Reviewerly/exHarmony huggingface dataset:
ADS-B-Based Spatial-Temporal Multi-scale Object Detection Network for Airport Scenes

Aircraft detection is important for intelligent airport applications. This task is challenging due to some problems, e.g. the aircraft is usually small and the appearance varies dramatically with view angle. In this paper, we introduce the Automatic Dependent Surveillance-Broadcast (ADS-B) signal into the object detection framework. ADS-B is a kind of airport-specific data, which provides the aircraft location information in real-time. We use the ADS-B signal as prior information to guide aircraft detection. Firstly, from the spatial perspective, we construct an ADS-B-based saliency function, and use it to apply attention to certain spatial regions during feature extraction. Because the aircraft is likely to be in the area of attention, the detection accuracy can be improved, especially for small aircraft. Secondly, from the temporal perspective, we predict the motion direction of moving aircraft based on historical ADS-B data, and use it to generate real-time updated anchors. In addition, the shape and scale prior are also considered in the anchor generation process. The generated anchor is able to fit aircraft shape well, even in the case of drastic viewangle changes. Finally, experiments are conducted on the AGVS-T dataset to verify the effectiveness of the proposed method.
Table Structure Recognition of Historical Dongba Documents

The analysis of table structures in historical documents has been a crucial area of research. Its objective is to identify the location of tables within the documents and segment each table cell. However, this process is often hindered by irregular structures and damage caused by inadequate preservation. Similarly, analyzing historical records written in the pictographic Dongba script of the Naxi people presents significant challenges. In this paper, we propose a novel method for extracting the table structure of historical Dongba documents. Our approach involves annotating the document image with specific patterns and applying an object detection model to precisely locate the regions of interest in the image. We then extract the table structure using a set of post-processing techniques. We evaluate the effectiveness of our proposed method by conducting experiments using various object detection models and providing a detailed analysis of our dataset in the Experiments section. Our results demonstrate that our approach is highly beneficial.
PBEMS: A Permissioned Blockchain-based Equipment Maintenance System in Smart Manufacturing

In the realm of smart manufacturing, equipment plays a pivotal role, impacting the efficiency and quality of production. Ensuring equipment availability and optimal performance is crucial, necessitating effective maintenance strategies. This paper addresses the complexities of smart device maintenance, which often involves collaboration between multiple entities like device owners, manufacturers, suppliers, and external maintenance services. To enhance this cooperative maintenance, we propose a novel maintenance system based on permissioned blockchain technology, offering superior security and reliability compared to conventional cloud-based platforms. Our contributions include developing an integrated maintenance system framework for smart manufacturing, which facilitates interactions among diverse stakeholders. Additionally, we have developed a prototype of this maintenance system, serving as a practical model for real-world application. Our research primarily demonstrates the practicality and advantages of using permissioned blockchain for maintenance systems in the manufacturing sector, paving the way for more secure, efficient, and collaborative maintenance practices.
Penentuan Prioritas Pendaftaran Tanah Sistematis Lengkap Melalui Clustering Jumlah Sertifikat Hak Atas Tanah Menggunakan K-Means

In carrying out its duties and functions to serve the community in the land sector, BPN Magelang Regency is still experiencing several obstacles such as in terms of filing and determining service priorities in 21 sub-districts in Magelang Regency. To improve the quality of BPN services in Magelang Regency, this research will build a system to assist file management and grouping of service priorities by implementing the K-Means Clustering method. The K-Means Clustering method was chosen because this method can calculate the grouping of several alternatives based on the criteria. The result of this study is that the application of a synchronized web-based PTSL application can make it easier for PTSL officers to manage land title registration files. In addition, the integration of K-Means Clustering calculations that are implemented into the system can be used as an approach tool in determining which districts are the priority for handling land certificate.
DensityLayout: Density-Conditioned Layout GAN for Visual-Textual Presentation Designs

