Reviewerly/exHarmony huggingface dataset:
Modeling and Implementation of Real Estate Management System Database Based on Data Integration

research-article Modeling and Implementation of Real Estate Management System Database Based on Data Integration Author: Xiwen Huang Chongqing Institute of Architecture Science and Technology, China Chongqing Institute of Architecture Science and Technology, ChinaView Profile Authors Info & Claims IPEC '22: Proceedings of the 3rd Asia-Pacific Conference on Image Processing, Electronics and ComputersApril 2022Pages 826–831https://doi.org/10.1145/3544109.3544362Published:18 July 2022Publication History 0citation31DownloadsMetricsTotal Citations0Total Downloads31Last 12 Months31Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Jakarta Persistence Query Language

The Jakarta Persistence API utilizes a query language for communicating with underlying data stores. Although Jakarta EE uses entities rather than SQL for database access, it provides a query language so that developers can obtain the required information via the entities. The Jakarta Persistence Query Language does just that because it provides a facility for querying and working with Jakarta EE entity objects. Although it is very similar to SQL, it is an object-relational query language, so there are some minor differences of which developers should be aware. Using the Jakarta Persistence Query Language along with Jakarta EE entities allows developers to create versatile applications because it is not database specific, and applications can be written once and deployed to run on top of a myriad of databases.
Jakarta Security

One of the most important components to an enterprise-level application is security. It is a fact that enterprise applications must be rock solid and secure so that data and application functionality cannot fall into the wrong hands. Utilizing a combination of application container security and application-level security can help secure applications from thugs who are targeting enterprise data.
W-Hash: A Novel Word Hash Clustering Algorithm for Large-Scale Chinese Short Text Analysis

AbstractShort text clustering is an unsupervised learning technique for pattern discovery and analysis of short text datasets, which has been applied to many scenarios such as business risk control and audit. With the development of digitalization over the last few years, the data scale in various scenarios has increased rapidly. Traditional short text clustering methods such as K-means face many challenges in large-scale data analysis, such as difficult to preset hyperparameters and high computational complexity. To alleviate this problem, we propose a novel clustering algorithm called Word Hash clustering algorithm (W-Hash) for Chinese short text analysis. Specifically, W-Hash does not require a pre-specified number of clusters, and it has much lower computational complexity than the traditional clustering approaches. To verify the effectiveness of W-Hash, we apply it to solve a real-life business audit problem. The corresponding experimental results show that W-Hash outperforms traditional clustering algorithms in both training time and result rationality.KeywordsShort text clusteringClusteringK-meansBusiness audit
Balancing RDF Generation from Heterogeneous Data Sources

Knowledge graphs in RDF are often generated from heterogeneous data sources to power services. However, knowledge graph generation is an unbalanced effort for producers compared to consumers of a knowledge graph. In this paper, I present my research about (i) investigating current RDF knowledge graph production and consumption approaches, and (ii) how to involve the consumer into a hybrid RDF generation approach to reduce the necessary resources for generating RDF for producers & consumers. I discuss the shortcomings of existing approaches for RDF generation from heterogeneous data sources (i.e., materialization and virtualization) and how I will address these: a Systematic Literature Review; an analysis and a set of guidelines for producers to select the right approach for an use case; and a combined hybrid approach to balance the producer's and consumer's effort in RDF generation. I already performed a Systematic Literature Review to get an overview of the existing approaches for RDF production from heterogeneous data sources. These results will be used to establish a set of producer guidelines, a benchmark to compare the current materialization and virtualization approaches, and evaluate the proposed hybrid approach. Thanks to my research, knowledge graph production and consumption will be more balanced and accessible to smaller companies and individuals. This way, they can focus on providing better services on top of a knowledge graph instead of being limited by the lack of computing resources to harvest enormous amounts of data from the Web and integrate it into a knowledge graph.
Students’ Perceptions of the Use Android Based Interactive Multimedia in Mathematics Learning in Elementary Schools

