Sharif Digital Repository

Content base image retrieval is an important research field with many applications. In this paper we presents a new approach for finding similar images to a given query, in a general-purpose image database using content-based image retrieval. Color and texture are used as basic features to describe images. In addition, a binary tree structure is used to describe higher level features of an image. It has been used to keep information about separate segments of the images. The performance of the proposed system has been compared with the SIMPLIcity system using COREL image database. Our experimental results showed that among 10 image categories available in COREL database, our system had a... 

An important security challenge in database outsourcing scenarios is the correctness verification of query results. The proposed approaches in the literature, impose high overhead on both the service provider and specially the clients. In this paper, we propose the Trust-Based Fake Tuples approach to audit the correctness of query results. In this approach, some fake tuples are included among the real ones in order to verify the correctness of the results. The experience learnt from past results is used in this paper to evaluate the trust toward the service provider. This trust value is used to tune the number of fake tuples and subsequently the imposed overhead. As the trust value toward... 

The COVID-19 epidemic is spreading day by day. Early diagnosis of this disease is essential to provide effective preventive and therapeutic measures. This process can be used by a computer-aided methodology to improve accuracy. In this study, a new and optimal method has been utilized for the diagnosis of COVID-19. Here, a method based on fuzzy C-ordered means (FCOM) along with an improved version of the enhanced capsule network (ECN) has been proposed for this purpose. The proposed ECN method is improved based on mayfly optimization (MFO) algorithm. The suggested technique is then implemented on the chest X-ray COVID-19 images from publicly available datasets. Simulation results are... 

In recent years, analyzing genome-wide expression profiles to find genetic markers has received much attention as a challenging field of research aiming at unveiling biological mechanisms behind complex disorders. The identification of reliable and reproducible markers has lately been achieved by integrating genome-scale functional relationships and transcriptome datasets, and a number of algorithms have been developed to support this strategy. In this paper, we present a promising and easily applicable tool to accomplish this goal, namely CytoGTA, which is a Cytoscape plug-in that relies on an optimistic game theoretic approach (GTA) for identifying subnetwork markers. Given transcriptomic... 

Security of database outsourcing, due to the untrustworthiness of service provider, is a basic challenge to have Database As a Service in a cloud computing environment. Having disparate assumptions to solve different aspects of security such as confidentiality and integrity is an obstacle for an integrated secure solution through the combination of existing approaches. Concentrating on confidentiality and integrity aspects of database outsourcing, this paper proposes an approach in which each attribute value is split up between several data servers using a customized threshold secret sharing scheme. Our approach preserves data confidentiality and at the same time provides the correctness... 

This paper explores design and implementation of an event-based simulator namely DBMSS for analyzing concurrency protocols in database systems. In the design of DBMSS we interested in event-based approach to create a real world with variety range of event possibilities. DBMSS is explored in the context of the development of a complex simulator for simulating real world conditions and provide an event-based environment to test and evaluate concurrency protocols in database systems. First we discuss the architecture of DBMSS and use it to provide arrival transactions to test and evaluate concurrency protocols in database system. We present the comparison of 4 concurrency protocols in variety... 

In this paper, a comprehensive accelerogram database of the Iranian plateau containing 3585 data with all three components is gathered. The raw data are processed by the wavelet-based denoising method, and results are compared with the contaminated data. All the data are classified into mainshock and aftershock categories using the time and spatial window method. Afterward, the data are categorized into the pulse-like and non-pulse-like events based on the detection of velocity pulse in any of horizontal and/or vertical directions. Eventually, among 3585 data, the ones with an average shear wave velocity of top 30 m of subsurface soil profile are selected and their important ground motion... 

A signaling pathway is a sequence of proteins and passenger molecules that transmits information from the cell surface to target molecules. Understanding signal transduction process requires detailed description of the involved pathways. Several methods and tools resolved this problem by incorporating genomic and proteomic data. However, the difficulty of obtaining prior knowledge of complex signaling networks limited the applicability of these tools. In this study, based on the simulation of signal flow in signaling network, we introduce a method for determining dominant pathways and signal response to stimulations. The model uses topology-weighted transit compartment approach and comprises... 

Early diagnosis of any disease at a lower cost is preferable. Automatic medical diagnosis classification tools reduce financial burden on health care systems. In medical diagnosis, patterns consist of observable symptoms and the results of diagnostic tests, which have various associated costs and risks. In this paper, we have experimented and suggested an automated pattern classification method for classifying four diseases into two classes. In the literature on machine learning or data mining, regression and classification problems are typically viewed as two distinct problems differentiated by continuous or categorical dependent variables. There are endeavors to use regression methods to... 

This paper proposes a distributed attack detection and mitigation technique based on distributed estimation over a multi-agent network, where the agents take partial system measurements susceptible to (possible) biasing attacks. In particular, we assume that the system is not locally observable via the measurements in the direct neighborhood of any agent. First, for performance analysis in the attack-free case, we show that the proposed distributed estimation is unbiased with bounded mean-square deviation in steady-state. Then, we propose a residual-based strategy to locally detect possible attacks at agents. In contrast to the deterministic thresholds in the literature assuming an upper... 

