API Library Research Articles

A Python-Based Automation Script to Mark Computer-Aided Design Assessments

The integration of automated tools in engineering education has the potential to improve student assessments, ensuring consistency and reducing instructor workload. This study introduces a Python-based automation tool designed to evaluate student Computer-Aided Design (CAD) submissions. The tool utilises software API and Open Cascade library to calculate model parameters. These parameters are compared against expected values from a solution file and marks are assigned based on deviations relative to the solution file. As a use case, seventy-five Solid Edge CAD files were assessed for geometric properties such as volume, surface area, and centre of gravity location to evaluate inter- and intra-marker reliability. The results showed perfect agreement, with a Cohen kappa of 1.0 for both metrics. Furthermore, the automated tool reduced grading time by 89.7% compared to manual evaluation. The potential of automation in improving marking efficiency, consistency, and objectivity in engineering education has been shown, providing a foundation for further integration of software. The python-based automation script is openly available on GitHub.

RuMono: Fuzz Driver Synthesis for Rust Generic APIs

Fuzzing is a popular technique for detecting bugs, which can be extended to libraries by constructing executables that call library APIs, known as fuzz drivers. Automated fuzz driver synthesis has been an important research topic in recent years since it can facilitate the library fuzzing process. Nevertheless, existing approaches generally ignore generic APIs or simply treat them as non-generic APIs. As a result, they cannot generate effective fuzz drivers for generic APIs. This paper explores the challenge of automating fuzz driver synthesis for Rust libraries with generic APIs. The problem is essential because Rust prioritizes security and generic APIs are widely employed in Rust libraries. We propose a novel approach and develop a prototype, RuMono, to tackle the problem. Our approach initially infers the API reachability from the generic API dependency graph, discovering the reachable and valid monomorphic APIs within the library. Further, we apply a similarity-based filter to eliminate redundant monomorphic APIs. Experimental results from 29 popular open-source libraries demonstrate that RuMono can achieve promising generic API coverage with a low rate of invalid fuzz drivers. Besides, we have identified 23 previously unknown bugs in these libraries, with 18 related to generic APIs.

Crabtree: Rust API Test Synthesis Guided by Coverage and Type

Rust type system constrains pointer operations, preventing bugs such as use-after-free. However, these constraints may be too strict for programming tasks such as implementing cyclic data structures. For such tasks, programmers can temporarily suspend checks using the unsafe keyword. Rust libraries wrap unsafe code blocks and expose higher-level APIs. They need to be extensively tested to uncover memory-safety bugs that can only be triggered by unexpected API call sequences or inputs. While prior works have attempted to automatically test Rust library APIs, they fail to test APIs with common Rust features, such as polymorphism, traits, and higher-order functions, or they have scalability issues and can only generate tests for a small number of combined APIs. We propose Crabtree, a testing tool for Rust library APIs that can automatically synthesize test cases with native support for Rust traits and higher-order functions. Our tool improves upon the test synthesis algorithms of prior works by combining synthesis and fuzzing through a coverage- and type-guided search algorithm that intelligently grows test programs and input corpus towards testing more code. To the best of our knowledge, our tool is the first to generate well-typed tests for libraries that make use of higher-order trait functions. Evaluation of Crabtree on 30 libraries found four previously unreported memory-safety bugs, all of which were accepted by the respective authors.

Application of Attribute-Based Encryption in Military Internet of Things Environment.

The Military Internet of Things (MIoT) has emerged as a new research area in military intelligence. The MIoT frequently has to constitute a federation-capable IoT environment when the military needs to interact with other institutions and organizations or carry out joint missions as part of a coalition such as in NATO. One of the main challenges of deploying the MIoT in such an environment is to acquire, analyze, and merge vast amounts of data from many different IoT devices and disseminate them in a secure, reliable, and context-dependent manner. This challenge is one of the main challenges in a federated environment and forms the basis for establishing trusting relationships and secure communication between IoT devices belonging to different partners. In this work, we focus on the problem of fulfillment of the data-centric security paradigm, i.e., ensuring the secure management of data along the path from its origin to the recipients and implementing fine-grained access control mechanisms. This problem can be solved using innovative solutions such as applying attribute-based encryption (ABE). In this work, we present a comprehensive solution for secure data dissemination in a federated MIoT environment, enabling the use of distributed registry technology (Hyperledger Fabric), a message broker (Apache Kafka), and data processing microservices implemented using the Kafka Streams API library. We designed and implemented ABE cryptography data access control methods using a combination of pairings-based elliptic curve cryptography and lightweight cryptography and confirmed their suitability for the federations of military networks. Experimental studies indicate that the proposed cryptographic scheme is viable for the number of attributes typically assumed to be used in battlefield networks, offering a good trade-off between security and performance for modern cryptographic applications.

