JavaScript Programs Research Articles

Meningkatkan Kompetensi dan Peluang Siap Kerja melalui Program Beasiswa di Bidang Teknologi Informasi

The increasing need for workers in the information technology industry isn't in line with the education gap in Indonesia. One thing that can be solved to overcome this is providing scholarships in the information technology industry to provide financial assistance to support the education being pursued. PT Hacktivate Teknologi Indonesia (Hacktiv8) as a training institution of information technology that transforms beginners into digital talents also plays a role in combating this educational gap by providing Fullstack Javascript and Fulltime Data Science scholarship programs at the Hacktiv8 Surabaya Campus. This research uses qualitative research methods and uses primary and secondary data. The scholarship program was awarded to 6 selected participants who passed the acceptance test selection from 215 applicants. Scholarships are awarded with a full or partial scholarship scheme. Bootcamp education will be run according to the chosen program to improve competencies and skills that support work readiness and global competence.

Java Script Programs for Calculation of Dihedral Angles with Manifold Equations

Java Script programs for calculation dihedral angles from NMR data with manifold equations of 3-Sphere approach: rectangle, Villarceau circles of cyclide (Torus – Dupin Cyclide), polar equations, Euler-Conic. Manifolds are curves or surface in higher dimension used for calculation of dihedral angles under wave character of NMR data, carbon and/or proton chemical shift δ<sub>Xn</sub>[ppm] and vicinal coupling constant <sup>3</sup><I>J</I><sub>HnHn+1</sub>[Hz]. 3-Sphere approach for calculation of the dihedral angles from NMR data in four steps: 1. Prediction, or more exactly calculation of the dihedral angles from vicinal coupling constant with trigonometric equations, 2. Calculation of the dihedral angles from manifold equations; 3. Building units from angle calculated with one of the manifold equations; 4. Calculation the vicinal coupling constant of the manifold dihedral angle. In this paper are presented Java Script programs of step 2 and from step 3 only the Java Script program for calculation of seven sets angles. The bond distances l<sub>CnCn+1</sub>[A<sup>0</sup>] between two atoms of carbon are under different polar equations (<i>i.e.</i> limaçons or cardioid, rose or lemniscale), our expectation was to find different manifold equations for calculation the best angle, differences are smaller but can be find sometimes a preferred one for a vicinal coupling constant. 3-Sphere approach has the advantages of calculation from vicinal angle or/and chemical shift the dihedral angle, tetrahedral angle and the bond distance l<sub>CnCn+1</sub>[A<sup>0</sup>], with application on conformational and configurational analysis.

Vulnerable JavaScript functions detection using stacking of convolutional neural networks.

System security for web-based applications is paramount, and for the avoidance of possible cyberattacks it is important to detect vulnerable JavaScript functions. Developers and security analysts have long relied upon static analysis to investigate vulnerabilities and faults within programs. Static analysis tools are used for analyzing a program's source code and identifying sections of code that need to be further examined by a human analyst. This article suggests a new approach for identifying vulnerable code in JavaScript programs by using ensemble of convolutional neural networks (CNNs) models. These models use vulnerable information and code features to detect related vulnerable code. For identifying different vulnerabilities in JavaScript functions, an approach has been tested which involves the stacking of CNNs with misbalancing, random under sampler, and random over sampler. Our approach uses these CNNs to detect vulnerable code and improve upon current techniques' limitations. Previous research has introduced several approaches to identify vulnerable code in JavaScript programs, but often have their own limitations such as low accuracy rates and high false-positive or false-negative results. Our approach addresses this by using the power of convolutional neural networks and is proven to be highly effective in the detection of vulnerable functions that could be used by cybercriminals. The stacked CNN approach has an approximately 98% accuracy, proving its robustness and usability in real-world scenarios. To evaluate its efficacy, the proposed method is trained using publicly available JavaScript blocks, and the results are assessed using various performance metrics. The research offers a valuable insight into better ways to protect web-based applications and systems from potential threats, leading to a safer online environment for all.