Generating layouts for visual-textual presentation designs aims at arranging elements such as logo, text, and underlay on the given images, which is the key to automating poster designs. It is challenging since the compositions of images, the spatial patterns of layout elements, and their cross-relationships need to be simultaneously considered. Existing works focus on the cross-relationships and either (1) suffer from the instability of adopting off-the-shelf saliency maps as prior knowledge or (2) require the semantic content of each element. To this end, this paper presents an efficient density paradigm that requires neither off-the-shelf models nor additional training data other than image-layout pairs. Under this paradigm, a three-stage approach using GAN is proposed, entitled DensityLayout. First, a density mapping network weakly supervised by the custom consistency loss will translate given images to spatial distributions of elements. Second, a multi-scale strategy is proposed to enhance understanding of the maps, and a generator conditioned on these visual features will generate preliminary layouts. Finally, a directed graph representation illustrating the inclusion relationships between elements is presented, and a graph convolution network will fine-tune the layouts. The effectiveness of the proposed approach is validated on CGL-Dataset, showing it achieves the best performance by generating visually appealing layouts for visual-textual presentation designs of diverse images.
Agile Effort Estimation: Comparing the Accuracy and Efficiency of Planning Poker, Bucket System, and Affinity Estimation methods

Published studies on agile effort estimation predominantly focus on comparisons of the accuracy of different estimation methods, while efficiency comparisons, i.e. how much time the estimation methods consume was not in the forefront. However, for practical use in software development, the time required can be a very important cost factor for enterprises, especially when the accuracy of different agile effort estimations is similar. In this study, we thus try to advance the current standard accuracy comparison between methods by introducing efficiency, i.e. time it takes to use a method as an additional dimension of comparison. We conduct this comparison between three agile effort estimation methods that were not yet compared in the literature, namely, Planning Poker, Bucket System and Affinity Estimation. For the comparison, we used eight student teams with 29 students who had to use all the effort estimation methods during the course where they had to finish a programming project in 3 weeks. The results indicate that after the students get used to using the different methods the accuracy between them is not statistically significantly different, however, the efficiency is. On average, Bucket System and Affinity Estimation methods take half as much time as Planning Poker.
Adversarial Training Method for Machine Learning Model in a Resource-Constrained Environment

Within the rapidly growing Adversarial Machine Learning (AML) domain, scholars strive to build robust defense mechanisms against adversarial attacks. Such advancements often need significant computing resources. This paper offers an innovative AML defense method designed explicitly for resource-restricted scenarios. The core strategy employs Adversarial Training with Elastic Weight Consolidation (AT-EWC) to reduce the computational demands of adversarial training, thereby fortifying model robustness without compromising accuracy. This regularization technique deters overfitting adversarial instances by adding a penalty term tied to the Fisher Information Matrix to the loss function. Our method emerges as a promising strategy to augment the security of machine learning systems, particularly in resource-limited environments.
Decision Support System for Loan Eligibility using the Simple Additive Weighting (SAW) Method

The Solok City Bakti Husada Employees Cooperative is a non-bank financial institution in the form of a cooperative that serves the needs of its members in lending services with collateral in the form of member savings. The aim of this research is to make it easier for the Bakti Husada Cooperative to determine the suitability of loans to borrowers and to create a Decision Making System using the Simple Additive Weighting (SAW) method which is able to assist the Bakti Husada Cooperative in determining the suitability of loans to borrowers. The data collection method was carried out by direct observation and interviews with employees from the Bakti Husada Cooperative, Solok City. The results obtained are effective in making decisions about loan eligibility at the Bakti Husada Cooperative in Solok City in accordance with the criteria that have been determined in the selection. This result is proven. By using this system, members who apply for a loan get a score that is in accordance with the criteria and weights that have been set and from there it can be seen if there are criteria that are not in accordance with the standard requirements of the member's application for a loan so that members can improve the loan requirements so that Loan applications are accepted by the cooperative. Gusnelawati obtained the calculation results using the SAW method to determine the feasibility of providing loans to cooperatives with a value of 0.9625 and was the 1st Best in making this decision.
Using AR in Technical Education. The ARphymedes case

To attract students to the study of technical sciences, Augmented reality (AR) brings high hopes. But the use of AR in education has not yet reached a level of maturity. The paper first makes a short review with the main results in the field reported in the literature. Then, it analyzes the ARphymedes project, emphasizing the goal, the achievements but also the difficulties encountered, with the goal to understand better this kind of educational approach.
A model of Solidity-style smart contracts in the theorem prover Agda

The use of smart contracts is transforming traditional industry and business practices. It enables the automatic enforcement of contractual terms without the need for a trusted third party. Smart contracts can automate a variety of transactions on Blockchain. Despite their numerous benefits, some challenges, such as security vulnerabilities, still need to be addressed before smart contracts can be widely adopted.This paper introduces two models of smart contracts – one simple and one more complex – using the interactive theorem prover Agda. This is a step towards converting the previous work of verifying Bitcoin smart contracts using weakest preconditions [1], [2] to Ethereum’s Solidity-style [3] smart contracts. Since Ethereum’s contracts are object-oriented, this model is substantially more complex than Bitcoin’s. We provide models supporting simple and complex executions, the calling of other contracts, and functions referring to addresses and messages. Furthermore, these models also support transferring money to other contracts and updating specific contracts, and the more complex model includes gas cost and pure functions.
Privacy-Preserving Digital Vaccine Passport

