Popular Programming Research Articles

Beyond The Basics: A Detailed Survey of Advanced Python Applications and Innovations

Abstract: Python is the general purpose, high-level, scriptable, interpreted, object oriented and easy to learn language was developed by Guido van Rossum. Python is a popular programming language because of its simplicity, ease of use, open-source licensing, and accessibility. It provides various libraries and frameworks for learning and real world programming. Python’s versatility and active community make it an ideal language for Machine Learning, Data Science, Artificial Intelligence, Networking, Game Development, Web Development and various other domains. In this paper, we will provide an introduction to the main Python programming software tools used for Web development, Data science, Machine learning techniques, Educational Development, etc. In this paper will first introduce Python as a language, and give introduction about Web Development, Data science, Machine learning, Educational Development & Financial Analysis and then describe packages that are popular in that sector

Algorithm XXXX: UltimateKalman, Flexible Kalman Filtering and Smoothing Using Orthogonal Transformations

UltimateKalman is a flexible linear Kalman filter and smoother implemented in three popular programming languages: MATLAB, C, and Java. UltimateKalman is a slight simplification and slight generalization of an elegant Kalman filter and smoother that was proposed in 1977 by Paige and Saunders. Their algorithm appears to be numerically superior and more flexible than other Kalman filters and smoothers, but curiously has never been implemented or used before. UltimateKalman is flexible: it can easily handle time-dependent problems, problems with state vectors whose dimensions vary from step to step, problems with varying number of observations in different steps (or no observations at all in some steps), and problems in which the expectation of the initial state is unknown. The programming interface of UltimateKalman is broken into simple building blocks that can be used to construct filters, single or multi-step predictors, multi-step or whole-track smoothers, and combinations. The paper describes the algorithm and its implementation as well as a test suite of examples and tests.

Detecting and Explaining Python Name Errors

Python has become one of the most popular programming languages nowadays but has not received enough attention from the software engineering community. Many errors, either fixed or not yet, have been scattered in the lifetime of Python projects, including popular Python libraries that have already been reused. NameError is among one of those errors that are widespread in the Python community, as confirmed in our empirical study. Yet, our community has not put effort into helping developers mitigate its introductions. To fill this gap, we propose in this work a static analysis-based approach called DENE (short for Detecting and Explaining Name Errors) to automatically detect and explain name errors in Python projects. To this end, DENE builds control-flow graphs for Python projects and leverages a scope-aware reaching definition analysis to locate identifiers that may cause name errors at runtime and report their locations. Experimental results on carefully crafted ground truth demonstrate that DENE is effective in detecting name errors in real-world Python projects. The results also confirm that unknown name errors are still widely presented in popular Python projects and libraries, and the outputs of DENE can indeed help developers understand why the name errors are flagged as such.

Look after gardens and they look after you: Cultivating wellbeing in a pandemic through a popular TV gardening programme

Over the past few years there has been intense policy interest in the idea that nature-related activities such as gardening are good for wellbeing. Several studies have indeed found links between such activities and self-reported wellbeing. However, there are also sympathetic critiques of the limited conceptions of what kinds of activities are considered beneficial. Therefore, more attention should be paid to the social and cultural roots of the assumption that nature is good for us rather than treating connection to nature as an innate need. This article contributes to this task by showing how commonly held ideas about gardening and wellbeing were reproduced in the long-running and popular UK TV gardening programme Gardeners’ World ( GW) during 2020. As an exemplar of lifestyle programming, GW provides both information and entertainment and appeals to viewers to try out the techniques they observe on screen in their own gardens. Within the programme there is an emphasis on gardens as secluded havens from the outside world and on the attractive characteristics of individual plants. However, in many cases it is the work involved and the challenge that interests gardeners, as much as the qualities of the plants. Gardeners design a space and use it to express their identity. But the results of their actions are not guaranteed. Gardening can therefore be understood through a political ecology lens as a process whereby the social and nature constitute each other.