Binding the past with digital: Film curation at Batu Api Library

Background: Digital curation is a growing concept that involves the selection, organization, and presentation of digital information for specific purposes. In this study, digital curation manages Batu Api Library’s film collection using digital technology. This trend is critical to keeping cultural information relevant and accessible to the public. Purpose: This study intends to examine the curation of digital films at the Batu Api Library and how users react to and value the library’s digital film curation results. Methods: This study is based on qualitative research. Batu Api Library conducted this study for three months. Interviews and questionnaires are research methods. Quantitative and qualitative data are collected in two phases. The types of data require different treatment methods. Thematic analysis provides qualitative data, while descriptive statistics uses quantitative data. The methodology provides a comprehensive understanding of the impact of technology on the curation of films and audience participation at Batu Api Library. Results: The study found overwhelmingly positive results for integrating digital technology into film curation at Batu Api Library. The experience engaged and educated many visitors and helped them understand historical and cultural differences. The youngest users were particularly engaged. Conclusion: The study found that digitizing film curation at Batu Api Library enhanced the visitor experience and historical insight. Responses, especially from younger participants, indicate that digital advances are beneficial. Implications: Digital film curation can enhance the museum experience and historical and cultural knowledge. Digital technology preserves and communicates cultural heritage in practical and theoretical ways.

Seismic Full Waveform Inversion Accelerated by Overlapping Data Input and Computation

Seismic full waveform inversion (FWI) is a powerful technology to obtain high-precision and high-resolution images of subsurface structures. However, FWI is a data-intensive algorithm that needs to read extensive seismic data from disks, which significantly affects its performance. We proposed a portable parallel framework to improve FWI by overlapping data input and computation (ODIC). The framework is based on POSIX threads (Pthreads), which is a standard thread API library and can create a parent thread and a child thread in the FWI process. The former is used to perform computation and the latter to read data from disks, both running simultaneously. This framework has two attractive features. First, it is broadly applicable; it can run on almost any computer from a laptop to a supercomputer. Second, it is easy to implement; it can be readily applied to existing FWI programs. A 3D FWI example shows that the framework speeds up FWI considerably.

Visualizing Google Sheets Data in Web Applications with Laravel

The study presents the design implementation of visualizing Google Sheets data in web applications using the Rapid Application Development (RAD) approach. It focuses on integrating Google Sheets data seamlessly into a web application using the Laravel PHP framework and employs effective data visualization techniques. Real-time data access is achieved through the integration of Google Sheets API, while the Google Maps API and High charts Library enabled interactive and informative data visualizations. The result is a user-friendly web application, empowering users to explore data insights and make well-informed decisions with ease

Discussion forum model in the Covid-19 pandemic era at Batu Api Library

Discussion forums are activities to distribute information, ideas, and question. The Batu Api Library held discussion forums to exchange ideas that are discussed and presented through a problem or question that must be resolved based on a joint opinion or decision. This research aimed to find out the discussion forum model at the Batu Api Library during the pandemic with analysis discussion forum model and the development of library services through discussion forums. The research method used is a qualitative approach with a case study research method. The research was carried out for four months, from February to May 2022, using in-depth interview techniques with five people as the number of subjects in this study. The study results stated that during the pandemic, the Batu Api Library continued to conduct discussion forum activities, despite several changes due to the pandemic. Discussion forum at the Batu Api Library is sharing knowledge activity that source library collections. The discussion forum model in the pandemic era at Batu Api Library can be a bridge between users and the library. The discussion forum was also to develop library services so that more people would visit the library. In conclusion, the Batu Api Library can still conduct discussion forum activities as part of library services during the Covid-19 pandemic; this aligns with the statement that libraries are organisms that can constantly develop even during the Covid-19 pandemic.

