Sharif Digital Repository

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... 

Much efforts have been made in the area of energy security in recent years, but due to its wide scope, it is necessary to review relevant knowledge activities and to analyze the state of knowledge of the field of energy security measurement. The purpose of this article is to present a comprehensive map of knowledge in the field of energy security. For this reason, many documents and articles have been collected during the period 2002–2019 using 7 large and reputable scientific databases as well as 53 different journals, 90% white Q1 quality. There is employed meta-synthesis, scientometrics and network analysis. In the initial survey stage where 1290 articles were found, after analyzing the... 

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... 

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... 

Supervised speech emotion recognition requires a large number of labeled samples that limit its use in practice. Due to easy access to unlabeled samples, a new semi-supervised method based on auto-encoders is proposed in this paper for speech emotion recognition. The proposed method performed the classification operation by extracting the information contained in unlabeled samples and combining it with the information in labeled samples. In addition, it employed maximum mean discrepancy cost function to reduce the distribution difference when the labeled and unlabeled samples were gathered from different datasets. Experimental results obtained on different emotional speech datasets... 

We introduce the notion of private set operations (PSO) as a symmetric-key primitive in the cloud scenario, where a client securely outsources his dataset to a cloud service provider and later privately issues queries in the form of common set operations. We define a syntax and security notion for PSO and propose a general construction that satisfies it. There are two main ingredients to our PSO scheme: an adjustable join (Adjoin) scheme (MIT-CSAIL-TR-2012-006 (2012) Cryptographic treatment of CryptDB's adjustable join. http://people.csail.mit.edu/nickolai/papers/popa-join-tr.pdf) and a tuple set (TSet) scheme (Cash, D., Jarecki, S., Jutla, C. S., Krawczyk, H., Rosu, M.-C., and Steiner, M.... 

Abstract The aim of this study was to determine the effects of Mg supplementation on anthropometric indices consisting of body weight, waist circumference (WC), BMI and body fat percentage. In this systematic review and dose-response meta-analysis, we searched PubMed, Cochrane Library, Scopus, Web of Science and Google Scholar from databases inception up to February 2020 for relevant randomised controlled trials. Quality of evidence was evaluated using the Cochrane Collaboration Tool. All the outcomes of this meta-analysis were pooled using the random effect model. Analysis of dose-response for Mg dosage was carried out using a fractional polynomial model. The systematic review and... 

Database management systems (DBMS) are one of cloud services with major applications in industry and business. In the use of such services, since the cloud service provider cannot be entrusted with the plain data, the databases are typically encrypted prior to outsourcing. One of the most challenging issues in designing these services is supporting SQL join queries on the encrypted database. The multi-adjustable join scheme (M-Adjoin) [Khazaei-Rafiee 2020], an extension of Adjoin [Popa-Zeldovich 2012 and Mironov-Segev-Shahaf 2017], is a symmetric-key primitive that supports the join queries for a list of column labels on an encrypted database. In previous works, the following security... 

Gelatin-based functional films were prepared by the addition of grapefruit seed extract (GSE, 5 wt% based on gelatin) and various amounts of TiO2 (0.5, 1.0, 3.0, and 5.0 wt% based on gelatin). TiO2 was evenly dispersed in the gelatin film, but the film surface roughness was increased as the concentration of TiO2 increased. The mechanical strength and water contact angle (WCA) of the composite film were the highest, while the water vapor permeability (WVP) was the lowest when 0.5 wt% TiO2 was used. The addition of GSE slightly reduced the UV light transmittance, but the addition of TiO2 almost completely prevented the UV light transmission. The addition of GSE and TiO2 did not significantly... 

Liquefaction is a phenomenon through which saturated sandy soil loses its shear strength and turns into a liquefied state. One of the most detrimental consequences of liquefaction is the reconsolidation volumetric settlements after the earthquakes, which is due to the dissipation of excess pore pressure caused by earthquakes. Severe floods can follow these settlements in free fields such as grounds close to the sea or rivers. Several researchers studied this phenomenon using data obtained from experiments in the lab or observations in the fields. Previous works were mainly based on a limited number of experimental observations and considered loadings and boundary conditions that were... 

