Artykuły w czasopismach: „JavaScript programs”

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Walker, Stuart, and Norihito Kawana. "JavaScript freeware programs." JALT CALL Journal 1, no. 1 (2005): 25–29. http://dx.doi.org/10.29140/jaltcall.v1n1.3.

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Walker, Stuart, and Norihito Kawana. "JavaScript freeware programs." JALT CALL Journal 1, no. 1 (2005): 25–29. http://dx.doi.org/10.29140/jaltcall.v1n2.r3.

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Sun, Kwangwon, and Sukyoung Ryu. "Analysis of JavaScript Programs." ACM Computing Surveys 50, no. 4 (2017): 1–34. http://dx.doi.org/10.1145/3106741.

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Ikram, Muhammad, Hassan Jameel Asghar, Mohamed Ali Kaafar, Anirban Mahanti, and Balachandar Krishnamurthy. "Towards Seamless Tracking-Free Web: Improved Detection of Trackers via One-class Learning." Proceedings on Privacy Enhancing Technologies 2017, no. 1 (2017): 79–99. http://dx.doi.org/10.1515/popets-2017-0006.

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Abstract Numerous tools have been developed to aggressively block the execution of popular JavaScript programs in Web browsers. Such blocking also affects functionality of webpages and impairs user experience. As a consequence, many privacy preserving tools that have been developed to limit online tracking, often executed via JavaScript programs, may suffer from poor performance and limited uptake. A mechanism that can isolate JavaScript programs necessary for proper functioning of the website from tracking JavaScript programs would thus be useful. Through the use of a manually labelled dataset composed of 2,612 JavaScript programs, we show how current privacy preserving tools are ineffective in finding the right balance between blocking tracking JavaScript programs and allowing functional JavaScript code. To the best of our knowledge, this is the first study to assess the performance of current web privacy preserving tools in determining tracking vs. functional JavaScript programs. To improve this balance, we examine the two classes of JavaScript programs and hypothesize that tracking JavaScript programs share structural similarities that can be used to differentiate them from functional JavaScript programs. The rationale of our approach is that web developers often “borrow” and customize existing pieces of code in order to embed tracking (resp. functional) JavaScript programs into their webpages. We then propose one-class machine learning classifiers using syntactic and semantic features extracted from JavaScript programs. When trained only on samples of tracking JavaScript programs, our classifiers achieve accuracy of 99%, where the best of the privacy preserving tools achieve accuracy of 78%. The performance of our classifiers is comparable to that of traditional two-class SVM. One-class classification, where a training set of only tracking JavaScript programs is used for learning, has the advantage that it requires fewer labelled examples that can be obtained via manual inspection of public lists of well-known trackers. We further test our classifiers and several popular privacy preserving tools on a larger corpus of 4,084 websites with 135,656 JavaScript programs. The output of our best classifier on this data is between 20 to 64% different from the tools under study. We manually analyse a sample of the JavaScript programs for which our classifier is in disagreement with all other privacy preserving tools, and show that our approach is not only able to enhance user web experience by correctly classifying more functional JavaScript programs, but also discovers previously unknown tracking services.

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Vardanyan, V. "Profile-based optimizations for JavaScript programs." Proceedings of the Institute for System Programming of the RAS 28, no. 1 (2016): 5–20. http://dx.doi.org/10.15514/ispras-2016-28(1)-1.

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Zhuykov, Roman, та Eugene Sharygin. "Аhead of time optimization for JavaScript programs". Proceedings of the Institute for System Programming of the RAS 27, № 6 (2015): 67–86. http://dx.doi.org/10.15514/ispras-2015-27(6)-5.

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Alimadadi, Saba, Di Zhong, Magnus Madsen, and Frank Tip. "Finding broken promises in asynchronous JavaScript programs." Proceedings of the ACM on Programming Languages 2, OOPSLA (2018): 1–26. http://dx.doi.org/10.1145/3276532.

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Zhuykov, R., and E. Sharygin. "Ahead-of-time compilation of JavaScript programs." Programming and Computer Software 43, no. 1 (2017): 51–59. http://dx.doi.org/10.1134/s036176881701008x.

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Christian, Wolfgang, Mario Belloni, Robert M. Hanson, Bruce Mason, and Lyle Barbato. "Converting Physlets and Other Java Programs to JavaScript." Physics Teacher 59, no. 4 (2021): 278–81. http://dx.doi.org/10.1119/10.0004157.

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Younang, Astrid, and Lunjin Lu. "Static Checking of Range Assertions in JavaScript Programs." International Journal of Computer Theory and Engineering 9, no. 5 (2017): 346–50. http://dx.doi.org/10.7763/ijcte.2017.v9.1164.

