Programming Language For Processing Research Articles

Why Natural Language Processing is Not Reading: Two Philosophical Distinctions and their Educational Import

This paper explores two important ways in which close reading differs from natural language processing, the use of computer programming to decode, process, and replicate messages within a human language. It does so in order to highlight distinctive features of close reading that are not replicated by natural language processing. The first point of distinction concerns the nature of the meaning generated in each case. While natural language processing proceeds on the principle that a text’s meaning can be deciphered by applying the rules governing the language in which the text is written, close reading is premised on the idea that this meaning lies in the interplay that the text prompts within readers. While the semantic theory of meaning upon which natural language processing programs are based is often taken for granted today, I draw from phenomenological and hermeneutic theories, particularly Wolfgang Iser and Hans-Georg Gadamer, to explain why a different theory of meaning is necessary for understanding the meaning generated by close reading. Second, while natural language processing programs are considered successful when they generate what epistemologists call true beliefs about a text, I argue that close reading aims first and foremost at the development, not of true belief, but of understanding. To develop this distinction, I draw from recent scholarship on the epistemology of education, including work by Duncan Pritchard, to explain how understanding differs from true belief and why attainment of the latter is less educationally significant than the former.

ASSESSMENT OF THE EVAPOTRANSPIRATION COMPONENTS DYNAMICS IN DIFFERENT AGRO-CLIMATIC ZONES OF UKRAINE USING THE PENMAN-MONTEITH-LEUNING MODEL

The results of the assessment of evapotranspiration (ET) and its components based on remote sensing data are presented in the paper. To obtain this assessment, a special software was developed, namely the scripts using the JavaScript programming language for remote data processing in the Google Earth Engine (GEE) cloud software environment. This software allows to adjust the Penman-Monteith-Leuning (PML) model for the conditions of Ukraine and visualize the spatial distribution of ET. The usage of the cloud capabilities of the service made it possible to access the collection of images and carry out their remote processing using the Penman-Monteith-Leuning algorithm calibrated according to the data of the global network of eddy covariance monitoring stations. The result of such an assessment was a composite mosaic - a spatially distributed generalized image of evapotranspiration and its three main components: transpiration from vegetation (Ec), evaporation from the soil (Es) and evaporation of precipitation intercepted by vegetation cover (Ei) on the territory of Ukraine for the 2020 growing season. Understanding the dynamics of these components helps to optimize the water resources use and develop effective irrigation schemes, especially in the climate change conditions. As a result of the analysis of the evapotranspiration components’ dynamics during the growing season, the most important component of evapotranspiration for different agro-climatic zones was determined. However, the models, which are using remote data to estimate evapotranspiration dynamics, require additional validation and comparison with field measurements to improve their accuracy. Quantitative indicators obtained through modeling should be consistent with the data from ground-based greenhouse gas flows monitoring stations, which will contribute to the improvement of the methodology and its adaptation to the conditions of different agricultural regions. In addition, the use of the maps of geospatial distributed evapotranspiration allows to identify regions with increased transpiration and potential shortage of water resources. Such maps become a valuable tool for planning and forecasting water resources, which is critically important for the agricultural sector.

Abstract 4144529: Cardiac Allograft Hypertrophy (CAH) at 6 Months Post Transplant Is a Prognostic Marker of Long-Term Outcomes: Are We Missing a Simple Biomarker?

