Reviewerly/exHarmony huggingface dataset:
Does Rust SPARK Joy? Safe Bindings from Rust to SPARK, Applied to the BBQueue Library

Both Rust and SPARK are memory-safe programming languages and feature stronger safety guarantees than other popular programming languages for embedded software. However, modern systems often combine software written in multiple programming languages using the Foreign Function Interface (FFI). When using safety-oriented programming languages such as Rust and SPARK, maintaining compile-time safety guarantees across a language boundary is a challenge. The objective of this study is to explore if/how the inherent safety guarantees of these languages are preserved, and their potential benefits when establishing a library interface between them. In particular, we apply our method to the BBQueue circular buffer library that features complex ownership hand-over patterns when using FFI. Results reveal that most of the inherent consistency and safety features of these languages can be maintained. Yet, special caution is required at the FFI boundary to prevent potential vulnerabilities.
Solving Queries for Boolean Fault Tree Logic via Quantified SAT

Fault trees (FTs) are hierarchical diagrams used to model the propagation of faults in a system. Fault tree analysis (FTA) is a widespread technique that allows to identify the key factors that contribute to system failure. In recent work we introduced BFL, a Boolean Logic for Fault trees. BFL can be used to formally define simple yet expressive properties for FTA, e.g.: 1) we can set evidence to analyse what-if scenarios; 2) check whether two elements are independent or if they share a child that can influence their status; 3) and set upper/lower boundaries for failed elements. Furthermore, we provided algorithms based on binary decision diagrams (BDDs) to check BFL properties on FTs. In this work, we evaluate usability of a different approach by employing quantified Boolean formulae (QBFs) instead of BDDs. We present a translation from BFL to QBF and provide an implementation—making it the first tool for checking BFL properties—that builds on top of the Z3 solver. We further demonstrate its usability on a case study FT and investigate runtime, memory consumption and scalability on a number of benchmark FTs. Lastly, qualitative differences from a BDD-based approach are discussed.
Implementation of ICT-Based Learning Media to Enhance Digital Literacy in Elementary Schools

This research explores the implementation of ICT-based (Information and Communication Technology) instructional media at the elementary school level and its impact on students' digital literacy. The study was conducted at MI Ma'arif NU Penaruban, with research subjects including the school principal, teachers, and students. Key findings include the proactive role of the school principal in developing a vision for ICT usage, the use of diverse instructional media by teachers, positive responses from students to technology in learning, and challenges in ICT implementation such as infrastructure issues and teacher training. The research recommendations encompass technology infrastructure development, teacher training, sustained school support, and ongoing evaluation of ICT implementation. The results of this study provide valuable insights into how technology can be effectively used to enhance digital literacy at the elementary school level.
Global Positioning Scheme via Quantum Teleportation

Quantum teleportation scheme is undoubtedly an inspiring theoretical discovery as an amazing application of quantum physics, which was experimentally realized several years later. For the purpose of quantum communication via this scheme, an entangled ancillary pair shared by Alice and Bob is the essential ingredient, and a quantum memory in Bob’s system is necessary for him to keep the quantum state until the classical message from Alice arrives. Yet, the quantum memory remains a challenge in both technology and rationale. Here we show that quantum teleportation provides fresh perspectives in terms of an alternative scheme for global positioning system. Referring to fixed locations of Bob and Charlie, Alice can determine her relative position by comparing quantum states before and after teleporting around via Bob and Charlie successively. This may open up a new scene in the stage of the application of quantum physics without quantum memories.
ROMANIAN TOPIC MODELING – AN EVALUATION OF PROBABILISTIC VERSUS TRANSFORMER-BASED TOPIC MODELING FOR DOMAIN CATEGORIZATION

A primary challenge for digital library systems when digitizing millions of volumes is to automatically analyze and group the huge document collection by categories while identifying patterns and extracting the main themes. A common method to be leveraged on unlabeled texts is topic modeling. Given the wide range of datasets and evaluation criteria used by researchers, comparing the performance and outputs of existing unsupervised algorithms is a complex task. This paper introduces a domain-based topic modeling evaluation applied to Romanian documents. Several variants of Latent Dirichlet Allocation (LDA) combined with dimensionality reduction techniques were compared to Transformer-based models for topic modeling. Experiments were conducted on two datasets of varying text lengths: abstracts of novels and full-text documents. Evaluations were performed against coherence and silhouette scores, while the validation considered classification and clustering tasks. Results highlighted meaningful topics extracted from both datasets.
Escalating SVM-Efficiency by Sequential QP LP and Slack Variable Analysis