TrojanProbe: Fingerprinting Trojan tunnel implementations by actively probing crafted HTTP requests

Trojan is a well-known hidden tunnel protocol widely used to bypass Internet censorship and thus presents a big challenge to transparent network management and forensics. As claimed by the protocol designer, Trojan maintains its anti-identifiability by proxying real HTTPS/TLS traffic to react to unauthenticated requests, eliminating any subtle differences between the Trojan traffic and the legitimate HTTPS. Despite such a protocol seeming unidentifiable by design, the diverse Trojan implementations adopting very different programming languages will likely have varied coding logic and networking API calls, opening a new door to be identified and fingerprinted from the implementation level. In this paper, we propose TrojanProbe, a new class of active probing methods that can be used to fingerprint Trojan implementations by triggering their identifiable responses. Our basic idea is to audit the source code of the Trojan programs and discover the subtle logic discrepancy compared with the legitimate HTTPS counterparts, to craft specific HTTP requests as probes to trigger these differences for fingerprinting. By this idea, we choose the five most popular open-source Trojan programs off-the-shelf as our targets to audit, covering the majority of Trojan market share and the mainstream programming languages from traditional C++ to the cutting-edge Go and Rust, and design a suite of novel HTTP probes to differentiate them from their web server masquerades. Our probes exploit either the different responding/buffering logic to the malformed HTTP requests and the different HTTP versions, or the varied timeouts set in the different networking APIs by default. To this end, we have conducted extensive experiments to evaluate the TrojanProbe against a comprehensive set of configuration and networking conditions. The experimental results show that our TrojanProbe can effectively fingerprint our selected Trojan targets in most conditions, but leave a single Rust implementation with a minority market occupied that can only be identified in some constraint cases. Despite such an exception, our research sheds light on a new kind of possibility to fingerprint Trojans at their implementation level, even if such a hidden tunnel is widely known as unidentifiable at the protocol level.

DrYFiT: An Excel Freeware Tool to Describe Thin Layer Drying of Foods

ABSTRACTWe introduced DrYFiT (drying data fitting tool), a Microsoft Excel freeware tool to be used for modeling thin‐layer drying of foods, which is available at https://drive.google.com/drive/folders/1ouompmNkmdmw1KMTJUnY0t8dJqSJ9iKv?usp=drive_link. There are 12 models in the tool and it can be used without any modeling and programming skills. Time and moisture ratio data can be entered and one of models available (one at a time) can be selected to describe the drying data. Parameter values, standard error of the parameters, p value and a statement that indicates whether the parameter is statistically significant or not (α = 0.05) are reported. Moreover, R2, adjusted R2 and root mean square error values are calculated for each model. Users can instantaneously observe the experimental data and the model fit on the same graph. Residual plot is given next to this graph. It is possible for the users to have the results of all models applied to drying data within a couple of minutes. The results of DrYFiT were compared with some popular software programs used for nonlinear regression and identical values (parameters, standard errors, p values, goodness‐of‐fit statistics) were obtained for 40 datasets.

Descriptive Analysis Of K-Means and Apriori Methods To Find Promotion Strategies For University Bhayangkara.

The increasing number of higher education institutions in Indonesia has intensified competition between universities. Universitas Bhayangkara Jakarta Raya (Ubhara Jaya) must develop an effective marketing plan to stand out. This study used segmentation and associative analysis on 2023 student enrolment data. The Apriori algorithm identified patterns in student preferences for study programmes, while the K-Means method categorized students based on demographics and family income. Three income-based clusters were identified: C0 ‘Already stable’ (IDR 1,000,000 - 2,500,000), C1 ‘Focus on promotion strategy’ (IDR 20,000,000), and C2 ‘Maximise promotion again’ (IDR 5,000,000 - 10,000,000). The Davies-Bouldin Index (DBI) indicated k=5 as the optimal cluster number, but k=3 was adequate with a minimal score difference. The most popular programmes were Communication Science and Management, with high support and confidence values. This data helps Ubhara Jaya manage study programme demand and room availability. Combining K-Means and Apriori algorithms is expected to enhance data segmentation[1] and support effective marketing strategies, aiding strategic decisions in higher education marketing.

