Security analysis and enhancements of an improved multi-factor biometric authentication scheme

Many remote user authentication schemes have been designed and developed to establish secure and authorized communication between a user and server over an insecure channel. By employing a secure remote user authentication scheme, a user and server can authenticate each other and utilize advanced services. In 2015, Cao and Ge demonstrated that An's scheme is also vulnerable to several attacks and does not provide user anonymity. They also proposed an improved multi-factor biometric authentication scheme. However, we review and cryptanalyze Cao and Ge's scheme and demonstrate that their scheme fails in correctness and providing user anonymity and is vulnerable to ID guessing attack and server masquerading attack. To overcome these drawbacks, we propose a security-improved authentication scheme that provides a dynamic ID mechanism and better security functionalities. Then, we show that our proposed scheme is secure against various attacks and prove the security of the proposed scheme using BAN Logic.111Ysciescopu

From Values to Opinions: Predicting Human Behaviors and Stances Using Value-Injected Large Language Models

Being able to predict people's opinions on issues and behaviors in realistic scenarios can be helpful in various domains, such as politics and marketing. However, conducting large-scale surveys like the European Social Survey to solicit people's opinions on individual issues can incur prohibitive costs. Leveraging prior research showing influence of core human values on individual decisions and actions, we propose to use value-injected large language models (LLM) to predict opinions and behaviors. To this end, we present Value Injection Method (VIM), a collection of two methods -- argument generation and question answering -- designed to inject targeted value distributions into LLMs via fine-tuning. We then conduct a series of experiments on four tasks to test the effectiveness of VIM and the possibility of using value-injected LLMs to predict opinions and behaviors of people. We find that LLMs value-injected with variations of VIM substantially outperform the baselines. Also, the results suggest that opinions and behaviors can be better predicted using value-injected LLMs than the baseline approaches.Comment: EMNLP 2023 main paper accepte

NeBLa: Neural Beer-Lambert for 3D Reconstruction of Oral Structures from Panoramic Radiographs

Panoramic radiography (panoramic X-ray, PX) is a widely used imaging modality for dental examination. However, its applicability is limited as compared to 3D Cone-beam computed tomography (CBCT), because PX only provides 2D flattened images of the oral structure. In this paper, we propose a new framework which estimates 3D oral structure from real-world PX images. Since there are not many matching PX and CBCT data, we used simulated PX from CBCT for training, however, we used real-world panoramic radiographs at the inference time. We propose a new ray-sampling method to make simulated panoramic radiographs inspired by the principle of panoramic radiography along with the rendering function derived from the Beer-Lambert law. Our model consists of three parts: translation module, generation module, and refinement module. The translation module changes the real-world panoramic radiograph to the simulated training image style. The generation module makes the 3D structure from the input image without any prior information such as a dental arch. Our ray-based generation approach makes it possible to reverse the process of generating PX from oral structure in order to reconstruct CBCT data. Lastly, the refinement module enhances the quality of the 3D output. Results show that our approach works better for simulated and real-world images compared to other state-of-the-art methods.Comment: 10 pages, 4 figure

Security and Privacy for Modern Wireless Communication Systems

The aim of this reprint focuses on the latest protocol research, software/hardware development and implementation, and system architecture design in addressing emerging security and privacy issues for modern wireless communication networks. Relevant topics include, but are not limited to, the following: deep-learning-based security and privacy design; covert communications; information-theoretical foundations for advanced security and privacy techniques; lightweight cryptography for power constrained networks; physical layer key generation; prototypes and testbeds for security and privacy solutions; encryption and decryption algorithm for low-latency constrained networks; security protocols for modern wireless communication networks; network intrusion detection; physical layer design with security consideration; anonymity in data transmission; vulnerabilities in security and privacy in modern wireless communication networks; challenges of security and privacy in node–edge–cloud computation; security and privacy design for low-power wide-area IoT networks; security and privacy design for vehicle networks; security and privacy design for underwater communications networks

Low-Surface-Brightness Galaxies are missing in the observed Stellar Mass Function

We investigate the impact of the surface brightness (SB) limit on the galaxy stellar mass functions (GSMFs) using mock surveys generated from the Horizon Run 5 (HR5) simulation. We compare the stellar-to-halo-mass relation, GSMF, and size-stellar mass relation of the HR5 galaxies with empirical data and other cosmological simulations. The mean SB of simulated galaxies are computed using their effective radii, luminosities, and colors. To examine the cosmic SB dimming effect, we compute $k$-corrections from the spectral energy distributions of individual simulated galaxy at each redshift, apply the $k$-corrections to the galaxies, and conduct mock surveys based on the various SB limits. We find that the GSMFs are significantly affected by the SB limits at a low-mass end. This approach can ease the discrepancy between the GSMFs obtained from simulations and observations at $0.625\le z\le 2$. We also find that a redshift survey with a SB selection limit of \left^e = 28 mag arcsec${}^{-2}$ will miss 20% of galaxies with $M_\star^g=10^{9}~{\rm M_\odot}$ at $z=0.625$. The missing fraction of low-surface-brightness galaxies increases to 50%, 70%, and 98% at $z=0.9$, 1.1, and 1.9, respectively, at the SB limit.Comment: 27 pages, 30 figures, accepted for publication in Ap

The Horizon Run 5 Cosmological Hydrodynamic Simulation: Probing Galaxy Formation from Kilo- to Giga-parsec Scales

Horizon Run 5 (HR5) is a cosmological hydrodynamical simulation which captures the properties of the Universe on a Gpc scale while achieving a resolution of 1kpc. Inside the simulation box we zoom-in on a high-resolution cuboid region with a volume of 1049×114×114cMpc3.The sub-grid physics chosen to model galaxy formation includes radiative heating/cooling, UV background, star formation, supernova feedback, chemical evolution tracking the enrichment of oxygen and iron, the growth of supermassive black holes and feedback from active galactic nuclei (AGN) in the form of a dual jet-heating mode. For this simulation we implemented a hybrid MPI-OMP version of RAMSES, specifically targeted for modern many-core many thread parallel architectures. In addition to the traditional simulation snapshots, light-cone data was generated on the fly. For the post-processing, we extended the Friends-of-Friend (FoF) algorithm and developed a new galaxy finder PGalF to analyse the outputs of HR5. The simulation successfully reproduces observations, such as the cosmic star formation history and connectivity of galaxy distribution, We identify cosmological structures at a wide range of scales, from filaments with a length of several cMpc, to voids with a radius of ~100 cMpc. The simulation also indicates that hydrodynamical effects on small scales impact galaxy clustering up to very large scales near and beyond the baryonic acoustic oscillation (BAO) scale. Hence, caution should be taken when using that scale as a cosmic standard ruler: one needs to carefully understand the corresponding biases. The simulation is expected to be an invaluable asset for the interpretation of upcoming deep surveys of the Universe