Processing Library Research Articles

The Comparative study of Python Libraries for Natural Language Processing (NLP)

Natural Language Processing (NLP) has seen significant advancements in recent years, driven largely by the availability of powerful Python libraries. This comparative study aims to analyze and compare the performance, language support, community support and ease of use of many popular Python libraries for NLP like NLTK (Natural Language Toolkit), spaCy, TextBlob, Flair, Jina, Gensim etc. The study evaluates these libraries across various NLP tasks such as tokenization, part-of-speech tagging, named entity recognition, sentiment analysis, and text summarization. Additionally, the paper discusses the strengths and weaknesses of each library, providing insights into their suitability for different NLP applications. Through detailed experimentation and analysis, this study aims to guide researchers and practitioners in selecting the most appropriate library for their NLP projects.

Comparison of an Ensemble of Machine Learning Models and the BERT Language Model for Analysis of Text Descriptions of Brain CT Reports to Determine the Presence of Intracranial Hemorrhage.

Seven machine learning algorithms and three text vectorization techniques were selected as models to solve the binary classification problem. These models were trained on textual data represented by 3980 brain CT reports from 56 inpatient medical facilities in Moscow. The study utilized three text vectorization techniques: bag of words, TF-IDF, and word2vec. The resulting data were then processed by the following machine learning algorithms: decision tree, random forest, logistic regression, nearest neighbors, support vector machines, Catboost, and XGboost. Data analysis and pre-processing were performed using NLTK (Natural Language Toolkit, version 3.6.5), libraries for character-based and statistical processing of natural language, and Scikit-learn (version 0.24.2), a library for machine learning containing tools to tackle classification challenges. MedRuBertTiny2 was taken as a BERT transformer model pre-trained on medical data. Based on the training and testing outcomes from seven machine learning algorithms, the authors selected three algorithms that yielded the highest metrics (i.e. sensitivity and specificity): CatBoost, logistic regression, and nearest neighbors. The highest metrics were achieved by the bag of words technique. These algorithms were assembled into an ensemble using the stacking technique. The sensitivity and specificity for the validation dataset separated from the original sample were 0.93 and 0.90, respectively. Next, the ensemble and the BERT model were trained on an independent dataset containing 9393 textual radiology reports also divided into training and test sets. Once the ensemble was tested on this dataset, the resulting sensitivity and specificity were 0.92 and 0.90, respectively. The BERT model tested on these data demonstrated a sensitivity of 0.97 and a specificity of 0.90. When analyzing textual reports of brain CT scans with signs of intracranial hemorrhage, the trained ensemble demonstrated high accuracy metrics. Still, manual quality control of the results is required during its application. The pre-trained BERT transformer model, additionally trained on diagnostic textual reports, demonstrated higher accuracy metrics (p<0.05). The results show promise in terms of finding specific values for both binary classification task and in-depth analysis of unstructured medical information.

An efficient algorithm for estimating gate-level power consumption in large-scale integrated circuits

Estimating power dissipation in Very Large Scale Integrated (VLSI) circuits, particularly large-scale sequential circuits, is a significant challenge in Electronic Design Automation (EDA). Benchmarked against PrimeTime PX, the proposed algorithm proficiently analyzes large-scale combinational and sequential circuits. This research begins with a power analysis algorithm for combinational circuits, focusing on signal probability (SP) and transition count (TC). It then extends to sequential circuits, introducing methodologies for loopback issues and a validity-checking mechanism for spatial dependency topology. Developed on the OpenTimer open-source framework using the SMIC 40-nm process library, the algorithm was validated on academic and industrial datasets. Experimental evaluations show that our algorithm outperforms in terms of speed and accuracy. For power analysis with vector annotations, it achieves an average error within 0.16% in power testing of large-scale timing circuits. It also performs gate-level vectorless power analysis on complex topology and large-scale integrated circuits, demonstrating computational robustness.