Relationship between tetrahedral and dihedral on hypersphere coordinates

The wave character of the NMR data (chemical shift ΔδCn[ppm] – vicinal coupling constant 3JHH[Hz]) enable calculation the tandem dihedral-vicinal or tetrahedral-internal angles with 3-sphere approach. Golden ratio, invers of Fibonacci number, and manifold inversion torus to Dupin cyclide under 3-sphere units ensure the calculation of the tetrahedral angles of five membered ring in opposite with dihedral angles sin versus tan functions. The transformation from space/time to space under electric and magnetic rule gives dodecahedron and icosahedron values for calculation tetrahedral angles from polyhedron and hypersphere equations. Tetrahedral angles of five membered ring can be calculated from carbon chemical shift ΔδCn[ppm] with Java Script program and hypersphere equations.

3-Sphere Torsional Angles and Six Membered Ring Conformation

The conformation of six membered ring, 1,3,4-tri-o-acetyl-2-azido-6-bromo-2,6-dideoxy-D-galactose α-1 and β-1, and 2,6-dideoxy-2,6-imino- D-talonic acid 2D and L-talonic acid 2L, is analyzed inscribing two five membered ring on six membered ring. Altona’s five membered ring conformational parameters, phase angles of the pseudorotation P₁, P₂ [deg] and angle of deviation from planarity θ_m [deg] = f(P [deg]), are calculated with Java Script program using 3-Sphere torsional angles. The correlation between Altona map and six membered ring Stoddart’s diagram conformations was confirmed with VISION molecular models. 3-Sphere torsional angles θ_nn+1 [deg] for all <i>cis</i>, <i>trans</i>-ee, -<i>aa</i> stereochemistry are disclosed from trigonometric point of view, Hopf fibration confirmed by Lie algebra. The <i>cis</i>, <i>trans</i> stereochemistry on two units with six sets angles ensure relationships between dihedral θ_HnHn+1 [deg]/vicinal angles ϕ [deg] (θ^Bn + A = θ^An, θ^An = θ_HnHn+1, θ^Bn = ϕ), and between dihedral θ_HnHn+1 [deg]/torsional angles θ_nn+1 [deg] (θ_nn+1 = A +/- θ_HnHn+1); in first case A = 150 for <i>trans-aa</i>^6,1 and A = 90 for <i>trans-aa</i>^5,2 and <i>trans-ee</i> stereochemistry, in last case A = 180 [deg] for <i>cis</i> and <i>trans-aa</i> stereochemistry and A = 120 [deg] for <i>trans-ee</i> stereochemistry. The number of dihedral angles with positive and negative sign results from <i>3-sphere approach</i> for only one vicinal coupling constant ³<I>J</I>_HnHn+1 [Hz] are restricted by the VISION molecular models.

User-Customizable Transpilation of Scripting Languages

A transpiler converts code from one programming language to another. Many practical uses of transpilers require the user to be able to guide or customize the program produced from a given input program. This customizability is important for satisfying many application-specific goals for the produced code such as ensuring performance, readability, ease of exposition or maintainability, compatibility with external environment or analysis tools, and so on. Conventional transpilers are deterministic rule-driven systems often written without offering customizability per user and per program. Recent advances in transpilers based on neural networks offer some customizability to users, e.g. through interactive prompts, but they are still difficult to precisely control the production of a desired output. Both conventional and neural transpilation also suffer from the "last mile" problem: they produce correct code on average, i.e., on most parts of a given program, but not necessarily for all parts of it. We propose a new transpilation approach that offers fine-grained customizability and reusability of transpilation rules created by others, without burdening the user to understand the global semantics of the given source program. Our approach is mostly automatic and incremental, i.e., constructs translation rules needed to transpile the given program as per the user's guidance piece-by-piece. Users can rely on existing transpilation rules to translate most of the program correctly while focusing their effort locally, only on parts that are incorrect or need customization. This improves the correctness of the end result. We implement the transpiler as a tool called DuoGlot, which translates Python to Javascript programs, and evaluate it on the popular GeeksForGeeks benchmarks. DuoGlot achieves 90% translation accuracy and so it outperforms all existing translators (both handcrafted and neural-based), while it produces readable code. We evaluate DuoGlot on two additional benchmarks, containing more challenging and longer programs, and similarly observe improved accuracy compared to the other transpilers.

