Reviewerly/exHarmony huggingface dataset:
<scp>GeoDeepShovel</scp>: A platform for building scientific database from geoscience literature with <scp>AI</scp> assistance

Abstract With the rapid development of big data science, the research paradigm in the field of geosciences has also begun to shift to big data‐driven scientific discovery. Researchers need to read a huge amount of literature to locate, extract and aggregate relevant results and data that are published and stored in PDF format for building a scientific database to support the big data‐driven discovery. In this paper, based on the findings of a study about how geoscientists annotate literature and extract and aggregate data, we proposed GeoDeepShovel, a publicly available AI‐assisted data extraction system to support their needs. GeoDeepShovel leverages state‐of‐the‐art neural network models to support researcher(s) easily and accurately annotate papers (in the PDF format) and extract data from tables, figures, maps, etc., in a human–AI collaboration manner. As a part of the Deep‐Time Digital Earth (DDE) program, GeoDeepShovel has been deployed for 8 months, and there are already 400 users from 44 geoscience research teams within the DDE program using it to construct scientific databases on a daily basis, and more than 240 projects and 50,000 documents have been processed for building scientific databases.
Parameters not empirically identifiable or distinguishable, including correlation between Gaussian observations

Note: Accepted version, published in Statistical Papers, https://doi.org/10.1007/s00362-023-01414-3. It is shown that some theoretically identifiable parameters cannot be empirically identified, meaning that no consistent estimator of them can exist. An important example is a constant correlation between Gaussian observations (in presence of such correlation not even the mean can be empirically identified). Empirical identifiability and three versions of empirical distinguishability are defined. Two different constant correlations between Gaussian observations cannot even be empirically distinguished. A further example are cluster membership parameters in $k$-means clustering. Several existing results in the literature are connected to the new framework. General conditions are discussed under which independence can be distinguished from dependence.
An Entity Extraction and Categorization Technique on Twitter Streams

As social media platforms have gained huge momentum in recent years, the amount of information generated from the social media sites is growing exponentially and gives the information retrieval systems a great challenge to extract the potential named entities. Researchers have utilized the semantic annotation mechanism to retrieve the entities from the unstructured documents, but the mechanism returns with too many ambiguous entities. In this work, the DBpedia knowledge base is adopted for entity extraction and categorization. To achieve the entity extraction task precisely, a two-step process is proposed: (a) train the unstructured datasets with Word2Vec and classify the entities into their respective categories. (b) crawl the web pages, forums, and other web sources to identifying the entities that are not present in the DBpedia. The evaluation shows the results with more precision and promising F 1 score.
Design of an efficient graph-based traversing method for establishing authentication mechanism in cloud

With the broader development of networking, data acquired from IoT and big data have grown significantly in various real-time applications. It provides a transparent role of human life as it shows an extensive bond with data provisioning. On the other hand, it has some challenging issues like verification of authenticity of the enormous data over an un-trusted environment like cloud. It causes privacy leakage, delay, and unidentified data size by third-party. Thus, there is a need for a novel authentication to handle all these problems using a tree data structure for establishing authentication for privacy preservation. Here, a novel graph-based traversing algorithm (GTA) is proposed for realizing the data stream verification and validates the arrival of data streams. This model reduces the failure rate and improves the authentication mechanism and robustness. An integrity verification scheme is used to handle privacy leakage issues during third-party analysis. The simulation is performed in MATLAB 2016b environment to project the significance of the model, and the outcomes are more desirable for authentication and privacy preservation during practical examination. Here, metrics like path length, total verification time, and total insertion time are evaluated. The proposed model gives a better trade-off compared to existing approaches. The path length variation of GTA is 1.3, 1.4, and 1.5 for depth rates 6, 8, and 10, respectively. The total verification time with data size is 1, 2, 3, and 4[Formula: see text]GB, respectively. The total insertion time of GTA is 5, 10, 15, and 18[Formula: see text]s for 1, 2, 3, and 4[Formula: see text]GB, respectively. It is remarkably lesser than P-ATHAT and Merkle tree, respectively. The time needed for two successive data verification is 16, 23, 43, and 57[Formula: see text]s for data sizes of 1, 2, 3, and 4[Formula: see text]GB, respectively. The proposed model consumes 15, 28, 44, and 54[Formula: see text]s, respectively, for the data size from 1[Formula: see text]GB to 4[Formula: see text]GB.
Temporal logic explanations for dynamic decision systems using anchors and Monte Carlo Tree Search

