Tarlac State University Library

M. Jubur, M. Shirvanian, S. Duraibi and N. Saxena, "𝐂𝐨𝐦𝐩𝐚𝐫𝐢𝐧𝐠 𝐚 𝐒𝐭𝐨𝐫𝐞-𝐋𝐞𝐬𝐬 𝐏𝐚𝐬𝐬𝐰𝐨𝐫𝐝 𝐌𝐚𝐧𝐚𝐠𝐞𝐫 𝐖𝐢𝐭𝐡 𝐓𝐫𝐚𝐝𝐢𝐭𝐢𝐨𝐧𝐚𝐥 𝐏𝐚𝐬𝐬𝐰𝐨𝐫𝐝-𝐎𝐧𝐥𝐲 𝐀𝐮𝐭𝐡𝐞𝐧𝐭𝐢𝐜𝐚𝐭𝐢𝐨𝐧" in IEEE Internet Computing, vol. 30, no. 02, pp. 76-86, March-April 2026, doi: 10.1109/MIC.2026.3668165.

𝐊𝐞𝐲𝐰𝐨𝐫𝐝𝐬: {Passwords; Usability; Authentication; Security; Browsers; Cryptography; Servers; Synchronization; Protocols; Standards; Data integrity; Authentication; Access control}

𝐀𝐛𝐬𝐭𝐫𝐚𝐜𝐭: This article presents a comprehensive usability evaluation of hidden-password online password (HIPPO), a novel store-less password manager, compared to traditional password-only authentication methods. In the digital age, robust password management solutions are essential to mitigate cognitive burdens and enhance security. Unlike conventional store-based password managers, HIPPO dynamically generates passwords without storing them, significantly reducing the risk of data breaches. Our study involved 25 participants who performed tasks such as installation, configuration, password updating, and login using both HIPPO and traditional methods. The findings reveal that HIPPO offers significant security advantages and is perceived as more secure and trustworthy by users. While HIPPO introduces additional steps for password generation and entry, participants reported higher satisfaction and ease of use compared to traditional password-only authentication. This study highlights the tradeoffs between security and usability in password management, providing valuable insights for developing more user-friendly and secure password management tools.
URL:

CSDL | IEEE Computer Society

N. Kshetri and J. Voas, "𝐑𝐞𝐭𝐫𝐚𝐜𝐭𝐢𝐧𝐠 𝐒𝐜𝐢𝐞𝐧𝐭𝐢𝐟𝐢𝐜 𝐏𝐚𝐩𝐞𝐫𝐬 𝐚𝐧𝐝 𝐀𝐜𝐤𝐧𝐨𝐰𝐥𝐞𝐝𝐠𝐢𝐧𝐠 𝐂𝐫𝐢𝐦𝐢𝐧𝐚𝐥 𝐉𝐮𝐬𝐭𝐢𝐜𝐞 𝐅𝐚𝐢𝐥𝐮𝐫𝐞𝐬" in Computer, vol. 59, no. 06, pp. 16-20, June 2026, doi: 10.1109/MC.2026.3684130.

𝗞𝗲𝘆𝘄𝗼𝗿𝗱𝘀: {Criminal Court, Scientific Papers, False Positive, Scalable, Use Of Imaging, Global South, Scientific Publications, Research Integrity, Risk Of False Positives, Scientific Misconduct, Duplicate Images, Hundreds Of Papers, Scientific Community, Rule Based, Flame Retardant, Non English Speaking, Paper Mill, Artificial Intelligence Tools, Non Native English}

𝗔𝗯𝘀𝘁𝗿𝗮𝗰𝘁: Scientific misconduct, poses a growing threat to research integrity. This article examines how AI can detect such issues on a scale while emphasizing the need for human oversight to prevent false positives and protect researchers.
URL:

CSDL | IEEE Computer Society

Y. Li, D. Wen, M. Xia, M. Chen and X. Fu,"𝐑𝐨𝐛𝐮𝐬𝐭 𝐃𝐞𝐜𝐞𝐧𝐭𝐫𝐚𝐥𝐢𝐳𝐞𝐝 𝐎𝐧𝐥𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐀𝐠𝐚𝐢𝐧𝐬𝐭 𝐓𝐚𝐫𝐠𝐞𝐭𝐞𝐝 𝐚𝐧𝐝 𝐔𝐧𝐭𝐚𝐫𝐠𝐞𝐭𝐞𝐝 𝐌𝐚𝐥𝐢𝐜𝐢𝐨𝐮𝐬 𝐃𝐚𝐭𝐚 𝐅𝐞𝐚𝐭𝐮𝐫𝐞 𝐌𝐚𝐧𝐢𝐩𝐮𝐥𝐚𝐭𝐢𝐨𝐧" in IEEE Transactions on Mobile Computing, vol. 25, no. 06, pp. 7611-7625, June 2026, doi: 10.1109/TMC.2025.3642873.

𝗞𝗲𝘆𝘄𝗼𝗿𝗱𝘀: {Generators; Delays; Classification algorithms; Data models; Telecommunication traffic; Heuristic algorithms; Costs; Biological system modeling; Threat modeling; Quality of service}

𝗔𝗯𝘀𝘁𝗿𝗮𝗰𝘁: Motivated by real-world applications, we study the problem of decentralized online learning with dynamic feedback delays in the presence of malicious data generators under different threat models. In this problem, multiple agents collaborate to classify the features of streaming data samples generated online and receive dynamically delayed feedback on the ground-truth labels. While some data generators are benign, others—due to internal motives or external factors such as cyberattacks—may maliciously manipulate data features to compromise the classification performance. In this work, we first investigate the targeted attacks by malicious data generators, i.e., feature manipulation with aims to gain preferred classification outcomes from the agents. In response, we propose two robust algorithms, RDOC-TO and RDOC-TC, countering ordinary and clairvoyant adversaries that can access certain outdated and the latest classification models of the agents, respectively. Subsequently, we address the untargeted attacks by malicious data generators, which aim to disrupt the classification outcomes without targeting any particular class, by proposing another algorithm, RDOC-U. Our theoretical analysis establishes that all three proposed algorithms achieve sublinear regret bounds. The evaluations conducted in the application of network traffic classification with two real-world datasets demonstrate the competitiveness of the proposed algorithms compared to advanced baselines.
URL:

CSDL | IEEE Computer Society

📚 Magandang Buhay, TSU!

Here's the library schedule for the coming week, June 1–6, 2026. Plan your visit and make the most of our resources for your study, research, and learning needs.

See you at the library! 😊