DeepMerge: Learning to Merge Programs

In collaborative software development, program merging is <i>the</i> mechanism to integrate changes from multiple programmers. Merge algorithms in modern version control systems report a conflict when changes interfere textually. Merge conflicts require manual intervention and frequently stall modern continuous integration pipelines. Prior work found that, although costly, a large majority of resolutions involve re-arranging text without writing any new code. Inspired by this observation we propose the <i>first data-driven approach</i> to resolve merge conflicts with a machine learning model. We realize our approach in a tool DEEPMERGE that uses a novel combination of (i) an edit-aware embedding of merge inputs and (ii) a variation of pointer networks, to construct resolutions from input segments. We also propose an algorithm to localize manual resolutions in a resolved file and employ it to curate a ground-truth dataset comprising 8,719 non-trivial resolutions in JavaScript programs. Our evaluation shows that, on a held out test set, DEEPMERGE can predict correct resolutions for 37&#x0025; of non-trivial merges, compared to only 4&#x0025; by a state-of-the-art semistructured merge technique. Furthermore, on the subset of merges with upto 3 lines (comprising 24&#x0025; of the total dataset), DEEPMERGE can predict correct resolutions with 78&#x0025; accuracy.

Effective Techniques for Protecting the Privacy of Web Users

With the rapid growth of web networks, the security and privacy of online users are becoming more compromised. Especially, the use of third-party services to track users’ activities and improve website performance. Therefore, it is unavoidable that using personal information to create unique profiles may violate individuals’ privacy. Recently, several tools have been developed such as anonymity, anti-tracking, and browser plugins to ensure the protection of users from third-party tracking methods by blocking JavaScript programs and other website components. However, the current state lacks an efficient approach that provides a comprehensive solution. In this paper, we conducted a systematic analysis of the most common privacy protection tools based on their accuracy and performance by evaluating their effectiveness in correctly classifying tracking and functional JavaScript programs, then evaluating the estimated time the browser takes to render the pages for each tool. To achieve this, we automatically browsed the most 50 websites determined in 2022 and categorized them according to different fields to get the in-page (as part of HTML script tags), and all external JavaScript programs. Then we collected data and datasets of 1578 JavaScript elements and obtained six diverse Firefox profiles when the tools were enabled. The results found that Ghostery has the highest percentage of allowing most functioning scripts with the lowest average error rate (AER). While at the same time NoScript achieved the highest percentage of blocking most tracking scripts since it is the highest blocker of third-party services. After that, we examined the speed of the browser finding that, Ghostery improved the load time by 36.2% faster than the baseline, while Privacy Badger only reduced the load time by 7.1%. We believe that our findings can help users decide on a privacy tool that meets their needs. Moreover, researchers and developers can use our findings to improve the privacy of internet users by designing more effective privacy protection techniques.

An empirical study on bugs in JavaScript engines

JavaScript is a prototype-based dynamic type scripting language. The correct running of a JavaScript program depends on the correctness of both the program and the JavaScript engine.

Hamster (Cricetinae) Motion Assisted by Video Tracker Application on Motion Materials in Junior High School

This study aims to explain the phenomenon of motion in hamsters moving in a rolling wheel. Motion is one of the materials taught at the 7th-grade junior high school. The study can be used in learning with the practicum method, namely animal motion. Hamster (Cricetinae) motion is analyzed using a video tracker analysis application with a javascript program. This study used a direct experiment using a hamster using four reference points, namely the forelegs, head, middle body, and hind legs. The results of data analysis from the tracker application were processed using the Microsoft Excel program to obtain a graph of changes in the position and velocity of the hamster. Hamster motion on the rolling wheel is a type of translational motion, namely uniform linear motion. Changes in the hamster's position indicate motion on a horizontal line. The velocity obtained in the tracker application is more accurate than manual calculations.