There is growing interest in applying distributed machine learning to edge computing, forming federated edge learning. Federated edge learning faces non-i.i.d. and heterogeneous data, and the communication between edge workers, possibly through distant locations and with unstable wireless networks, is more costly than their local computational overhead. In this work, we propose ${{sf DONE}}$DONE, a distributed approximate Newton-type algorithm with fast convergence rate for communication-efficient federated edge learning. First, with strongly convex and smooth loss functions, ${{sf DONE}}$DONE approximates the Newton direction in a distributed manner using the classical Richardson iteration... 

Recently, Grid infrastructures have provided wide integrated use of resources. DotGrid intends to introduce required Grid services and toolkits that are implemented as a layer wrapped over the existing operating systems. Our DotGrid has been developed based on Microsoft .NET in Windows and MONO .NET in Linux and UNIX. Using DotGrid APIs, Grid middlewares and applications can be implemented easily. We evaluated our DotGrid capabilities by implementing some applications including a grid-based distributed cryptographic engine and also a typical computational problem. © 2006 IEEE  

In this Paper a new approach is proposed for electrocardiogram (ECG) based human identification using wavelet distance measurement. The main advantage of this method is that it guarantees high accuracy even in abnormal cases. Furthermore, it possesses low sensitivity to noise. The algorithm was applied on 11 normal subjects and 10 abnormal subjects of MIT-BIH Database using single lead data, and a 100% human identification rate was on both normal and abnormal subjects. Adding artificial white noise to signals shows that the method is nearly accurate in SNR level above 5dB in normal subjects and 20dB in abnormal subjects  

This paper presents a multi-stage algorithm for QRS complex classification into normal and abnormal categories using an unsupervised sequential beat clustering and a cross-distance analysis algorithm. After the sequential beat clustering, a search algorithm based on relative similarity of created classes is used to detect the main normal class. Then other classes are labeled based on a distance measurement from the main normal class. Evaluated results on the MIT-BIH ECG database exhibits an error rate less than 1% for normal and abnormal discrimination and 0.2% for clustering of 15 types of arrhythmia existing on the MIT-BIH database. © 2001 IEEE  

In this paper, we propose a novel method for extracting fiducial points (FPs) of the beats in electrocardiogram (ECG) signals using switching Kalman filter (SKF). In this method, according to McSharry's model, ECG waveforms (P-wave, QRS complex and T-wave) are modeled with Gaussian functions and ECG baselines are modeled with first order auto regressive models. In the proposed method, a discrete state variable called “switch” is considered that affects only the observation equations. We denote a mode as a specific observation equation and switch changes between 7 modes and corresponds to different segments of an ECG beat. At each time instant, the probability of each mode is calculated and... 

In this paper, we propose a novel method for extracting fiducial points (FPs) of electrocardiogram (ECG) signals. We propose the use of multi hidden Markov model (MultiHMM) as opposed to the traditional use of Classic HMM. In the MultiHMM method, each segment of an ECG beat is represented by a separate ergodic continuous density HMM. Each HMM has different state number and is trained separately. In the test step, the log-likelihood of two consecutive HMMs is compared and a path is estimated, which shows the correspondence of each part of the ECG signal to the HMM with the maximum log-likelihood. Fiducial points are estimated from the obtained path. For performance evaluation, the Physionet... 

Using stochastic point source and finite fault modeling, the stochastic stress drop is estimated for 52 large shallow earthquakes listed in the 'Pacific Earthquake Engineering Research Center (PEER) Next Generation Attenuation of Ground Motions (NGA)' database. The Pseudo Spectral Acceleration (PSA) of 541 accelerograms, recorded at National Earthquake Hazards Reduction Program (NEHRP) C-class sites from 52 earthquakes are simulated and compared with the PSA listed in the PEER NGA database. The magnitude of the analyzed earthquakes ranged from M4:4 to M7:6. The stress drop is calibrated by trial and error and based on the analysis of residuals where the residual is defined as the log of the... 

Federated learning (FL) and split learning (SL) are state-of-the-art distributed machine learning techniques to enable machine learning without accessing raw data on clients or end devices. However, their comparative training performance under real-world resource-restricted Internet of Things (IoT) device settings, e.g., Raspberry Pi, remains barely studied, which, to our knowledge, have not yet been evaluated and compared, rendering inconvenient reference for practitioner. This work firstly provides empirical comparisons of FL and SL in real-world IoT settings regarding learning performance and on-device execution overhead. Our analyses demonstrate that the learning performance of SL is... 

Sequential pattern recognition has long been an important topic of soft computing research with a wide area of applications including speech and handwriting recognition. In this paper, the performance of a novel fuzzy sequential pattern recognition tool named “Fuzzy Elastic Matching Machine” has been investigated. This tool overcomes the shortcomings of the HMM including its inflexible mathematical structure and inconsistent mathematical assumptions with imprecise input data. To do so, “Fuzzy Elastic Pattern” was introduced as the basic element of FEMM. It models the elasticity property of input data using fuzzy vectors. A sequential pattern such as a word in speech or a piece of writing is... 

A novel approach for selecting proper cover images in steganography is presented in this paper. The proposed approach consists of two stages. The first stage is an evolutionary algorithm that extracts the signature of cover images against stego images in the form of fuzzy if-then rules. This algorithm is based on an iterative rule learning approach to construct an accurate fuzzy rule base. The rule base is generated in an incremental way by optimizing one fuzzy rule at a time using an evolutionary algorithm. In the second stage of the proposed approach, the fuzzy rules generated in the first stage are used for selecting suitable cover images for steganography. We applied our approach to some...