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Ruiling Chen

dblp:334/7228 · DBLP profile ↗
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2ranked-venue papers
1as first author
2since 2021 · last 2023
0009-0007-6925-9327ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Software engineering, systems software and programming languages · 1 · 1 first-author · 1 since 2021Human-computer interaction and ubiquitous computing · 1 · 1 since 2021Applied, interdisciplinary, general and emerging computing · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Network and information security
1 paper
Cryptographic primitives and cryptanalysis · 100%

Topics — the 1 heaviest of 1, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Cryptographic primitives and cryptanalysis › cryptographic implementation
cryptographic implementation verification
0.712023
llvm2CryptoLine: Verifying Arithmetic in Cryptographic C Programs · ESEC/SIGSOFT FSE 2023

Methods — techniques the papers use, named apart from their topics

static analysis · 0.7formal verification · 0.7
YearPublicationVenuePosition
2023 llvm2CryptoLine: Verifying Arithmetic in Cryptographic C Programs
abstract
Correct implementations of cryptographic primitives are essential for modern security. These implementations often contain arithmetic operations involving non-linear computations that are infamously hard to verify. We present llvm2CryptoLine, an automated formal verification tool for arithmetic operations in cryptographic C programs. llvm2CryptoLine successfully verifies 51 arithmetic C programs from industrial cryptographic libraries OpenSSL, wolfSSL and NaCl. Most of the programs are verified fully automatically and efficiently. A screencast that showcases llvm2CryptoLine can be found at https://youtu.be/QXuSmja45VA. Source code is available at https://github.com/fmlab-iis/llvm2cryptoline.
Ruiling Chen, Jiaxiang Liu 0001, Xiaomu Shi, Ming-Hsien Tsai 0001, Bow-Yaw Wang, Bo-Yin Yang
ESEC/SIGSOFT FSE1
2022 Motion Arc Analysis in Virtual Reality Environment
abstract
With the popularity of VR, the body interaction in VR has not received enough corresponding attention, and the interaction design that violates the law of the human body happens a lot. Therefore, we study the action interaction in virtual reality through motion arc, an important parameter of human action, to get the factors that affect the user’s body interaction in virtual reality. A within-subject experiment (n=18) was conducted, in which all participants played three VR games and responded to the post-game questionnaire. Through video recordings of the front and right side of them, 3D skeleton modeling was reconstructed by using OpenPose and the conversion relationship between the four coordinate systems under computer vision. After the results of operations such as skeleton standardization and motion segmentation, clustering algorithms are used to cluster similar users, and Spearman’s Rank Correlation Coefficient is used to study the influence of user characteristics on motion arc. Our results indicated that in an unconstrained game, the tutorial makes the motion arc increase, while in a constrained game, the change of motion arc is more complex. It is also found that the participants’ instructions learning has the greatest impact on the average motion arc, followed by individual factors (including age gender, etc.) and sports experience.
Chenxin Qu, Ruiling Chen, Xiaoping Che
SMC2