PERFORMANCE EVALUATION OF PYTHON LIBRARIES FOR MULTITHREADING DATA PROCESSING

Topicality. The rapid growth of data in various domains has necessitated the development of efficient tools and libraries for data processing and analysis. Python, a popular programming language for data analysis, offers several libraries, such as NumPy and Numba, for numerical computations. However, there is a lack of comprehensive studies comparing the performance of these libraries across different tasks and data sizes. The aim of the study. This study aims to fill this gap by comparing the performance of Python, NumPy, Numba, and Numba.Cuda across different tasks and data sizes. Additionally, it evaluates the impact of multithreading and GPU utilization on computation speed. Research results. The results indicate that Numba and Numba.Cuda significantly optimizes the performance of Python applications, especially for functions involving loops and array operations. Moreover, GPU and multithreading in Python further enhance computation speed, although with certain limitations and considerations. Conclusion. This study contributes to the field by providing valuable insights into the performance of different Python libraries and the effectiveness of GPU and multithreading in Python, thereby aiding researchers and practitioners in selecting the most suitable tools for their computational needs.

Analisis Penyebab Menurunnya Minat dan Partisipasi Generasi Muda dalam Sektor Pertanian

This research aims to determine the causes of the decline in the interest and participation of the younger generation in the agricultural sector and its impact, so that appropriate steps can be taken to improve the situation. The research method used is a literature study with a secondary data collection approach from the library by reading, taking notes, and then processing library materials to be used as discussion and research results. The results of the study show that the decline in interest and participation of the younger generation is caused by negative perceptions of agriculture, urbanization, and modernization, limited access to resources, economic uncertainty, level of education, and changes in social and values. This phenomenon can threaten a country's food security and the sustainability of the agricultural sector, so a comprehensive and sustainable strategy is needed, such as increasing education and training, skills development, infrastructure development and access to resources, increasing economic welfare, promotion and campaigning, and building partnerships and collaboration.

FDIP—A Fast Diffraction Image Processing Library for X-ray Crystallography Experiments

Serial crystallography (SX) is a cutting-edge technique in structural biology, involving the systematic collection of X-ray diffraction data from numerous randomly oriented microcrystals. To extract comprehensive three-dimensional information about the studied system, SX utilises thousands of measured diffraction patterns. As such, SX takes advantages of the properties of modern X-ray sources, including Free Electron Lasers (FELs) and third and fourth generation synchrotrons, as well as contemporary high-repetition-rate detectors. Efficient analysis of the extensive datasets generated during SX experiments demands fast and effective algorithms. The FDIP library offers meticulously optimised functions tailored for preprocessing data obtained in SX experiments. This encompasses tasks such as background subtraction, identification and masking of parasitic streaks, elimination of unwanted powder diffraction (e.g., from ice or salt crystals), and pinpointing useful Bragg peaks in each diffraction pattern. The library is equipped with a user-friendly graphical interface for facile parameter adjustment tailored to specific datasets. Compatible with popular SX processing software like OnDA, Cheetah, CrystFEL, and Merge3D, the FDIP library enhances the capabilities of these tools for streamlined and precise serial crystallography analyses.

Boosting HPC data analysis performance with the ParSoDA-Py library

Developing and executing large-scale data analysis applications in parallel and distributed environments can be a complex and time-consuming task. Developers often find themselves diverted from their application logic to handle technical details about the underlying runtime and related issues. To simplify this process, ParSoDA, a Java library, has been proposed to facilitate the development of parallel data mining applications executed on HPC systems. It simplifies the process by providing built-in scalability mechanisms relying on the Hadoop and Spark frameworks. This paper presents ParSoDA-Py, the Python version of the ParSoDA library, which allows for further support of commonly used runtimes and libraries for big data analysis. After a complete library redesign, ParSoDA can be now easily integrated with other Python-based distributed runtimes for HPC systems, such as COMPSs and Apache Spark, and with the large ecosystem of Python-based data processing libraries. The paper discusses the adaptation process, which takes into consideration the new technical requirements, and evaluates both usability and scalability through some case study applications.

