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In this paper, we propose the development of an interactive platform between humans and a dual-arm robotic system based on the Robot Operating System (ROS) and a multimodal artificial intelligence model. Our proposed platform consists of two main components: a dual-arm robotic hardware system and software that includes image processing tasks and natural language processing using a 3D camera and embedded computing. First, we designed and developed a dual-arm robotic system with a positional accuracy of less than 2 cm, capable of operating independently, performing industrial and service tasks while simultaneously simulating and modeling the robot in the ROS environment. Second, artificial intelligence models for image processing are integrated to execute object picking and classification tasks with an accuracy of over 90%. Finally, we developed remote control software using voice commands through a natural language processing model. Experimental results demonstrate the accuracy of the multimodal artificial intelligence model and the flexibility of the dual-arm robotic system in interactive human environments., Comment: In The 25th National Conference on Electronics, Communications and Information Technology (REV-ECIT 2022), Hanoi, Vietnam. in Vietnamese language

Existing fuzzy extractors and similar methods provide an effective way for extracting a secret key from a user's biometric data, but are susceptible to impersonation attack: once a valid biometric sample is captured, the scheme is no longer secure. We propose a novel multi-factor fuzzy extractor that integrates both a user's secret (e.g., a password) and a user's biometrics in the generation and reconstruction process of a cryptographic key. We then employ this multi-factor fuzzy extractor to construct personal identity credentials which can be used in a new multi-factor authenticated key exchange protocol that possesses multiple important features. First, the protocol provides mutual authentication. Second, the user and service provider can authenticate each other without the involvement of the identity authority. Third, the protocol can prevent user impersonation from a compromised identity authority. Finally, even when both a biometric sample and the secret are captured, the user can re-register to create a new credential using a new secret (reusable/reissued identity credentials). Most existing works on multi-factor authenticated key exchange only have a subset of these features. We formally prove that the proposed protocol is semantically secure. Our experiments carried out on the finger vein dataset SDUMLA achieved a low equal error rate (EER) of 0.04%, a reasonable averaged computation time of 0.93 seconds for the user and service provider to authenticate and establish a shared session key, and a small communication overhead of only 448 bytes., Comment: 17 pages

In this article, we address the challenges of image super-resolution and noise reduction, which are crucial for enhancing the quality of images derived from low-resolution or noisy data. We compared and assessed several approaches for upgrading low-resolution images to higher resolutions and for eliminating unwanted noise, all while maintaining the essential characteristics of the original images and recovering images from poor quality or damaged data using deep learning models. Our analysis and the experimental outcomes on image quality metrics indicate that the EDCNN neural network model, enhanced with pretrained weights, significantly outperforms other methods with a Train PSNR of 31.215, a Valid PSNR of 29.493, and a Test PSNR of 31.6632., Comment: This article is no longer relevant

The Glauber-Exclusion process is a superposition of a Glauber dynamics and the Symmetric Simple Exclusion Process (SSEP) on the lattice. The model was shown to admit a reaction-diffusion equation as the hydrodynamic limit. In this article, we define a notion of temperature regimes via the reaction function in the equation and prove cutoff in the full high-temperature regime for the attractive model in dimensions $1$ and $2$ with periodic boundary condition. Our results show that the equation in the hydrodynamic limit reflects the mixing behavior of the large but finite system. Besides, cutoff is proved under the lack of reversibility and an explicit formula for the invariant measure. We also provide the spectral gap and prove pre-cutoff in all dimensions. Our proof involves a new interpretation of attractiveness, the information percolation framework introduced by Lubetzky and Sly, anti-concentration of simple random walk on the lattice, and a coupling inspired by excursion theory. We hope that this approach can find new applications in the future., Comment: 68 + 3 pages, 5 figures, comments welcome!