Logical methods of forensic expertise of Android devices

Mobile devices are now so ubiquitous that they have revolutionized the way we do most of our activities. As a result, the mobile device is now a huge repository of confidential and personal information about its owner. This has led to the development of mobile forensics, which focuses on the recovery and investigation of mobile data. The purpose of the diagnostic process is to retrieve and recover any information from a mobile device without changing the data on the device. Over the years, digital forensics has expanded with the rapid growth of mobile devices. There are different branches of digital forensics depending on the type of digital device, such as computer forensics, network forensics, mobile forensics, etc. Data obtained from telephones becomes an invaluable source of evidence for investigations in criminal and civil cases. It's rare to conduct digital forensics without using a phone. Third-party applications are an integral part of the investigation mobile device. This requires understanding where app data is stored on the device, what app data is stored for that platform, and which tool best helps uncover the evidence. Although some commercial tools, such as Magnet IEF, are known for supporting application parsing, no tool is perfect, and it is nearly impossible for tools to keep up with the frequent updates that are released for each application. The most commonly available commercial tools analyze the most popular programs on the market. For example, when Facebook acquired WhatsApp, Cellebrite, IEF, and Oxygen Forensics started supporting the app. This is where all programs differ.

Overcoming Sampling Issues and Improving Computational Efficiency in Collective-Variable-Based Enhanced-Sampling Simulations: A Tutorial.

This tutorial is designed to help users overcome sampling challenges and improve computational efficiency in collective-variable (CV)-based enhanced-sampling, or importance-sampling, simulations. Toward this end, we introduce well-tempered metadynamics-extended adaptive biasing force (WTM-eABF) and its integration with Gaussian accelerated molecular dynamics (GaMD). Additionally, use will be made of a method for identifying the least-free-energy pathway (LFEP) and multiple concurrent pathways on high-dimensional free-energy surfaces. We illustrate these sampling techniques with the conformational equilibria of trialanine and chignolin in aqueous solution as test cases. This tutorial assumes that the user has prior experience with molecular dynamics (MD) simulations, in general, with the popular program NAMD, and to some extent with Colvars, the module for CV-based calculations. This tutorial can, however, in large measure be used in conjunction with alternate MD engines that support the Colvars module such as GROMACS, LAMMPS, and Tinker-HP.

AI Image Generator Utilizing OpenAI and Replit

In this research, we leverage OpenAI's DALL- E 3 API in the assistance of generating images from a web interface which has been built on Replit. DALL-E 3 is a very advanced generative AI model due to its ability of generating complex and creative images from textual inputs. We intend to make use of the capabilities of DALL-E 3 and have it available to users in the generation of high-quality, custom images. Replit-the popular cooperative programming platform-secures the setup of the development environment, API request and response management, and user interfaces which allow users to interact with these AI models. The following apparently consider these factors at the implementation of AI in graphic design: better creativity, effective usage of time, and chance of creating unique aesthetic contents. Key Words: DALL-E 3 API, Generative AI, Web interface, Replit, Custom images, Graphic design.

Current cases of and motivations for second screen use by generation Z: university students

ABSTRACT Second screen use is becoming increasingly popular among young people. Türkiye can be expressed as a place where the use of devices such as TV, phone, tablet and computer is widespread. This study, which is based on the uses and gratifications approach, seeks answers to the following questions: a) which devices are used as second screens, b) with which TV programs they are used, c) with what kind of motivations they are used by Generation Z members who still watch TV. We collected data from university students using a questionnaire and the convenience sampling method, which is a type of non-probabilistic sampling, through SPSS. The data showed that Generation Z primarily uses smartphones as second screens, and only a few use second screen applications on their mobile phones. Music programs are the most popular thematic programs watched together with the second screen. Women tend to use their second screens for watching series and shopping. Generation Z prefers using a ‘smartphone with computer’ as a second screen. Future studies could investigate the motivations of students and academics to use second screens in distance education. Additionally, future studies can investigate the usage habits of second screens in second language learning.