Introduction: Cardiac hypertrophy is a known adverse prognostic marker in various non-transplant pathologies. In heart transplant patients, due to many confounders, it has been controversial on the relevance and timing of cardiac hypertrophy as an adverse remodeling vs acute injury pattern in an immunologically hostile environment. Previous studies have shown prognostication of hypertrophy on echocardiogram at 1-year post-heart transplant. Research Questions: Does cardiac hypertrophy within a year after transplant have long-term prognostic implications? Methods: We collected relevant clinical variables for all heart transplants using EPIC EHR’s Clarity database. Hypertrophy was defined based on LV Mass Indexed to body surface area where LV Mass = 0.8 x (1.04 x (((LVIDD + IVSd + PWd) 3 - LVIDD 3 ))) + 0.6. Relative Wall Thickness was defined as RWT = 2 x PWd / LVIDD. We used a rule-based natural language processing program validated by correlation with manual readings by trained cardiologists (r=0.96, p=0.007) to abstract echo variables. Results: Inclusion criteria were heart transplants performed from 2015 to 2023 at our center, with an echocardiogram closest to 6 months (+/- 1 month). Ten percent (n=33) showed hypertrophy on echocardiograms at 6 months (Table 1). Of these, 20 (61%) had mild, 3 (9%) severe, and 10 (30%) moderate hypertrophy. Of 33 patients, 28 (85%) had concentric, and 5 (15%) had eccentric hypertrophy. Patients with hypertrophy at 6 months had significantly worse survival at 5 years (p=0.01) and 10 years (p=0.05) compared to patients without hypertrophy (Fig 1). Survival at 5 and 10 years was not statistically different for patients with hypertrophy at 3 months (5 yrs p=0.17, 10 yrs p=0.06), 12 months (5 yrs p=0.38, 10 yrs p=0.30), and 18 months (5 yrs p=0.15, 10 yrs p=0.08) compared to those without hypertrophy. Conclusion: Cardiac hypertrophy on echocardiogram at 6 months predicts adverse long-term survival, while other time points did not.

Analysis of the Application the Promethee Method in Determining Work Partners at the Central Statistics Agency of Central Aceh Regency

The selection of partners at the Central Statistics Agency of Central Aceh Regency faces challenges because manual selection methods are considered inefficient and inaccurate. Therefore, this study aims to improve the accuracy and efficiency of the selection process by applying the Promethee (Preference Ranking Organization Method for Enrichment Evaluations) method. This method is used as a multi-criteria decision-making technique and is implemented using the Python programming language for automatic and real-time data processing. The data used comes from the 2024 partner recruitment process at the Central Statistics Agency of Central Aceh Regency, consisting of candidates who have met the administrative requirements. The validity of the promethee method is tested through the calculation of an error percentage that reaches 10%, demonstrating the method's ability to filter partners according to the established criteria. In conclusion, the application of the promethee method has been proven to improve the efficiency and accuracy of the partner selection process, where the Python-based system developed can reduce the time and effort required in decision-making while producing more valid and reliable partner recommendations.

Исследование линейности цветовосприятия человека в графическом интерфейсе систем управления

The work is aimed at studying the process of human perception of visual information in conditions of interaction with a certain interface. The authors describe experimental research of the linearity of human colour perception in the graphical interface of control systems when observing colour pictograms in a conditional interface. To analyze the mechanism operation of the visual information perception, an information model of visual perception is used. Features of human perception of visual information can be of both an individual nature associated with a specific individual, and an external nature associated with the systemic interference of the operator’s direct interaction with the interface. The paper defines the factors of the study and describes the methodology for preparing stimulus material using the Processing programming language. As a tool for conducting the experiment, a software and hardware complex recording oculomotor activity Eye-tracker SMI RED 250 is used. The importance of creating effective user interfaces for control systems of complex objects and insufficient study of adapting interfaces to the features of human perception of visual information at the perceptual level of awareness determine the relevance of the study. The means of mathematical statistics process the obtained parametric data of the template for examining the stimulus material. The results got in the experiment allow concluding that the mechanism of human visual information perception, when the colour spot brightness decreases, remains sensitive to yellow colour the longest.

Improving dynamic prognostication of patients with hepatocellular carcinoma by node fusion algorithm equipped survival path mapping.