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Ko, Yoonseok, Xavier Rival, and Sukyoung Ryu. "Weakly sensitive analysis for JavaScript object‐manipulating programs." Software: Practice and Experience 49, no. 5 (2019): 840–84. http://dx.doi.org/10.1002/spe.2676.

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Zhuykov, Roman, Dmitry Melnik, Ruben Buchatskiy, Vahagn Vardanyan, Vladislav Ivanishin, and Eugene Sharygin. "Dynamic and ahead of time optimization for JavaScript programs." Proceedings of the Institute for System Programming of RAS 26, no. 1 (2014): 297–314. http://dx.doi.org/10.15514/ispras-2014-26(1)-10.

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Jiang, Zijian, Hao Zhong, and Na Meng. "Investigating and recommending co-changed entities for JavaScript programs." Journal of Systems and Software 180 (October 2021): 111027. http://dx.doi.org/10.1016/j.jss.2021.111027.

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Alshraide, Mohammad. "A Complete Automation of Unit Testing for JavaScript Programs." Journal of Computer Science 4, no. 12 (2008): 1012–19. http://dx.doi.org/10.3844/jcssp.2008.1012.1019.

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Tilkov, Stefan, and Steve Vinoski. "Node.js: Using JavaScript to Build High-Performance Network Programs." IEEE Internet Computing 14, no. 6 (2010): 80–83. http://dx.doi.org/10.1109/mic.2010.145.

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Richards, Gregor, Sylvain Lebresne, Brian Burg, and Jan Vitek. "An analysis of the dynamic behavior of JavaScript programs." ACM SIGPLAN Notices 45, no. 6 (2010): 1–12. http://dx.doi.org/10.1145/1809028.1806598.

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Shoikhedbrod, Michael. "Using JavaScript in web design." BOHR International Journal of Engineering 2, no. 1 (2023): 7–14. http://dx.doi.org/10.54646/bije.2023.12.

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JavaScript was created to bring a web page to life. Scripts are programs written in JavaScript. Each script is connected directly to HyperText Markup Language (HTML) in any browser and is immediately executed when the web page is loaded. JavaScript scripts are written in plain text that does not require special training, which is why JavaScript is very different from the Java programming language, to which it is very similar. A special interpreter program allows JavaScript to be used not only in the browser but anywhere, and therefore the process of executing script commands is called “interpretation.” The interpreter executes the program’s JavaScript ource code (script) “as is” in browsers. Modern interpreters convert JavaScript to machine code before execution, optimize it, and only then perform it. Because of this, JavaScript is very fast. All known browsers have a JavaScript interpreter built into them, which is why they can execute scripts on the page. However, JavaScript can be used outside of the browser. This is a full-fledged language, with programs that can be run on a server and even on consumer electronic devices, if they have the appropriate interpreter installed. This article presents various JavaScript applications, used by the author, embedded in HTML files that allow a web designer to make a text animation presentation on a web page, to make a slide show, to create a questionnaire with ready-made answers so that the user reading the web page can easily master the necessary material contained in the questionnaire, to conduct an exam with the user on any topic of interest to him or her in a multitask mode with assessments of the exam results and a table of correct answers, place scientific applications on the web page, for example, a table of chemical elements by Mendeleev with printing of all parameters of a chemical element at the request of the user or calculators that the user can use online. The author has managed to build HTML using JavaScript that allows displaying beautiful Java applets accompanied by a banner, designed as a Java applet, contained within a moving window that expands from the left end and moves to the right end at the top of the web page.

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Rokotyanskaya, Violyetta Valeryevna, and Vadim Sergeevich Abramov. "Studying WebAssembly and comparison of its performance with JavaScript." Vestnik of Astrakhan State Technical University. Series: Management, computer science and informatics 2023, no. 2 (2023): 93–100. http://dx.doi.org/10.24143/2072-9502-2023-2-93-100.

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Over the long history of the Internet, JavaScript has been the primary programming language in web development. Because of its simple syntax and support in all popular browsers, the language has gained popularity among the developers. However, as computer power and user demands evolved, simple sites turned into the web applications that are full-fledged analogues of the desktop applications. As capabilities grew, so did the performance requirements of such programs. Browser engines have developed ways to optimize the code they run, and intense competition between browsers has contributed to a qualitative increase in performance. Despite all the ways to increase the speed of execution of JavaScript code, the main stopping factor was the dynamic typing of the language. Because of dynamic typing, the browser engine needs to check each time the program is executed whether the variable is an integer, a float, or any other valid type. Thus, each JavaScript instruction has to go through several type checks and conversions, which slows down the execution. This led to the idea of using languages with strict typing, which could compensate for this drawback. But the browser engines cannot execute the code of other languages, that is why there appeared the technology WebAssembly. It allows writing code in languages with static typing, and then parses it into  a more native and machine-readable format, which speeds up execution of programs compared to JavaScript. The technology is cross-platform and supports the main programming languages: C++, C, Java, C#. WebAssembly is also implemented using JavaScript, which allows to use the concise JavaScript syntax and computing power of WebAssembly together.

