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The Influence of Big Data‐Driven Educational Technologies on College Teaching Development

Engineering Reports, Volume 8, Issue 2, February 2026.
Exploring the impact of big data on college instructors: enhancing teaching, fostering professional growth, and addressing challenges in data adoption and privacy. ABSTRACT The rapid development of big data and mobile Internet technologies has significantly influenced the instructional growth of college instructors. This study investigated how big data
Ling Yu, Wenye Li, Ying Luo
wiley   +1 more source

Artificial intelligence‐powered microscopy: Transforming the landscape of parasitology

Journal of Microscopy, Volume 301, Issue 2, Page 280-329, February 2026.
Abstract Microscopy and image analysis play a vital role in parasitology research; they are critical for identifying parasitic organisms and elucidating their complex life cycles. Despite major advancements in imaging and analysis, several challenges remain. These include the integration of interdisciplinary data; information derived from various model
Mariana De Niz, Sara Silva Pereira, David Kirchenbuechler, Leandro Lemgruber, Constadina Arvanitis   +4 more
wiley   +1 more source

A Survey of SIR‐Based Differential Epidemic Models for Control and Security Against Malware Propagation in Computer Networks

SECURITY AND PRIVACY, Volume 9, Issue 1, January/February 2026.
ABSTRACT Unarguably, malware and their variants have metamorphosed into objects of attack and cyber warfare. These issues have directed research focus to modeling infrastructural settings and infection scenarios, analyzing propagation mechanisms, and conducting studies that highlight optimized remedial measures.
Chukwunonso Henry Nwokoye
wiley   +1 more source

Trojans in Early Design Steps—An Emerging Threat [PDF]

, 2016
Hardware Trojans inserted by malicious foundries during integrated circuit manufacturing have received substantial attention in recent years. In this paper, we focus on a different type of hardware Trojan threats: attacks in the early steps of design
Becker, Georg T., Polian, Ilia, Regazzoni, Francesco   +2 more
core  

Natural Language Processing for Hardware Security: Case of Hardware Trojan Detection in FPGAs

Cryptogr.
Field-programmable gate arrays (FPGAs) offer the inherent ability to reconfigure at runtime, making them ideal for applications such as data centers, cloud computing, and edge computing. This reconfiguration, often achieved through remote access, enables
Jaya Dofe, Wafi Danesh, Vaishnavi More, Aaditya Chaudhari   +3 more
semanticscholar   +1 more source

Automated Test Generation for Hardware Trojan Detection using Reinforcement Learning

Asia and South Pacific Design Automation Conference, 2021
Due to globalized semiconductor supply chain, there is an increasing risk of exposing System-on-Chip (SoC) designs to malicious implants, popularly known as hardware Trojans.
Zhixin Pan, P. Mishra
semanticscholar   +1 more source

A Novel Two-Level Protection Scheme against Hardware Trojans on a Reconfigurable CNN Accelerator

Cryptography
With the boom in artificial intelligence (AI), numerous reconfigurable convolution neural network (CNN) accelerators have emerged within both industry and academia, aiming to enhance AI computing capabilities.
Zichu Liu, Jia Hou, Jianfei Wang, Chen Yang   +3 more
doaj   +1 more source

A New Characterization of Hardware Trojans

IEEE Access, 2016
This paper examines hardware trojan threats to semiconductor chips, which is particularly important for chips intended for vital infrastructure and critical applications.
Samer Moein, Thomas Aaron Gulliver, Fayez Gebali, Abdulrahman Alkandari   +3 more
doaj   +1 more source

Reusing Logic Masking to Facilitate Hardware Trojan Detection [PDF]

, 2016
Hardware Trojan (HT) and Integrated Circuit (IC)/ Intellectual Property (IP) piracy are important threats which may happen in untrusted fabrication foundries.
Beroulle, Vincent, Hely, David, Nejat, Arash   +2 more
core  

A Hybrid Transformer–CNN Framework for Semantic Behavioral Modeling in Office Malware Detection

SECURITY AND PRIVACY, Volume 9, Issue 1, January/February 2026.
ABSTRACT Office documents have emerged as a prevalent attack vector, with adversaries increasingly embedding executable payloads and malicious macros to evade signature‐based detection mechanisms. To address these challenges, this study presents a hybrid Transformer–CNN semantic behavioral modeling framework for Office malware detection.
Sheikh M. Zeeshan Javed, M. Faisal Amjad, Sajjad A. Ghauri, Shahzaib Tahir, Nauman Anwar Baig   +4 more
wiley   +1 more source