Reviewerly/exHarmony huggingface dataset:
Geoinformation Modeling of Determination a Soil Pollution by Lead Compounds in Highway Agroecosystems

This work examines the method of geoinformation modeling in determining the degree of soil pollution by Pb compounds in the highway ecosystems, used as agricultural areas. The methodology allows identifying the spatial parameters of the area of pollution and the territories safe for cultivating crops by determining the actual level of soil contamination. The actual level of pollution (ALP) reflects the estimated balance of Pb compounds in soils over a specified period of time and the duration of pollution (or decontamination) period under the conditions of existing anthropogenic pressure. ALP is defined as the difference between the calculated amount of Pb compounds that have been introduced into the soils of the geosystems from the main sources of emissions and their calculated amount that has been lost from the soil and/or removed from migratory flows (by being transferred to the state conditionally inaccessible by plants), for the same time interval. Methodology verification took place in the geographic area representative of forest-steppe zone of Ukraine by its environmental conditions and agricultural use. Methodology implementation was conducted by building spatial regional databases, utilizing the data from field and laboratory soils studies and open sources of digital spatial data. Based on geoinformation modeling, we determined the configuration and quantitative characteristics of the primary zone of contamination of the soil surface layer with Pb compounds. We presented the calculation procedure of quantitative indicators of soil decontamination from Pb compounds as they redistribute through the soil profile via main geochemical scenarios. We also provided the rate of this process under the conditions of the existing anthropogenic pressure. Using modeling results, we mapped areas of removal, transit and accumulation of Pb compounds and territories receiving pollution from highway geosystems. Verification of the modeling results during laboratory studies proved the objectivity of the proposed methodology in determining the current levels of pollution with Pb compounds. Our methodology could be utilized for a set of different environmental factors as well as other heavy metals with a purpose of identifying areas safe for cultivating crops.
Information Gathering and Controlling over the Internet by Internet of Things (IoT)

The main goal of this work is to create an application that has the ability to communicate, connect, and control physical entities via the Internet.This purpose can be achieved by the Internet of Things or Internet of everything (The more complete form) which is a new revolution of the Internet to allow objects to be connected to the internet.Connected objects can communicate with each other or with us via an HTML interface that allows to create a communication between the user and the server ESP8266 through its IP address.Objects have been connected wirelessly (WiFi communication).A sensor is used to collect data then transfer it to the user via the Internet in order to make correct decisions in anywhere at any time.
Collecting People’s Preferences in Immersive Virtual Reality: A Case Study on Public Spaces in Singapore, Germany, and France

In the design of urban public spaces, the inclusion of diverse voices enhances the development of products and services by synchronising designer expertise with people's preferences.Multiple participatory methods exist, each with their respective benefits and drawbacks in terms of the quality of results, time and cost needed for preparing and conducting studies, and knowledge required for participation.Providing more concrete representations of abstract or intangible design concepts would be beneficial for laypeople unfamiliar with design or the case study.We propose a Virtual Reality (VR) platform to discover subjective preferences on public waiting rooms through immersive design experiences.The VR platform was tested with 463 participants with variety in age and cultural background.Following a qualitative data analysis, we discuss the suitability of our VR platform for fostering inclusive participation and how it impacts the role of the designer, as well as propose design guidelines for future VR studies.
THE EFFECT OF PROBING PROMPTING ON STUDENTS’ WRITING ACHIEVEMENT AT TENTH GRADE OF MAN 1 BOMBANA

The objective of this research was to find out the effect of using probing prompting on students’ writing achievement at tenth grade of MAN 1 Bombana. This research applied quasi experimental design that consist of experimental class and control class. The sample of this research were students at class X IPA and X IPS in MAN 1 Bombana who were register in academic years 2019/2020. The sample taken by using cluster random sampling and the total numbers of students are 46, 22 for experimental class (X IPA) and 24 for control class (X IPS). The research instrument was written test which focus on narrative text. The researcher collected the data by giving pre-test in both of the classes, giving treatment in experimental by giving probing prompting and non-probing prompting in control class, and giving post-test in both of the classes. In analyzing the data were used SPSS 16.0 version. To examine the hypothesis the researcher used an independent sample t-test. Based on the result, it showed that students’ mean score of post-test in experimental class is 64.35. It is raised 9.83 point from its pre-test (54.52). While students’ mean score of post-test in control class is 56.82. It is only raised 4.97 point from its pre-test (51.85). Besides, the value of degree of freedom (df)= 42, t=4.574, and sig (2 tailed)=0.000. It means that the probability (sig) value is lower than α ( p value &lt; α or 0.000 &lt; 0.05). Therefore,probingprompting had a significant effect on students’ writing achievement at tenth grade of MAN 1 Bombana.
An accurate signature verification system based on proposed HSC approach and ANN architecture