CONCEPTUAL METAPHORS OF ARTIFICIAL INTELLIGENCE AND AI DEVELOPMENT IN THE GUARDIAN NEWSPAPER

The whirlwind advent of ChatGPT in 2022 has marked a new age of artificial intelligence (AI), the general name for the technology that combines computer technology, big data bases and machines. This AI technology quickly makes its presence felt with hundreds of popular programs and chatbots such as the portrait-making AI diffusion art and the thesis-writing ChatGPT. This paper investigates the conceptual metaphors representing AI and AI development in The Guardian, a UK-based newspaper, to figure out how this technology and its growth have been introduced to ordinary people via mass media. Employing the Conceptual Metaphor Theory proposed by Lakoff & Johnson (1980), this study found three AI-related conceptual metaphors, namely, AI IS A HUMAN BEING, AI IS AN ANIMAL and AI IS A NATURAL FORCE, which are realized by more than 100 linguistic expressions across 33 news articles. Also, this research found five conceptual metaphors related to AI development, namely AI DEVELOPMENT IS WAR, AI DEVELOPMENT IS A RACE, AI DEVELOPMENT IS A CONVERSATION, AI DEVELOPMENT IS A DANCE, AI DEVELOPMENT IS A GAME and these metaphors are manifested by approximately 40 linguistic expressions. This paper discusses the way that these metaphors could influence the way people and technology companies think about AI and AI development.

The Limits to Food and Beverage Industry Influence over Fiscal and Regulatory Policy in Latin America.

Little is known about the political, institutional, and social contexts contributing to a decline in food and beverage industry power and influence over fiscal (soda taxes) and regulatory (sales/advertising restrictions and food labels) policy. This article addresses this issue by exploring why Mexico and Chile eventually saw such a decline in the food and beverage industry's influence whereas Brazil was not as successful. I argue that in Mexico and Chile, these outcomes are explained by shifts in presidential, congressional, and bureaucratic interests in pursuing policies that went against industry preferences. This article took a qualitative methodological approach to comparative historical research. Policymakers' interest in pursuing stronger food and beverage regulations were shaped by economic and public health concerns, new electoral contexts, epidemiological information, and normative beliefs. In Mexico, the infiltration of nutrition researchers within government facilitated this process. In contrast, Brazil's government was divided about pursuing regulatory policies, with presidents favoring partnerships with industry to implement a popular anti-hunger program; industry's power endured there with limited progress in policy reforms. Governments can eventually overcome industry power and policy influence, but it depends on a whole government commitment to reform.

Using Media to Stimulate Babies’ Language Activities Development

As the digital age continues to reshape educational paradigms, it is essential to investigate the purposeful integration of media into infants’ lives to enhance language acquisition “Harmonizing Voices” explores how age-appropriate media, such as videos, apps, and interactive content, can complement traditional methods, providing infants with a multi-sensory and engaging platform for language exploration. Today, various strategies are employed to stimulate babies from pregnancy through infancy, with mothers increasingly using media as a tool. This includes playing music during pregnancy and introducing nursery rhymes through media post-birth to create a sensory -rich environment. This study investigates the synergy between the melodic allure of English nursery rhymes and intricate process of a baby’s linguistic development. Digital platforms like YouTube and Netflix offer numerous nursery rhymes, such as Cocomelon and Bebefinn, which are popular among infants. This research aims to evaluate which of these nursery rhymes programs is more effective in stimulating infants and which elicits more positive reactions. Caregivers, educators, and researchers will gain insights into the connections between musicality, language and early cognitive development through this exploration. The study addresses the identification of high quality media content, strategies for joint engagement between caregivers and infants, and the potential impact of media on various facets of language development. By fostering an environment where technology harmonizes with natural language acquisition processes, this research aspires to contribute valuable insights for educators, caregivers, and researchers. The objective of this study is to observe and compare infants’ reactions to two popular nursery rhyme programs, Cocomelon and Bebefinn. The research aims to identify which program stimulates more positive engagement, thereby providing a deeper understanding of infants’ preferences and the effectiveness of media content in early language development.