The global lockdown imposed during the Covid-19 pandemic has resulted in significant social and economic challenges. In an effort to reopen economies and simultaneously control the spread of the disease, the implementation of contact tracing and digital vaccine passport technologies has been introduced. While contact tracing methods have been extensively studied and scrutinized for security concerns through numerous publications, vaccine passports have not received the same level of attention in terms of defining the problems they address, establishing security requirements, or developing efficient systems. Many of the existing methods employed currently suffer from privacy issues. This work introduces $$\textsf {PPass}$$ , an advanced digital vaccine passport system that prioritizes user privacy. We begin by outlining the essential security requirements for an ideal vaccine passport system. To address these requirements, we present two efficient constructions that enable $$\textsf {PPass}$$ to function effectively across various environments while upholding user privacy. By estimating its performance, we demonstrate the practical feasibility of $$\textsf {PPass}$$ . Our findings suggest that $$\textsf {PPass}$$ can efficiently verify a passenger’s vaccine passport in just 7 milliseconds, with a modest bandwidth requirement of 480 KB.
Toward Sustainable Model Services for Deep Learning: A Sub-Network-Based Solution Integrating Blockchain with IPFS and a Use Case in Intelligent Transportation

In the era of deep learning as a service, ensuring that model services are sustainable is a key challenge. To achieve sustainability, the model services, including but not limited to storage and inference, must maintain model security while preserving system efficiency, and be applicable to all deep models. To address these issues, we propose a sub-network-based model storage and inference solution that integrates blockchain and IPFS, which includes a highly distributed storage method, a tamper-proof checking method, a double-attribute-based permission management method, and an automatic inference method. We also design a smart contract to deploy these methods in the blockchain. The storage method divides a deep model into intra-sub-network and inter-sub-network information. Sub-network files are stored in the IPFS, while their records in the blockchain are designed as a chained structure based on their encrypted address. Connections between sub-networks are represented as attributes of their records. This method enhances model security and improves storage and computational efficiency of the blockchain. The tamper-proof checking method is designed based on the chained structure of sub-network records and includes on-chain checking and IPFS-based checking stages. It efficiently and dynamically monitors model correctness. The permission management method restricts user permission based on the user role and the expiration time, further reducing the risk of model attacks and controlling system efficiency. The automatic inference method is designed based on the idea of preceding sub-network encrypted address lookup. It can distribute trusted off-chain computing resources to perform sub-network inference and use the IPFS to store model inputs and sub-network outputs, further alleviating the on-chain storage burden and computational load. This solution is not restricted to model architectures and division methods, or sub-network recording orders, making it highly applicable. In experiments and analyses, we present a use case in intelligent transportation and analyze the security, applicability, and system efficiency of the proposed solution, particularly focusing on the on-chain efficiency. The experimental results indicate that the proposed solution can balance security and system efficiency by controlling the number of sub-networks, thus it is a step towards sustainable model services for deep learning.
Hubungan Interaksi Sosial dengan Kecerdasan Emosional Siswa di SMPN 1 Sungai Geringging

This research is motivated by the phenomenon of students who experience social interaction problems, students feel embarrassed when they start interacting with their friends where they feel they will not be accepted in their social environment, and students who have difficulty in social interaction in the school environment often act indifferent when their friends are speak. This research uses a quantitative approach with a descriptive correlational research type. The population of this study was 421 students and the sample was 205 students using the stratified random sampling formula. Data were collected using a Likert scale model questionnaire and validity tests were carried out with valid items totaling 36 social interaction questionnaire items and 29 emotional intelligence questionnaire items. Meanwhile, the reliability test on the social interaction questionnaire was 0.919 and the emotional intelligence questionnaire was 0.910. Data analysis uses descriptive statistics using the person product moment correlation formula. The results of the research show that (1) The social interaction of students at SMPN 1 Sungai Geringging is at varying levels, most of it is in the medium category with a percentage of 56.10% (2) The emotional intelligence of students is at varying levels, most of it is in the medium category with percentage 54.63% (3) there is a positive and significant relationship between Social Interaction and the Emotional Intelligence of students at SMPN 1 Sungai Geringging with a calculated r value of 0.423 with an aggressiveness level of 0.000). Therefore, guidance and counseling services are needed to improve students' social interaction and emotional intelligence through information services, content mastery services and group guidance services.
DATABASE USAGE IN SEMANTIC ONTOLOGIES