Introducing the Video In Situ Snowfall Sensor (VISSS)

Abstract. The open-source Video In Situ Snowfall Sensor (VISSS) is introduced as a novel instrument for the characterization of particle shape and size in snowfall. The VISSS consists of two cameras with LED backlights and telecentric lenses that allow accurate sizing and combine a large observation volume with relatively high pixel resolution and a design that limits wind disturbance. VISSS data products include various particle properties such as maximum extent, cross-sectional area, perimeter, complexity, and sedimentation velocity. Initial analysis shows that the VISSS provides robust statistics based on up to 10 000 unique particle observations per minute. Comparison of the VISSS with the collocated PIP (Precipitation Imaging Package) and Parsivel instruments at Hyytiälä, Finland, shows excellent agreement with the Parsivel but reveals some differences for the PIP that are likely related to PIP data processing and limitations of the PIP with respect to observing smaller particles. The open-source nature of the VISSS hardware plans, data acquisition software, and data processing libraries invites the community to contribute to the development of the instrument, which has many potential applications in atmospheric science and beyond.

YOLO-IHD: Improved Real-Time Human Detection System for Indoor Drones.

In the field of unmanned systems, the combination of artificial intelligence with self-operating functionalities is becoming increasingly important. This study introduces a new method for autonomously detecting humans in indoor environments using unmanned aerial vehicles, utilizing the advanced techniques of a deep learning framework commonly known as "You Only Look Once" (YOLO). The key contribution of this research is the development of a new model (YOLO-IHD), specifically designed for human detection in indoor using drones. This model is created using a unique dataset gathered from aerial vehicle footage in various indoor environments. It significantly improves the accuracy of detecting people in these complex environments. The model achieves a notable advancement in autonomous monitoring and search-and-rescue operations, highlighting its importance for tasks that require precise human detection. The improved performance of the new model is due to its optimized convolutional layers and an attention mechanism that process complex visual data from indoor environments. This results in more dependable operation in critical situations like disaster response and indoor rescue missions. Moreover, when combined with an accelerating processing library, the model shows enhanced real-time detection capabilities and operates effectively in a real-world environment with a custom designed indoor drone. This research lays the groundwork for future enhancements designed to significantly increase the model's accuracy and the reliability of indoor human detection in real-time drone applications.

APLIKASI PERPUSTAKAAN BERBASIS WEB MENGGUNAKAN FRAMEWORK CODEIGNITER (STUDI KASUS: SMPN 3 PACET)

The role of information technology is very beneficial for many industries. It is necessary for data to meet the needs of data users. Support in the field of education can be included in one of these fields. The library of SMPN 3 Pacet Bandung contains various textbooks and other complementary books. The library itself still uses a manual system in recording or bookkeeping for loan transactions, book returns, book checks, and library reports. Such a manual method is not possible, because the volume of transaction data is increasing, resulting in increased difficulty in processing library data. With these problems, SMPN 3 Pacet Bandung needs an information system to overcome the problems of using a manual system. This system is a web-based application using the codeigniter framework to facilitate the design of the system. The author hopes that making this library application can help the library more easily, effectively and efficiently for staff and all parties involved in library data processing.

Library Management Advocacy Study of IAIN Fattahul Muluk Papua Library Program in Realizing Digital Library in 2023

The study of the IAIN Fattahul Muluk Papua Library program in realizing a digital library is a form of adjustment to technological developments in improving its competence as a supporter of the implementation of the Tri Dharma of Higher Education according to the digital era. The discussion of this study uses the theory of Challenge and Response proposed by Arnold Y. Toynbee. The challenges and responses in the program have reinforced the theory. Some responses are shown by the existence of programs in various components, namely the digital collection component of e-books has held 421 titles, 1,151 copies, developing an institutional Repository as a forum for campus community papers in digital form, and other supporting facilities, such as Turnitin Plagiarism Checker Online. The components of library services, both user services and technical services, also lead to the digitization system. For lending and returning printed books, e-books, free library certificates according to computerized and digitized data. Technical services through the migration program of processing library materials, especially printed books consisting of 5,668 titles, 15,589 copies have been and are being reprocessed to comply with national library standards. Library implementation and management component programs that emphasize reporting systems are a hallmark of good library governance. The Cooperation Program followed up by comparative studies with UIN Mataram and with the National Library of the Republic of Indonesia is a step forward. The existence of additional programs from librarianship organizations implemented such as the Indonesian Higher Education Library Forum (FPPTI), the Indonesian Librarian Association (IPI) of Papua Province shows the level of public trust in the ability of librarians in particular and the IAIN Fattahul Muluk Papua Library in general

