Reviewerly/exHarmony huggingface dataset:
Design of online interactive education system of art course based on B/S architecture

In order to improve the accuracy of resource mining and operation efficiency of online interactive education system for art courses, an online interactive education system for art courses based on B/S architecture is proposed and designed. In the B/S architecture, the overall structure of the system is designed. The hardware part of the system includes system login module, art course resource interactive communication module and interactive teaching module, which can fully improve the interactive education performance of the system. In the software part of the system, the feature clustering method is used to excavate the teaching resources of art course, so as to improve the utilization ability of art course resources and complete the overall design of the system. The experimental results show that, compared with the traditional education system, the designed education system has higher mining accuracy of art curriculum resources, and the designed system has lower response time. Therefore, it shows that the designed education system has better performance in practical application.
Efficient Distributed Database Clustering Algorithm for Big Data Processing

When clustering efficient distributed database, the conventional algorithm has long time cost and low clustering accuracy. To solve the above problems, an efficient distributed database clustering algorithm for big data processing is designed. Calculating the eigenvalues of the database, and linking the efficient distributed database with similar characteristics. The cross correlation matrix is used to ensure the consistency of cluster label. To improve the performance of K-means algorithm, input the database to be clustered, output <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> clustering centers, and divide the clustering groups. Mapping database to clustering center, clustering low dimensional big data. Experimental results show that the proposed algorithm can reduce the running time and mean square error of data clustering, and improve the efficiency and accuracy of clustering.
Geological interpretation of remote sensing data for deep faults identifying in the Dnieper-Donets basin

Summary The article offers an interpretation of the lineaments obtained by decoding SRTM, using the example of the western part of the Dnieper-Donetsk depression. A new approach is based on data on the interaction of deep faults with the formation of transmission and movement structures, which significantly affect the conductivity of deep faults. According to geophysical data published in open sources, the formation as a result of such interaction of fault fields of different stages of maturity has been traced, which affects the fluid conductivity of the faults, as well as the destruction of mineral deposits. The use of this approach will increase the quality and reliability of interpretation of the results of decoding the remote sensing data, and this, in turn, will increase the efficiency of forecasting geodynamically active zones and, as a consequence, the oil and gas prospects of specific areas, structures and individual blocks.
SECONDARY SCHOOL STUDENTS' PERCEPTION TO READING COMPREHENSION IN ENGLISH LANGUAGE IN IJEBU ODE LOCAL GOVERNMENT OF OGUN STATE

Reading is a very important skill that students must master because it cannot be neglected in the teaching and learning process. Reading experts note that when the reading skill is mastered, transfer of knowledge to other school subjects is facilitated and this assists students to tackle problems of comprehension not only in English Language but also in other school subjects. Hence, this study examines the perception of secondary students towards reading comprehension in English language in Ijebu-Ode Local Government Area of Ogun State. This study used questionnaire as an instrument to obtain data from four hundred and eighty-five (485) students who were drawn from ten (10) randomly selected secondary schools. The frequency counts, percentage, t-test and ANCOVA were the analytical tools used in the study. The findings show that there is no significant gender difference in the perception of secondary school students towards reading comprehension and there is no significant difference in the perception of Junior Secondary School students and Senior Secondary School students towards reading comprehension in English Language. The study, therefore, recommends that reading culture should be inculcated into the students at early stage and that Government should provide enabling school environment that will stimulate students to read through provision of well-equipped libraries to schools.
Fruit-Fly optimization based feature integration in image retrieval

<abstract> The content-based image retrieval (CBIR) system searches and retrieves the similar images from the huge database using the significant features extracted from the image. Feature integration techniques used in the CBIR system assign static weights to each feature involved in the retrieval process that gives a smaller number of similar images as a result. Moreover, the retrieval time of the CBIR system increases due to the entire database search. To overcome this disadvantage the proposed work introduced a two-level searching process in the CBIR system. The initial level of the proposed framework uses the image selection rule to select more relevant images for the second-level process. The second level of the framework takes the proposed dominant color and radial difference pattern details from the query and selected images. By using color and texture features of the selected images, similarity measure is calculated. The proposed work assigns optimal dynamic weight to the similarity measure of color and texture features using the fruit fly optimization algorithm. This improves the retrieval performance of the CBIR system. </abstract>
Simplification on mobile devices reduces personal (cyber) safety