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Møller, Anders, Benjamin Barslev Nielsen, and Martin Toldam Torp. "Detecting locations in JavaScript programs affected by breaking library changes." Proceedings of the ACM on Programming Languages 4, OOPSLA (2020): 1–25. http://dx.doi.org/10.1145/3428255.

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Serrano, Manuel. "Of JavaScript AOT compilation performance." Proceedings of the ACM on Programming Languages 5, ICFP (2021): 1–30. http://dx.doi.org/10.1145/3473575.

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The fastest JavaScript production implementations use just-in-time (JIT) compilation and the vast majority of academic publications about implementations of dynamic languages published during the last two decades focus on JIT compilation. This does not imply that static compilers (AoT) cannot be competitive; as comparatively little effort has been spent creating fast AoT JavaScript compilers, a scientific comparison is lacking. This paper presents the design and implementation of an AoT JavaScript compiler, focusing on a performance analysis. The paper reports on two experiments, one based on standard JavaScript benchmark suites and one based on new benchmarks chosen for their diversity of styles, authors, sizes, provenance, and coverage of the language. The first experiment shows an advantage to JIT compilers, which is expected after the decades of effort that these compilers have paid to these very tests. The second shows more balanced results, as the AoT compiler generates programs that reach competitive speeds and that consume significantly less memory. The paper presents and evaluates techniques that we have either invented or adapted from other systems, to improve AoT JavaScript compilation.

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Hong Son, Nguyen, and Ha Thanh Dung. "Malicious Javascript Detection based on Clustering Techniques." International Journal of Network Security & Its Applications 13, no. 6 (2021): 11–21. http://dx.doi.org/10.5121/ijnsa.2021.13602.

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Malicious JavaScript code is still a problem for website and web users. The complication and equivocation of this code make the detection which is based on signatures of antivirus programs becomes ineffective. So far, the alternative methods using machine learning have achieved encouraging results, and have detected malicious JavaScript code with high accuracy. However, according to the supervised learning method, the models, which are introduced, depend on the number of labeled symbols and require significant computational resources to activate. The rapid growth of malicious JavaScript is a real challenge to the solutions based on supervised learning due to the lacking of experience in detecting new forms of malicious JavaScript code. In this paper, we deal with the challenge by the method of detecting malicious JavaScript based on clustering techniques. The known symbols that will be analyzed, the characteristics which are extracted, and a detection processing technique applied on output clusters are included in the model. This method is not computationally complicated, as well as the typical case experiments gave positive results; specifically, it has detected new forms of malicious JavaScript code.

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Tang, Jun. "A Lightweight Distributed Computing System Based on JavaScript Technology." Applied Mechanics and Materials 34-35 (October 2010): 1911–15. http://dx.doi.org/10.4028/www.scientific.net/amm.34-35.1911.

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Because the web is not only the platform for information exchange but also the computational platform based on JavaScript engine, every computer having installed modern browser on the Internet can easily access the web and execute some JavaScript programs. Under above conditions, we develop a lightweight distributed computing system based on the web and JavaScript technologies. Our system plays an intermediary role between the IT expert who has to solve large-scale computational problem and end users on the Internet. In the other words, people could easily cooperate with each other to finish complicated computational problem through the support of our system.

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Song, Xuyan, Chen Chen, Baojiang Cui, and Junsong Fu. "Malicious JavaScript Detection Based on Bidirectional LSTM Model." Applied Sciences 10, no. 10 (2020): 3440. http://dx.doi.org/10.3390/app10103440.

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JavaScript has been widely used on the Internet because of its powerful features, and almost all the websites use it to provide dynamic functions. However, these dynamic natures also carry potential risks. The authors of the malicious scripts started using JavaScript to launch various attacks, such as Cross-Site Scripting (XSS), Cross-site Request Forgery (CSRF), and drive-by download attack. Traditional malicious script detection relies on expert knowledge, but even for experts, this is an error-prone task. To solve this problem, many learning-based methods for malicious JavaScript detection are being explored. In this paper, we propose a novel deep learning-based method for malicious JavaScript detection. In order to extract semantic information from JavaScript programs, we construct the Program Dependency Graph (PDG) and generate semantic slices, which preserve rich semantic information and are easy to transform into vectors. Then, a malicious JavaScript detection model based on the Bidirectional Long Short-Term Memory (BLSTM) neural network is proposed. Experimental results show that, in comparison with the other five methods, our model achieved the best performance, with an accuracy of 97.71% and an F1-score of 98.29%.

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Christophe, Laurent, Coen De Roover, Elisa Gonzalez Boix, and Wolfgang De Meuter. "Orchestrating dynamic analyses of distributed processes for full-stack JavaScript programs." ACM SIGPLAN Notices 53, no. 9 (2020): 107–18. http://dx.doi.org/10.1145/3393934.3278135.

