مجله رایانش نرم و فناوری اطلاعات
سال یازدهم شماره 3 (پاییز 1401)

 Investigations have revealed that human emotions are resulted from their internal neural operations. The feedback of each emotion, sent from the skull surface, can be received and processed as a signal. Brain signals can be received and recorded by the EEG setup. In recent years, researchers and scholars have utilized various methods to capture and pre-process the signal, feature selection, dimensionality reduction and classification of brain signals. But, the number and type of extracted features play key roles in classification. Since it is unknown which feature operates more effectively and due to the fact that the number of used features are typically high and dependent on person, reduction in number of features and improving the efficiency of the classifier have been focused by many researchers. The purpose of this article is to provide a multi-task learning method to reduce the dimension and achieve a common space of features that describes the feelings of different people well. To show the efficiency of the proposed method, three well-known datasets are used, i.e. DEAP, SEED and DREAMER. Experiments are performed in two forms. In the first experiment, each channel is investigated separately. Channels with high efficiency are selected. The second experiment is performed by considering channels related to different parts of the brain (frontal, occipital, left hemisphere, right hemisphere). In the first experiment, the highest efficiency is about 80% and in the second experiment it is about 84%. Experimental results have shown that the proposed method have a higher efficiency than other comparing methods.

in visual secret sharing, the secret image is transformed into several share images and distributed among different people. The share images do not contain any information about the original image, and these images are similar to the noise image. When needed, the original image can be retrieved in the presence of all stakeholders and by stacking the share images. In this regard, the appearance of noisy images may attract the attention of the attackers. To solve this problem, meaningful visual secret sharing was presented. In this article, a method for sharing the image with meaningful shares is introduced. There are a number of hyper parameters in the proposed algorithm. To improve performance, an attempt has been made to determine these hyper parameters using a genetic algorithm. The cost function of the genetic algorithm is the difference between the two values of the correct number of bits of the recovered image and the original image and the correct number of integer bits between the cover image and the shares. The proposed method was evaluated using PSNR, MSE, BCR criteria and presented good results on different images with different number of shares.

The Routing Protocol for Low Power and Lossy Networks (RPL) is defined as a standard routing protocol for the Internet of Things (IETF) based on the definitions of the Internet Engineering Task Force (IETF). This protocol uses the objective function to select the optimum path. Generally, Routing depends on a variety of factors. Thus, it is desirable to use more objective functions to select the best path in the routing process. Therefore, in this paper, an RPL-based Multi-objective routing method is proposed for the Internet of Things. In the proposed method, in addition to the metric of trust, other comprehensive metrics have been used. Despite popular attacks such as rank and Sybil attacks, packet loss rates have decreased and the stability of a node has increased in relation to the rank changes. The advantages of the proposed method are that, despite the rank and Sybil attacks, the average rate of lost packets in different scenarios is between 5 and 13%, while in MRHOF-RPL and SecTrust- RPL is between 62% to 89% and 26% to 37% respectively. On the other hand, the degree of stability of a node compared to the rank changes of the proposed method has increased between 2 and 8 times compared to the mentioned methods. Finally, the Cooja simulator is used to evaluate the proposed method.

Considering the increasing expansion of the Internet of Things (IoT) applications and the limitation of end devices in energy resources, sending/receiving fewer data in order to reduce energy consumption is essential. The authentication and key agreement in the IoT networks face many important challenges, most of which are the high computational overhead of existing group key agreements, having negative impacts on network performance. In this paper, we propose an authentication and key agreement protocol that support group communication and uses the Chebyshev polynomial in its calculations, which compare to linear pairing and elliptic curve cryptography, while having enough security, imposes less overhead on the IoT networks. Our proposed protocol is a good option for use in IoT applications including home automation, smart meters, and vehicular sensor networks. The proposed group authentication improves the efficiency of communication compared to other similar protocols and is resistant to various types of internal and external attacks. The security assessment of the proposed protocol has been done using a Random Oracle Model (ROM) and informally.

The advent of Internet of Things (IoT) technology has led to the concept of the smart city, in which smart devices are recognized as a necessity. The applications installed on these devices generate large volumes of data that often require real-time processing. However, these devices have limited capabilities and are not capable of processing large amounts of data. Moving all this data to cloud data centers results in higher bandwidth usage, latency, cost and energy consumption. Therefore, providing services to delay-sensitive smart city applications in the cloud is a challenging issue, and meeting the requirements of these applications requires the use of a hybrid cloud and fog paradigm. Fog computing as a complement to the cloud allows data to be processed near smart devices. However, the resources in the fog layer are heterogeneous and have different capabilities, hence, appropriate scheduling of these resources is of great importance. In this paper, the problem of task scheduling for the smart city applications in the cloud-fog environment has been addressed. To this purpose, the task scheduling problem has been modeled as a multi-objective optimization problem, which aims to minimize service delay and energy consumption of the system under deadline constraint. Then, in order to solve this problem and achieve an appropriate scheduling strategy, non-dominated sorting genetic algorithm II (NSGA-II) with customized operators has been applied. In addition, in order to improve the diversity of the population and the convergence speed of the proposed algorithm, a combination of chaotic map and opposition-based learning methods have been used to generate the initial population. Also, the approach based on the penalty function has been employed to penalize the solutions that do not meet the deadline constraint. The simulation results reveal that the proposed scheduling algorithm, compared to its best competitor, improves service response delay, waiting time, execution delay and system energy consumption by 1.49%, 1.70%, 2.7% and 1.86%, respectively. Furthermore, by properly assigning tasks to the computing nodes, compared to the best competitor, the percentage of missed-deadline tasks is reduced by 1.89%.

Attention deficit/hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders in children. In this study, we investigated alterations in functional connectivity in children with ADHD compared to healthy controls (HC) using high-density electroencephalographic (EEG) data. We computed current source densities using the exact low-resolution electromagnetic tomography (eLORETA) method. Then, the lagged phase synchronization was used to evaluate group differences in functional connectivity. We also explored differences in network topological properties between ADHD and HC. Compared to HC, ADHD was characterized with a decrease in delta LPS in the occipital regions. ADHD also showed higher LPS in theta and alpha bands over frontal, temporal, central and occipital areas. Both ADHD and HC showed the small-world properties (S>1) in all frequency bands. Compared to HC, ADHD was further characterized by increased global efficiency and decreased average clustering coefficient in delta and theta band, respectively. Our findings show that the EEG source connectivity analysis can better characterize alterations in functional connectivity in children with ADHD.

In this paper, analytical models are presented for modeling cloud load balancing mechanism combing queueing networks (QNs) and generalized stochastic Petri nets (GSPNs). To this end, a hybrid model is proposed to model a cluster inside an Infrastructure-as-a-Service (IaaS) cloud data center in the first step. The model includes several real aspects of such environments, such as request queueing, virtual machine (VM) provisioning, VM servicing, and powering on and off of physical machines (PMs). Based on the presented cluster model, a hybrid monolithic model is proposed in the second step that encompasses requests arrival and load balancing component of the cloud. The proposed monolithic model supports heterogeneity of requests in terms of the arrival process and the number and execution time of requested VMs. To demonstrate the applicability of the hybrid monolithic model, several load balancing algorithms that can be applied to such a model are introduced and evaluated based on different performance metrics of interest.