&lt;span&gt;With the rapid development of technology in all life fields, and due to the huge daily needs for banking systems process, documents processing and other similar systems. The authentication became more required key for these systems. One of the successful system to verify the any person is the signature verification system. However, a reliable and accurate system is still needed. For this reason, the security challenge is take place via authentic signatures. However, a reliable and accurate system is still needed. For this reason, the security challenge is take place via authentic signatures. Therefore, this paper present a reliable signature verification system using proposed histogram of sparse codes (HSC) feature extraction approach and artificial neural networks (ANN) architecture for classification. The system achieved fast computing 0.09 ms and accurate verification results that is 99.7% using three different signature images datasets CEDAR, UTSig, and ICDAR.&lt;/span&gt;
Variant-based Equational Unification under Constructor Symbols

Equational unification of two terms consists of finding a substitution that, when applied to both terms, makes them equal modulo some equational properties.A narrowing-based equational unification algorithm relying on the concept of the variants of a term is available in the most recent version of Maude, version 3.0, which provides quite sophisticated unification features.A variant of a term t is a pair consisting of a substitution σ and the canonical form of tσ .Variant-based unification is decidable when the equational theory satisfies the finite variant property.However, this unification procedure does not take into account constructor symbols and, thus, may compute many more unifiers than the necessary or may not be able to stop immediately.In this paper, we integrate the notion of constructor symbol into the variant-based unification algorithm.Our experiments on positive and negative unification problems show an impressive speedup.
Multichannel Correlation Clustering Target Detection

During target tracking, certain multi-modal background scenes are unsuitable for off-line training model. To solve this problem, based on the Gaussian mixture model and considering the pixels’ time correlation, a method that combines the random sampling operator and neighborhood space propagation theory is proposed to simplify the model update process. To accelerate the model convergence, the observation vector is constructed in the time dimension by optimizing the model parameters. Finally, a three channel-multimodal background model fusing the HSI color space and gradient information is established in this study. Hence the detection of moving targets in a complicated environment is achieved. Experiments indicate that the algorithm has good detection performance when inhibiting ghosts, dynamic background, and shade; thus, the execution efficiency can meet the needs of real-time computing.
COMPARISON INTENT RECOGNITION ON FOOD DELIVERY SERVICE COMPLAINT IN TWITTER WITH RECURRENT AND CONVOLUTIONAL NEURAL NETWORK

In this research, the case of intent classification for Customer Relation Management (CRM) how to handle complaints as a domain to be followed up, where datasets are extracted from the conversation on Twitter. The research objectives support three key findings to comparing the CNNs and BRNNs model to intent recognition by vectorization text: (1) Which architecture performs better (accuracy) depends on how important it is to semantically understand the whole sequence and (2) Learning rate changes performance relatively smoothly, while the optimal result iterated by change hidden size and batch size result in large fluctuations. (3) Last, how word vectorization is able to define sub-domain of the complaints by word vector classification.
Methods for Predict Memory Usage of Big Data Computing System: A Comparison Study

Memory is a very important resource in big-data computing system. Reasonable allocation of memory is actually meaningful. But sometimes it is hard to achieve it. Mammoth Data Platform is a big data computing platform developed by NetEase. When we take a look at the jobs running on the data platform, we can find that the memory requested by the users will actually be much larger than the memory used on Mammoth Data Platform, which is not a good phenomenon. So this paper tries some machine learning methods to predict the actual memory usage so that we can allocate the memory reasonably. After trying these methods, we get a good prediction for memory used by the jobs on the platform (R2=0.95), which will improve the memory utilization of the data platform.
Lipsticks Elemental Analysis by Energy Dispersive X-Ray Used as Criminal Evidence

Since ancient times, man has been interested in showing his appearance in the best shape and has used make-up materials including lipstick for this purpose.Four lipstick samples were selected for one brand with different colors (Lipstick-03, Lipstick-23, Lipstick-29 and Lipstick-51) as well as two different colors for the so-called magic lipstick (Luxe orang and Luxe yellow).Energy-dispersive X-ray spectroscopy (EDX) is an analytical technique was used for the elemental analysis for the selected six samples.These tests aims to explain that there are clear elemental differences for the selected samples which can be used to define the identity of each sample.Lipstick-51 has been selected to contaminate some substances that can be commonly used in homes, offices and others.The selected substances were (Plastic bottle, Plastic cup, Glass, Cigarette filter and Tissue paper).The elemental results indicate a partial match for the types of elements in lipstick contaminated with substances taken from the crime scene compared to the original Lipstick-51.Results could be more accurate when identifying the contaminated material and select the most concentrated positions for samples taken from the crime scene and for conducting tests.
Prufer Coding: A Vectorization Method for Undirected Labeled Graph

