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IACR Transactions on Cryptographic Hardware and Embedded Systems 2021年第4期2021卷 388-411页

作者： Zarei, Sara Shahmirzadi, Aein Rezaei Soleimany, Hadi Salarifard, Raziyeh Moradi, Amir Shahid Beheshti University Cyber Research Center Tehran Iran Ruhr University Bochum Horst Görtz Institute for IT Security Bochum Germany Shahid Beheshti University Faculty of Computer Science and Engineering Tehran Iran

Correct application of masking on hardware implementation of cryptographic primitives necessitates the instantiation of registers in order to achieve the non-completeness (commonly said to stop the propagation of glitches). This sometimes leads to a high latency overhead, making the implementation not necessarily suitable for the underlying application. As a concrete example, this holds for Keccak. Application of d + 1 Domain Oriented Masking (DOM) on a round-based implementation of Keccak leads to the introduction of two register stages per round, i.e., two times higher latency. On the other hand, Rhythmic-Keccak, introduced in CHES 2018, unrolls two rounds to half the latency compared to an unprotected ordinary round-based implementation. To that end, td + 1 masking is used which requires a notable area, and – apart from the difficulty to construct – its extension to higher orders seems beyond the bounds of feasibility. In this paper, we focus on d + 1 masking and introduce a methodology which enables us to stay with the latency of an unprotected round-based implementation, i.e., one register stage per round. While being secure under glitch-extended probing model, we provide a general design where the desired security order can be easily adjusted without any effect on the above-given latency. Compared to the Rhythmic-Keccak, the synthesis results show that our first-order design is able to accomplish the entire operations of Keccak-f[200] in the same period of time while decreasing the area by 74.5%. Notably, our implementations achieve around 30% less delay compared to the corresponding original DOM-Keccak designs. © 2021, Ruhr-University of Bochum. All rights reserved.

关键词： Domain-oriented masking Hardware implementation Keccak Low latency Masking Threshold implementation

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FarsEval-PKBETS: A new diverse benchmark for evaluating Persian large language models

arXiv

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arXiv 2025年

作者： Shamsfard, Mehrnoush Saaberi, Zahra Manesh, Mostafa Karimi Hashemi, Seyed Mohammad Hossein Vatankhah, Zahra Ramezani, Motahareh Pourazin, Niki Zare, Tara Azimi, Maryam Chitsaz, Sarina Khoraminejad, Sama Mortazavi, Morteza Mahdavi Chizari, Mohammad Mahdi Maleki, Sahar Majd, Seyed Soroush Masumi, Mostafa Khoeini, Sayed Ali Musavi Mohseni, Amir Alipour, Sogol NLP Lab Faculty of Computer Science and Engineering Shahid Beheshti University Tehran Iran Faculty of Computer Engineering Amirkabir University of Technology Tehran Iran Faculty of Law Qom University Qom Iran Faculty of Medicine Tehran Medical Sciences Islamic Azad University Tehran Iran Computer Engineering Department Sharif University of Technology Tehran Iran

Research on evaluating and analyzing large language models (LLMs) has been extensive for high-resource languages such as English, yet their performance in languages such as Persian has received considerably less attention. This paper introduces FarsEval-PKBETS benchmark, a subset of FarsEval project for evaluating large language models in Persian. This benchmark consists of 4,000 questions and answers in various formats, including multiple-choice, short-answer, and descriptive responses. It covers a wide range of domains and tasks, including medicine, law, religion, Persian language, encyclopedic knowledge, human preferences, social knowledge, ethics and bias, text generation, and respecting others’ rights. This benchmark incorporates linguistic, cultural, and local considerations relevant to the Persian language and Iran. To ensure the questions are challenging for current LLMs, three models—Llama3-70B, PersianMind, and Dorna—were evaluated using this benchmark. Their average accuracy was below 50%, meaning they provided fully correct answers to fewer than half of the questions. These results indicate that current language models are still far from being able to solve this *** Codes 68T50 © 2025, CC0.

