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Showing 1 - 20 of 6,605

1

A Hybrid JavaScript-WebAssembly Framework for Efficient Deep Learning Inference in Web Browsers.
Lee, Chaeeun ; Jeon, Sanghoon
KSII Transactions on Internet & Information Systems. Jan2026, Vol. 20 Issue 1, p243-264. 22p.

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2

PyRHOH: A meta-learning analysis framework for determining the impact of compilation on malicious JavaScript identification
Fulkerson, Eli ; Yocam, Eric ; Vaidyan, Varghese ; et al.
In Machine Learning with Applications August 2025

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4

Accelerating page loads via streamlining JavaScript engine for distributed learning
Liang, Chen ; Wang, Guoyu ; Li, Ning ; et al.
In Information Sciences July 2024 675

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5

Learning JavaScript: a hands-on guide to the fundamentals of modern JavaScript
Learning Java Script
Wright, Tim, 1982- ; Wright, Tim, 1982-

JavaScript (Computer pro... Scripting languages (Com...
Book
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7

Learning JavaScript robotics: design, build, and program your own remarkable robots with JavaScript and open source hardware
Perch, Kassandra, author. ; Perch, Kassandra, author.

Robots -- Programming. Robotics. Arduino (Programmable co... JavaScript (Computer pro...
Book
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8

Learning JavaScript
JavaScript
Learning Java Script
Powers, Shelley. ; Powers, Shelley.

JavaScript (Computer pro...
Book
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9

Learning JavaScript design patterns
Learning Java Script design patterns
Osmani, Addy. ; Osmani, Addy.

JavaScript (Computer pro... Software patterns. Computer software -- Reu... Scripting languages (Com...
Book
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10

Learning JavaScript
Powers, Shelley. ; Powers, Shelley.

JavaScript (Computer pro...
Book
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11

ZipAST: Enhancing malicious JavaScript detection with sequence compression
Chen, Zixian ; Wang, Weiping ; Qin, Yan ; et al.
In Computers & Security June 2025 153

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12

A machine learning approach to detection of JavaScript-based attacks using AST features and paragraph vectors
Ndichu, Samuel ; Kim, Sangwook ; Ozawa, Seiichi ; et al.
In Applied Soft Computing Journal November 2019 84

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13

Detection and defending the XSS attack using novel hybrid stacking ensemble learning-based DNN approach
Krishnan, Muralitharan ; Lim, Yongdo ; Perumal, Seethalakshmi ; et al.
In Digital Communications and Networks June 2024 10(3):716-727

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14

Learning JavaScript : a hands-on guide to the fundamentals of modern JavaScript by Tim Wright
Teodorovici, Vasile G.
ACM SIGSOFT Software Engineering Notes. 38(3):35-36

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15

Obfuscated Clone Search in JavaScript based on Reinforcement Subsequence Learning
Song, Leo ; Ding, Steven H. H. ; Tian, Yuan ; et al.
ACM Transactions on Software Engineering and Methodology. 34(6):1-28

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16

Mutation-Based Deep Learning Framework Testing Method in JavaScript Environment
Zou, Yinglong ; Zhai, Juan ; Fang, Chunrong ; et al.
Proceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering. :970-981

Conference
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17

A Design of Phrase Fill-in-Blank Problem in JavaScript-Based Web-Client Programming Learning Assistant System
Qi, Huiyu ; Li, Zhikang ; Funabiki, Nobuo ; et al.
Proceedings of the 2024 7th International Conference on Computational Intelligence and Intelligent Systems. :146-150

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18

Detection of malicious javascript on an imbalanced dataset
Phung, Ngoc Minh ; Mimura, Mamoru
In Internet of Things March 2021 13

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19

Fuzzing JavaScript Interpreters with Coverage-Guided Reinforcement Learning for LLM-Based Mutation
Eom, Jueon ; Jeong, Seyeon ; Kwon, Taekyoung
Proceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis. :1656-1668

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20

Investigating and recommending co-changed entities for JavaScript programs
Jiang, Zijian ; Zhong, Hao ; Meng, Na
In The Journal of Systems & Software October 2021 180

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