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Barrie, Patrick J. "JavaScript Programs To Calculate Thermodynamic Properties Using Cubic Equations of State." Journal of Chemical Education 82, no. 6 (2005): 958. http://dx.doi.org/10.1021/ed082p958.

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Barrie, Patrick J. "JavaScript Programs To Calculate Thermodynamic Properties Using Cubic Equations of State." Journal of Chemical Education 82, no. 6 (2005): 960. http://dx.doi.org/10.1021/ed082p960.2.

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Vardanyan, V., V. Ivanishin, S. Asryan, A. Khachatryan, and J. Hakobyan. "Dynamic compilation of JavaScript programs to the statically typed LLVM intermediate representation." Proceedings of the Institute for System Programming of the RAS 27, no. 6 (2015): 33–48. http://dx.doi.org/10.15514/ispras-2015-27(6)-3.

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Antal, Gábor, Zoltán Tóth, Péter Hegedűs, and Rudolf Ferenc. "Enhanced Bug Prediction in JavaScript Programs with Hybrid Call-Graph Based Invocation Metrics." Technologies 9, no. 1 (2020): 3. http://dx.doi.org/10.3390/technologies9010003.

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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.

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MORALES, JOSE F., RÉMY HAEMMERLÉ, MANUEL CARRO, and MANUEL V. HERMENEGILDO. "Lightweight compilation of (C)LP to JavaScript." Theory and Practice of Logic Programming 12, no. 4-5 (2012): 755–73. http://dx.doi.org/10.1017/s1471068412000336.

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AbstractWe present and evaluate a compiler from Prolog (and extensions) to JavaScript which makes it possible to use (constraint) logic programming to develop the client side of web applications while being compliant with current industry standards. Targeting JavaScript makes (C)LP programs executable in virtually every modern computing device with no additional software requirements from the point of view of the user. In turn, the use of a very high-level language facilitates the development of high-quality, complex software. The compiler is a back end of the Ciao system and supports most of its features, including its module system and its rich language extension mechanism based onpackages. We present an overview of the compilation process and a detailed description of the run-time system, including the support for modular compilation into separate JavaScript code. We demonstrate the maturity of the compiler by testing it with complex code such as a CLP(FD) library written in Prolog with attributed variables. Finally, we validate our proposal by measuring the performance of some LP and CLP(FD) benchmarks running on top of major JavaScript engines.

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Wang, Yuchen, Kwok Sun Cheng, Myoungkyu Song, and Eli Tilevich. "A declarative enhancement of JavaScript programs by leveraging the Java metadata infrastructure." Science of Computer Programming 181 (July 2019): 27–46. http://dx.doi.org/10.1016/j.scico.2019.05.005.

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Bubukayr, Maryam, and Mounir Frikha. "Effective Techniques for Protecting the Privacy of Web Users." Applied Sciences 13, no. 5 (2023): 3191. http://dx.doi.org/10.3390/app13053191.

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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.

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Goel, Aviral, Pierre Donat-Bouillud, Filip Křikava, Christoph M. Kirsch, and Jan Vitek. "What we eval in the shadows: a large-scale study of eval in R programs." Proceedings of the ACM on Programming Languages 5, OOPSLA (2021): 1–23. http://dx.doi.org/10.1145/3485502.

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Most dynamic languages allow users to turn text into code using various functions, often named <tt>eval</tt>, with language-dependent semantics. The widespread use of these reflective functions hinders static analysis and prevents compilers from performing optimizations. This paper aims to provide a better sense of why programmers use <tt>eval</tt>. Understanding why <tt>eval</tt> is used in practice is key to finding ways to mitigate its negative impact. We have reasons to believe that reflective feature usage is language and application domain-specific; we focus on data science code written in R and compare our results to previous work that analyzed web programming in JavaScript. We analyze 49,296,059 calls to <tt>eval</tt> from 240,327 scripts extracted from 15,401 R packages. We find that <tt>eval</tt> is indeed in widespread use; R’s <tt>eval</tt> is more pervasive and arguably dangerous than what was previously reported for JavaScript.

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Ponomarenko, G. S., and P. G. Klyucharev. "Detection of Obfuscated Javascript Code Based on Abstract Syntax Trees Coloring." Mathematics and Mathematical Modeling, no. 2 (June 9, 2020): 1–24. http://dx.doi.org/10.24108/mathm.0220.0000218.