e13594 Background: Patients with hepatocellular carcinoma (HCC) always require routine surveillance and repeated treatment. Our previous work (1) developed a machine learning methodology called “Survival Path Mapping”, which converted the time series data into a cascading survival map to facilitate dynamic prognosis prediction and treatment planning. However, in previous work, the exponential growth of paths raised challenges of exhausting limited cohort and over-fitting risk. Novel algorithm enhancing data utilization in the Survival Path Mapping was in urgent need. Methods: A node fusion algorithm was designed. First, feature selection algorithm bifurcated each parent-nodes into temporary child-nodes, which constitutes temporary child-nodes set. Then, clustering was performed within temporary child-nodes set according to key features of each child-node. Finally, in each cluster, multiple child-nodes would be merged into one if there was no significant difference in survival. For nodes with bifurcation, a treatment recommendation algorithm was developed. A total of 19446 patients with newly diagnosed primary HCC at Sun Yat-sen University Cancer Center (SYSUCC) between 2008 and 2023 were retrospectively included as internal cohort (divided into internal train and holdout validation cohort); meanwhile 1135 patients from five hospitals were included as multicenter external validation cohort. Dedicated natural language processing program was developed to extract structured features from medical imaging reports. The time series data of patients were converted into data of time slices, with an interval of 3 months. Results: The original survival path mapping (raw-SP) consists of 463 different nodes, while the node fusion survival path mapping (fusion-SP) consisted of 84 nodes (81.8% decrease from raw-SP). The 3-year c-index of fusion-SP were 0.745 (95% CI 0.717-0.772), significantly better (p<0.001) than that of raw-SP: 0.725 (95% CI 0.696-0.755), in the internal holdout validation. Both raw-SP and fusion-SP significantly (p < 0.001) outperformed conventional BCLC and CNLC staging. In the multicenter external validation, the fusion-SP also demonstrated better performance than raw-SP and BCLC staging system. The recommended therapy calculated based fusion-SP system for patients receiving second-line or subsequent treatment had superior or non-inferior efficacy compared to BCLC guideline’s recommendation. Conclusions: The proposed node fusion algorithm solved exponential growth of paths in previous survival path algorithm, improving the accuracy and external validity of dynamic prognosis prediction for HCC patients. The fusion-SP model also retained its transparency and could be used to guide dynamic treatment planning, with its effectiveness in treatment guidance evaluated in prospective trials in the future. 1. Nat Commun 9, 2230; 2018.

Development of an error correction algorithm for Kazakh language

This article discusses a method for correcting spelling errors in the Kazakh language using the advantages of morphological analysis and a model based on noisy channels. To achieve this goal, modern problems of automatic processing of Kazakh textual information were analyzed, existing linguistic resources and processing systems of the Kazakh language were systematized, the basic requirements for the development of a system for analyzing Kazakh textual information based on machine learning were determined, and models and algorithms for extracting facts from unstructured and poorly structured text arrays were developed. The search function, an enhanced spelling correction algorithm, was utilized in this work and has the ability to recommend the proper spelling of the input text. The maximum editing distance, whether to include the original word when near matches are not found, and how to handle case sensitivity and exclusion based on regular expressions are all easily adjustable features of this functionality. Because of their adaptability, algorithms can be applied to a wide range of problems, from straightforward spell checks in user interfaces to intricate natural language processing assignments. Because of the way it’s designed, the search function finds possible corrections and verifies the context of words while accounting for user preferences like verbosity and ignore markers. Most modern multilingual natural language processing programs use only the graphical stage of text processing. On the other hand, semantic text analysis or analysis of the meaning of natural language is still an important problem in the theory of artificial intelligence and computational linguistics. But in order to process the grammar and semantics of multilingual information, precreated semantic and grammatical corpora of each natural language are necessary. To solve this problem, several tasks were considered and solved. These tasks included the analysis of research in the field of machine learning methods used in the processing of textual information, the existing problems of formalization and modeling of the Kazakh language, as well as the development and implementation of models, methods and algorithms for morphological and semantic analysis of texts of the Kazakh language.

Time Series Analysis of Landsat Images for Monitoring Flooded Areas in the Inner Niger Delta, Mali