Due pandemic reasons, the phishing attack numbers are 6 times bigger than normally, approximately. If we assume the defense stays in the same level, this means 6 times more loss than before. Phishing attacks are targeting not just companies but also private individuals, however actual data is not available on individual attack, so the real effect on individuals, their cumulative loss with business is unknown. This study targets the review of mobile attacks and available prevention assets and materials. This paper collects evidences on how simplified - mobile - interfaces according to PC reduces the opportunity to identify cyberattacks as phishing attack. The goal of this document is to point out minor differences between PC-s and mobiles and their user behavior. Mobile devices are widely used and widely targeted by attackers and mobile devices in contrast with PC provides fewer opportunity and feedback to users to avoid attacks.
Identifying Negativity Factors from Social Media Text Corpus Using Sentiment Analysis Method

Automatic sentiment analysis play vital role in decision making. Many organizations spend a lot of budget to understand their customer satisfaction by manually going over their feedback/comments or tweets. Automatic sentiment analysis can give overall picture of the comments received against any event, product, or activity. Usually, the comments/tweets are classified into two main classes that are negative or positive. However, the negative comments are too abstract to understand the basic reason or the context. organizations are interested to identify the exact reason for the negativity. In this research study, we hierarchically goes down into negative comments, and link them with more classes. Tweets are extracted from social media sites such as Twitter and Facebook. If the sentiment analysis classifies any tweet into negative class, then we further try to associates that negative comments with more possible negative classes. Based on expert opinions, the negative comments/tweets are further classified into 8 classes. Different machine learning algorithms are evaluated and their accuracy are reported.
Kegiatan preservasi naskah kuno Syekh Abdul Manan di Museum Bandar Cimanuk Indramayu

The spread of Islam in Indramayu stems from trading activities at Cimanuk Port, Indramayu. This is completely contained in the hundred years old manuscripts of Sheikh Abdul Manan. This study aimed to determine how the preservation of Syekh Abdul Manan's ancient manuscripts at the Bandar Cimanuk Museum, Indramayu. The research used a qualitative approach through descriptive research method. Based results of research, the Bandar Cimanuk Museum has carried out activities for the preservation of Sheikh Abdul Manan’s ancient manuscripts through restrictions to direct access to museum users, routine cleaning of manuscripts and the museum environment, fumigation, and digitization. Restrictions to direct access to museum users by placing the ancient manuscripts in glass covered displays and special boxes. Routine maintenance of the museum is conducted by regulating the light and humidity, using camphor in every corner of the room, dehumidifier settings, and directing the museum visitors. Fumigation is conducted through pesticide compund to eliminate pests once a year. The digitization of ancient manuscripts into the form of a digital book (flipbook) which is carried out in collaboration with museums with various institutions, such as, a collaboration with researchers and students from Padjadjaran University. The entire process of preservation activities was carried out in a structured and panned methods. The study concludes that the importance of preservation to maintain and preserve the heritage of ancient manuscripts to be accessed and utilized by wider societies.
A hybrid regularizers model for multiplicative noise removal

In this paper, we propose a variational method for restoring images corrupted by multiplicative noise. Computationally, we employ the alternating minimization method to solve our minimization problem. We also study the existence and uniqueness of the proposed problem. Finally, experimental results are provided to demonstrate the superiority of our proposed hybrid model and algorithm for image denoising in comparison with state-of-the-art methods.
A network-based computational model showing responses to changing environmental constraints similar to patterns observed in the dynamics of the Covid-19 event

The Covid-19 event is special with respect to two aspects: First, the contacts between people are strongly reduced[RW1] . Second, curfews and similar interventions often have a strong initial effect but then rather suddenly stop to yield further reduction. For example, the 7d average of new cases in Portugal came down from 12890 January 28th 2021 to 1005 February 28th. But it was 513 March 16th and 512 April 16th, and remained in the band between 420 and 614 over the period before Delta changed the picture, and, notwithstanding effects of vaccination and change of rules, was 641 June 11th after a minimum of 327 May 11th. Similar patterns were observed elsewhere, e.g. in Belgium and Ireland . The curves look like what would be expected from a system that has different steady states, depending on environmental constraints. In such systems, change of the environment will trigger a possibly strong initial response but as the steady state corresponding to the new constraints is...
Integrating an Automated Prover for Projective Geometry as a New Tactic in the Coq Proof Assistant