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The paper deals with a problem of the obfuscated JavaScript code detection and classification based on Abstract Syntax Trees (AST) coloring. Colors of the AST vertexes and edges are assigned with regard to the types of the AST vertexes specified by the program lexical and syntax structure and the programming language standard. Research involved a few stages. First of the all, a non-obfuscated JavaScript programs dataset was collected by the public repositories evaluation. Secondly, obfuscated samples were created using eight open-source obfuscators. Classifier models were built using an algorithm of gradient boosting on the decision trees (GBDT). We built two types of the classifiers. The first one is the model that classifies the program according to the type of the obfuscator used, i.e. based on what obfuscator created the sample. The second one tries to detect samples obfuscated by the obfuscator whose samples are not observed during training. The quality of the obtained models is on par with the known published results. The feature engineering method proposed in the paper does not require a preliminary analysis of the obfuscators and obfuscating transformations. In the final part of the paper we analyze a quality of models estimated, discussing the certain statistical properties of the obfuscated and non-obfuscated samples obtained and corresponding colored ASTs. Analysis of generated samples of obfuscated programs has shown that the method proposed in the paper has some limitations. In particular, it is difficult to recognize minifiers or other obfuscating programs, which change the lexical structure to a greater extent and the syntax to a lesser extent. To improve the quality of detection of this kind of obscuring transformations, one can built combined classifiers using both the method based on the AST coloring and the additional information about lexemes and punctuation, for example, entropy of identifiers and strings, proportion of characters in upper and lower case, usage frequency of certain characters etc.

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Stothard, Paul. "The Sequence Manipulation Suite: JavaScript Programs for Analyzing and Formatting Protein and DNA Sequences." BioTechniques 28, no. 6 (2000): 1102–4. http://dx.doi.org/10.2144/00286ir01.

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Haghish, E. F. "On the importance of syntax coloring for teaching statistics." Stata Journal: Promoting communications on statistics and Stata 19, no. 1 (2019): 83–86. http://dx.doi.org/10.1177/1536867x19830892.

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In this article, I underscore the importance of syntax coloring in teaching statistics. I also introduce the statax package, which includes JavaScript and LATEX programs for highlighting Stata code in HTML and LATEX documents. Furthermore, I provide examples showing how to implement this package for developing educational materials on the web or for a classroom handout.

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Tiwow, Olivia Benazir Ester, and Adhi Kusnadi. "Implementasi Algoritma Prim Dalam Penentuan Ikatan Senyawa Kimia." Jurnal ULTIMA Computing 9, no. 2 (2018): 48–51. http://dx.doi.org/10.31937/sk.v9i2.789.

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This research concerns the implementation of Prim algorithm in the determination of bond of a chemical compound. By doing a minimum spanning tree search of a weight graph that has been created. Input from the user in the form of chemical compounds will be processed so that the compound can be divided into elements that will act as nodes on the prime algorithm. Weight of the tangent node is obtained from the comparison of the minimum number of atoms that can react between the two elements that will be bonded to each other. Applications developed on the basis of the web by considering the functionality of the design can be accessed by many people. System design is poured into HTML and Javascript programming language. All design functionality is poured into the Javascript programming language. This study is still limited to programs that can only process the input of compounds that are in class A in the periodic table. And preformed compounds such as SO42-, HO3-. This application has an accuracy rate of 77.41% and 66.67% of testers said that this application is useful for Indonesian education. Index Terms—Chemical Compound, prim algoritm, minimum spanning tree, website, Javascript.

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Arceri, Vincenzo, Isabella Mastroeni, and Sunyi Xu. "Static Analysis for ECMAScript String Manipulation Programs." Applied Sciences 10, no. 10 (2020): 3525. http://dx.doi.org/10.3390/app10103525.

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In recent years, dynamic languages, such as JavaScript or Python, have been increasingly used in a wide range of fields and applications. Their tricky and misunderstood behaviors pose a great challenge for static analysis of these languages. A key aspect of any dynamic language program is the multiple usage of strings, since they can be implicitly converted to another type value, transformed by string-to-code primitives or used to access an object-property. Unfortunately, string analyses for dynamic languages still lack precision and do not take into account some important string features. In this scenario, more precise string analyses become a necessity. The goal of this paper is to place a first step for precisely handling dynamic language string features. In particular, we propose a new abstract domain approximating strings as finite state automata and an abstract interpretation-based static analysis for the most common string manipulating operations provided by the ECMAScript specification. The proposed analysis comes with a prototype static analyzer implementation for an imperative string manipulating language, allowing us to show and evaluate the improved precision of the proposed analysis.

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Kang, Ah, Young-Seob Jeong, Se Kim, and Jiyoung Woo. "Malicious PDF Detection Model against Adversarial Attack Built from Benign PDF Containing JavaScript." Applied Sciences 9, no. 22 (2019): 4764. http://dx.doi.org/10.3390/app9224764.