A Benchmark Test of High-Throughput Atomistic Modeling for Octa-Acid Host–Guest Complexes

Years of massive applications of high-throughput atomistic modeling tools such as molecular docking and end-point free energy calculations in the drug industry and academic exploration have made them indispensable parts of hierarchical screening. While the similarities between host–guest and protein–ligand complexes lead to the direct extension of techniques for protein–ligand screening to host–guest systems, the practical performance of these hit identification tools remains unclear in host-–-guest binding. Recent reports on specific host–guest complexes suggest that the experience on the accuracy ladder accumulated from protein–ligand cases could be invalid in host–guest complexes, which makes it an urgent need to perform a systematic benchmark to secure solid numerical supports and guidance of practical setups. Concerning molecular docking, there still lacks a comprehensive benchmark considering popular docking programs. As for end-point reranking, quantitative and rigorous free energy estimation via end-point formulism requires establishing statistically meaningful measurements of uncertainties due to finite sampling, which is neglected or underestimated by a significant portion in almost all main-stream applications. Further, a face-to-face comparison between different screening tools is required for the design of a hierarchical workflow. To fill the above-mentioned critical gaps, in this work, using a dataset containing tens of host–guest complexes involving basket-like macromolecular hosts from the octa acid family, we extensively benchmark seven academic docking protocols and perform post-docking end-point rescoring with twenty protocols. The resulting comprehensive benchmark provides conclusive pictures of the practical value of docking and end-point screening in OA host–guest binding.

The onerous granting of the right to build: an alternative form of financing in the popular housing programs of the municipality of Goiânia

The City Statute (Law nº 10.257/2001) established general norms for the elaboration of the Master Plan by the Municipalities, including defining that not only the OODC, but all urban policy instruments defined therein, must follow the general guidelines of urban policy, such as the fair distribution of burdens and benefits, the recovery of government investments that result in the appreciation of urban properties and, in the specific case of Onerous Grants, that part of the amounts collected with their concession are applied in social housing programs. In this sense, the scope of the implementation of the urban instrument of the Onerous Grant of the Right to Build is discussed, based on its criticisms and distortions, in case there is a legal binding expressed in the local legislation of the percentage of collection of its revenues linked to housing programs of social interest.

The 68th annual conference on magnetism and magnetic materials

The 2023 Conference on Magnetism and Magnetic Materials (MMM) was held from October 30–November 3 at the Dallas Hyatt Regency, at the base of the iconic Reunion Tower. There, 703 on-site attendees from 37 different countries discussed cutting-edge research in fundamental and applied magnetism, made new connections, and met old friends and colleagues at one of the first primarily in-person conferences in our field since the major disruptions of the pandemic era. The format of the conference was primarily in-person, with supporting online content that included recordings of the most popular programs and presentations, made available after the conference (including all symposia, the tutorial, and selected other special content). All presenters had the option to contribute online content, as did 96 participants who did not travel to the venue and elected to present virtual posters. Attendees enjoyed a vibrant celebration of the magnetism research community that included a Texas-sized welcome reception, tutorials on machine learning in magnetism, seven symposia, a lively exhibit hall that hosted poster sessions and bierstuben, a full roster of special events, coffee breaks to facilitate active scientific discussions, and of course, excellent technical presentations. These technical presentations, organized into 45 oral sessions, 23 in-person poster sessions, and 17 virtual poster sessions, were selected by a Program Committee of 57 members, capably coordinated by three Program Chairs: Hendrik Ohldag (ALS-LBNL), Karin Leistner (TU Chemnitz), and Takahiro Moriyama (Nagoya University). These three Program Chairs worked seamlessly and tirelessly over many months to make the central mission of MMM a huge success. The Program Committee selected seven symposia from nominations provided by the magnetism community: “Frontier Topics in Antiferromagnetism: Altermagnetism and Topology,” “Rare Earth Spintronics,” “Recent Advances in Cavity Magnonics,” “Emerging Topics in Magnetic Tunnel Junctions: Altermagnetism, Probabilistic Computing and Energy Efficient Switching,” “Orbitronics: From Orbital Currents Created by Charge Currents to Creation by Light or RF Excitation,” “Imaging Magnetic Textures at the Nanoscale,” and finally “Magnetization Dynamics in Two-Dimensional van der Waals Magnets.” These symposia highlighted a wide range of exciting new areas for magnetism, and were part of a total of 101 invited talks, 28% of which were presented by women. The contributed program was selected by the Program Committee from more than 900 submitted abstracts.