Abstract This paper presents an R-based approach to mapping dynamics of the flooded areas in the Inner Niger Delta (IND), Mali, using time series analysis of Landsat 8–9 satellite images. As the largest inland wetland in West Africa, the habitats of IND offers high potential for biodiversity of the flood-dependent eco systems. IND is one of the most productive areas in West Africa. Mapping flooded areas based on satellite images enables to provide strategies for land management and rice planting and modelling vegetation types of IND. Our approach is based on using libraries of R programming language for processing six Landsat images, and each image was taken on November from 2013 to 2022. By capturing spatial and temporal structures of the satellite images on 2013, 2015, 2018, 2020, 2021 and 2022, the remote sensing data are combined to yield estimates of landscape dynamics that is temporally coherent, while helping to analyse fluctuations of spatial extent in fluvial wetlands caused by the hydrological processes of seasonal flooding. Further, by allowing packages of R to support image processing, an approach to mapping vegetation by NDVI, SAVI and EVI indices and visualising changes in distribution of different land cover classes over time is realised. In this context, processing Earth observation data by advanced scripting tools of R language provides new insights into complex interlace of climate-hydrological processes and vegetation responses. Our study contributes to the sustainable management of natural resources and improving knowledge on the functioning of IND ecosystems in Mali, West Africa.

AI Chatbots And Psychotherapy: A Match Made In Heaven?

Dear Editor, Artificial Intelligence (AI) is revolutionizing psychotherapy by combating its inaccessibility.1 AI chatbots and conversational agents are among the most promising integrations in psychiatry and psychology. By harnessing the power of machine learning and artificial intelligence (Figure I), AI chatbots and conversational agents can provide a level of interaction and support previously unattainable in the landscape of mental health. In this letter, I will explore some of their numerous benefits. Chatbots have evolved from early examples such as ELIZA, a programmable natural language processing program, and PARRY, a program that mimicked the behaviour of a human with schizophrenia, to modern ones such as Woebot, Tess, and Replika, which offer cognitive behavioural therapy, sessions, and self-understanding, respectively. Some chatbots such as Kaspar and Nao are also embodied in physical robots, which help children with autism spectrum disorder (ASD) learn social skills and facial recognition. AI chatbots can address the challenges of mental health care, such as the shortage of professionals2, especially in underdeveloped countries; the stigma of seeking help, which is prevalent in many cultures; the cost of therapy, which can be prohibitive for many people; and the limitations of human therapists, who can have variable attention spans, attention-to-detail, and memory capacities. AI chatbots can also offer benefits such as accessibility, anonymity, personalization, and effectiveness. Studies have shown that AI chatbots can improve the outcomes and experiences of mental health patients in various ways, such as enhancing the performance of those with ASD with robotic "therapists"1, increasing their disclosure of sensitive information to chatbots2, and reducing their distress after talking about their problems with conversational agents3. The benefits of AI chatbots for mental health are clear. However, they also face many challenges that limit their usability and reliability. They may lack the emotional skills of human therapists and may not be accountable for their diagnoses and recommendations. They may also not be based on sound mental health knowledge and evidence, as technology companies may not have expertise and experience in this field. Therefore, technology companies and mental health professionals must collaborate and adopt a rational approach to develop and evaluate chatbots. Only then can chatbots realize their full potential and complement existing mental health services safely and ethically.

The Effects of Syntactic Priming on Language Processing and Learning

Priming can facilitate the language processing and learning by affecting speakers response to the subsequent stimulus after introducing the new language form, for both comprehension and production. Syntactic priming, as a new branch of priming, is the main focus of this research as it is more practically applicable under psycholinguistic background. In this review, the effects of syntactic priming on language learning and the effects of syntactic priming on the link between language comprehension and production were both explored. It is assumed that there is a common lexical-boost effect as a mediator during the syntactic priming process through the implicit language learning. However, such effect can be somehow independent to the language comprehension based on the current research. Moreover, some neurological measures are applicable when investigating the neuronal correlates of the syntactic priming and lexical boost effect during sentence comprehension and production. In general, this study discussed the non-linear relationship between implicit language learning and syntactic priming highlighted with the lexical mediator effect, the independent effect between lexical and structural content on language comprehension, as well as the similar mechanism shared by language comprehension and production. The previous experiments still lacked the reliability and validity as the variables are difficult, and the tools or strategies used may not be appropriate to accurately measure the result. Hence, causations cannot be formed in a solid state even if some correlations are well evolved with research findings. The future direction, naturally, should keep track of how syntactic priming can better intervene on the language processing and learning program, which in turn reduce the overload of the computational brain and boost the language learning. This review can provide some guidance for the design of educaitonal programs using syntactic priming as well as relevant research.