Securing Operating Systems Through Fine-Grained Kernel Access Limitation for IoT Systems

With the development of Internet of Things (IoT), it is gaining a lot of attention. It is important to secure the embedded systems with low overhead. The Linux Seccomp is widely used by developers to secure the kernels by blocking the access of unused syscalls, which introduces less overhead. However, there are no systematic Seccomp configuration approaches for IoT applications without the help of developers. In addition, the existing Seccomp configuration approaches are coarse-grained, which cannot analyze and limit the syscall arguments. In this article, a novel static dependent syscall analysis approach for embedded applications is proposed, which can obtain all of the possible dependent syscalls and the corresponding arguments of the target applications. So, a fine-grained kernel access limitation can be performed for the IoT applications. To this end, the mappings between dynamic library APIs and syscalls according with their arguments are built, by analyzing the control flow graphs and the data dependency relationships of the dynamic libraries. To the best of our knowledge, this is the first work to generate the fine-grained Seccomp profile for embedded applications.

Shrinking the Kernel Attack Surface Through Static and Dynamic Syscall Limitation

Linux Seccomp is widely used by the program developers and the system maintainers to secure the operating systems, which can block unused syscalls for different applications and containers to shrink the attack surface of the operating systems. However, it is difficult to configure the whitelist of a container or application without the help of program developers. Docker containers block about only 50 syscalls by default, and lots of unblocked useless syscalls introduce a big kernel attack surface. To obtain the dependent syscalls, dynamic tracking is a straight-forward approach but it cannot get the full syscall list. Static analysis can construct an over-approximated syscall list, but the list contains many false positives. In this paper, a systematic dependent syscall analysis approach, sysverify, is proposed by combining static analysis and dynamic verification together to shrink the kernel attack surface. The semantic gap between the binary executables and syscalls is bridged by analyzing the binary and the source code, which builds the mapping between the library APIs and syscalls systematically. To further reduce the attack surface at best effort, we propose a dynamic verification approach to intercept and analyze the security of the invocations of indirect-call-related or rarely invoked syscalls with low overhead.

Energy Consumption of Electric Vehicles: Analysis of Selected Parameters Based on Created Database

Electric vehicles in a short time will make up the majority of the fleet of vehicles used in general. This state of affairs will generate huge sets of data, which can be further investigated. The paper presents a methodology for the analysis of electric vehicle data, with particular emphasis on the energy consumption parameter. The prepared database contains data for 123 electric vehicles for analysis. Data analysis was carried out in a Python environment with the use of the dabl API library. Presentation of the results was made on the basis of data classification for continuous and categorical features vs. target parameters. Additionally, a heatmap Pearson correlation coefficient was performed to correlate the energy consumption parameter with the other parameters studied. Through the data classification for the studied dataset, it can be concluded that there is no correlation against energy consumption for the parameter charging speed; in contrast, for the parameters range and maximum velocity, a positive correlation can be observed. The negative correlation with the parameter energy consumption is for the parameter acceleration to 100 km/h. The methodology presented to assess data from electric vehicles can be scalable for another dataset to prepare data for creating machine learning models, for example.

EXPANSION: a webserver to explore the functional consequences of protein-coding alternative splice variants in cancer genomics.

EXPANSION (https://expansion.bioinfolab.sns.it/) is an integrated web-server to explore the functional consequences of protein-coding alternative splice variants. We combined information from Differentially Expressed (DE) protein-coding transcripts from cancer genomics, together with domain architecture, protein interaction network, and gene enrichment analysis to provide an easy-to-interpret view of the effects of protein-coding splice variants. We retrieved all the protein-coding Ensembl transcripts and mapped Interpro domains and post-translational modifications on canonical sequences to identify functionally relevant splicing events. We also retrieved isoform-specific protein-protein interactions and binding regions from IntAct to uncover isoform-specific functions via gene-set over-representation analysis. Through EXPANSION, users can analyze precalculated or user-inputted DE transcript datasets, to easily gain functional insights on any protein spliceform of interest. EXPANSION is freely available at http://expansion.bioinfolab.sns.it/. The code of the scripts used for EXPASION is available at: https://github.com/raimondilab/expansion. Datasets associated to this resource are available at the following URL: https://doi.org/10.5281/zenodo.8229120. The web-server was developed using Apache2 (https://https.apache.org/) and Flask (v2.0.2) (http://flask.pocoo.org/) for the web frontend and for the internal pipeline to handle back-end processes. We additionally used the following Python and JavaScript libraries at both back- and front-ends: D3 (v4), jQuery (v3.2.1), DataTables (v2.3.2), biopython (v1.79), gprofiler-officia l(v1.0.0), Mysql-connector-python (v8.0.31). To construct the API, Fast API library (v0.95.1) was used.