Android technology has a very reliable potential as a means of playing a learning role, namely in giving assignments, as well as explaining material.With interactive multimedia based on Android, students can learn not only in theory but also in practice because multimedia contains elements of audio, video, text, images and animation, which makes it easier for students to learn independently in this covid-19 era.This study aims to explore student participation in the use of Android-based interactive multimedia in learning mathematics in post-covid-19 elementary schools This study used survey research using questionnaire tools involving 53 elementary school students in Buluspesantren Subdistrict.From the data, the student experience in using android-based interactive multimedia shows 91% of students have never used and 9% have ever used android-based interactive multimedia.Student perception data in media use shows that 85% of students choose to use interactive multimedia, 9% of students choose to use audio-visual media, and 6% of students choose to use visual media.From the results of the study, it will be used as a reference to apply learning using android-based interactive multimedia in learning mathematics in elementary school.Android technology has a very reliable potential as a means of playing a learning role, namely in giving assignments, as well as explaining the material.With interactive multimedia based on Android, students can learn not only in theory but also in practice because multimedia contains elements of audio, video, text, images, and animation, which makes it easier for students to learn independently in this covid-19 era.This study aims to explore student participation in the use of Android-based interactive multimedia in learning mathematics in post-covid-19 elementary schools This study used survey research using questionnaire tools involving 53 elementary school students in Buluspesantren Subdistrict.From the data, the student experience in using android-based interactive multimedia shows 91% of students have never used and 9% have ever used Android-based interactive multimedia.Student perception data in media use shows that 85% of students choose to use interactive multimedia, 9% of students choose to use audio-visual media, and 6% of students choose to use visual media.From the results of the study, it will be used as a reference to apply to learning using android-based interactive multimedia in learning mathematics in elementary school.
Learning in Your “Pocket”: Secure Collaborative Deep Learning with Membership Privacy

Organizations tend to collaboratively train the deep learning model over their combined datasets for a common benefit (e.g., better-trained model or learning a complicated model). However, due to the consideration about privacy leakage, organizations cannot share their data directly, especially related to sensitive domains. In this paper, a privacy-preserving collaborative deep learning mechanism, namely Sigma, is designed to allow participating organizations to train a collective model without exposing their local training data to the others. Specifically, a single-server-aided private collaborative architecture is introduced to achieve the private collaborative learning, which protects organizations&#x2019; data even if <inline-formula><tex-math notation="LaTeX">$n-1$</tex-math></inline-formula> out of <inline-formula><tex-math notation="LaTeX">$n$</tex-math></inline-formula> participants colluded. We also design a practical protocol to perform the secure model training, which can resist the typical inference attack through the sharing information. After that, we propose a fair model releasing mechanism for participants and introduce differential privacy to prevent model stealing and membership inference attack. Furthermore, we prove that Sigma can ensure participants&#x2019; privacy preservation and analyze the communication overhead in theory. To evaluate the effectiveness and efficiency of Sigma, we conduct an experiment over two real-world datasets and the simulation results demonstrate that Sigma can efficiently achieve the collaborative model training and effectively resist the membership inference attack.
Tree Edit Distance Based Ontology Merging Evaluation Framework

Merging structured knowledge has been widely investigated to build common resources in recent years. Indeed, many merging operators have been proposed and developed. However, the majority of them lack comparison and evaluation. Finding ontology sources for evaluation is not an effortless task. To this end, we propose a framework for evaluating the quality of ontology merging operators. The primary strategy starts with an original ontology as a gold standard to create noisy ontologies as datasets and use them to evaluate the merging operators. We generate the noisy ontologies using some perturbations of the tree structure of the original ontology based on tree edit operations. Then, we use tree edit distance to measure the existing merging operators with these noisy sources. We provide the details to assess the merging operators’ efficiency in the computation time and their ability to cover (or be close to) the original ontology.
Cooperative Localization for Human Pose Estimation

The existing methods for human pose estimation are needed to predict either dense heatmap, which requires more complex model architecture, or to execute long-distance regression, which makes the model unable to achieve good performance. This work proposes an accurate and efficient deep learning framework for human pose estimation called cooperative localization. It uses a sparse heatmap to find the keypoint’s approximate location while two short-distance offsetmaps obtain its precise coordinates. To realize the framework, we construct two types of cooperative localization networks: CLNet-ResNet and CLNet-Hourglass. We evaluate the networks on three benchmark datasets: the Leeds Sports Pose dataset, the MPII Human Pose dataset, and the COCO keypoints detection dataset. Experimental results show that our framework consistently improves the average precision of their plain counterparts. The CLNet-ResNet50 outperforms SimpleBaseline by 1.14% with about 1/2 GFLOPs, and the CLNet-Hourglass outperforms the original stacked-hourglass by 4.45% on COCO.
Local Applied Academic Result on Constructing Course Teaching Reform and Practice

Localization courses teaching reform is applied fusion one of the best way to development, production and education in colleges and universities from the early project teaching, project teaching, development to today's studio system teaching mode, the form is novel, but follow the teaching philosophy has always been to improve the students' ability of practical application.Through the summary and analysis, to find suitable for applied undergraduate colleges and universities teaching model of the studio system.
The Influence of University Entrance Examination Results, Gender and Field of Study on Semester Cumulative Grade-point Averages (CGPAs) of First Year Students: The Case of Raya University