Comparison of Laravel and Symfony - the most popular PHP frame-works, based on a simple CRUD application

The main purpose of the study is to compare the performance of two popular PHP language programming frameworks - Laravel 10 and Symfony 6 based on test CRUD-type applications. The benchmarking also considered the implementation process and support from the development community. The study was conducted using two simple and small test applications with the same sets of functionality. Performance was measured based on the execution times of single CRUD-type operations and handling dozens and hundreds of requests simultaneously. Execution times were measured via the Postman application from when the request was sent to the server until the response was received. The results showed that Symfony 6 is a better solution for applications where performance matters.

Utilizing Python for Scalable Data Processing in Cloud Environments

In the age of big data and cloud computing, enterprises need to effectively analyze enormous datasets to get meaningful insights and stay ahead. Python, a popular programming language, is a strong tool for cloud-scale data processing. This research study examines Python's integration with cloud platforms and its effects on performance and efficiency in scalable data processing. The study introduces scalable data processing and cloud computing. It then discusses Python's ecosystem, including Dask, Apache Spark with PySpark, TensorFlow, and PyTorch for data processing and machine learning. The study also examines Python's interoperability with cloud services like AWS, Google Cloud Platform, and Microsoft Azure in data input, transformation, and analysis. Many case studies and real-world applications demonstrate how Python has been used in banking, healthcare, and e-commerce. Python is useful for managing massive amounts of data, streamlining processing processes, and scaling cloud applications, as shown in the case studies. The report also analyzes Python-based cloud systems' performance indicators and cost consequences, revealing best practices and possible issues. The article explores Python's involvement in cloud computing trends and technology. Serverless architectures, Docker and Kubernetes, and Python interaction with cloud-native tools and services are examples. These patterns show how data processing is changing and how Python is improving to meet current data needs. This study concludes that Python is a reliable and scalable cloud data processing option. The language's strengths, alignment with cloud technologies, and practical applications in many areas are covered in detail. The results indicate that Python's versatility and cloud scalability provide a robust foundation for handling and analyzing massive datasets, enabling better decision-making and innovation across fields.

Are Disaster Impact Estimates Distorted by Errors in Popular Night-Time Lights Data?

Satellite-detected night lights data are widely used to evaluate economic impacts of disasters. Growing evidence from elsewhere in applied economics suggests that impact estimates are potentially distorted when popular Defense Meteorological Satellite Program (DMSP) night lights data are used. The low resolution DMSP sensor provides blurred and top-coded images compared to those from the newer and more precise Visible Infrared Imaging Radiometer Suite (VIIRS) images. Despite this, some disaster impact studies continue to use DMSP data, which have also been given a new lease of life through the use of harmonized series linking DMSP and VIIRS data to provide a longer sample. We examine whether use of DMSP data affects evaluations, using expected typhoon damages in the Philippines from 2012–19 as our case study. With DMSP data, negative impacts on economic activity from expected damages at the municipality level appear over 50% larger than when the VIIRS data are used. The DMSP data give the appearance of greater spatial autocorrelation in luminosity and through this channel may tend to spread apparent spatial impacts of shocks. Harmonized data that adjust VIIRS images to be like the DMSP data also have this spurious autocorrelation so researchers should be cautious in using these data for disaster assessments.