Automated Driver Drowsiness Detection

This project uses image processing and Python programming language to create an automatic sleepiness detection system for drivers. The system's objective is to develop an algorithm that can reliably gauge a driver's sleepiness under various circumstances by using real-time grayscale photographs. The driver will be observed by the system as it scans their face and eyelids for hypo vigilance, which is a precursor to tiredness. To properly determine the driver's level of sleepiness, the system entails three steps: feature selection, eye-pair state detection, and decision-making. The algorithm will differentiate between the two states on a collection of captured photos of both tired and awake drivers. To identify sleepiness in drivers, facial feature recognition algorithms has been employed, with an emphasis on the eye pair. Images will be taken with a mounted camera. For the system to successfully be integrated into current driver assistance systems, it will be measured on how well it is able to identify tiredness. The project's importance stem from its ability is to increase road safety by identifying driver sleepiness, a major contributing factor to accidents on the road. In conclusion, this research will significantly impact road safety by tackling the problem of accidents caused by drowsiness.

Depression and anxiety are associated with an increased risk of infection, revision, and reoperation following total hip or knee arthroplasty.

The aim of this study was to determine the prevalence of depressive and anxiety disorders prior to total hip (THA) and total knee arthroplasty (TKA) and to assess their impact on the rates of any infection, revision, or reoperation. Between January 2000 and March 2019, 21,469 primary and revision arthroplasties (10,011 THAs; 11,458 TKAs), which were undertaken in 15,504 patients at a single academic medical centre, were identified from a 27-county linked electronic medical record (EMR) system. Depressive and anxiety disorders were identified by diagnoses in the EMR or by using a natural language processing program with subsequent validation from review of the medical records. Patients with mental health diagnoses other than anxiety or depression were excluded. Depressive and/or anxiety disorders were common before THA and TKA, with a prevalence of 30% in those who underwent primary THA, 33% in those who underwent revision THA, 32% in those who underwent primary TKA, and 35% in those who underwent revision TKA. The presence of depressive or anxiety disorders was associated with a significantly increased risk of any infection (primary THA, hazard ratio (HR) 1.5; revision THA, HR 1.9; primary TKA, HR 1.6; revision TKA, HR 1.8), revision (THA, HR 1.7; TKA, HR 1.6), re-revision (THA, HR 2.0; TKA, HR 1.6), and reoperation (primary THA, HR 1.6; revision THA, HR 2.2; primary TKA, HR 1.4; revision TKA, HR 1.9; p < 0.03 for all). Patients with preoperative depressive and/or anxiety disorders were significantly less likely to report "much better" joint function after primary THA (78% vs 87%) and primary TKA (86% vs 90%) compared with those without these disorders at two years postoperatively (p < 0.001 for all). The presence of depressive or anxiety disorders prior to primary or revision THA and TKA is common, and associated with a significantly higher risk of infection, revision, reoperation, and dissatisfaction. This topic deserves further study, and surgeons may consider mental health optimization to be of similar importance to preoperative variables such as diabetic control, prior to arthroplasty.

Clinical Implementation of a Combined Artificial Intelligence and Natural Language Processing Quality Assurance Program for Pulmonary Nodule Detection in the Emergency Department Setting.

This quality assurance study assessed the implementation of a combined artificial intelligence (AI) and natural language processing (NLP) program for pulmonary nodule detection in the emergency department setting. The program was designed to function outside of normal reading workflows to minimize radiologist interruption. In all, 19,246 CT examinations including at least some portion of the lung anatomy performed in the emergent setting from October 1, 2021, to June 1, 2022, were processed by the combined AI-NLP program. The program used an AI algorithm trained on 6-mm to 30-mm pulmonary nodules to analyze CT images and an NLP to analyze radiological reports. Cases flagged as negative for pulmonary nodules by the NLP but positive by the AI algorithm were classified as suspected discrepancies. Discrepancies result in secondary review of examinations for possible addenda. Out of 19,246 CT examinations, 50 examinations (0.26%) resulted in secondary review, and 34 of 50 (68%) reviews resulted in addenda. Of the 34 addenda, 20 patients received instruction for new follow-up imaging. Median time to addendum was 11 hours. The majority of reviews and addenda resulted from missed pulmonary nodules on CT examinations of the abdomen and pelvis. A background quality assurance process using AI and NLP helped improve the detection of pulmonary nodules and resulted in increased numbers of patients receiving appropriate follow-up imaging recommendations. This was achieved without disrupting in-shift radiologist workflow or causing significant delays in patient follow for the diagnosed pulmonary nodule.