Alignment of Unsupervised Machine Learning with Human Understanding: A Case Study of Connected Vehicle Patents

As official public records of inventions, patents provide an understanding of technological trends across the competitive landscape of various industries. However, traditional manual analysis methods have become increasingly inadequate due to the rapid expansion of patent information and its unstructured nature. This paper contributes an original approach to enhance the understanding of patent data, with connected vehicle (CV) patents serving as the case study. Using free, open-source natural language processing (NLP) libraries, the author introduces a novel metric to quantify the alignment of classifications by a subject matter expert (SME) and using machine learning (ML) methods. The metric is a composite index that includes a purity factor, evaluating the average ML conformity across SME classifications, and a dispersion factor, assessing the distribution of ML assigned topics across these classifications. This dual-factor approach, labeled the H-index, quantifies the alignment of ML models with SME understanding in the range of zero to unity. The workflow utilizes an exhaustive combination of state-of-the-art tokenizers, normalizers, vectorizers, and topic modelers to identify the best NLP pipeline for ML model optimization. The study offers manifold visualizations to provide an intuitive understanding of the areas where ML models align or diverge from SME classifications. The H-indices reveal that although ML models demonstrate considerable promise in patent analysis, the need for further advancements remain, especially in the domain of patent analysis.

The Tradition of Henna Night in the Hadrami Arab Community in Jakarta

The Hadhrami Arab is a community comes from Hadhramaut, South Yemen, and they still practice their traditions, one of which is the henna night tradition. This is an Arab tradition that is usually observed at weddings. The bride do henna the night before the wedding ceremony in this tradition. The Hadhrami Arab community in Tanah Abang district Jakarta is the subject of this study. The objective of conducting this research was to help the community understand the context of the henna night by providing new information about how the henna night tradition is still practiced by the Arab Hadhrami community in Tanah Abang Jakarta. A qualitative descriptive approach, a literature review, and interviews were used in this study. In this study, data was collected by processing library data and data from direct interviews with sources using seven questions related to the existing problem formulation. This study addressed two issues: first, what is the role of henna night in the tradition of the Arab Hadhrami community in Tanah Abang, Jakarta; and second, how is henna night conducted among the Arab Hadhrami community in Tanah Abang, Jakarta.

Postmortem Muscle Proteome Characteristics of Silver Carp (Hypophthalmichthys molitrix): Insights from Full-Length Transcriptome and Deep 4D Label-Free Proteomic.

The coverage of the protein database directly determines the results of shotgun proteomics. In this study, PacBio single-molecule real-time sequencing technology was performed on postmortem silver carp muscle transcripts. A total of 42.43 Gb clean data, 35,834 nonredundant transcripts, and 15,413 unigenes were obtained. In total, 99.32% of the unigenes were successfully annotated and assigned specific functions. PacBio long-read isoform sequencing (Iso-Seq) analysis can provide more accurate protein information with a higher proportion of complete coding sequences and longer lengths. Subsequently, 2671 proteins were identified in deep 4D proteomics informed by a full-length transcriptomics technique, which has been shown to improve the identification of low-abundance muscle proteins and potential protein isoforms. The feature of the sarcomeric protein profile and information on more than 30 major proteins in the white dorsal muscle of silver carp were reported here for the first time. Overall, this study provides valuable transcriptome data resources and the comprehensive muscle protein information detected to date for further study into the processing characteristic of early postmortem fish muscle, as well as a spectral library for data-independent acquisition and data processing. This batch of muscle-specific dependent acquisition data is available via PRIDE with identifier PXD043702.

