Sharif Digital Repository

The Putting human commonsense knowledge into computers has always been a long standing dream of artificial intelligence (AI). The cost of several tens of millions of dollars and time have been covered so that the computers could know about "objects falling, not rising.", "running is faster than walking". The large database was built, automated and semi-automated methods were introduced and volunteers' efforts were utilized to achieve this, but an automated, high-throughput and low-noise method for commonsense collection still remains as the holy grail of AI. The aim of this study was to build commonsense knowledge ontology using three approaches namely Hearst method, machine translation and... 

This paper addresses some of the issues learned during the course of building a written language resource, called 'Peykare', for the contemporary Persian. After defining five linguistic varieties and 24 different registers based on these linguistic varieties, we collected texts for Peykare to do a linguistic analysis, including cross-register differences. For tokenization of Persian, we propose a descriptive generalization to normalize orthographic variations existing in texts. To annotate Peykare, we use EAGLES guidelines which result to have a hierarchy in the part-of-speech tags. To this aim, we apply a semi-automatic approach for the annotation methodology. In the paper, we also give a... 

The outlook of brain-computer interfacing (BCI) is very bright. The real-time, accurate detection of a motor movement task is critical in BCI systems. The poor signal-to-noise-ratio (SNR) of EEG signals and the ambiguity of noise generator sources in brain renders this task quite challenging. In this paper, we demonstrate a novel algorithm for precise detection of the onset of a motor movement through identification of event-related-desynchronization (ERD) patterns. Using an adaptive matched filter technique implemented based on an optimized continues Wavelet transform by selecting an appropriate basis, we can detect single-trial ERDs. Moreover, we use a maximum-likelihood (ML),... 

Fake news detection has become an emerging and critical topic of research in recent years. One of the major complications of fake news detection lies in the fact that news in social networks is multilingual, and therefore developing methods for each and every language in the world is impossible, especially for low resource languages like Persian. In an effort to solve this problem, researchers use machine translation to uniform the data and develop a method for the uniformed data. In this paper, we aim to explore the impacts of machine translation on fake news detection. For this purpose, we extracted and labeled a dataset of Persian Tweets from Twitter on the subject of COVID-19 and... 

This paper presents a solution in order to solve the problem of using HMM-based POS tagger in some languages where a word can be comprised of several tokens. Viterbi algorithm is modified in order to support segment of words within a model state. In the other word, the proposed system has a built-in tokenizer where indicates words boundaries as well as its corresponding tag sequence  

One of the roles of a Natural Language Processing (NLP) model in Continuous Speech Recognition (CSR) systems is to find the best sentence hypothesis by ranking all n-best sentences according to the grammar. This paper describes a robust parsing algorithm for Spoken Language Recognition (SLR) which utilizes a technique that improves the efficiency of parsing. This technique integrates grammatical and statistical approaches, and by using a best-first parsing strategy improves the accuracy of recognition. Preliminary experimental results using a Persian continuous speech recognition system show effective improvements in accuracy with little change in recognition time. The word error rate was... 

In this paper, we present our attempts to design and implement a large-coverage computational grammar for the Persian language based on the Generalized Phrase Structured Grammar (GPSG) model. This grammatical model was developed for continuous speech recognition (CSR) applications, but is suitable for other applications that need the syntactic analysis of Persian. In this work, we investigate various syntactic structures relevant to the modern Persian language, and then describe these structures according to a phrase structure model. Noun (N), Verb (V), Adjective (ADJ), Adverb (ADV), and Preposition (P) are considered basic syntactic categories, and X-bar theory is used to define Noun... 

An efficient method for optimal allocation of wavelengths in a hybrid dense-wavelength-division-multiplexing system, carrying both quantum and classical data, is proposed. The transmission of quantum bits alongside intense classical signals on the same fiber faces major challenges arising from the background noise generated by classical channels. Raman scattering, in particular, is shown to have detrimental effects on the performance of quantum key distribution systems. Here, by using a nearly optimal wavelength allocation technique, we minimize the Raman induced background noise on quantum channels, hence maximize the achievable secret key generation rate for quantum channels. It turns out... 

We propose orthogonal frequency-division multiplexing (OFDM), as a spectrally efficient multiplexing technique, for quantum key distribution at the core of trusted-node quantum networks. Two main schemes are proposed and analyzed in detail, considering system imperfections, specifically, time misalignment issues. It turns out that while multiple service providers can share the network infrastructure using the proposed multiplexing techniques, no gain in the total secret key generation rate is obtained if one uses conventional passive all-optical OFDM decoders. To achieve a linear increase in the key rate with the number of channels, an alternative active setup for OFDM decoding is proposed,... 

In this paper, we propose and investigate several crosstalk reduction techniques for hybrid quantum-classical dense-wavelength-division-multiplexing systems. The transmission of intense classical signals alongside weak quantum ones on the same fiber introduces some crosstalk noise, mainly due to Raman scattering and non-ideal channel isolation, that may severely affect the performance of quantum key distribution systems. We examine the conventional methods of suppressing this crosstalk noise, and enhance them by proposing an appropriate channel allocation method that reduces the background crosstalk effectively. Another approach proposed in this paper is the usage of orthogonal frequency... 