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Improving Pre-Trained Language Model for Relation Extraction Using Syntactic Information in Persian 2

Improving Pre-Trained Language Model for Relation Extraction...

引用

2nd International Workshop on NLP Solutions for Under Resourced Languages, NSURL 2021

作者： Jafari, Mohammad Mahdi Behmanesh, Somayyeh Talebpour, Alireza Ghomsheh, Ali Nadian Faculty of Computer Science and Engineering Shahid Beheshti University Tehran Iran Cyberspace Research Institute Shahid Beheshti University Tehran Iran

ISBN: (纸本)9781955917193

Relation classification is an essential task in NLP to identify relationships between entities. The state-of-the-art methods for relation classification are primarily based on deep learning and pre-trained BERT methods. This paper presents U-Bert and T-BERT methods and is submitted to the Second Workshop on NLP Solutions for Under Resourced Languages (NSURL2021) (Taghizadeh et al., 2021). In this paper, we focus on the optimal use of the syntactic features in pre-trained language models. First, we extract the syntactic properties and then feed them by a new embedding layer. This work achieved third place in NSURL-2021 task 1: Semantic Relation Extraction in Persian. Our results in this competition are 59.44 and 57.6 macro-average F1-score, respectively, in U-BERT and T-BERT evaluation. © NSURL 2021. All Rights Reserved.

关键词： Natural language processing systems

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Cultural-Aware AI Model for Emotion Recognition

Cultural-Aware AI Model for Emotion Recognition

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Iranian Machine Vision and Image Processing (MVIP)

作者： Mehrdad Baradaran Payam Zohari Abtin Mahyar Hossein Motamednia Dara Rahmati Saeid Gorgin Department of Computer and Data Sciences Shahid Beheshti University Tehran Iran Department of Computer Engineering Khaje Nasir University Of Technology Tehran Iran High Performance Computing Laboratory School of Computer Science Institute for Research in Fundamental Sciences Tehran Iran Faculty of Computer Science and Engineering Shahid Beheshti University Tehran Iran Department of Computer Engineering Chosun University Gwangju South Korea

Emotion AI is a research domain that aims to understand human emotions from visual or textual data. However, existing methods often ignore the influence of cultural diversity on emotional interpretation. In this paper, we propose a multimodal deep learning model that integrates cultural awareness into emotion recognition. Our model uses images as the primary data source and comments from individuals across different regions as the secondary data source. Our results show that our model achieves robust performance across various scenarios. Our contribution is to introduce a novel fusion approach that bridges cultural gaps and fosters a more nuanced understanding of emotions. Due to the best of our knowledge, few works are using this approach, for Emotion AI, combining different types of data sources and models. We evaluate our model on the ArtELingo dataset, which contains image-comment pairs with Chinese, Arabic, and English annotations. The experimental results in the evaluation phase demonstrate an impressive 80% recognition accuracy for the model that merges image-text features.

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Evolutionary Deep Reinforcement Learning Using Elite Buffer: A Novel Approach Towards DRL Combined with EA in Continuous Control Tasks

arXiv

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arXiv 2022年

作者： Esmaeeli, Marzieh Sadat Malek, Hamed Faculty of Computer Science and Engineering Shahid Beheshti University Tehran Iran

