed into a Deep Learning model to classify the type of pattern as either attack or non-attack kind.
Digital pathology: an overview

This article is an overview of digital pathology (DP) and its myriad applications. In addition, the trajectory of DP in India, with its successes, limitations and barriers is described. Covid pandemic provided new impetus to development and adoption of DP worldwide and India was no exception. With growing interest in Artificial Intelligence based algorithms for diagnosis and abilities to prognosticate and predict, the field of DP continues to evolve relentlessly to provide a new dimension to precision oncology.