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Ấn phẩm

Robust finite-time supoptimal control of large-scale systems with interacted state and control delays

(2021-10) Vu, Ngoc Phat; Pham, T. Huong

This paper concerns with a problem of supoptimal fiite-time control for a class of linear large-scale delay systems. The system under consideration is subjected to the state and control delays interacted between subsystems. Based on improved LMI approach combining with new estimation techniques, we derive suffient conditions for solving H∞ fiite-time control and guaranteed cost control of the system. A numerical example is given to illustrate the validity and effctiveness of the theoretical results.

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The traveling salesman problem with multi-visit drone

(2021-10) Ha, Quang Minh; Vu, Duy Manh; Le, Xuan Thanh; Hoang, Minh Ha

This paper deals with the Traveling Salesman Problem with Multi-Visit Drone (TSPMVD) in which a truck works in collaboration with a drone that can serve up to q ≥ 1 customers consecutively during each sortie. We propose a Mixed Integer Linear Programming (MILP) formulation and a metaheuristic based on Iterated Local Search (ILS) to solve the problem. Benchmark instances collected from the literature of the special case with q = 1 are used to test the performance of our algorithms. The obtained results show that our MILP model can solve a number of instances to optimality. This is the fist time optimal solutions for these instances are reported. Our ILS performs better than other algorithms in terms of both solution quality and running time on several instance classes. The numerical results obtained by testing the methods on new randomly generated instances show again the effctiveness of the methods as well as the positive impact of using the multi-visit drone.

Ấn phẩm

Privacy in advanced cryptographic protocols: Prototypical examples

(2021-10) Phan, Duong Hieu; Yung, Moti

Cryptography is a fundamental cornerstone of cybersecurity, traditionally supporting data confientiality, integrity, and authenticity. However, when cryptographic protocols are deployed in emerging applications such as cloud services or big data, the demand for security grows beyond these requirements. Data nowadays are being extensively stored in the cloud, and users also need to trust the cloud servers/ authorities that run powerful applications. Collecting user data, combined with powerful tools (e.g., machine learning), can come with a huge risk of mass surveillance or of undesirable data-driven strategies for profi making, while ignoring users’ needs. Privacy, therefore, becomes more and more important, and new techniques should be developed, fist, to protect personal privacy, and, second, to reduce centralized trust in authorities or in technical solutions providers.

Ấn phẩm

Dual transformer encoders for session-based recommendation

(2021-10) Pham, Hoang Anh; Ngo, Xuan Bach; Tu, Minh Phuong

When long-term user profies are not available, session-based recommendation methods are used to predict the user’s next actions from anonymous sessions-based data. Recent advances in session-based recommendation highlight the necessity of modeling not only user sequential behaviors but also the user’s main interest in a session, while avoiding the effct of unintended clicks causing interest drift of the user. In this work, we propose a Dual Transformer Encoder Recommendation model (DTER) as a solution to address this requirement. The idea is to combine the following recipes: (1) A Transformer-based model with dual encoders capable of modeling both sequential patterns and the main interest of the user in a session; (2) A new recommendation model that is designed for learning richer session contexts by conditioning on all permutations of the session prefi. This approach provides a unifid framework for leveraging the ability of the Transformer’s self-attention mechanism in modeling session sequences while taking into account the user’s main interest in the session. We empirically evaluate the proposed method on two benchmark datasets. The results show that DTER outperforms state-of-the-art session-based recommendation methods on common evaluation metrics.

Ấn phẩm

Post-quantum blind signature protocol on non-commutative algebras

(2021-10) N.H. Minh; D.N. Moldovyan; N.A. Moldovyan; A.A. Kostina; L.Q. Minh

A method for constructing a blind signature scheme based on a hidden discrete logarithm problem defied in fiite non-commutative associative algebras is proposed. Blind signature protocols are constructed using four-dimensional and six-dimensional algebras defied over a ground fiite fild GF(p) and containing a global two-sided unit as an algebraic support. The basic properties of the used algebra, which determine the choice of protocol parameters, are described.

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Modeling amino acid substitutions for whole genomes

(2021-10) Le, Sy Vinh

Modeling amino acid substitution process is a core task in bioinformatics. New advanced sequencing technologies have generated huge datasets including whole genomes from various species. Estimating amino acid substitution models from whole genome datasets provides us unprecedented opportunities to accurately investigate relationships among species. In this paper, we review state-ofthe-art computational methods to estimate amino acid substitution models from large datasets. We also describe a comprehensive pipeline to practically estimate amino acid models from whole genome datasets. Finally, we apply amino acid substitution models to build phylogenomic trees from bird and plant genome datasets. We compare our newly reconstructed phylogenomic trees and published ones and discuss new fidings.

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