Despite the numerous applications and success of deep reinforcement learning in many control tasks, it still suffers from many crucial problems and limitations, including temporal credit assignment with sparse reward, absence of effective exploration, and a brittle convergence that is extremely sensitive to the hyperparameters of the problem. The problems of deep reinforcement learning in continuous control, along with the success of evolutionary algorithms in facing some of these problems, have emerged the idea of evolutionary reinforcement learning, which attracted many controversies. Despite successful results in a few studies in this field, a proper and fitting solution to these problems and their limitations is yet to be presented. The present study aims to study the efficiency of combining the two fields of deep reinforcement learning and evolutionary computations further and take a step towards improving methods and the existing challenges. The "Evolutionary Deep Reinforcement Learning Using Elite Buffer" algorithm introduced a novel mechanism through inspiration from interactive learning capability and hypothetical outcomes in the human brain. In this method, the utilization of the elite buffer (which is inspired by learning based on experience generalization in the human mind), along with the existence of crossover and mutation operators, and interactive learning in successive generations, have improved efficiency, convergence, and proper advancement in the field of continuous control. According to the results of experiments, the proposed method surpasses other well-known methods in environments with high complexity and dimension and is superior in resolving the mentioned problems and limitations. © 2022, CC BY.

关键词： Reinforcement learning

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UNFIS: A NOVEL NEURO-FUZZY INFERENCE SYSTEM WITH UNSTRUCTURED FUZZY RULES FOR CLASSIFICATION

arXiv

引用

arXiv 2022年

作者： Salimi-Badr, Armin Faculty of Computer Science and Engineering Shahid Beheshti University Tehran Iran

An important constraint of Fuzzy Inference Systems (FIS) is their structured rules defined based on evaluating all input variables. Indeed, the length of all fuzzy rules and the number of input variables are equal. However, in many decision-making problems evaluating some conditions on a limited set of input variables is sufficient to decide properly (unstructured rules). Therefore, this constraint limits the performance, generalization, and interpretability of the FIS. To address this issue, this paper presents a neuro-fuzzy inference system for classification applications that can select different sets of input variables for constructing each fuzzy rule. To realize this capability, a new fuzzy selector neuron with an adaptive parameter is proposed that can select input variables in the antecedent part of each fuzzy rule. Moreover, in this paper, the consequent part of the Takagi-Sugeno-Kang FIS is also changed properly to consider only the selected set of input variables. To learn the parameters of the proposed architecture, a trust-region-based learning method (General quasi-Levenberg-Marquardt (GqLM)) is proposed to minimize cross-entropy in multiclass problems. The performance of the proposed method is compared with some related previous approaches in some real-world classification problems. Based on these comparisons the proposed method has better or very close performance with a parsimonious structure consisting of unstructured fuzzy. © 2022, CC BY-NC-ND.

关键词： Fuzzy neural networks

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Jahan: A Framework for Procedural Map Generation from Design Specifications

SSRN

引用

SSRN 2022年

作者： Amiri-Chimeh, Saeed Haghighi, Hassan Vahidi-Asl, Mojtaba Faculty of Computer Science and Engineering Shahid Beheshti University Tehran Iran

Procedural content generation is a field that focuses on creating game assets using algorithms. A critical asset in many game genres is the map, a virtual environment that acts as an infrastructure for other elements of game content and hosts the gameplay and story. Due to the complexity of the game maps, many procedural techniques have tried to engage human designers in the generation process to take their insights and improve the output of algorithmic procedures. To this end, this paper presents a framework for procedural map generation that places map designers at the center of a pipeline architecture. This framework allows designers to configure different pipeline modules and specify the characteristics of their desired map. We demonstrate the capabilities of the presented framework through one running example for generating a simple open-world RPG map, along with two case studies for generating FPS and RTS maps. © 2022, The Authors. All rights reserved.

关键词： Pipelines

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A Persian ASR-based SER: Modification of Sharif Emotional Speech Database and Investigation of Persian Text Corpora

arXiv

引用

arXiv 2022年

作者： Yazdani, Ali Shekofteh, Yasser Faculty of Computer Science and Engineering Shahid Beheshti University Tehran Iran

Speech Emotion Recognition (SER) is one of the essential perceptual methods of humans in understanding the situation and how to interact with others, therefore, in recent years, it has been tried to add the ability to recognize emotions to human-machine communication systems. Since the SER process relies on labeled data, databases are essential for it. Incomplete, low-quality or defective data may lead to inaccurate predictions. In this paper, we fixed the inconsistencies in Sharif Emotional Speech Database (ShEMO), as a Persian database, by using an Automatic Speech Recognition (ASR) system and investigating the effect of Farsi language models obtained from accessible Persian text corpora. We also introduced a Persian/Farsi ASR-based SER system that uses linguistic features of the ASR outputs and Deep Learning-based *** Codes 68T10 (Primary) 68T50, 68T07 (Secondary) Copyright © 2022, The Authors. All rights reserved.