A private information retrieval (PIR) scheme allows a client to retrieve a data item $x_i$ among $n$ items $x_1,x_2,\ldots,x_n$ from $k$ servers, without revealing what $i$ is even when $t < k$ servers collude and try to learn $i$. Such a PIR scheme is said to be $t$-private. A PIR scheme is $v$-verifiable if the client can verify the correctness of the retrieved $x_i$ even when $v \leq k$ servers collude and try to fool the client by sending manipulated data. Most of the previous works in the literature on PIR assumed that $v < k$, leaving the case of all-colluding servers open. We propose a generic construction that combines a linear map commitment (LMC) and an arbitrary linear PIR scheme to produce a $k$-verifiable PIR scheme, termed a committed PIR scheme. Such a scheme guarantees that even in the worst scenario, when all servers are under the control of an attacker, although the privacy is unavoidably lost, the client won't be fooled into accepting an incorrect $x_i$. We demonstrate the practicality of our proposal by implementing the committed PIR schemes based on the Lai-Malavolta LMC and three well-known PIR schemes using the GMP library and blst, the current fastest C library for elliptic curve pairings., Comment: Accepted at ESORICS 2023

We consider the Symmetric Simple Exclusion Process (SSEP) on the segment with two reservoirs of densities $p, q \in (0,1)$ at the two endpoints. We show that the system exhibits cutoff with a diffusive window, thus confirming a conjecture of Gantert, Nestoridi, and Schmid in \cite{Gantert2020}. In particular, our result covers the regime $p \neq q$, where the process is not reversible and there is no known explicit formula for the invariant measure. Our proof exploits the information percolation framework introduced by Lubetzky and Sly, the negative dependence of the system, and an anticoncentration inequality at the conditional level. We believe this approach is applicable to other models., Comment: 23 pages, comments welcome

We present an open-source toolkit for neural machine translation (NMT). The new toolkit is mainly based on vaulted Transformer (Vaswani et al., 2017) along with many other improvements detailed below, in order to create a self-contained, simple to use, consistent and comprehensive framework for Machine Translation tasks of various domains. It is tooled to support both bilingual and multilingual translation tasks, starting from building the model from respective corpora, to inferring new predictions or packaging the model to serving-capable JIT format.

Recently, there is great interest to investigate the application of deep learning models for the prediction of clinical events using electronic health records (EHR) data. In EHR data, a patient's history is often represented as a sequence of visits, and each visit contains multiple events. As a result, deep learning models developed for sequence modeling, like recurrent neural networks (RNNs) are common architecture for EHR-based clinical events predictive models. While a large variety of RNN models were proposed in the literature, it is unclear if complex architecture innovations will offer superior predictive performance. In order to move this field forward, a rigorous evaluation of various methods is needed. In this study, we conducted a thorough benchmark of RNN architectures in modeling EHR data. We used two prediction tasks: the risk for developing heart failure and the risk of early readmission for inpatient hospitalization. We found that simple gated RNN models, including GRUs and LSTMs, often offer competitive results when properly tuned with Bayesian Optimization, which is in line with similar to findings in the natural language processing (NLP) domain. For reproducibility, Our codebase is shared at https://github.com/ZhiGroup/pytorch_ehr., Comment: This podium abstract was peer-reviewed and accepted in 2018, and presented at Medinfo-2019 in Leon France in August 2019

We consider the mean-field Zero-Range process in the regime where the potential function $r$ is increasing to infinity at sublinear speed, and the density of particles is bounded. We determine the mixing time of the system, and establish cutoff. We also prove that the Poincar\'e constant is bounded away from zero and infinity. This mean-field estimate extends to arbitrary geometries via a comparison argument. Our proof uses the path-coupling method of Bubley and Dyer and stochastic calculus., Comment: 29 pages, accepted version

We investigate chemo-photothermal effects of gold nanorods (GNRs) coated using mesoporous silica (mSiO2) loading doxorubicin (DOX). When the mesoporous silica layer is embedded by doxorubicin drugs, a significant change in absorption spectra enable to quantify the drug loading. We carry out photothermal experiments on saline and livers of mice having GNRs@mSiO2 and GNRs@mSiO2-DOX. We also inject the gold nanostructures into many tumor-implanted mice and use laser illumination on some of them. By measuring weight and size of tumors, the distinct efficiency of photothermal therapy and chemotherapy on treatment is determined. We experimentally confirm the accumulation of gold nanostructures in liver., Comment: 8 pages, 6 figures, accepted for publication