For many automated perception and decision tasks, state-of-the-art performance may be obtained by algorithms that are too complex for their behavior to be completely understandable or predictable by human users, e.g., because they employ large machine learning models. To integrate these algorithms into safety-critical decision and control systems, it is particularly important to develop methods that can promote trust into their decisions and help explore their failure modes. In this article, we combine the anchors methodology with Monte Carlo Tree Search to provide local model-agnostic explanations for the behaviors of a given black-box model making decisions by processing time-varying input signals. Our approach searches for descriptive explanations for these decisions in the form of properties of the input signals, expressed in Signal Temporal Logic, which are highly likely to reproduce the observed behavior. To illustrate the methodology, we apply it in simulations to the analysis of a hybrid (continuous-discrete) control system and a collision avoidance system for unmanned aircraft (ACAS Xu) implemented by a neural network.
Spectrum‐based fault localization using empirical mode decomposition algorithm

Abstract Spectrum‐based fault localization (SBFL) is considered as the most popular lightweight fault localization method. However, pure SBFL is proved to be tedious and time‐consuming for programmers to detect faults. This is because the suspiciousness is duplicated and they usually involve only the first few suspicious elements in the debugging process before losing patience. For this reason, benefiting from abundant spectrum information created by SBFL, we propose a new spectral fault localization technique using empirical mode decomposition method (EMD) to improve the accuracy of automatic software debugging. To accomplish that, the faulty program is evaluated by SBFL, and then, the resultant suspiciousness scores are taken as signals and nonfaulty elements as noise. Next, EMD is employed to decompose the suspicious scores of SBFL to eliminate massively repeated ones. Hence, elements are reranked according to new scores, and the ranking list is reconstructed by enlarging the high‐performance range (checking 5% elements) and TOP‐5 region to detect more faults. The performance of EMD‐SBFL is tested and compared with pure SBFL with EXAM scores and Top‐n ranks both on Siemens programs with seeded faults and large‐sized Defects4j programs with real faults. The result reveals that EMD‐SBFL is significantly effective to locate nearly over doubled faults by only check Top‐1 and outperforms the state‐of‐the art SBFL techniques.
Deriving Thresholds of Object-Oriented Metrics to Predict Defect-Proneness of Classes: A Large-Scale Meta-Analysis

Many studies have explored the methods of deriving thresholds of object-oriented (i.e. OO) metrics. Unsupervised methods are mainly based on the distributions of metric values, while supervised methods principally rest on the relationships between metric values and defect-proneness of classes. The objective of this study is to empirically examine whether there are effective threshold values of OO metrics by analyzing existing threshold derivation methods with a large-scale meta-analysis. Based on five representative threshold derivation methods (i.e. VARL, ROC, BPP, MFM, and MGM) and 3268 releases from 65 Java projects, we first employ statistical meta-analysis and sensitivity analysis techniques to derive thresholds for 62 OO metrics on the training data. Then, we investigate the predictive performance of five candidate thresholds for each metric on the validation data to explore which of these candidate thresholds can be served as the threshold. Finally, we evaluate their predictive performance on the test data. The experimental results show that 26 of 62 metrics have the threshold effect and the derived thresholds by meta-analysis achieve promising results of GM values and significantly outperform almost all five representative (baseline) thresholds.
Healthcare Management System using Blockchain with ML Integration

Today, every industry has access to a vast amount of data, and with the development of technology, it is now possible to offer answers to a wide range of issues. This project will use machine learning and blockchain technology to offer answers to issues with healthcare data management. With the aid of machine learning, it is feasible to extract only the pertinent data from the data. Using trained algorithms, this is done. The trustworthiness of data exchange becomes a challenge after this data has been stored. Blockchain can be used in this situation. Blockchain technology's consensus ensures that data is authentic and transactions are secure. By putting the patient at the heart of healthcare administration, blockchain technology has the ability to improve it.
A Survey of Image Semantic Segmentation Algorithm Based on Deep Learning

Image semantic segmentation technology is one of the core research contents in the field of computer vision, and has a wide range of applications in production and life. With the improvement of computer performance and the continuous development of deep learning technology, researchers have increasingly high research enthusiasm for the performance of image semantic segmentation. This paper summarizes the research status of image semantic segmentation based on deep learning and introduces the common datasets used in the field of semantic segmentation. Finally, we point out the existing problems and future development trend of image semantic segmentation algorithms.
Research on the Evaluation and Prediction of the Operation Efficiency of China's Science and Technology Business Incubators