Recently, we developed an automated theorem prover for projective incidence geometry.This prover, based on a combinatorial approach using matroids, proceeds by saturation using the matroid rules.It is designed as an independent tool, implemented in C, which takes a geometric configuration as input and produces as output some Coq proof scripts: the statement of the expected theorem, a proof script proving the theorem and possibly some auxiliary lemmas.In this document, we show how to embed such an external tool as a plugin in Coq so that it can be used as a simple tactic.
Geochemical records of the sediments and their significance in Dongping Lake Area, the lower reach of Yellow River , North China

Dongping Lake area, located in the lower reaches of Yellow River, is an ideal place to study the changes of modern river and lake sedimentary environment. The sediment samples of Dawen River, Yellow River, and Dongping Lake were collected, and the major elements, trace elements and organic matter geochemical composition of the samples were analyzed. Cluster analysis, characteristic element ratio method and graphic method were used to explore the geochemical characteristics of sediments and their environmental implication. The results show that the contents of SiO2, Na2O, TiO2 and Zr in sediments of Dawen River and Yellow River are relatively high, and the contents of iron and manganese oxides, organic matter, CaO, P2O5 and Sr in lake sediments are relatively high. That reveals the differences of sedimentary environments between the rivers and the lake. The contents of Sr and Zr in Dawen River are affected by the rapid migration of clastic materials in the upstream carbonate source area during the flood season; the δCe, ΣREE and REE’s ratios in the sediments of the Yellow River reflect the influence of the Loess source; and the distribution of elements changes along the flow direction during the flood season. The characteristics of pH, element composition and LREE & HREE fractionation of the lake sediments indicate that the sediment source is complex, and the lake environment is affected by the flood season. The study shows that the geochemical content and its variation characteristics of sediments effectively reveal the sedimentary environment, material composition and characteristics of flood season of rivers and the lake in the study area.
Social and Psychological Issues of Smart Education

The study aims at analyzing social and psychological issues of Smart education (the education involving artificial intelligence technologies). Modern education and its subjects are increasingly changing. And the changes are not confined to the forms and content of education,but make a person reinterpret themselves as a personality, partner, student, and professional, they also lead to transformation and deformation of personal relations with themselves and the world, their functioning in various spheres: academic and labor, family and friends, everyday life and hobby, sport and recreation. Properly implemented Smart education will intensify and enhance education quality, attaining these goals, though, requires development and introduction of appropriate smart technologies, and consonance of educational organization’s objectives with the goals of education. The authors' contribution to the study of social and psychological problems of smart education is associated with an attempt to clarify the conceptual and technological, value and target boundaries of smart education, which will increase the effectiveness of using smart technologies in teaching and upbringing in schools, universities and other educational institutions.
A Natural Language Processing–Assisted Extraction System for Gleason Scores: Development and Usability Study

Background Natural language processing (NLP) offers significantly faster variable extraction compared to traditional human extraction but cannot interpret complicated notes as well as humans can. Thus, we hypothesized that an “NLP-assisted” extraction system, which uses humans for complicated notes and NLP for uncomplicated notes, could produce faster extraction without compromising accuracy. Objective The aim of this study was to develop and pilot an NLP-assisted extraction system to leverage the strengths of both human and NLP extraction of prostate cancer Gleason scores. Methods We collected all available clinical and pathology notes for prostate cancer patients in an unselected academic biobank cohort. We developed an NLP system to extract prostate cancer Gleason scores from both clinical and pathology notes. Next, we designed and implemented the NLP-assisted extraction system algorithm to categorize notes into “uncomplicated” and “complicated” notes. Uncomplicated notes were assigned to NLP extraction and complicated notes were assigned to human extraction. We randomly reviewed 200 patients to assess the accuracy and speed of our NLP-assisted extraction system and compared it to NLP extraction alone and human extraction alone. Results Of the 2051 patients in our cohort, the NLP system extracted a prostate surgery Gleason score from 1147 (55.92%) patients and a prostate biopsy Gleason score from 1624 (79.18%) patients. Our NLP-assisted extraction system had an overall accuracy rate of 98.7%, which was similar to the accuracy of human extraction alone (97.5%; P=.17) and significantly higher than the accuracy of NLP extraction alone (95.3%; P&lt;.001). Moreover, our NLP-assisted extraction system reduced the workload of human extractors by approximately 95%, resulting in an average extraction time of 12.7 seconds per patient (vs 256.1 seconds per patient for human extraction alone). Conclusions We demonstrated that an NLP-assisted extraction system was able to achieve much faster Gleason score extraction compared to traditional human extraction without sacrificing accuracy.
Edge-Based Convolutional Neural Network for Improving Breast Cancer Prediction Performance