A new method for simulation modelling of leaner remanufacturing in PaaS settings

The application of simulation technologies (STs) in the industry 4.0 paradigm can accelerate the implementation of the circular economy (CE) in companies by faster modeling and optimization of value retention processes (VRPs), such as remanufacturing or refurbishment. In business practices, the efficiency of the collection of used products and their recovery for a new or extended lifecycle is influenced by a high level of uncertainty. The product-as-a-service (PaaS) model might change the boundary conditions, which will decisively influence the operational factors such as time for a disassembly, and material recovery rate. The state-of-the-art in commercial simulation software (e.g., FlexSim, Simio, Arena) is based on the object-oriented approach, which requires that users must build the model themselves from a set of objects (object libraries), which is effective for a traditional linear process but is less suitable for multiple flow of a product between PaaS contracts. Remanufacturing in PaaS for consumer markets is currently a niche practice, due to the difficulties of providing its economic viability. Lean management practices are promising in lowering the cost of remanufacturing. The aim of this paper is to develop a new method for simulation modeling using a high-level script language, similar to those used by industry experts, according to lean remanufacturing principles under PaaS conditions. This novel method is built on the user-centric principle. Thus, it enables to build the simulation models and to use them by decision-makers in business practice who are not specialists in simulation modeling and do not have programming competences. The library of reference processes for remanufacturing in PaaS is defined with the industrial partners.

A Method for Extracting BPMN Models from Textual Descriptions Using Natural Language Processing

Business Process Model and Notation (BPMN) is a standard for formally modeling complex business processes. Manual creation of BPMN models can be time-consuming and error-prone, prompting a need for automation. Existing approaches, such as rule-based methods, machine learning, and machine translation, have progressed but face accuracy and real-world applicability challenges. In this research paper, we propose a novel method for automated extraction of BPMN models from textual descriptions using natural language processing (NLP) tools and deep learning models, including the spaCy library for text processing, a fine-tuned BERT model, and state-of-the-art large language models like GPT-3.5-Turbo and GPT-4. We utilize Graphviz, an open-source graph visualization software, to visualize the extracted processes. Our method supports representing tasks, exclusive gateways, parallel gateways, and start and end events in the generated BPMN models. The evaluation of 31 textual descriptions shows that our method generates process models with 96% accuracy using GPT-4 and 80% accuracy using GPT-3.5-Turbo large language models. Although subject to certain limitations, such as occasional inaccuracies in model outputs and reliance on well-formed input text, our approach offers a valuable contribution to the growing body of research on automating BPMN model generation.

Automated system of SMM lead generation in Telegram messenger

In the article the development of automated system for search of interested users, so called leads, in Telegram messenger environment. While Telegram is not a social network and is strongly different with its interaction mode to ay web-service like blog, image or news board or forum, then the search of motivated target audience is a complex task. It is primarily complex because no recommendation system for content or finding new channels, chats, content sources is provided, the news and posts feed does not exist like in other social media. In current paper the process of development of a tool for searching interested users, created as a Telegram-bot, which interacts with Telegram API to gather the data and with different language tools analyses messages in the chat, helping to find discussions related to required theme. Particularly, to detect users that a potentially interested in specific themes, it is required to analyze the very texts of the discussion and detect the themes, users of the current chat discuss. Specifically for this analysis natural language tools are needed, as well as the tools that allow to process discussion’s context. Bot was created in the following technologies stack: the main programming language is Python, the framework pyTelegramBotAPI is responsible for interaction with Telegram servers via API, the gathered and processed data is stored in a database based on MySQL, language processing is performed in multiple steps, in which natural language processing libraries for Python and AI particularly big language model ChatGPT are involved. The bot gathers and processes information from the chat messages and then provides a report of how many mentions in the administrator defined theme made certain chat users, these users are potential leads. This data helps to build and improve marketing models of goods and services promotion and detect the level of involvement and the degree of interest in current theme