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Intelligent attacks using document-based malware that exploit vulnerabilities in document viewing software programs or document file structure are increasing rapidly. There are many cases of using PDF (portable document format) in proportion to its usage. We provide in-depth analysis on PDF structure and JavaScript content embedded in PDFs. Then, we develop the diverse feature set encompassing the structure and metadata such as file size, version, encoding method and keywords, and the content features such as object names, keywords, and readable strings in JavaScript. When features are diverse, it is hard to develop adversarial examples because small changes are robust for machine-learning algorithms. We develop a detection model using black-box type models with the structure and content features to minimize the risk of adversarial attacks. To validate the proposed model, we design the adversarial attack. We collect benign documents containing multiple JavaScript codes for the base of adversarial samples. We build the adversarial samples by injecting the malware codes into base samples. The proposed model is evaluated against a large collection of malicious and benign PDFs. We found that random forest, an ensemble algorithm of a decision tree, exhibits a good performance on malware detection and is robust for adversarial samples.

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Miyaji, Isao. "Comparison of Term Recognition and Awareness in Blended Classes of JavaScript and PHP Programming." Asian Education Studies 2, no. 1 (2016): 28. http://dx.doi.org/10.20849/aes.v2i1.108.

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A lecture course is provided by projecting slides on contents of the lecture course on a screen and the grammar in textbook and JavaScript program examples are explained in a lecture course on programming. It was then followed by explanation with materials describing program examples to practice the grammar and exercise problems. We conducted exercises to prepare programs in exercise problems with reference to the program examples. Instructions were given to complete the program during the lecture course as soon as possible, and the students were asked to submit the program and report files using the functions of e-learning. During the 14th week, each student was asked to prepare a program of voluntary subject he or she came up with, execute it and evaluate one another. With reference to the results, they were asked to correct them by the final 15th week and to evaluate one another again in the 15th week. The degree of term recognition and awareness were investigated before and after the lecture course. This article reports on the results obtained regarding this information on JavaScript programming. Then the difference between JavaScript and PHP programming is reported by comparing them with the results regarding PHP programming reported previously.

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Rao, Xiaojia, Aïna Linn Georges, Maxime Legoupil, et al. "Iris-Wasm: Robust and Modular Verification of WebAssembly Programs." Proceedings of the ACM on Programming Languages 7, PLDI (2023): 1096–120. http://dx.doi.org/10.1145/3591265.

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WebAssembly makes it possible to run C/C++ applications on the web with near-native performance. A WebAssembly program is expressed as a collection of higher-order ML-like modules, which are composed together through a system of explicit imports and exports using a host language, enabling a form of higher- order modular programming. We present Iris-Wasm, a mechanized higher-order separation logic building on a specification of Wasm 1.0 mechanized in Coq and the Iris framework. Using Iris-Wasm, we are able to specify and verify individual modules separately, and then compose them modularly in a simple host language featuring the core operations of the WebAssembly JavaScript Interface. Building on Iris-Wasm, we develop a logical relation that enforces robust safety: unknown, adversarial code can only affect other modules through the functions that they explicitly export. Together, the program logic and the logical relation allow us to formally verify functional correctness of WebAssembly programs, even when they invoke and are invoked by unknown code, thereby demonstrating that WebAssembly enforces strong isolation between modules.

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Leger, Paul, Felipe Ruiz, Hiroaki Fukuda, and Nicolás Cardozo. "Benefits, challenges, and usability evaluation of DeloreanJS: a back-in-time debugger for JavaScript." PeerJ Computer Science 9 (February 24, 2023): e1238. http://dx.doi.org/10.7717/peerj-cs.1238.

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JavaScript Web applications are a common product in industry. As with most applications, Web applications can acquire software flaws (known as bugs), whose symptoms are seen during the development stage and, even worse, in production. The use of debuggers is beneficial for detecting bugs. Unfortunately, most JavaScript debuggers (1) only support the “step into/through” feature in an execution program to detect a bug, and (2) do not allow developers to go back-in-time at the application execution to take actions to detect the bug accurately. For example, the second limitation does not allow developers to modify the value of a variable to fix a bug while the application is running or test if the same bug is triggered with other values of that variable. Using concepts such as continuations and static analysis, this article presents a usable debugger for JavaScript, named DeloreanJS, which enables developers to go back-in-time in different execution points and resume the execution of a Web application to improve the understanding of a bug, or even experiment with hypothetical scenarios around the bug. Using an online and available version, we illustrate the benefits of DeloreanJS through five examples of bugs in JavaScript. Although DeloreanJS is developed for JavaScript, a dynamic prototype-based object model with side effects (mutable variables), we discuss our proposal with the state-of-art/practice of debuggers in terms of features. For example, modern browsers like Mozilla Firefox include a debugger in their distribution that only support for the breakpoint feature. However DeloreanJS uses a graphical user interface that considers back-in-time features. The aim of this study is to evaluate and compare the usability of DeloreanJS and Mozilla Firefox’s debugger using the system usability scale approach. We requested 30 undergraduate students from two computer science programs to solve five tasks. Among the findings, we highlight two results. First, we found that 100% (15) of participants recommended DeloreanJS, and only 53% (eight) recommended Firefox’s debugger to complete the tasks. Second, whereas the average score for DeloreanJS is 71.6 (“Good”), the average score for Firefox’s debugger is 55.8 (“Acceptable”).