There are many research studies in the field of breast cancer prediction, but it has been observed that the time taken for prediction needs to be reduced. The problem in the existing research is space consumption by graphical content. The proposed research is supposed to minimize the prediction time and space consumption. In this paper, research has focused on the study of existing breast cancer research and techniques and eliminating their limitation. It has been observed that when the number of datasets increases, every comparison makes a huge gap in size and comparison time. This research proposes a methodology for breast cancer prediction using an edge-based CNN (convolutional neural network) algorithm. The elimination of useless content from the graphical image before applying CNN has reduced the time consumption along with space consumption. The edge detection mechanism would retail only edges from the image sample in order to detect the pattern to predict breast cancer. The proposed work is supposed to implement the proposed methodology. A comparison of the proposed methodology and algorithm with the existing algorithm is made during simulation. The proposed work is found to be more efficient compared to the existing techniques used in breast cancer prediction. The utilization of proposed in the work area of medical science is supposed to enhance the capability in case of CNN at the time of decision-making. The proposed work is supposed to be more accurate compared to the existing works. It has been observed that the proposed work is fourteen to fifteen percent more accurate. It is taking 9/4 times less space and 1.0849004/0.178971 times less time compared to the general CNN model. Accuracy might vary as per size of the image and alteration performed in dataset of the image.
WawPart: Workload-Aware Partitioning of Knowledge Graphs

Large-scale datasets in the form of knowledge graphs are often used in numerous domains, today. A knowledge graph’s size often exceeds the capacity of a single computer system, especially if the graph must be stored in main memory. To overcome this, knowledge graphs can be partitioned into multiple sub-graphs and distributed as shards among many computing nodes. However, performance of many common tasks performed on graphs, such as querying, suffers, as a result. This is due to distributed joins mandated by graph edges crossing (cutting) the partitions. In this paper, we propose a method of knowledge graph partitioning that takes into account a set of queries (workload). The resulting partitioning aims to reduces the number of distributed joins and improve the workload performance. Critical features identified in the query workload and the knowledge graph are used to cluster the queries and then partition the graph. Queries are rewritten to account for the graph partitioning. Our evaluation results demonstrate the performance improvement in workload processing time.
Neural Markov Logic Networks

We introduce neural Markov logic networks (NMLNs), a statistical relational learning system that borrows ideas from Markov logic. Like Markov logic networks (MLNs), NMLNs are an exponential-family model for modelling distributions over possible worlds, but unlike MLNs, they do not rely on explicitly specified first-order logic rules. Instead, NMLNs learn an implicit representation of such rules as a neural network that acts as a potential function on fragments of the relational structure. Similarly to many neural symbolic methods, NMLNs can exploit embeddings of constants but, unlike them, NMLNs work well also in their absence. This is extremely important for predicting in settings other than the transductive one. We showcase the potential of NMLNs on generation of molecular (graph) data, knowledge-base completion and triple classification.
Prediction of Fake Toll-Free Logistic Vehicles Based on Historical Traffic Data

At present, the green channel policy is fully implemented. However, the detection is relatively lagging, and truck drivers are susceptible to fake the toll-free logistics vehicles (TFLVs), causing huge losses to the operation. In order to improve the accuracy and efficiency of TFLVs inspection, this paper establishes a toll evasion prediction model for fake TFLVs based on the historical TFLVs traffic dataset derived from the highway network toll collection system. First, according to the importance and reliability, we used the data mining technology to differentiate and extract the data attributes. And the spatiotemporal characteristics and other characteristics of fake TFLVs through-traffic entering and exiting toll booths are studied and analyzed based on the preprocessed data. This study then used the Borderline-SMOTE oversampling method to balance the pass data set and the ChiMerge algorithm to discretize continuous attributes. In order to ensure the effective matching and the correlation between the large contribution attributes and the results, correlation item test and collinearity test are used for discrete related attributes. Finally, adopting the processed discrete TFLVs data which passed correlation item test and collinearity test, and using the decision tree to establish the prediction model, the classification results of the escape behavior prediction model and other models are compared by using the historical TFLVs data set. The results show that the accuracy of the escape behavior prediction model proposed in this paper is 83.4%, which is higher than that of the Logistic regression model (61.8%) and the Random forest model (81%). This research model can effectively warn of fake TFLVs, and on the basis of simplifying the inspection process of TFLVs, can reduce the probability of the occurrence of fake TFLVs evasion, which has practical application significance.