Medical data formatting to improve physician interpretation speed in the Military Healthcare System.

The purpose of this project was to improve ease and speed of physician comprehension when interpreting daily laboratory data for patients admitted within the Military Healthcare System (MHS). A JavaScript program was created to convert the laboratory data obtained via the outpatient electronic medical record (EMR) into a "fishbone diagram" format that is familiar to most physicians. Using a balanced crossover design, 35 internal medicine trainees and staff at Naval Medical Center Portsmouth were asked to complete timed comprehension tests for laboratory data sets formatted in the outpatient EMR's format and in fishbone diagram format. The number of responses per second and error rate per response were measured for each format. Participants were asked to rate relative ease of use for each format and indicate which format they preferred. Comprehension speed increased 37% (6.28 seconds per interpretation) with the fishbone diagram format with no observed increase in errors. Using a Likert scale of 1-5 (1 being hard, 5 easy), participants indicated the new format was easier to use (4.14 for fishbone vs 2.14 for table) with 89% expressing preference for the new format. The publically available web application that converts tabular lab data to fishbone diagram format is currently used 10000-12000 times per month across the MHS, delivering significant benefit to the enterprise in terms of time saved and improved physician experience. This study supports the use of fishbone diagram formatting for laboratory data for inpatients within the MHS.

Development and Evaluation of a Three-Dimensional-Printed Pediatric Intraosseous Infusion Simulator To Enhance Medical Training.

Vascular access is an essential and rate-limiting step during pediatric resuscitation efforts. Intraosseous (IO) access, an effective resuscitative strategy, remains underutilized in emergency departments. Many medical graduates report never performing the procedure before graduation, and it has been recommended that continuing education and in-servicing programs be implemented to increase the use and familiarity of IO access. The goal of this technical report is to describe the development and evaluation of a three-dimensional (3D)-printed Pediatric IO Infusion Model for simulation-based medical education. The simulator was designed by combining open-source models of a human skeleton and a lower leg surface scan in Blender (Blender Foundation, Amsterdam, Netherlands; www.blender.org), scaled to a pediatric size, and manipulated further using a JavaScript program. Polylactic acid was used to simulate bone while silicone molds were used as skin and soft tissue. Two trainers were produced and evaluated by seven emergency medicine physicians, two family medicine residents, and three medical students.Overall, the simulator was positively received with all participants indicating they would recommend it to assist in the training of others. Suggestions focused on enhancing the anatomical representations of both the skin and bones to enhance the learner experience. The content and outcomes of this report support the use of this simulator as part of simulation-based medical education.

Hopf Fibration on Relationship Between Dihedral Angle θ&amp;lt;sub&amp;gt;HnHn+1&amp;lt;/sub&amp;gt;[deg] and Vicinal Angle ϕ[deg], Angles Calculated from NMR Data with 3-Shere Approach and Java Script

3-Sphere theory, a hypersphere in four dimensions, is applied for calculation dihedral angles with the right stereochemistry and sign in D, -L ribitol series from proton and carbon chemical shift (Δδ_XnXn+1[ppm], X = H, C) and vicinal coupling constant (³<i>J</i>_HnHn+1[Hz]) with Java Script. A method in three steps, easy to calculate by hand or with Java Script program: 1. prediction of the dihedral angle only from ³<i>J</i>_HnHn+1[Hz], 2. calculation the angle of set A with manifold equation (conic section, Villarceau circles) from chemical shift, 3. building of the seven sets unit or six sets units, from which is chose an angle almost equal with the predicted one having its stereochemistry and sign. Angles of set A and set B, relationships between vicinal angle and dihedral angle (X₀-X₁₅) are introduced instead of polar angle and azimuthal angle in spherical coordinates (eq. 1 <i>versus</i> eq. 3). Hopf coordinates, trigonometric equations, confirmed by algebraic equations are disclosed for all <i>cis</i>-ae/ea, <i>trans</i>-ee, <i>trans-aa</i> stereochemistry. Octonionic fibration S⁷→S¹⁵→S⁸ in R¹⁶, with real fibration S⁰ →S¹ →S¹ as unit, reassembles all possible stereochemistry gives by the HCCH fragment on two congruent disks, each centered on the perimeter of the other with equilateral triangles as vertices. Complex Hopf fibration in R⁴ ensuring the calculation of the dihedral angle from vicinal angle and <i>vice versa</i>, demonstrating the relationships between sets A, B, C.