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Biibosunov, Bolotbek, and Jenish Beksulanov. "Information technologies for landslides and mudflows research." E3S Web of Conferences 177 (2020): 06005. http://dx.doi.org/10.1051/e3sconf/202017706005.

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This article presents the results of research using computer technology and mathematical modeling in relation to hydrodynamic processes that determine such natural disasters as landslides and mudflows common in the territory of the Kyrgyz Republic. A specialized website is proposed, which contains the results of scientific research on natural and man-made disasters and exogenous geological processes (EGP). The following systems were used as the main database management systems (DBMS): MS Access, My SQL and PostgreSQL. Thus, the main means of developing computer programs and computational procedures are Delphi, Python, Visual Basic, Java and JavaScript. Web technologies and the following software tools were used to design and create the site: Python, JavaScript, PhP and HTML. Modern level of scientific research presumes and obliges development and using of new information technologies. In this regard there was defined a problem on mathematical modelling and information technologies using for research and forecasting of EGP on the territory of Kyrgyzstan. There are proposed hydrodynamic models and numerical methods of their solution. Information system is developed for landslides, mudflows, and other EGP types, typical for Kyrgyzstan.

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Gabryel, Marcin, Magdalena M. Scherer, Łukasz Sułkowski, and Robertas Damaševičius. "Decision Making Support System for Managing Advertisers By Ad Fraud Detection." Journal of Artificial Intelligence and Soft Computing Research 11, no. 4 (2021): 331–39. http://dx.doi.org/10.2478/jaiscr-2021-0020.

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Abstract Efficient lead management allows substantially enhancing online channel marketing programs. In the paper, we classify website traffic into human- and bot-origin ones. We use feedforward neural networks with embedding layers. Moreover, we use one-hot encoding for categorical data. The data of mouse clicks come from seven large retail stores and the data of lead classification from three financial institutions. The data are collected by a JavaScript code embedded into HTML pages. The three proposed models achieved relatively high accuracy in detecting artificially generated traffic.

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Mironov, Denis Dmitrievich, Daniil Alekseevich Sigalov, and Maxim Petrovich Malkov. "Research into Occurrence of Insecurely-Serialized Objects in Client-Side Code of Web-Applications." Proceedings of the Institute for System Programming of the RAS 35, no. 1 (2023): 223–36. http://dx.doi.org/10.15514/ispras-2023-35(1)-14.

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This paper studies the occurrence of insecure deserialization in communication between client-side code and the server-side of a web application. Special attention was paid to serialized objects sent from JavaScript client-side code. Specific patterns of using serialized objects within the client-side JavaScript code were identified and unique classes were formulated, whose main goal is to facilitate manual and automatic analysis of web applications. A tool that detects a serialized object in the client-side code of a web page has been designed and implemented. This tool is capable of finding encoded serialized objects as well as serialized objects encoded using several sequentially applied encodings. For found samples of serialized objects, the tool determines the context in which the found object appears on the page. For objects inside JavaScript code, the tool identifies the previously mentioned classes by mapping the vertices of the abstract syntax tree (AST) of the code. Web application endpoints were checked for whether programming objects were deserialized on the server side, after obtaining the results of the study. As a result of this check, previously unknown vulnerabilities were found, which were reported to the developers of this software. One of them was identified as CVE-2022-24108. Based on the results of this research, a method was proposed to facilitate both manual and automated searches for vulnerabilities of the "Deserialization of untrusted data". The proposed algorithm was tested on more than 50,000 web application pages from the Alexa Top 1M list, as well as on 20,000 web application pages from Bug Bounty programs.

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Wai, Richard. "XERIS/APEX." ACM SIGAda Ada Letters 40, no. 2 (2021): 65–69. http://dx.doi.org/10.1145/3463478.3463484.

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Modern day cloud native applications have become broadly representative of distributed systems in the wild. However, unlike traditional distributed system models with conceptually static designs, cloud-native systems emphasize dynamic scaling and on-line iteration (CI/CD). Cloud-native systems tend to be architected around a networked collection of distinct programs ("microservices") that can be added, removed, and updated in real-time. Typically, distinct containerized programs constitute individual microservices that then communicate among the larger distributed application through heavy-weight protocols. Common communication stacks exchange JSON or XML objects over HTTP, via TCP/TLS, and incur significant overhead, particularly when using small size message sizes. Additionally, interpreted/JIT/VM-based languages such as Javascript (NodeJS/Deno), Java, and Python are dominant in modern microservice programs. These language technologies, along with the high-overhead messaging, can impose superlinear cost increases (hardware demands) on scale-out, particularly towards hyperscale and/or with latency-sensitive workloads.