Developing a machine learning model to detect diagnostic uncertainty in clinical documentation.

Diagnostic uncertainty, when unrecognized or poorly communicated, can result in diagnostic error. However, diagnostic uncertainty is challenging to study due to a lack of validated identification methods. This study aims to identify distinct linguistic patterns associated with diagnostic uncertainty in clinical documentation. This case-control study compares the clinical documentation of hospitalized children who received a novel uncertain diagnosis (UD) diagnosis label during their admission to a set of matched controls. Linguistic analyses identified potential linguistic indicators (i.e., words or phrases) of diagnostic uncertainty that were then manually reviewed by a linguist and clinical experts to identify those most relevant to diagnostic uncertainty. A natural language processing program categorized medical terminology into semantic types (i.e., sign or symptom), from which we identified a subset of these semantic types that both categorized reliably and were relevant to diagnostic uncertainty. Finally, a competitive machine learning modeling strategy utilizing the linguistic indicators and semantic types compared different predictive models for identifying diagnostic uncertainty. Our cohort included 242 UD-labeled patients and 932 matched controls with a combination of 3070 clinical notes. The best-performing model was a random forest, utilizing a combination of linguistic indicators and semantic types, yielding a sensitivity of 89.4% and a positive predictive value of 96.7%. Expert labeling, natural language processing, and machine learning methods combined with human validation resulted in highly predictive models to detect diagnostic uncertainty in clinical documentation and represent a promising approach to detecting, studying, and ultimately mitigating diagnostic uncertainty in clinical practice.

Carbonate texture identification using multi-layer perceptron neural network

Abstract This study proposes an approach to identifying carbonate rocks depositional texture utilizing more than 1250 thin-section photos as a data set from shallow-water carbonates of the Kruja zone (Albania). The proposed technique uses a numerical methodology based on digitized images of thin sections. According to the Dunham classification, it permits the user to recognize the carbonate texture automatically. The carbonate rock should not be affected by post-depositional processes. This technique uses as input gray-scale digital images taken from thin sections. Image processing can extract (as output) a set of 22 numerical parameters measured in the entire image, including white areas representing calcite cement. A multi-layer perceptron (MLP) neural network inputs these numerical parameters and produces, as output, the estimated depositional texture. We utilized 654 images of thin sections to evaluate this technique to train the neural network. We used 348 pictures taken from the same data set and 250 from the Burizana section (Kruja zone, Albania) to test the technique. The proposed method has shown 90.5 and 91.3% accuracy in identifying the automatically depositional texture using digitized images on the 348 and 250 test sets. Based on the good results obtained, this technique can be extending not only in identifying carbonate rocks texture but in every type of rock, getting fast and correct results. Python was used as a computer programming language for image processing and displaying images.