Support Vector Machine (SVM) is a highly attractive algorithm among many machine learning models due to its generalization power and classification performance based on sound mathematical formulation being convex that offers global minimum. However, despite being sparse, its high classification cost from kernel execution with Support Vectors (SVs) reduces the user's interest when there are hard computational constraints in the application, especially, for large and difficult data. So far in our knowledge, out of many existing works to overcome this problem, some are really interesting and heavy but get less attractive due to improper training difficulties for example, excessive cost-memory requirement, initialization, and parameter selection trouble because of the non-convexity of the problems while the other few that avoid these problems, cannot generate sparsity and complexity simultaneously of the final discriminator upto satisfactory level for very large and tricky data. In this direction, we propose a novel algorithm Efficiency Escalated SVM (EESVM) that solves two convex problems using Quadratic Programming (QP) and Linear Programming (LP) in sequence. This is followed by computational analysis on the remaining smallest set of slack variables that ultimately build two very essential properties of the machine: (i) Highly efficient by being heavily sparse and optimally complex and (ii) Able to handle very large and noise-effected complicated data. Benchmarking shows that this EESVM demands kernel computation as little as 6.8% of the standard QPSVM while posing almost the same classification accuracy on test data and requiring 42.7, 27.7 and 46.6% that of other three implemented state-of-the-art heavy-sparse machines while offering similar classification accuracy. It claims the lowest Machine Accuracy Cost (MAC) value among all of these machines though showing very similar generalization performance that is evaluated numerically using the term Generalization Failure Rate (GFR). Being quite pragmatic for modern technological advancement, it is indispensable for optimum manipulation of the troublesome massive, and difficult data.
Architectural Implications of GNN Aggregation Programming Abstractions

Graph neural networks (GNNs) have gained significant popularity due to the powerful capability to extract useful representations from graph data. As the need for efficient GNN computation intensifies, a variety of programming abstractions designed for optimizing GNN Aggregation have emerged to facilitate acceleration. However, there is no comprehensive evaluation and analysis upon existing abstractions, thus no clear consensus on which approach is better. In this letter, we classify existing programming abstractions for GNN Aggregation by the dimension of data organization and propagation method. By constructing these abstractions on a state-of-the-art GNN library, we perform a thorough and detailed characterization study to compare their performance and efficiency, and provide several insights on future GNN acceleration based on our analysis.
Exploring the Practicality of Differentially Private Federated Learning: A Local Iteration Tuning Approach

Although Federated Learning (FL) prevents the exposure of original data samples when collaboratively training machine learning models among decentralized clients, it has been revealed that vanilla FL is still susceptible to adversarial attacks if model parameters are leaked to malicious attackers. To enhance the protection level of FL, Differential Private Federated Learning (DPFL) has been proposed in recent years. DPFL injects zero-mean noises randomly generated by differential private (DP) mechanisms on local model parameters before they are disclosed. Nevertheless, DP noises can significantly deteriorate model utility jeopardizing the practicality of DPFL. In this paper, we are among the first to explore how to improve the model utility of DPFL by tuning the number of local iterations (LIs) on DPFL clients. Our work shows that such a local iteration tuning approach can well mitigate the adverse influence of DP noises on the final model utility. Formally, we derive the sensitivity (a measure of the maximum change of the output given two adjacent inputs) with respect to the number of LIs conducted on DPFL clients for the Laplace mechanism, and the aggregated variances of Laplace noises at the server side. We further conduct convergence rate analysis to quantify the influence of the Laplace noises on the final model accuracy and determine how to optimally set the number of LIs. Finally, to verify our theoretical findings, we perform extensive experiments using three real-world datasets, namely, Lending Club, MNIST and Fashion-MNIST. The results not only corroborate our analysis, but also demonstrate that our approach significantly improves the practicality of DPFL.
Transforming Ideas into Code: Visual Sketching for ML Development

We propose a novel code assistant and generation paradigm aimed at closing the gap between visual sketching and code creation for Machine Learning (ML) development. This approach empowers developers and ML practitioners to translate hand-drawn sketches into functional code with enhanced accuracy and usability. Developers are recruited to assess the tool's performance. This research contributes to the future of low-code approaches, facilitating ML application development, and promoting an intuitive and accessible programming environment.
Semi-Supervised Semantic Generative Networks For Remote Sensing Image Segmentation

Semi-supervised remote sensing semantic segmentation is an efficient way to increase the use of unlabeled data and cut labelling costs. The unlabeled-to-labeled data ratio is employed in more recent methods, which is very different from what is really used in practise. In this paper, we propose a semi-supervised semantic generative network for remote sensing images, introducing a self-supervised learning method to enhance the feature representation of the model when the data ratio is high. Specifically, we design a new branch for unlabeled data, which includes modules for both semantic reconstruction and appearance reconstruction. It can effectively alleviate the category confusion in complicated remote sensing image when there are few labeled data. Comprehensive experiments on the ISPRS POTSDAM dataset demonstrate that the proposed method achieves promising results.
Seeing is Not Believing: Toward Forgery Detection for Hyperspectral Image