The purpose of this study was to assess the influence of university entrance examination results, gender and field of study on semester CGPA score of freshman students.A descriptive research design was employed for the present study.For the study 200 participants (119 males and 81 females) were selected through stratified random sampling technique.The data were gathered mainly from secondary sources found in the registrar office of Raya University.Descriptive statistics, Spearman correlation, multiple regression and Independent samples T-tests were applied to analyze the data.Sex of students (0=female and 1=male) had a statistically significant relationship with semester CGPA scores; university entrance exam results and field of study revealed very weak positive and statistically non-significant relationship with semester CGPA scores.Multiple regression model selected sex and university entrance examination results as statistically significant predictors of semester CGPA scores and field of study was not statistically significant predictor, but only sex was statistically significant contributor in their relative contribution to students' CGPA scores.Independent samples t-test revealed a statistically sex differences on semester CGPA scores of students (favoring males).Further research with advanced approaches was recommended for other investigators on students CGPA scores and their influencing factors.
Scaffolding strategy to enhance reading sub-skills among young-adult learners

The notion of scaffolding is widely used in education, where the real protagonist of the whole process is the learner. The teacher’s role, then, becomes more of that of a facilitator. The main objective of the present study was to improve the reading sub-skills of second semester students at the Quevedo State Technical University. The methodology used was semi-structured interviews and expert judgments. The results indicated that it is important that the teacher be familiar with the students’ competence and knowledge to know how to best help them, and what is most appropriate for their progress and pace of learning. To determine this, students should participate in different activities. In addition, it is essential that the university's language center promote ongoing training so that instructors can update their knowledge by taking professional training courses. Lastly, the approach of this research is purely qualitative since it is based on students' perceptions of which scaffolding strategies they believed were most useful to understand the readings.
Cloud based Medical Image De-Noising using Deep Convolution Neural Network

It is difficult to remove noise from images because of the many sources of noise.Among the many sources of noise in imaging, Gaussian, impulse, salt, pepper, and speckle are the most complex.Image processing for medical purposes has no other major aim, such as beautifying an image or generating art, whereas conventional image processing has primary goals such as improving an image's aesthetics.This may include enhancing the picture itself, as well as the extraction of information either manually or automatically, depending on the needs of the work.Deep Convolutional Neural Networks (DnCNN) are the kinds of deep neural networks that do visual processing of images.An old but still relevant area of image processing research is denoising images.This subject has seen a surge with the advent of Deep Convolutional Neural Networks thanks to the several advantages.The first advantage is that, It saves time and affords, Denoising networks that have been pre-trained are very well tuned.There are no noticeable artifacts after denoising and It generates excellent denoising results.In proposed work, The implementation process has been divided into four parts.Working with cloud-based medical images in the initial phase.The previously trained network will be loaded in the second step.In the third stage, the denoised picture is obtained by sending the noisy image to the network and then performing.Afterwards, in the last stage, the resulting image is denoised image.The result is compared with various existing denoising methods.The outcome result is better in the terms of PSNR and SSIM.
Accelerating Optimization Using Vectorized Moth-Flame Optimizer (vMFO)

In this chapter, Moth-Flame Optimization (MFO) algorithm is discussed and improved from the computational cost point of view. While many of the adjustments and improvements on an optimization algorithm, such as Moth-Flame Optimizer (MFO), aim to enhance its performance in terms of the accuracy of optimal solutions obtained, here we concentrate on the implementation of the algorithm and show how a vectorized MFO (vMFO) can significantly boost the computation time.
SST-Sal: A Spherical Spatio-Temporal Approach for Saliency Prediction in 360º Videos

We present a deep learning approach to visual attention prediction in 360º videos. We resort to recurrent neural networks to model the inherent spatio-temporal features of visual behavior, while tailoring our model to the particularities of 360º content. Our model outperforms previous state-of-the-art works by a large margin.
Unification in Pretabular Extensions of S4

L.L. Maksimova and L. Esakia, V. Meskhi showed that the modal logic S4 has exactly 5 pretabular extensions PM1–PM5. In this paper, we study the problem of unification for all given logics. We showed that PM2 and PM3 have finitary, and PM1, PM4, PM5 have unitary types of unification. Complete sets of unifiers in logics are described.
STUDENT PRESENTATION-BASED TEACHING TO PROVIDE TRANSACTIONAL ANALYSIS TRAINING TO PRODUCE EMOTIONALLY INTELLIGENT ENGINEERING GRADUATE STUDENTS