Prufer algorithm is a powerful method for topology vectorization, but the traditional prufer algorithm method can only encode a rootless labeled tree, and no prior work has studied the method of applying it to the graph vectorization. This paper proposes a vectorization method for undirected labeled graphs based on the prufer algorithm, including graph encoding and decoding algorithms. A particular case was discovered by preliminary experiments, which will reduce the accuracy of the coding algorithm (when the node size reaches more than 150, the accuracy can only reach about 60%), so a connectivity check mechanism that based on the Warshall algorithm is proposed and added to the coding algorithm. A large number of experimental verifications show that the accuracy of the coding algorithm can reach 100% after introducing this mechanism. Then the length of the vector generated by the coding algorithm is analyzed, and the results show that graph vectorization can improve the efficiency of partial topology calculation. Finally, the defects of the algorithm are discussed. The most significant defect is that the length of the vector generated by the encoding algorithm is uncertain, which will prevent it from being applied to more topological calculations.
Pest Detection Expert System And Method Using Bayes Rice Diseases

Indonesia is included in an agricultural country that is rich in agriculture, fertile land in Indonesia allows for the growth of various types of plants, one of which is rice. But in the cultivation of rice plants, it will never be separated from pests and diseases of rice plants, so that the growth of rice plants is not optimal. Due to the large number of pests and diseases that can attack rice plants, it is necessary to take proper care in dealing with pests and diseases of rice plants to avoid losses that can result in reduced production of rice plants. Therefore an expert system was created which was expected to help farmers in consulting about diseases and pests contained in their rice plants to the experts without having to meet with experts directly. In its identification, the expert system for diagnosing ahama and rice disease uses the Bayes method and uses the Website in its implementation.
A Novel Content Based Image Retrieval Based on a New Approach of Color String Coding and Meta-Heuristic Algorithms

Content based image retrieval (CBIR) is ranked among the most important domain of image processing.This technique allows us to find the relevant images from a query image in a very large image database.This approach is based on the visual features of the image.Therefore, the choice of these features is a very decisive factor for improving the robustness and efficiency of the CBIR system.This paper aims at the development of a novel technique which is based on the color string coding method implemented in the previous work.This new technique combines the two color spaces: RGB and HSV, and introduces the texture features to improve the results obtained previously by increasing the number of relevant images and decreasing the computational complexity and the response time whatever the size of the images.Accordingly, the image retrieval is performed using the meta-heuristic algorithms.Meanwhile, our system is evaluated based on the precision and recall measures.The obtained results show the efficiency and the performance of the proposed method compared to other CBIR systems.
Figure It Out: Creative Language Expressions in ESL Writing Classrooms

This study, which investigates ways to enhance students" creative writing skills in the Malaysian University English Test (MUET) writing task, utilised a mixed method design.It comprised two phases: Phase One consisted of an experimental group who was given a writing pre-test before playing the game Figure it Out! four times with only one level of game cards.The group then attempted a written post-test.The students showed a marginal improvement in their performance; pre-test =11.5172 (m) and post-test =12.3966 (m).These students in the structured interview expressed joy playing the game despite finding it somewhat challenging due to insufficient exposure to creative language expressions.Phase Two consisted of experimental and control groups.This phase incorporated three different levels of game cards.The students in the experimental group had the opportunity to play all three levels of the game.Students in both groups sat for the pre-and post-tests and participated in the written task individually or in small groups.Creative language expressions were taught in a conventional classroom setting for the students in the control group.The students from the experimental group, however, played Figure it Out! six times.The results indicated that both groups improved in their post-test scores; the control group scored average means of 16.1471 in the pre-test and 17.8095 in the post-test.However, the experimental group showed a significant improvement in the post-test (M=18.5294),p+.000 compared to the pre-test (M=16.1471).The students in the experimental group shared that the game (Figure it Out!) provided them the opportunity to use creative language expressions in the right context and setting.
Applicability of Deep Neural Networks on the Task of Document Retrieval