Evolving JavaScript Code to Reduce Load Time

JavaScript is one of the most used programming languages for front-end development of Web applications. The increase in complexity of front-end features brings concerns about performance, especially the load and execution time of JavaScript code. In this paper, we propose an evolutionary program improvement technique to reduce the size of JavaScript programs and, therefore, the time required to load and execute them in Web applications. To guide the development of this technique, we performed an experimental study to characterize the patches applied to JavaScript programs to reduce their size while keeping the functionality required to pass all test cases in their test suites. We applied this technique to 19 JavaScript programs varying from 92 to 15,602 LOC and observed reductions from 0.2 to 73.8 percent of the original code, as well as a relationship between the quality of a program's test suite and the ability to reduce the size of its source code.

Investigating and recommending co-changed entities for JavaScript programs

JavaScript (JS) is one of the most popular programming languages due to its flexibility and versatility, but maintaining JS code is tedious and error-prone. In our research, we conducted an empirical study to characterize the relationship between co-changed software entities (e.g., functions and variables), and built a machine learning (ML)-based approach to recommend additional entity to edit given developers’ code changes. Specifically, we first crawled 14,747 commits in 10 open-source projects; for each commit, we created at least one change dependency graph (CDG) to model the referencer–referencee relationship between co-changed entities. Next, we extracted the common subgraphs between CDGs to locate recurring co-change patterns between entities. Finally, based on those patterns, we extracted code features from co-changed entities and trained an ML model that recommends entities-to-change given a program commit.According to our empirical investigation, (1) three recurring patterns commonly exist in all projects; (2) 80%–90% of co-changed function pairs either invoke the same function(s), access the same variable(s), or contain similar statement(s); (3) our ML-based approach CoRec recommended entity changes with high accuracy (73%–78%). CoRec complements prior work because it suggests changes based on program syntax, textual similarity, as well as software history; it achieved higher accuracy than two existing tools in our evaluation.

ML-CB: Machine Learning Canvas Block

Abstract With the aim of increasing online privacy, we present a novel, machine-learning based approach to blocking one of the three main ways website visitors are tracked online—canvas fingerprinting. Because the act of canvas fingerprinting uses, at its core, a JavaScript program, and because many of these programs are reused across the web, we are able to fit several machine learning models around a semantic representation of a potentially offending program, achieving accurate and robust classifiers. Our supervised learning approach is trained on a dataset we created by scraping roughly half a million websites using a custom Google Chrome extension storing information related to the canvas. Classification leverages our key insight that the images drawn by canvas fingerprinting programs have a facially distinct appearance, allowing us to manually classify files based on the images drawn; we take this approach one step further and train our classifiers not on the malleable images themselves, but on the more-difficult-to-change, underlying source code generating the images. As a result, ML-CB allows for more accurate tracker blocking.

A pragmatic controller for a gantry crane

A gantry crane presents a control problem with ten state variables. The undamped swinging of the load has led earlier researchers to apply cumbersome control strategies. The essence of pragmatic control is gleaned from autopilot designs of half a century ago. The control is designed as a set of ‘nested loops’, where the innermost loop takes the form of a velocity loop wrapped around a motor to give crisp velocity control. Target values are derived from states in the outer loops, to be applied to a succession of inner loops. These target values are subjected to constraints. Thus in the case of the crane, the horizontal position of the load can be considered to be the two outermost variables. In this study, from the load position error, two target velocity components are calculated and subjected to limits. The velocity error leads in turn to a target acceleration of the load and subsequently, to the target displacement that takes place between the horizontal coordinates of the hoist and the load. In this paper, the pragmatic control technique is demonstrated with the aid of a JavaScript and MATLAB software programs using a state-space model.