Semantic ontology languages are a way for experts to write down their knowledge in a commonly accepted way, it allows information to be understood by humans and machines. However, ontology tools do not provide the ability to use this data in a database for geodata-based analysis easily. The project is focused on ontology web language (OWL) usage for web content generation for business-to-business communication using geodata analysis. This specific paper is focused on selecting a database that could support the ontology tools. In the research scope, different databases were checked (relationship, document, and graph databases). In conclusion, graph databases were the most similar to the structure of the ontology, so it was chosen to use Neo4j as the database.
AUTONOMOUS UNMANNED DRONES FLIGHT PLANNING, USING A MODIFIED SHORTEST PATH ALGORITHM WITH A LIMITED TIME FRAME

The aim of this work is to develop an algorithm to find the shortest path for drone flight planning with a limited time frame. Author used the local search shortest path algorithm to find the most efficient algorithm to use for further modification to apply to a drones flight calculation. The algorithm was modified to use the distance between points as a unit of time to limit the flight path length depending on the drone's maximum flight time. As a result of the work, an algorithm was created which, upon receiving an array of points, finds the shortest distance between the points, but when it reaches the limit of the flight duration, it returns to the drone station to charge, and resumes flight once it’s done charging.
Human- centered Design Research of Visualization Approaches to Health Popularization

With the improvement of people's health consciousness and the development of new media, the visual representation of health science based on media has flourished. However, the current visualization cannot meet people 's emotional needs. This article conducted literature research and in-depth case studies on types of visualization for health science popularization, questionnaire survey and comparative analyses were carried out between the existing visualization methods of health science popularization. The article then discusses the design strategy for building a suitable visual html five(H5) for health science popularization. The results of practical verification show that the html five quiz game can guide people to actively think about and understand how to use medicine correctly, optimize the user's experience when acquiring knowledge. This research offers an insight into human-centered design of visualization of health science popularization.
Quantitative evaluation method examination for mock classes in a faculty development program

This research focuses on the mock classes in the faculty development program to nurture university teachers. This research aims to establish a quantitative evaluation method for mock classes and examine the possibility. In this paper, we analyzed the data from mock classes conducted by 23 participants of the faculty development program who mainly aim to become teachers. First, based on the subjective evaluation results by the Likert scale currently used in the mock classes, multiple regression analysis was performed using the comprehensive evaluation of the mock classes as the objective variable and the 11 evaluation items as the explanatory variables. As a result, four items were extracted as variables included in the regression equation: clarity of objectives and goals, class flow, time allocation, and clarity of lecturer’s explanation. The coefficient of determination of this multiple regression equation was 0.93, and the multiple correlation coefficient was 0.97. Next, in this paper, we attempted to quantify gestures which we considered the component of the “clarity of explanation” related to the delivery method among the multiple regression equation variables. We adopted OpenPose for motion analysis using image processing for the recorded video of the mock classes. As a result, of the 23 mock class participants, when comparing the higher and lower groups than the average value of overall subjective evaluation, the higher evaluated group showed a tendency of large value in the total amount of movement of both hands, the range of both hands’ motion, and face direction.
Exploring the challenges in classroom assessment: A mixed-method study of secondary schools in Pakistan

&lt;span lang="EN-US"&gt;In the process of classroom assessment, data is collected regarding the skills, understanding and knowledge of students. It has very important role in enhancing academic achievement of students. Present study was designed at exploring the challenges of classroom assessment. The sample of study contained a sample of 360 participants and a concurrent mixed-method design was deployed to conduct the research. Data collection was carried out using researchers’ self-developed questionnaire. The collected data was analyzed using statistical software of SPSS version 24.0 for descriptive and inferential stats. For collection and analyses of qualitative data, classroom observation and semi-structured interviews were also conducted. The study explored those major challenges&lt;/span&gt;&lt;span lang="EN-US"&gt; included lack of interest of both, the parents and the students; insufficient guidance on the assessment by school administration; and less or no provision of professional training in assessment to the teachers. It was recommended that the school administration should take appropriate steps for the involvement of all stakeholders. Extensive professional training should be provided to all the teachers on regular basis by the concerned departments.&lt;/span&gt;