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Distributed detection and mitigation of biasing attacks over multi-agent networks
, Article IEEE Transactions on Network Science and Engineering ; Volume 8, Issue 4 , 2021 , Pages 3465-3477 ; 23274697 (ISSN) ; Zarrabi, H ; Rabiee, H. R ; Khan, U. A ; Charalambous, T ; Sharif University of Technology
IEEE Computer Society
2021
Abstract
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...
Evaluation and optimization of distributed machine learning techniques for internet of things
, Article IEEE Transactions on Computers ; 2021 ; 00189340 (ISSN) ; Kim, M ; Thapa, C ; Abuadbba, S ; Zhang, Z ; Camtepe, S ; Kim, H ; Nepal, S ; Sharif University of Technology
IEEE Computer Society
2021
Abstract
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...
ECG beat classification based on a cross-distance analysis
, Article 6th International Symposium on Signal Processing and Its Applications, ISSPA 2001, Kuala Lumpur, 13 August 2001 through 16 August 2001 ; Volume 1 , 2001 , Pages 234-237 ; 0780367030 (ISBN); 9780780367036 (ISBN) ; Nayebi, K ; Sharif University of Technology
IEEE Computer Society
2001
Abstract
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
DONE: Distributed approximate newton-type method for federated edge learning
, Article IEEE Transactions on Parallel and Distributed Systems ; Volume 33, Issue 11 , 2022 , Pages 2648-2660 ; 10459219 (ISSN) ; Tran, N. H ; Nguyen, T. D ; Bao, W ; Balef, A. R ; Zhou, B. B ; Zomaya, A. Y ; Sharif University of Technology
IEEE Computer Society
2022
Abstract
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...
Finding an unknown object by using piezeoelectric material: A finite element approach
, Article 2nd International Conference on Environmental and Computer Science, ICECS 2009, 28 December 2009 through 30 December 2009, Dubai ; 2009 , Pages 156-160 ; 9780769539379 (ISBN) ; Durali, L ; Zareie, S ; Parvari Rad, F ; Sharif University of Technology
IEEE
2009
Abstract
This paper presents a method to determine material of an unknown sample object. The main objective of this study is to design a database for specifying material of an object. We produce the database for different materials which is subjected to different forces. For this purpose we use a Polyvinidilene Fluoride (PVDF) sensor which is a piezoelectric material. Also we study the effect of changing place of sensor on our study. The detailed design was performed using finite element method analysis. Furthermore, if we have an object which we do not know its material by use of this database we can find out what this object is and how much its Yanoung's modules is. This study will be suitable for...
COVID-19 diagnosis using capsule network and fuzzy c -means and mayfly optimization algorithm
, Article BioMed Research International ; Volume 2021 , 2021 ; 23146133 (ISSN) ; Salekshahrezaee, Z ; Mohammadi Tofigh, A ; Ghanavati, R ; Arandian, B ; Chapnevis, A ; Sharif University of Technology
Hindawi Limited
2021
Abstract
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...
A model-driven approach to semi-structured database design
, Article Frontiers of Computer Science ; Volume 9, Issue 2 , 2015 , Pages 237-252 ; 20952228 (ISSN) ; Mirian Hosseinabadi, S ; Sharif University of Technology
Higher Education Press
2015
Abstract
Recently XML has become a standard for data representation and the preferred method of encoding structured data for exchange over the Internet. Moreover it is frequently used as a logical format to store structured and semi-structured data in databases. We propose a model-driven and configurable approach for modeling hierarchical XML data using object role modeling (ORM) as a flat conceptual model. First a non-hierarchical conceptual schema of the problem domain is built using ORM and then different hierarchical views of the conceptual schema or parts of it are specified by the designer using transformation rules. A hierarchical modeling notation called H-ORM is proposed to show these...
Evaluation of a novel fuzzy sequential pattern recognition tool (fuzzy elastic matching machine) and its applications in speech and handwriting recognition
, Article Applied Soft Computing Journal ; Volume 62 , January , 2018 , Pages 315-327 ; 15684946 (ISSN) ; Bagheri Shouraki, S ; Sharif University of Technology
Elsevier Ltd
2018
Abstract
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...
Predictive equations for drift ratio and damage assessment of RC shear walls using surface crack patterns
, Article Engineering Structures ; Volume 190 , 2019 , Pages 410-421 ; 01410296 (ISSN) ; Dolatshahi, K. M ; Sharif University of Technology
Elsevier Ltd
2019
Abstract
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...
You are what you eat: Sequence analysis reveals how plant microRNAs may regulate the human genome
, Article Computers in Biology and Medicine ; Volume 106 , 2019 , Pages 106-113 ; 00104825 (ISSN) ; Hasani Bidgoli, M ; Motahari, S. A ; Sedaghat, N ; Modarressi, M. H ; Sharif University of Technology
Elsevier Ltd
2019
Abstract
Background: Nutrigenomic has revolutionized our understanding of nutrition. As plants make up a noticeable part of our diet, in the present study we chose microRNAs of edible plants and investigated if they can perfectly match human genes, indicating potential regulatory functionalities. Methods: miRNAs were obtained using the PNRD database. Edible plants were separated and microRNAs in common in at least four of them entered our analysis. Using vmatchPattern, these 64 miRNAs went through four steps of refinement to improve target prediction: Alignment with the whole genome (2581 results), filtered for those in gene regions (1371 results), filtered for exon regions (66 results) and finally...