Based on the division of the three major economic regions in the east, the middle and the west, the operational efficiency of science and technology incubators in 31 provinces (cities) from 2010 to 2019 and 2019 is analyzed by DEA-BCC model, and the development trend of science and technology incubators in the next six years and their contribution to GDP are predicted by using the grey model GM (1,1). It is concluded that while giving consideration to fairness, we should pay attention to the total allocation of scientific and technological resources between the eastern, central and western regions to improve the efficiency of scientific and technological resources allocation, promote the development of high-tech in late-developing regions, accurately allocate resources and stabilize regional economic development.
Characteristics of Longitudinal Study Design Based on School Participation through Cooperation between Local Universities, Office of Education, and Schools: Case Study of Chungnam innovation school

This study aims to analyze the characteristics of regional-level education longitudinal studies designed in the process of communication and cooperation between local universities, education offices, and schools, and to explore the role and function of future regional-level education longitudinal studies. As a result of the analysis, it was found that the university and the Office of Education went through the process of research design through cooperation, the process of design refinement through adjustments and experiment, and the process of complementing during research execution, respectively. The differences from the existing longitudinal studies based on the local education office are as follows. First, the existing educational longitudinal studies were designed to track individual students, focusing mainly on individual growth, while the Chungnam Innovation School longitudinal study was designed to see the unit of growth as a school and track unit schools. Second, while existing educational longitudinal studies investigated students' competencies in a self-report manner for the efficiency of the survey, Chungnam Innovation School's longitudinal studies were designed to evaluate students' competencies through performance-type tasks. Third, in the existing educational longitudinal study, schools were regarded as the subject or cooperator of the study, while in the Chungnam Innovation School longitudinal study, schools were regarded as producers or participants of data. Finally, it was discussed how communication and cooperation between local universities, education offices, and schools influenced the design and operation of a new type of educational longitudinal research.
Dilemma and Solution of Intelligent Vehicle Tort Liability Regulation in The Post-Civil Code Era

Since the concept of "artificial intelligence" was first proposed in 1956, it has been applied in many fields, and China has also entered the era of artificial intelligence. Smart cars are developing more and more rapidly, bringing great convenience to people's lives, but they cannot completely avoid the risk of traffic accidents. After a smart car is involved in a traffic accident, how to allocate responsibility has become a difficult problem. Due to the autonomy of smart cars, existing laws do not seem to be able to fully solve this problem, and after the introduction of the Civil Code, it cannot solve it. Based on the research of relevant scholars, this paper clarifies the existing legal regulatory dilemma, and attempts to combine it with the classification standard of autonomous driving, so that the tort liability allocation of smart vehicles can be solved under the existing legal framework through the principle of grading proportionality.
Local Path Planning of USV Based on Millimeter Wave Radar

The USV (Unmanned Surface Vehicle) have unpredictable obstacles in the process of performing missions. To avoid obstacles smoothly, a local path planning method based on millimeter wave radar detection is proposed. An optimal path under the constraints of local path length and smoothness is screened out by improving the B-spline curve method, and the navigation safety and optimization of USV are improved through the actual verification of multiple launchings.
Differential Privacy-based Personalized Recommendation Service for "Helping Farmers" Tourism in Guizhou Province

The revitalization of the countryside is an important strategy proposed in the report of the 19th Party Congress. Rural tourism in Guizhou Province is the key to the development of "big tourism" in the 14th Five-Year Plan of the province, and also the driving force of rural revitalization. Most of the current rural tourism personalized recommendation systems rely on user information, and the exposure of sensitive user information has become an obstacle to the development of smart tourism. Through the genetic matrix decomposition recommendation algorithm based on satisfying differential privacy protection and differential privacy recommendation algorithm for sparse data, privacy protection of similarity calculation, accuracy of recommendation and data security are realized. Actively develop secure and personalized rural tourism recommendations so that the majority of farmers can benefit from them.
Green and Smart Energy Pave the Way for China's Dream

The strategic background of "dual carbon" will promote the green transformation of China's energy, promote economic stability and progress, and is of great significance to ensure energy security. A new pattern of development for the global digital economy has been established by the digital era, e-commerce innovation and development. The energy sector's digitalization, the regular image of new technologies and solutions, the innovation and modernization of new energy systems, the use of green and smart energy to stay on top of the times, these measures have made great contributions to China's leadership in global energy development.
UAV Three-dimensional Hierarchical Path Planning

Aiming at the disadvantages of low efficiency and long operation time of UAV path planning in traditional 3D mountain environment, this paper proposes a layered path planning model for 3D mountain environment. Firstly, three-dimensional elevation diagram is used to model the three-dimensional space, and the three-dimensional space is divided into horizontal and vertical directions, which effectively improves the efficiency of path planning. Secondly, B-spline curve method is used to fit the trajectory points of UAV. Finally, the simulation experiment is carried out. The results show that the generated UAV path is smoother and converges faster, which can meet the task requirements.
THE IMPLEMENTATION OF EXTENSIVE READING TO FOSTER STUDENTS’ READING SKILL