In this paper, we investigate the performance of orthogonal frequency division multiplexed quantum key distribution (OFDM-QKD) in an integrated quantum-classical wavelength-division-multiplexing system. The presence of an intense classical signal alongside the quantum one generates Raman background noise. Noise reduction techniques should, then, be carried out at the receiver to suppress this crosstalk noise. In this work, we show that OFDM-QKD enables efficient filtering, in time and frequency domains, making it an attractive solution for the high-rate links at the core of quantum-classical networks  

Optimal wavelength assignment in dense-wavelength-division-multiplexing (DWDM) systems that integrate both quantum and classical channels is studied. In such systems, weak quantum key distribution (QKD) signals travel alongside intense classical signals on the same fiber, where the former can be masked by the background noise induced by the latter. Here, we investigate how optimal wavelength assignment can mitigate this problem. We consider different DWDM structures and various sources of crosstalk and propose several near-optimal wavelength assignment methods that maximize the total secret key rate of the QKD channels. Our numerical results show that the optimum wavelength assignment... 

Three-dimensional porous scaffolds are essential in tissue engineering applications. One of the most conventional methods to form porosity in scaffolds is freeze-drying, which is not energy efficient and cost effective. Therefore in this work, it was experimentally investigated whether gelatin, with its unique mechanical properties and cell binding applications, could be used as a comprising polymer of scaffolds with porous structure made by the freeze-gelation method. Chitosan, gelatin and chitosan/gelatin scaffolds were fabricated by the freeze-gelation method and their behaviors, determined by analysis of scanning electron microscopy images, Fourier transform infrared spectroscopy,... 

Using word base n-gram language models in continuous speech recognition systems is so prevalent. For using this type of language models, we should extract them from large corpora. Since Persian corpora are not rich, therefore the extracted language models are not credible. For this reason, most researchers extract class n-grams instead of finding word n-grams. In this research a new idea for fuzzy word clustering is represented that each word can be assigned to more that one class. The Fuzzy c-mean algorithm is used for our clustering method and we have examined its various parameters of it. Finally, this algorithm was applied on 20000 most frequent Persian words extracted from "Persian Text... 

The global shift to renewable energy, especially wind power, is critical for achieving carbon-neutral power systems but poses challenges to grid stability due to reduced system inertia from inverter-based resources. Grid-forming (GFM) control modes are emerging as key solutions to these stability issues. Given the vast potential of wind energy, ensuring that wind turbine generators (WTGs) comply with GFM control standards is essential. This paper explores GFM capabilities in Type IV WTGs, focusing on the interactions between grid-side and machine-side control dynamics. A simplified small-signal model is developed to efficiently analyse the direct coupling between active power and DC-link... 

This article presents an enhanced power-synchronized grid-following inverter (ePSGFLI) for reliable and stable integration of renewable energy resources into the grid. By eliminating the use of a phase-locked loop (PLL) in the control loop, the ePSGFLI resolves instability issues commonly associated with conventional PLL-based grid-following inverters (GFLIs). Furthermore, in contrast to existing PLL-less strategies like linear parameter-varying control of power-synchronized GFLI (LPV-PSGFLI), which suffer from steady-state errors and instability during grid frequency deviations, the proposed ePSGFLI ensures stable performance without steady-state errors, regardless of the strength of the... 

The increasing use of inverter-based resources (IBRs) in power systems highlights the need for a deep understanding of their stability and dynamics. Traditional analytical models, limited by the black-box nature of IBRs, underscore the importance of frequency-domain methods through measured admittance. Facing challenges in real-world admittance measurement, such as the absence of ideal voltage or current sources, the use of measurement inverters as perturbation sources has emerged as a promising approach, despite drawbacks like reduced system stability and increased measurement time. This article introduces a novel control strategy for measurement inverters, elucidating the impact of abc/dq... 

In the realm of modern power systems, grid-connected inverters are essential for integrating renewable energy resources. While grid-following inverters (GFLIs) currently dominate the market, grid-forming inverters (GFMIs) are pivotal for technology future power systems. Furthermore, recent research emphasizes the advantages of power-synchronized GFLIs (PSGFLIs), which combine the reliable operation of conventional GFLIs in strong grids with the stability of GFMIs in weak grids. This paper presents a comprehensive analysis of transient angle stability for a specific variant of PSGFLI, referred to as the linear parameter-varying control PSGFLI (LPV-PSGFLI). First, a nonlinear dynamic model... 

A novel method for combining bigram model and Probabilistic Latent Semantic Analysis (PLSA) is introduced for language modeling. The motivation behind this idea is the relaxation of the bag of words assumption fundamentally present in latent topic models including the PLSA model. An EM-based parameter estimation technique for the proposed model is presented in this paper. Previous attempts to incorporate word order in the PLSA model are surveyed and compared with our new proposed model both in theory and by experimental evaluation. Perplexity measure is employed to compare the effectiveness of recently introduced models with the new proposed model. Furthermore, experiments are designed and...