Deep Learning Models for the Detection of Monkeypox Skin Lesion on Digital Skin Images

The study is an investigation testing the accuracy of deep learning models in the detection of Monkeypox. The disease is relatively new and difficult for physicians to detect. Data for the skins were obtained from Google via web-scraping with Python’s BeautifulSoup, SERP API, and requests libraries. The images underwent scrutiny by professional physicians to determine their validity and classification. The researcher extracted the images’ features using two CNN models - GoogLeNet and ResNet50. Feature selection from the images involved conducting principal component analysis. Classification employed Support Vector Machines, ResNet50, VGG-16, SqueezeNet, and InceptionV3 models. The results showed that all the models performed relatively the same. However, the most effective model was VGG-16 (accuracy = 0.96, F1-score = 0.92). It is an affirmation of the usefulness of artificial intelligence in the detection of the Monkeypox disease. Subject to the approval of national health authorities, the technology can be used to help detect the disease faster and more conveniently. If integrated into a mobile application, it can be members of the public to self-diagnose before seeking official diagnoses from approved hospitals. The researcher recommends further research into the models and building bigger image databases that will power more reliable analyses.

A Sentiment Analysis Model for the COVID-19 Vaccine in Indonesia Using Twitter API v2, TextBlob, and Googletrans

COVID-19 was declared a world pandemic in early-mid 2020. After about a year, several vaccines for this virus have been found and become alternative solutions to reduce the spread of the COVID-19 pandemic and build up herd immunity in society. Since early 2021, Indonesia has been one of the countries that participate in using vaccines for the public to fight the COVID-19 pandemic. However, there are a lot of positive and negative responses from Indonesian society related to these COVID-19 vaccines. Implementing a sentiment analysis model for a specific topic like a “vaccine” from social media could help us to see and understand the responses from society in Indonesia towards the vaccine program that is being conducted by the Indonesian government. Understanding society's response towards vaccines is expected to be able to support the Indonesian government, for example in formalizing the distribution strategy of vaccines in the future. This paper discusses how to develop a sentiment analysis model, by implementing several existing technologies such as Twitter API, TextBlob, and Googletrans Python libraries. The utilization of these existing technologies could show how a sentiment analysis model could be developed conveniently, for example in using cases to analyse Indonesian society's responses towards the COVID-19 vaccine program. Besides the solution design, this paper also shows a sample of data visualization of the sentiment analysis model in a meaningful infographics format.

A Numerical Code for a Wide Range of Compressible Flows on Hybrid Computational Architectures

The major points in the development of the parallel multiplatform multipurpose numerical code solving the full unsteady Navier–Stokes equations are presented. The developed code is primarily designed for running on multi-GPU computational devices but can also be used on traditional multicore CPUs and even on manycore processors such as Intel Xeon Phi. Physical models include calorically perfect inert gas, single- and multi-temperature approaches for chemically reactive flows and an Euler–Euler model for gas-particle suspensions. Main details of the implementation are described. Shock capturing TVD and WENO schemes in general curvilinear coordinates are used for spatial approximation. Explicit, semi-implicit and fully implicit schemes are employed for advancing solution in time. The code is written in C++ with CUDA API and opensource libraries, such as MPI, zlib and VTK. A few examples of numerical simulations are briefly described to provide general idea of the numerical code capabilities. They include a supersonic flow past a wedge, a jet exhausting from a square nozzle, a heavy gas bubble descending in a lighter medium and a heterogeneous detonation in gas-particle suspension.

SENTIMENT ANALYSIS FOR PHARMACEUTICAL COMPANY FROM SOCIAL MEDIA USING ADAPTIVE COMPRESSION (ADACOMP) WITH RANDOM UNDER SAMPLE (RUS) AND SYNTHETIC MINORITY OVER-SAMPLING (SMOTE)

Pharmaceutical company has become the most highlight company across the world lately because of the pandemic. Despite of the high demand market in pharmaceutical company, about 94% of large company across the world having difficulty in their supply chain that indirectly affect their services. The purpose of this research is to compare word embedding with compression model by doing sentiment analysis about the entity to find the best model that give better accuracy rates based on the opinion of Twitter, Instagram and Youtube, as they are the largest platform that its many users to express their opinions about an individual or an instance. Data is retrieved from Twitter, Instagram and Youtube using the R-Studio application by utilizing their API library, then preprocessing and stored in a database. Next step is labeling the data and then train the data using word2Vec and LSTM, GloVe and LSTM and lastly using Adaptive Compression (adaComp) to compress the both model word embedding. Unfortunately, we got imbalanced dataset after labeling process, so we add sampling technique to sampling the dataset using Random Under Sample (RUS) and Synthetic Minority Over-sampling Technique (SMOTE). After the data are trained and tested, the results will be evaluated using Confusion Matrix to get the best Accuracy. With several models that have been carried out,applying adaComp is proven to increase accuracy. In the Word2Vec word embedding with LSTM model, applying adaComp increasing its accuracy from 77% to 81%.