This paper aims to review some previous studies and theories related to the effect of extensive reading to boost students’ reading ability. Moreover, good reading ability is an important skill that should be mastered by students. It is used to enhance students’ knowledge and reading ability. However, students facing several difficulties in upgrading their reading skill, such as limited time to read, uninteresting reading materials given in the classroom, and unsupportive reading environment. Thus, this condition made students to have low reading ability. Extensive reading is chosen as a method to overcome students’ reading problems and difficulties. It is implemented by asking students to read as much as possible what they like outside the classroom. The purpose of this activity is to create supportive reading atmosphere for students. Furthermore, this paper will describe students’ difficulties in reading, the definition of extensive reading, and the advantages of implementing extensive reading in teaching reading. Extensive reading is also proven by some experts as an effective method to assist students reading challenges.
Comparison of Regression Algorithms to Predict Average Air Temperature

Regression algorithms are statistical techniques used to predict the value of a dependent variable, based on one or more independent variables. These algorithms are commonly used in fields such as economics, finance, and engineering. Temperature prediction is a specific application of regression analysis. In this case, the dependent variable is temperature and the independent variables include factors such as humidity, speed of the wind, direction of the wind, and precipitation. There are many different types of regression algorithms, each with its strengths and weaknesses. The study compares the performance of multiple regression models in predicting the average air temperature, using one month's weather data for the Beşiktaş district of Istanbul. A total of 6 different regression models, including ridge, lasso, linear, polynomial, random forest (RF), and support vector (SV) regressions, were included in the study. Among the regression models trained and tested on two different data sets, the three most successful models in predicting average air temperature were lasso, RF, and polynomial regressions (PRs), respectively.
The effect of using emergency remote teaching on Greek Mathematic classes in secondary education from mathematicians’ perspective

From March 2020 until June 2021 secondary education in Greece experienced the consequences of the COVID-19 pandemic. The first measure taken was the general closure of face-to-face teaching at all levels of education and the provision of distance learning through the so-called emergency remote teaching. Mathematicians dealt with unprecedented and difficult situations. In the present paper we record the views of 218 mathematicians of secondary education working in Greek public schools about the obstacles they faced in their teaching during the pandemic in a quantitative survey. According to the survey connectivity problems (affecting teachers and students), lack of teaching material suitable for distance learning, unsatisfactory support of the state, poor previous training in distance learning were major issues. This is the first survey monitoring the problems that Greek mathematicians faced during this period, how they dealt with them and one of a few worldwide regarding mathematicians.
Efficient medical image retrieval system using Geometric Invariant Point Bilateral Transformation (GIPBT)

Sensor rich environments assist CBIR system to provide granularity in detail for many applications. From sensor rich environments, CBIR can provide the process to exploit images that can integrate both search & retrieval mechanisms. Era of digital technology with Smart devices connected to internet so as to communicate is made possible with Internet of Things (IoT). As IoT deals with huge data in the form of images or videos, to be communicated between smart devices through IoT, deals with complex data processing. Proposed Image Retrieval System reduces the computation overhead for wide range of IoT applications. Medical Images are not only image which holds the pixel also the valuable information of medical diagnosis of every human being. Medical image retrieval has become an inescapable task in every hospital IT system due to the influx of medical pictures. A novel texture fusion technique and a rapid similarity measure metric were used in this study to create an efficient medical CBIR. Proposed feature extraction method of Geometric Invariant Point Bilateral Transformation (GIPBT) algorithm provides the texture information. In this method features are computed as point-to-point geometric information of bilateral filtered image. It automatically inoculates the universal information into the local descriptor and reinforces the feature depiction. To further enhance the descriptor, we used Histogram of Second Order Gradient (HSOG) feature extraction method. By use of these two features, we are performing the feature matching. To analyze and categorize the non-linearity between the features, we propose the kernalized discriminant analysis concept into the score prediction metric. Our proposed similarity measure method is Discriminant Kernalized Disparity (DKD) metric. In DKD, kernel applied Eigen decomposition is utilized for each feature set and disparity metric is evaluated. Finally the proposed system is simulated and evaluated with different medical database images and retrieval performance of Retrieval Efficiency, Normalized Average Rank of Retrieval, Precision, Recall, SSIM and processing Time. Some previous implementations are examined and compared to our results. The proposed method has produced Retrieval efficiency of 98.4%, NARR of 15.18%, Precision and Recall of 98.4%, F1 score of 98.39%, Correlation of 98.2%, SSIM of 97.92% and all at a speed of 30 s 5% more precision as well as less processing time are achieved compared to prior efforts by our proposed solution.