A Deep Neural Network (DNN) can be used to learn higher-level and more abstract representations of a particular input.DNNs have successfully been applied to analysis tasks including image processing, unsupervised feature learning, and natural language processing.DNNs furthermore can improve computing performance when compared to shallower networks, for example in pattern recognition tasks in machine learning.Recent usage of DNNs in search engines for the Web have impacted that technology in industrial scale applications.One example for such an application is deepgif -a search engine for Graphics Interchange Format (GIF) images that is based on a convolutional neural network and takes natural language text as query.In this study, we developed a tool and compared the performance of feed-forward neural networks and deep architectures of recurrent neural network using the case of document retrieval.This study first discusses two architectural setups used to build the models and then provide a detailed comparison of their performance.The goal is to identify the architecture that is most suited for the task of document retrieval.
Teamwork Distribution: Local vs. Global Software Engineering Project Development Teamwork

Deliverable and course project become the preferred mean to measure learner competency and attainment of intended learning outcomes in IT-fields. Proper setup and evaluation for teamwork projects remains a key challenge for e-learning systems. This study investigates the possibility to improve the early prediction of academic software engineering project failure by treating teamwork differently according to the distribution of teamwork participants. Two configurations of teamwork distribution are considered. In the first configuration, a teamwork may include international participants, but all team participants are affiliated to the same institution, namely local teamwork. In the second configuration, a teamwork may include participants from different institutions, namely global teamwork. Software engineering projects are approached from two distinct perspectives. First, obeying the best practices during the system development life cycle (SDLC), namely, process perspective. Second, characteristics of the final deliverable deployed at each milestone of the SDLC, namely product perspective. A publicly released dataset collected by a designated e-learning environment is leveraged to validate the proposed approach. Results indicate a noticeable variance among local and global distributions. These results puts evidence that the reasons behind software engineering teamwork project failures may vary depending on the distribution of the teamwork, local vs. global. Consequently, it advise to customize e-learning systems differently according to the teamwork distribution.
CBIR Features Extraction with GLCM, HC and HOG using SVM-RF Classifier

Content-Based Image Retrieval (CBIR)allows search and recovery of pictures that are similar to a known picture, by using attributes that represent the visual content of the pictures.Our proposed study aimed to model a method for the recovery of indexed pictures in databases from their visual content, without the need for textual annotations.Gray Level Co-Occurrence Matrix (GLCM), Harris Corner, and Histogram of Oriented Gradients (HOG) attributes are extracted from the publicly available databases (Corel and Caltech Datasets).A novel nonparametric method of texture combination is applied by means of Principal Components Analysis (PCA).Finally the classification is accomplished by using Support Vector Machine (SVM) and Random Forest Classifiers.The simulation outcomes show satisfactory performance accordant with accuracy, recall, precision and F-score.
Provably Secure Crossdomain Multifactor Authentication Protocol for Wearable Health Monitoring Systems

Wearable health monitoring systems (WHMSs) have become the most effective and practical solutions to provide users with low-cost, noninvasive, long-term continuous health monitoring. Authentication is one of the key means to ensure physiological information security and privacy. Although numerous authentication protocols have been proposed, few of them cater to crossdomain WHMSs. In this paper, we present an efficient and provably secure crossdomain multifactor authentication protocol for WHMSs. First, we propose a ticket-based authentication model for multidomain WHMSs. Specifically, a mobile device of one domain can request a ticket from the cloud server of another domain with which wearable devices are registered and remotely access the wearable devices with the ticket. Secondly, we propose a crossdomain three-factor authentication scheme based on the above model. Only a doctor who can present all three factors can request a legitimate ticket and use it to access the wearable devices. Finally, a comprehensive security analysis of the proposed scheme is carried out. In particular, we give a provable security analysis in the random oracle model. The comparisons of security and efficiency with the related schemes demonstrate that the proposed scheme is secure and practical.
Implementing Personal Devices in Math

This study examined the effect of implementing personal devices into a fifth-grade mathematics classroom.Thirty-eight fifth graders participated in this eight-week project with a focus on six students to track their growth.During this study, students engaged in technology-based stations that pertained to our geometry unit and coordinate unit.Students took pre and post tests on paper to track progress.In addition, students completed online assessments within their practice websites to track comprehension and growth of the content.Furthermore, students were observed to ensure they were staying engaged in the online activities and to track student engagement about using personal devices within math.
Covid-19: Government must publish results of pilots of contact tracing app, experts say

Health experts have urged the government to publish the results of pilots of its covid-19 contact tracing app to demonstrate that it is effective and won’t exacerbate health inequalities.The app is being launched on 24 September in England and Wales to help reduce the spread of covid-19. But the government has not yet released the results of pilots that took place in August on the Isle of Wight and Newham in east London.The charity the Health Foundation said that greater transparency was needed to show that the app was effective and ready for mass …