关键词： Database systems

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Virtual Machine Placement for Edge and Cloud Computing 8th

Virtual Machine Placement for Edge and Cloud Computing

引用

1st International Workshop on Edge Adoption and Migration, EdgeWays 2020, 16th International Workshop on engineering Service-Oriented Applications and Cloud Services, WESOACS 2020, ESOCC 2020 PhD Symposium, ESOCC 2020 EU Projects Track which took place at 8th European Conference on Service-Oriented and Cloud Computing, ESOCC 2020

作者： Partovi, Behdad Bagheri, Alireza Kazarji, Maryam Haddad Pahl, Claus Barzegar, Hamid R. Faculty of Computer Science and Engineering Shahid Beheshti University Tehran Iran Faculty of Computer Engineering Amirkabir University of Technology Hafez Tehran Iran Department of Computer Science Free University of Bozen-Bolzano Bolzano Italy

ISBN: (纸本)9783030719050

So far, the genetic algorithm has been presented for the energy-aware scheduling of virtual machines to minimize the total busy time of servers. However, this algorithm does not consider the criteria for service-level policies on real-time applications. The convergence speed of the genetic algorithm is quite low in solving many of the large hybrid optimization problems. In other similar studies, heuristic algorithms were used to solve the interval scheduling problem. Such algorithms are not able to find nearly optimal solutions to hard problems. Since the optimization of scheduling is part of the hard problems, it is wise to use meta-heuristic algorithms to find nearly optimal solutions. Accordingly, an energy-aware meta-heuristic scheduling algorithm is presented in this paper for real-time virtual machines. The main goal of this algorithm is to minimize the total busy time of the physical machines in an interval without violating the deadline for virtual machines. The results were collected from the genetic algorithm, the smart water drop algorithm, the optimization of the ant colony, and the first possible downward algorithm for comparison and evaluation. The optimization of the ant colony and the smart algorithm of water drops showed better results than did the other two algorithms. © 2021, Springer Nature Switzerland AG.

关键词： Genetic algorithms

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Novel Feynman-Based Reversible and Fault-Tolerant Nano-communication Arithmetic Architecture Based on QCA Technology

引用

SN computer science 2021年第6期2卷 422页

作者： Bagherian Khosroshahy, Milad Abdoli, Alireza Panahi, Mohammad Mehdi Department of Computer Science and Engineering Shahid Beheshti University G.C Tehran Iran Department of Electrical and Computer Engineering Faculty of Shahid Shamsipour Tehran Branch Technical and Vocational University (TVU) Tehran Iran Department of Computer Engineering Science and Research Branch Islamic Azad University Tehran Iran

Quantum-dot cellular automata (QCA) has advantages such as low energy dissipation and high density as a suitable alternative to CMOS technology. The arithmetic element is one essential and critical component of every microprocessor. In this paper, a novel reversible and fault-tolerant nano-communication parity-based arithmetic circuit is introduced. The nano-communication contains one parity checker gate and one parity generator gate. The proposed structure requires only two garbage outputs and does not require any rotated cells, only using one layer that improves the manufacturability of the design. The proposed structures are simulated using QCADesigner software. The proposed nano-communication can gain improved cell count, area, delay, and reduced number of garbage outputs by almost 17%, 16%, 50%, and 60%, respectively, compared to the existing QCA-based single-layer reversible nano-communication circuit. © 2021, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Arithmetic computing Fault-tolerant Quantum-dot cellular automata Reversible Reversible computing Single-layer circuit

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