Labeling vertebral discs from MRI scans is important for the proper diagnosis of spinal related diseases, including multiple sclerosis, amyotrophic lateral sclerosis, degenerative cervical myelopathy and cancer. Automatic labeling of the vertebral discs in MRI data is a difficult task because of the similarity between discs and bone area, the variability in the geometry of the spine and surrounding tissues across individuals, and the variability across scans (manufacturers, pulse sequence, image contrast, resolution and artefacts). In previous studies, vertebral disc labeling is often done after a disc detection step and mostly fails when the localization algorithm misses discs or has false... 

Analysis of cancer mutational signatures have been instrumental in identification of responsible endogenous and exogenous molecular processes in cancer. The quantitative approach used to deconvolute mutational signatures is becoming an integral part of cancer research. Therefore, development of a stand-alone tool with a user-friendly interface for analysis of cancer mutational signatures is necessary. In this manuscript we introduce CANCERSIGN, which enables users to identify 3-mer and 5-mer mutational signatures within whole genome, whole exome or pooled samples. Additionally, this tool enables users to perform clustering on tumor samples based on the proportion of mutational signatures in... 

Electrocardiogram (ECG) recording of electrical heart activities has a vital diagnostic role in heart diseases. We propose to tackle the problem of arrhythmia detection from ECG signals totally by a deep model that does not need any hand-designed feature or heuristic segmentation (e.g., ad-hoc R-peak detection). In this work, we first segment ECG signals by detecting R-peaks automatically via a convolutional network, including dilated convolutions and residual connections. Next, all beats are aligned around their R-peaks as the most informative section of the heartbeat in detecting arrhythmia. After that, a deep learning model, including both dilated convolution layers and a Long-Short Term... 

This study presents a new approach for designing optimal sensors layout based on accuracy of defect mapping. It is obtained from combination of the reference-free damage detection technique and the probability-based diagnostic imaging method. Considering damage indices based on continuous wavelet transform of sensors signals, the core of this study involves with development of a database of continuous wavelet transform features of a crack. In fact, the database contains the data from 594 different states in crack positions, orientations, and the considered sensing path lengths. Eventually, this database is used for localization of damage by interpolating the stored data collected from the... 

It is of fundamental importance to find algorithms obtaining optimal performance for learning of statistical models in distributed and communication limited systems. Aiming at characterizing the optimal strategies, we consider learning of Gaussian Processes (GP) in distributed systems as a pivotal example. We first address a very basic problem: how many bits are required to estimate the inner-products of some Gaussian vectors across distributed machines? Using information theoretic bounds, we obtain an optimal solution for the problem which is based on vector quantization. Two suboptimal and more practical schemes are also presented as substitutes for the vector quantization scheme. In... 

In this paper, learning of tree-structured Gaussian graphical models from distributed data is addressed. In our model, samples are stored in a set of distributed machines where each machine has access to only a subset of features. A central machine is then responsible for learning the structure based on received messages from the other nodes. We present a set of communication-efficient strategies, which are theoretically proved to convey sufficient information for reliable learning of the structure. In particular, our analyses show that even if each machine sends only the signs of its local data samples to the central node, the tree structure can still be recovered with high accuracy. Our... 

Considering the wide spread locations of wind farms in Iran, it is important to develop a suitable decision support system (DSS) to fulfill proper management of wind farms. Extensive literature survey indicates that there are no integrated forms of DSS to manage a set of wind farms. The existing wind farms are performing independently, and there is no practical method for exchanging the online data. DSS can contribute to optimal operation of wind farms, operation and maintenance scheduling, pricing policy, etc. In this study, a geographic information system and RETSCREEN software were linked to the designed DSS to achieve a more suitable result. Also, a huge number of data are constantly... 

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... 

In this paper, we argue that in many basic algorithms for machine learning, including support vector machine (SVM) for classification, principal component analysis (PCA) for dimensionality reduction, and regression for dependency estimation, we need the inner products of the data samples, rather than the data samples themselves.Motivated by the above observation, we introduce the problem of private inner product retrieval for distributed machine learning, where we have a system including a database of some files, duplicated across some non-colluding servers. A user intends to retrieve a subset of specific size of the set of the inner product of every pair of data items in the database with... 

The purpose of this paper is to quantify the extent of damage of rectangular reinforced concrete shear walls after an earthquake using surface crack patterns. One of the most important tasks after an earthquake is to assess the safety and classify the performance level of buildings. This assessment is usually performed by visual inspection that is prone to significant errors. In this research, an extensive database on the images of damaged rectangular reinforced concrete shear walls is collected from the literature. This database includes more than 200 images from experimental quasi-static cyclic tests. Using the concept of fractal geometry, several probabilistic models are developed by...