The knowledge map of energy security
, Article Energy Reports ; Volume 7 , 2021 , Pages 3570-3589 ; 23524847 (ISSN) ; Bagheri Moghaddam, N ; Maleki, A ; Nazemi, A ; Sharif University of Technology
Elsevier Ltd
2021
Abstract
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...
Numerical-probabilistic modeling of the liquefaction-induced free fields settlement
, Article Soil Dynamics and Earthquake Engineering ; Volume 149 , 2021 ; 02677261 (ISSN) ; Pak, A ; Pakzad, A ; Ayoubi, P ; Sharif University of Technology
Elsevier Ltd
2021
Abstract
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...
ECG segmentation and fiducial point extraction using multi hidden Markov model
, Article Computers in Biology and Medicine ; Volume 79 , 2016 , Pages 21-29 ; 00104825 (ISSN) ; Shamsollahi, M. B ; Sayadi, O ; Armoundas, A. A ; Jutten, C ; Sharif University of Technology
Elsevier Ltd
Abstract
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...
Preventing database schema extraction by error message handling
, Article Information Systems ; Volume 56 , 2016 , Pages 135-156 ; 03064379 (ISSN) ; Amini, M ; Sharif University of Technology
Elsevier Ltd
Abstract
Nowadays, a large volume of an organization's sensitive data is stored in databases making them attractive to attackers. The useful information attackers try to obtain in the preliminary steps, is the database structure or schema. One of the popular approaches to infer and extract the schema of a database is to analyze the returned error messages from its DBMS. In this paper, we propose a framework to handle and modify the error messages automatically in order to prevent schema revealing. To this aim, after identifying and introducing an appropriate set of categories of error messages, each error message that is returned from a DBMS is placed in a proper category. According to the policy...
ECG fiducial point extraction using switching Kalman filter
, Article Computer Methods and Programs in Biomedicine ; Volume 157 , 2018 , Pages 129-136 ; 01692607 (ISSN) ; Montazeri Ghahjaverestan, N ; Shamsollahi, M. B ; Jutten, C ; Sharif University of Technology
Elsevier Ireland Ltd
2018
Abstract
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...
Semi-supervised parallel shared encoders for speech emotion recognition
, Article Digital Signal Processing: A Review Journal ; Volume 118 , 2021 ; 10512004 (ISSN) ; Razzazi, F ; Sameti, H ; Sharif University of Technology
Elsevier Inc
2021
Abstract
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...
An access and inference control model for time series databases
, Article Future Generation Computer Systems ; Volume 92 , 2019 , Pages 93-108 ; 0167739X (ISSN) ; Amini, M ; Sharif University of Technology
Elsevier B.V
2019
Abstract
Today, many applications produce and use time series data. The data of this type may contain sensitive information. So they should be protected against unauthorized accesses. In this paper, security issues of time series data are identified and an access and inference control model for satisfying the identified security requirements is proposed. Using this model, administrators can define authorization rules based on various time-based granularities (e.g. day or month) and apply value-based constraints over the accessed times series data. Furthermore, they can define policy rules over the composition of multiple time-series other than the base time-series data. Detecting and resolving...
Gelatin-based functional films integrated with grapefruit seed extract and TiO2 for active food packaging applications
, Article Food Hydrocolloids ; Volume 112 , 2021 ; 0268005X (ISSN) ; Priyadarshi, R ; Rhim, J. W ; Bagheri, R ; Sharif University of Technology
Elsevier B.V
2021
Abstract
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...
BIM and machine learning in seismic damage prediction for non-structural exterior infill walls
, Article Automation in Construction ; Volume 139 , 2022 ; 09265805 (ISSN) ; TohidiFar, A ; Alvanchi, A ; Sharif University of Technology
Elsevier B.V
2022
Abstract
Despite the seismic vulnerability of non-structural Exterior Infill Walls (EIWs), their resilient design has received minimal attention. This study addresses the issue by proposing a novel framework for predicting possible damage states of EIWs. The framework benefits from an automated combination of Building Information Modeling as a visualized 3D database of the building's components and the Machine Learning classification as the prediction engine. The framework's applicability is studied in a Proof of Concept example of the exterior walls of the buildings damaged in the 2017 earthquake in Kermanshah, Iran. The Extremely Randomized Trees classifier produced the best results for predicting...
Using Web-GIS technology as a smart tool for resiliency management to monitor wind farms performances (Ganjeh site, Iran)
, Article International Journal of Environmental Science and Technology ; 2018 ; 17351472 (ISSN) ; Abbaspour, M ; Radfar, R ; Mohammadi, A ; Sharif University of Technology
Center for Environmental and Energy Research and Studies
2018
Abstract
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...