Amid the rapid growth of low-earth orbits (LEOs) and hyperspectral imaging (HSI) techniques, this study addresses the overlooked area of security challenges, specifically hyperspectral content manipulation or inpainting. We present HyperForensics, a novel dataset based on NASA’s AVIRIS data, covering diverse scenarios across a wavelength range of 0.4 to 2.5 micrometers. To confront the challenge of detecting small manipulated regions within HSIs, we develop a detection method using a high-resolution network (HRNet) that maintains high resolution and spatial accuracy. Experiments demonstrate this approach outperforms existing methods, highlighting the critical role of spatial-spectral features in HSI forgery detection. This pioneering work in HSI forgery benchmarking and detection invites further research to enhance HSI security.
Optical Flow-Based Moving Vehicle Detection from Single-Pass Worldview-3 Imagery

Satellite-based moving vehicle (MV) detection plays a crucial role in traffic monitoring and surveillance. However, it presents challenges due to the large scale of scenes and the small size of MV features in satellite images. This study presents an optical flow-based approach for MV detection using single-pass WorldView-3 (WV-3) images. Representative images from two different multispectral sensors in WV-3 were created, and the Gunnar Farnebäck optical flow algorithm was employed to estimate MV motions. The estimated motion fields were then transformed into the HSV color space and segmented into eight ranges representing different moving direction angles. To refine the MV shapes and eliminate noise, opening operations and the DBSCAN clustering algorithm were applied. Finally, the MV detection results were obtained by combining eight images with bounding boxes. Although the algorithm exhibits high precision, challenges remain in accurately extracting MVs from the motion field, handling adjacent MVs and avoiding misinterpretations of non-moving edge features.
Streaming Science #5: Using Virtual Reality Tours for Engagement With Your Target Audience

This is the fifth of seven publications in the Streaming Science EDIS series about using mobile instructional and communication technologies (ICTs) for outreach and engagement with your target audiences. This publication is intended as a guide for communication and education professionals to use various virtual reality (VR) hardware and software to create immersive online content about agricultural and natural resources spaces and places to communicate, teach, and engage with scientific content.
CastorEDC API: A Python Package for Managing Real World Data in Castor Electronic Data Capture

Real world data is being used increasingly in medical research. Castor Electronic Data Capture is a secure and user-friendly platform for managing study data. Integrating data from several databases into a single Castor database is complex. We developed CastorEDC API, a free and open source Python package which can be used to interact with the API of Castor, and through which data can be imported from multiple sources into a Castor database. The importer reads, cleans, validates and imports data while accounting for differences in column structure and variable coding between databases. https://github.com/reiniervlinschoten/castoredc_api.
Analysis of Digital Literacy Technology Design for the Department of Mechanical Engineering Based on a Website at Universitas Muhammadiyah Sumatera Barat

This research aims to analyze the level of importance of digital literacy understanding among students of Mechanical Engineering at the University of Muhammadiyah (UM) Sumatera Barat. The digital literacy developed in this study not only facilitates access to learning references, literature, research methodologies, and testing in the field of Mechanical Engineering but also designed as a data storage place for student practical and research testing results. This research used a questionnaire as a data collection technique, which was filled out by Mechanical Engineering students at Universitas Muhammadiyah Sumatera Barat. The first questionnaire was conducted to measure the level of students' understanding of digital literacy in the field of Mechanical Engineering, and the results were used as the basis for creating a literacy website. The second questionnaire was conducted to evaluate the created literacy website. Data analysis was performed using quantitative descriptive analysis. The results showed that 57% of respondents had good understanding of digital literacy in the field of Mechanical Engineering. However, only 15% of respondents often searched for references on digital technology, and 80% of respondents faced difficulties in finding credible digital literacy sources. Moreover, most respondents expressed their interest in learning and using digital technology in research and practical work. These results served as the basis for creating a digital literacy website. The digital literacy website was evaluated, and the evaluation result showed that most respondents stated that the created literacy website was easy to use and helpful in the learning or task completion process. However, some aspects needed improvement, such as relevant and understandable content and the quality of content presentation that is more interesting for respondents. Overall, the literacy website has a high level of satisfaction in several aspects.
Source Selection Essential to Inter-Source Cross-Correlation

Our latest paper, published in J. Geophys. Res.
The Mediating Effect of Self-Efficacy on the Effect of Lifelong Education Field Practice on Lifelong Education Career Decision