A geospatial workflow for the assessment of public transit system performance using near real‐time data

AbstractThis article presents the development of a Geographical Information Systems (GIS) workflow that harvests high‐volume and high‐frequency near real‐time data from a public General Transit Feed Specification (GTFS) and calculates metrics for the assessment of on‐time and route speed performance for a public transit system. The approach is applied to near real‐time and static GTFS data collected over a 9‐month period for the City of Calgary, Alberta, Canada. The workflow uses two Azure Virtual Machines (VMs), one to harvest the data and the other to process observations in parallel using Python and the ArcGIS API libraries. A Web GIS application is described that queries data from MongoDB to visualize the performance results in spatiotemporal form. The purpose of the workflow and Web GIS application is to provide actionable information to transit planners to improve public transportation systems. The data management and analysis workflow is transferable to similar GTFS data from other cities.

In-IDE Code Generation from Natural Language: Promise and Challenges

A great part of software development involves conceptualizing or communicating the underlying procedures and logic that needs to be expressed in programs. One major difficulty of programming is turning concept into code , especially when dealing with the APIs of unfamiliar libraries. Recently, there has been a proliferation of machine learning methods for code generation and retrieval from natural language queries , but these have primarily been evaluated purely based on retrieval accuracy or overlap of generated code with developer-written code, and the actual effect of these methods on the developer workflow is surprisingly unattested. In this article, we perform the first comprehensive investigation of the promise and challenges of using such technology inside the PyCharm IDE, asking, “At the current state of technology does it improve developer productivity or accuracy, how does it affect the developer experience, and what are the remaining gaps and challenges?” To facilitate the study, we first develop a plugin for the PyCharm IDE that implements a hybrid of code generation and code retrieval functionality, and we orchestrate virtual environments to enable collection of many user events (e.g., web browsing, keystrokes, fine-grained code edits). We ask developers with various backgrounds to complete 7 varieties of 14 Python programming tasks ranging from basic file manipulation to machine learning or data visualization, with or without the help of the plugin. While qualitative surveys of developer experience are largely positive, quantitative results with regards to increased productivity, code quality, or program correctness are inconclusive. Further analysis identifies several pain points that could improve the effectiveness of future machine learning-based code generation/retrieval developer assistants and demonstrates when developers prefer code generation over code retrieval and vice versa. We release all data and software to pave the road for future empirical studies on this topic, as well as development of better code generation models.

RefactoringMiner 2.0

Refactoring detection is crucial for a variety of applications and tasks: (i) empirical studies about code evolution, (ii) tools for library API migration, (iii) code reviews and change comprehension. However, recent research has questioned the accuracy of the state-of-the-art refactoring mining tools, which poses threats to the reliability of the detected refactorings. Moreover, the majority of refactoring mining tools depend on code similarity thresholds. Finding universal threshold values that can work well for all projects, regardless of their architectural style, application domain, and development practices is extremely challenging. Therefore, in a previous work [N. Tsantalis, M. Mansouri, L. M. Eshkevari, D. Mazinanian, and D. Dig, Accurate and efficient refactoring detection in commit history, in 40th International Conference on Software Engineering, 2018, pp. 483–494], we introduced the first refactoring mining tool that does not require any code similarity thresholds to operate. In this work, we extend our tool to support low-level refactorings that take place within the body of methods. To evaluate our tool, we created one of the most accurate, complete, and representative refactoring oracles to date, including 7,226 true instances for 40 different refactoring types detected by one (minimum) up to six (maximum) different tools, and validated by one up to four refactoring experts. Our evaluation showed that our approach achieves the highest average precision (99.6 percent) and recall (94 percent) among all competitive tools, and on median is 2.6 times faster than the second faster competitive tool.

A Survey on Authentication for ATM Transaction System

Abstract: In this paper, we proceed to discuss the various implications and concepts related to the hardware and software used in our proposed system of fingerprint authentication for ATM card transactions. This paper refers to many different sources based on the techniques that are being used in the system, i.e. a RFID scanner, RFID tags, a Fingerprint scanner, the Adafruit library and RESTful APIs.