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Zakharov, A. A., and Yu V. Zakharova. "The use of modern computer technologies in the study of the Geometric Modeling course." Journal of Physics: Conference Series 2308, no. 1 (2022): 012007. http://dx.doi.org/10.1088/1742-6596/2308/1/012007.

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Abstract The paper describes the educational discipline “Geometric modeling”, which is suitable for students of applied mathematics. The course covers spline representations and transfinite interpolation methods for modeling the shape of geometric objects, the methods for performing Boolean operations between objects and methods for visualizing the objects. Screenshots of some graphic windows of the developed visualization programs for supporting the implementation of individual assignments in the course are shown. The programs have web interfaces that use the WebGL graphics API in a browser, independent of an operating system, and are also available for smartphones and tablets. They provide graphical output for curves and surfaces in three-dimensional space, user interfaces for entering the initial data and interaction with geometric objects. Students only need to write their own calculation code for the programs in the JavaScript programming language. The interactive tools enable students to get practical skills in working with splines and visually analyze the impact of the input data on the final geometry. The individual assignments and programs are specially focused to stimulate students’ interests. The paper is addressed to teachers of the geometric modeling for mathematicians and computer scientists. It seems to be interesting to those who develop software interfaces for geometric modeling algorithms.

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Yu, Chung-Yen, Yung-Ting Chuang, and Hsi-Peng Kuan. "Understanding Faculty Collaboration and Productivity: A Case Study." Asian Social Science 13, no. 3 (2017): 1. http://dx.doi.org/10.5539/ass.v13n3p1.

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Research productivity has been always an important part of every academic’s job, since it has a profound effect on faculty promotion and tenure decisions. In addition, some scholars believe that co-authorship between faculty members has a great impact on their academic life and faculty advancement. Since 2005, the Ministry of Education of Taiwan (MOE) has developed two university programs and evaluation policies for improving the competitiveness and internationalization of Taiwan universities, and has clearly stated that there is a strong relationship between faculty promotion and research performance. However, none of them has used social network analysis (SNA) to examine research productivity and co-authorship under two university programs and evaluation policies from MOE in Taiwan. Therefore, in this study, we first uses SNA to analyze the research productivity, collaboration patterns, and publication strategies of faculty members in a Management Information Systems (MIS) department at a national university in Taiwan. Then, we used D3, a well-known drawing tool to create data visualization using JavaScript libraries, to visualize and discuss how these two university programs and evaluation policies from the MOE affected these patterns and strategies. We hope that our study not only provides beneficial information to the MIS department, but can be treated as an important source for MOE committees in their future adjustment of university programs and policies.

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Reitinger, Nathan, and Michelle L. Mazurek. "ML-CB: Machine Learning Canvas Block." Proceedings on Privacy Enhancing Technologies 2021, no. 3 (2021): 453–73. http://dx.doi.org/10.2478/popets-2021-0056.

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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.

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Areshchankau, I. S. "Development of the learning game environment "Bukashka" ("Ladyb ug") Using client web technologies." Informatics in school, no. 6 (September 25, 2021): 32–38. http://dx.doi.org/10.32517/2221-1993-2021-20-6-32-38.

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The theme "Algorithmization and programming" is one of the most difficult in the school informatics course. The computer programs that implement models of microworlds in which virtual actors operate help to explain the basic concepts of this theme for schoolchildren.The best visualization can be achieved using learning environments with drawing actors. The learning environment "Kangaroo Ru" provides the most simple model. It was developed in the early 1990s as a part of an educational project for IBM PS/2 computers running the DOS operating system.The usage of "Kangaroo Ru" is fraught with difficulties on modern computers. This article describes a case of the learning environment that is "Kangaroo Ru" compatible, but is implemented using web technologies HTML, CSS and JavaScript. It is runnable in web browsers of a wide range of hardware platforms.

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Quinn, Anne. "Learning to Program with SoloLearn." Mathematics Teacher 112, no. 3 (2018): 226–30. http://dx.doi.org/10.5951/mathteacher.112.3.0226.

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When looking for a simple activity to show eighth-grade students at the Hour of Code (a global activity for students) in December, I became familiar with an app called SoloLearn: Learn to Code (SoloLearn 2018). In fact, it was an eighth-grade student who introduced me to it. The program is simple enough that students can produce their first programs in minutes and comprehensive enough that users can progress through many levels as they master any of several computerprogramming languages. The twelve languages are C++, Java, Python, HTML, CSS, JavaScript, PHP, SQL, Ruby, JQuery, C#, and Swift. Various applied mathematics classes at the college level assume a basic knowledge of programming, and having this skill can open many careers for students. Coding also can serve as an enrichment activity for algebra students.

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