Objectives The purpose of this study is to confirm the mediating effect of self-efficacy in lifelong education ca-reer decisions by college students who have completed lifelong education field training.&#x0D; Methods To this end, a survey was conducted on students who completed the lifelong education field practice course at H University from February 6 to April 25, 2023, and 230 copies were used for analysis. The collected data used the IBM SPSS 26.0 statistical program. Hierarchical regression analysis and bootstrapping were conducted according to the procedures of Baron and Kenny (1986) to verify the mediating effect.&#x0D; Results First, as a result of analyzing the correlation between lifelong education field practice, self-efficacy, and lifelong education career decision, it was found that there was a significant static correlation between each variable. Second, among the sub-factors of lifelong education field practice, self-efficacy showed a partial medi-ating effect in the relationship between the factors of practice institutions and lifelong education career decisions. Third, as a result of verifying the mediating effect of self-efficacy in the relationship between lifelong education field practice and lifelong education career decision, partial mediating role.&#x0D; Conclusions In this study, it was confirmed that self-efficacy had a partial mediating effect in the career decision of college students who have experienced lifelong education field training. Based on the results of the study, we discussed ways to improve lifelong education field practice and self-efficacy to increase self-efficacy and lifelong education career decisions as mediating factors connecting lifelong education field practice and lifelong education career decisions.
An Analysis of Student and Instructor's Recognition and Needs for Core Competencies: A Case Study of A University

Objectives This study aims to analyze the perceptions and needs of both students and instructors on com-petency-based education in order to derive educational implication.&#x0D; Methods A survey was conducted on core competencies and major competencies of 13,109 college students and 316 instructors, and a t-test, Borich's needs analysis and The Locus for Focus analysis were performed.&#x0D; Results First, regarding the recognition of the competency model and system, the level of instructors was above average, and the level of students was found to be somewhat low. Second, as a result of analyzing the importance of existing core competencies and major competencies, the future importance of the existing core and major competencies were high, which was statistically significant (p&lt;.01~p&lt;.05). Third, as a result of the needs analysis on core and major competencies, it was found that students tend to aware of their competencies higher with high priorities for instructors' needs.&#x0D; Conclusions It is necessary to raise awareness of competency-based education of students and instructors. In order to improve the competency-based education and system, it is possible to consider supplementing based on the existing core competency and major competency. Also, thorough collecting opinion process from various stake holders in college is needed in order to build up the own competency-based education system.
The Effect of CPTED Elements and Crime Prediction by Using Big Data Analysis in Seoul

In various parts of the world, big data related methods is being utilized to predict criminal activities and facilitate prompt arrests. Advancements in big data and technology have made it possible to analyze and forecast crime patterns effectively. This research aims to analyze the effectiveness of Crime Prevention Through Environmental Design (CPTED) elements and predict crime rates using machine learning techniques based on data from 2016 to 2021 in Seoul. This study utilizes diverse data in CPTED elements obtained from the Smart Public Safety Big Data Platform. The significance of this study lies in demonstrating the effectiveness of CPTED and leveraging big data for crime prediction. The findings can serve as a reference for the policy-making and the establishment of crime prevention systems based on predictive insights.
Is this an e-School? e-Learning Using Information Communication Technologies in South Africa

The study reported on in this paper reviewed the readiness of a specific high school in the township of Soweto to the south of Johannesburg, South Africa, to deliver the curriculum by using Information and Communication Technologies (ICTs), as well as to find the barriers and limitations thereto. For the empirical research, a quantitative mode of inquiry was adopted. A sample of learners and teachers was selected to answer the research questions, using two different questionnaires. The principal responded to a questionnaire on the ICT infrastructure available to establish what the school had in this regard, as well as the skills available to implement pedagogy using ICTs. Data analysis was carried out using a common spreadsheet application and multiple criteria, and the findings are discussed to provide value to, and should be of use to, the stakeholders involved for development purposes. The work discussed in this study covered curriculum topics, as well as lesson planning and reporting. In other words, it covered the process of teaching and learning up to reporting for teachers. A summary of the findings of the research conducted at the high school showed that the school does not have a stable internet connection and more technical support is needed from the government to strengthen the ICT infrastructure to enable the school to fully implement the curriculum using ICTs, compared to using traditional or hybrid methods. These strengthening recommendations include a stable internet connection, ICT staff, to support both e-teachers and e-learners with ICT problems, as well as software to deliver the curriculum. The findings also showed that learners are more comfortable with mobile devices and a little uncomfortable using computers for learning, which means that some aspects of e-learning requirements are not met, and it is very difficult to achieve some of the curriculum goals using mobile devices.