ПРОЕКТУВАННЯ І ПРОГРАМНА РЕАЛІЗАЦІЯ АВТОМАТИЗОВАНОЇ СИСТЕМИ ВІДВІДУВАНОСТІ ТА АНАЛІЗУ ДАНИХ В ЗАКЛАДАХ ОСВІТИ

The work is devoted to the research of the optimal method of monitoring the attendance of educational institutions by students, both teachers and parents: the design and implementation of an automated system that allows to simplify this process, to develop the necessary reports, including notifying parents with information about the attendance of their children; creating modules for analyzing the received information using mathematical modeling methods and the Python programming language and libraries for data processing. The principle of operation of the system is that the information about the student who came to the educational institution is read by a scanner and sent to the local computer, where it is stored. On the same computer, the stored information is analyzed and the necessary reports are generated. Another approach to system development also uses a cloud server. Problems and shortcomings arising from various possibilities of organizing an automated system are analyzed. To save information on the computer to which the scanner is connected, approaches based on the use of the Python language are considered, which allows easy transfer of data to various databases, including Access and MySQL, and another where the scanned information is immediately stored in the Access database, which is located on the computer to which the scanner is connected. Database structures for storing and processing information have been developed and implemented. The system generates various reports with the necessary information, which can be the basis for making certain decisions: for example, a report for the dining hall on the number of students by class, on the total number of children in the school, the total number of people present and absent in absolute and relative terms, etc. The modern Telegram messenger was used to send messages. To implement the idea, Bot Father and the function of this platform were used, which allows you to create automatic robots to perform mailing tasks to its users. The bot logic is controlled using HTTPS requests to the bot system API. With the help of SQL queries, the necessary information is extracted, which is transferred to the developed software module for analysis on a separate group of students or on the institution as a whole. Regression analysis methods are used in software mode using Python tools. The work on the creation and implementation of an automated intellectual system was carried out on the order of a secondary education institution.

77 Results of a Novel National Emergency Department Chief Complaint Database

77 Results of a Novel National Emergency Department Chief Complaint Database

Penerapan Metode Convolution Neural Network (CNN) Pada Aplikasi Automatic Lip Reading

AbstractA Prototype of Automatic Lip-Reading or a prototype of automatic lip-reading is a device that is needed by people with hearing disabilities or the Deaf. The prototype will help people with hearing impairment move independently without depending on others. Advances in Nano technology have driven the development of Computer Vision and software that enables the creation of prototypes of Automatic Lip-Reading. The image processing applied to this prototype is focused on the movement of the lips, tongue, and the area around a person's mouth. Furthermore, the results of the image recognition will compare with the database that has been provided to produce certain words. The prototype design consists of a camera, Python with Tensorflow used as an image processing programming language with the Convolutional Neural Network (CNN) method as an image recognition method, and Machine Learning Technology used as processing and decision-making systems. This study, numbers and alphabets were used as trials or predictions of the Automated Lip-Reading system. By using CNN and Machine Learning methods, the test results show that in general, the system designed can predict numbers and alphabet with not quite high or less than 35%. Â Â

Statically identifying XSS using deep learning

Cross-site Scripting (XSS) is ranked first in the top 25 Most Dangerous Software Weaknesses (2020) of Common Weakness Enumeration (CWE) and places this vulnerability as the most dangerous among programming errors. This work explores static approaches to detect XSS vulnerabilities using neural networks. We compare two different code representations based on Natural Language Processing (NLP) and Programming Language Processing (PLP) and experiment with models based on different neural network architectures for static analysis detection in PHP and Node.js. We train and evaluate the models using synthetic databases. Using the generated PHP and Node.js databases, we compare our results with three well-known static analyzers for PHP code, ProgPilot, Pixy, RIPS, and a known scanner for Node.js, AppScan static mode. Our analyzers using neural networks overperform the results of existing tools in all cases. • We explore static approaches to detect XSS vulnerabilities using neural networks. • We compare two different code representations based on NLP and PLP. • PLP representations obtained better results than NLP representations. • Our models outperform the results of four well-known static analysis tools.

Multi-View Graph Representation for Programming Language Processing: An Investigation into Algorithm Detection

Program representation, which aims at converting program source code into vectors with automatically extracted features, is a fundamental problem in programming language processing (PLP). Recent work tries to represent programs with neural networks based on source code structures. However, such methods often focus on the syntax and consider only one single perspective of programs, limiting the representation power of models. This paper proposes a multi-view graph (MVG) program representation method. MVG pays more attention to code semantics and simultaneously includes both data flow and control flow as multiple views. These views are then combined and processed by a graph neural network (GNN) to obtain a comprehensive program representation that covers various aspects. We thoroughly evaluate our proposed MVG approach in the context of algorithm detection, an important and challenging subfield of PLP. Specifically, we use a public dataset POJ-104 and also construct a new challenging dataset ALG-109 to test our method. In experiments, MVG outperforms previous methods significantly, demonstrating our model's strong capability of representing source code.