Advancement in Integrated Crop Management System for Sustainable Agriculture

The Crop Management System (CMS) is an innovative web application designed to revolutionize agricultural practices by integrating advanced technologies. This project encompasses four essential modules: Crop Prediction, Disease Detection, Marketing, and Government Forum Dashboard. Leveraging the power of Python, the CMS aims to provide a comprehensive solution for modern farming. The Crop Prediction module employs machine learning algorithms to forecast optimal crops based on factors such as soil type, climate, and historical data. This feature empowers farmers to make informed decisions, enhancing crop yield and profitability. The Disease Detection module employs image processing techniques to identify and diagnose diseases affecting crops, allowing for timely intervention and reducing yield loss. The Marketing module serves as a platform for farmers to streamline the selling process. Through features like price tracking and market trends analysis, farmers can optimize their sales strategies. The Government Forum Dashboard acts as a central hub for stakeholders to exchange information, policies, and best practices, fostering a collaborative ecosystem. Implemented as a web application, the CMS ensures accessibility across devices, providing a user-friendly interface for farmers and stakeholders. The backend is built using Python, leveraging its versatility and robust libraries for data processing, machine learning, and web development. In conclusion, the Crop Management System addresses critical aspects of modern agriculture, ranging from crop selection to disease management, marketing, and policy advocacy. By harnessing the power of Python and cutting-edge technologies, this project stands as a pivotal tool for advancing agricultural practices, ultimately contributing to sustainable and efficient farming practices

Slendr: a framework for spatio-temporal population genomic simulations on geographic landscapes.

One of the goals of population genetics is to understand how evolutionary forces shape patterns of genetic variation over time. However, because populations evolve across both time and space, most evolutionary processes also have an important spatial component, acting through phenomena such as isolation by distance, local mate choice, or uneven distribution of resources. This spatial dimension is often neglected, partly due to the lack of tools specifically designed for building and evaluating complex spatio-temporal population genetic models. To address this methodological gap, we present a new framework for simulating spatially-explicit genomic data, implemented in a new R package called slendr (www.slendr.net), which leverages a SLiM simulation back-end script bundled with the package. With this framework, the users can programmatically and visually encode spatial population ranges and their temporal dynamics (i.e., population displacements, expansions, and contractions) either on real Earth landscapes or on abstract custom maps, and schedule splits and gene-flow events between populations using a straightforward declarative language. Additionally, slendr can simulate data from traditional, non-spatial models, either with SLiM or using an alternative built-in coalescent msprime back end. Together with its R-idiomatic interface to the tskit library for tree-sequence processing and analysis, slendr opens up the possibility of performing efficient, reproducible simulations of spatio-temporal genomic data entirely within the R environment, leveraging its wealth of libraries for geospatial data analysis, statistics, and visualization. Here, we present the design of the slendr R package and demonstrate its features on several practical example workflows.

Diagnosis and Study of Mechanical Vibrations in Cargo Vehicles Using ISO 2631-1:1997.

This study presents the design and implementation of an electronic system aimed at capturing vibrations produced during truck operation. The system employs a graphical interface to display vibration levels, ensuring the necessary comfort and offering indicators as a solution to mitigate the damage caused by these vibrations. Additionally, the system alerts the driver when a mechanical vibration that could potentially impact their health is detected. The field of health is rigorously regulated by various international standards and guidelines. The case of mechanical vibrations, particularly those transmitted to the entire body of a seated individual, is no exception. Internationally, ISO 2631-1:1997/Amd 1:2010 oversees this study. The system was designed and implemented using a blend of hardware and software. The hardware components comprise a vibration sensor, a data acquisition card, and a graphical user interface (GUI). The software components consist of a data acquisition and processing library, along with a GUI development framework. The system underwent testing in a controlled environment and demonstrated stability and robustness. The GUI proved to be intuitive and could be integrated into modern vehicles with built-in displays. The findings of this study suggest that the proposed system is a viable and effective method for capturing vibrations in trucks and informing drivers about vibration levels. This system has the potential to enhance the comfort and safety of truck drivers.