Enhanced Bug Prediction in JavaScript Programs with Hybrid Call-Graph Based Invocation Metrics

Bug prediction aims at finding source code elements in a software system that are likely to contain defects. Being aware of the most error-prone parts of the program, one can efficiently allocate the limited amount of testing and code review resources. Therefore, bug prediction can support software maintenance and evolution to a great extent. In this paper, we propose a function level JavaScript bug prediction model based on static source code metrics with the addition of a hybrid (static and dynamic) code analysis based metric of the number of incoming and outgoing function calls (HNII and HNOI). Our motivation for this is that JavaScript is a highly dynamic scripting language for which static code analysis might be very imprecise; therefore, using a purely static source code features for bug prediction might not be enough. Based on a study where we extracted 824 buggy and 1943 non-buggy functions from the publicly available BugsJS dataset for the ESLint JavaScript project, we can confirm the positive impact of hybrid code metrics on the prediction performance of the ML models. Depending on the ML algorithm, applied hyper-parameters, and target measures we consider, hybrid invocation metrics bring a 2–10% increase in model performances (i.e., precision, recall, F-measure). Interestingly, replacing static NOI and NII metrics with their hybrid counterparts HNOI and HNII in itself improves model performances; however, using them all together yields the best results.

Development and Evaluation of a 3D-Printed Adult Proximal Tibia Model for Simulation Training in Intraosseous Access.

Intraosseous infusion remains an underutilized technique for obtaining vascular access in adults, despite its potentially life-saving benefits in trauma patients and those presenting to the emergency department. There is a scarcity of cost-effective, anatomically correct trainers to improve physician confidence and competency in this skill. The purpose of this report is to describe the development and evaluation of a three-dimensional (3D) printed Adult Proximal Intraosseous (IO) Tibia task trainer for simulation-based medical education. The proposed trainer was designed by combining open-source models of a human skeleton and a lower leg surface scan in Blender (Blender Foundation - www.blender.org) and manipulating them further using a JavaScript program. Polylactic acid was used to simulate bone while cured silicone moulds were used to replicate skin and soft tissue. Two trainers were produced and tested by 15 rural family medicine residents, six rural emergency medicine physicians, and six registered nurses. Participants evaluated the realism of the trainer and its efficacy as a training tool through a structured survey.The trainer received overall positive feedback from all participants, and most participants felt that no improvements were required to use the trainer for medical education. Notable suggestions for improvement included adding an infusion component, increasing the size of the tibial tubercle for better landmarking, and creating a variety of sizes for different patient body types. Residents and emergency medicine physicians practising in rural Newfoundland and Labrador found the 3D-printed trainer to be a practical tool for practising intraosseous technique. The outcome of this report supports the use of this cost-effective trainer for simulation-based medical education.

Detecting locations in JavaScript programs affected by breaking library changes

JavaScript libraries are widely used and evolve rapidly. When adapting client code to non-backwards compatible changes in libraries, a major challenge is how to locate affected API uses in client code, which is currently a difficult manual task. In this paper we address this challenge by introducing a simple pattern language for expressing API access points and a pattern-matching tool based on lightweight static analysis. Experimental evaluation on 15 popular npm packages shows that typical breaking changes are easy to express as patterns. Running the static analysis on 265 clients of these packages shows that it is accurate and efficient: it reveals usages of breaking APIs with only 14% false positives and no false negatives, and takes less than a second per client on average. In addition, the analysis is able to report its confidence, which makes it easier to identify the false positives. These results suggest that the approach, despite its simplicity, can reduce the manual effort of the client developers.