Information about the paper titled "STUDENT PRESENTATION-BASED TEACHING TO PROVIDE TRANSACTIONAL ANALYSIS TRAINING TO PRODUCE EMOTIONALLY INTELLIGENT ENGINEERING GRADUATE STUDENTS" at IATED Digital Library
Peer review declaration

All papers published in this volume of Journal of Physics: Conference Series have been peer reviewed through processes administered by the Editors. Reviews were conducted by expert referees to the professional and scientific standards expected of a proceedings journal published by IOP Publishing. • Type of peer review: Single-blind peer review by two reviewers • Conference submission management system: web-based system with custom-made, submissions were received and handled via e-mail • Number of submissions received: 225 • Number of submissions sent for review: 121 • Number of submissions accepted: 102 • Acceptance Rate (Number of Submissions Accepted / Number of Submissions Received × 100): 45,3% • Average number of reviews per paper: 2 • Total number of reviewers involved: 18 • Any additional info on review process: All papers are plagiarism checked by Turnitin software • Contact person for queries: Name: Dr. Ramli (corresponding editor) Affiliation: Department of Physics, Universitas Negeri Padang E-mail: ramli@fmipa.unp.ac.id
Time of Concentration Estimated Using Some Methods and Application in The Ciliwung Watershed, Jakarta

Flood is one of the disasters that must be faced in the lowlands with poor drainage system, when there is high rainfall. Rainfall in the that upstream River can affect water level downstream River. Calculation of river discharge and forecast from that time that arrival from flood can Help prepare population to face that disaster. That method used in this lesson is a quantitative method with analysis from rainfall and planned flood repatriate. Then, time of concentration (TC) before calculated use that time traveling method, That Experience Resource Conservation Serve (NRCS) and Kirpich. That results is compared to with HEC RAS and observation method. That research location is in Ciliwung watershed, with tc calculation of Katulampa (Bogor) to MT. Haryono (Jakarta). That results show that results from design flood discharge using the HSS Nakayasu method for Q2 of 161.04 m3/s, Q5 of 187.07 m3/s, Q 10 of 215.08 m3/s, Q25 of 263.11m3/s, Q50 of 308.91m3/s and Q100 of 364.34m3/s. Time of concentration using the Time Travel method from Katulampa to MT. Haryono 12 hours 32 minutes, while using the NRCS method is 12 hours and 31 minutes while the Kirpich method is 12 hours 52 minutes. That time of concentration using observations is about 13 hour and using the HEC RAS is 14 hours. The closest method to calculate the time of concentration under actual conditions is the Kirpich method.
Pengembangan Handout Elektronik Menggunakan Flip Pdf Professional pada Materi Kingdom Plantae Siswa Kelas X SMA/MA

In the teaching and learning process, teachers often teach using printed books, modules, worksheets, using LCD projectors, and have never combined printed teaching materials with electronic media. Based on the needs analysis questionnaire data given to students of class X MIA at SMAN 6 Metro, as many as 63.3% who have difficulty learning the material Kingdom Plantae. Thus, innovation is needed in the form of electronic handout teaching materials. This study aims to develop Electronic Handouts using Flip PDF Professional on Kingdom Plantae Materials as Teaching Materials for Class X SMA/MA. This type of research is an R&amp;D (Research and Development) research with the ADDIE model which consists of five stages, namely Analysis-Design-Development-Implementation-Evaluate. The final result of Electronic Handout validation using Flip PDF Professional on Kingdom Plantae Materials as Teaching Materials for Class X SMA/MA obtained a percentage of 85.3% in the "very feasible" category by material expert validators and media expert validators obtained a percentage of 94.6% with "very decent" category. The results of the teacher trial obtained a percentage of 90% in the "very good" category and the results of the student trial obtained a percentage of 83.4% in the "good" category. Based on the overall results, the percentage of electronic handout scores obtained an overall average with a percentage of 88.32% and is included in the "Very Valid" category, and is suitable for use in the Biology learning process at school.
Buffer Overflow Vulnerabilities and Prevention

Either in terms of software error or being an attack itself, buffer overflow attacks have been one of the most important security problems accountable for a common vulnerability of software security and cyber risks. Buffer overflow vulnerabilities influence the zone of remote network penetration vulnerabilities, where an anonymous user tries to gain partial or complete control of the host. Buffer overflow attacks liable for some of the biggest data breaches in recent years. One of the notable data breach examples include Morris worm or internet worm of November 2, 1988. Morris worm is an ancient computer worm distributed via the internet, was noted for infecting around 6000 major Unix machines. This paper mainly focuses on buffer overflow attack vulnerabilities and the preventive measures to safeguard once system from being attacked. Keywords – Buffer overflow attack, susceptibility, impediment