Journal of Computer and Robotics
Volume:16 Issue: 2, Summer and Autumn 2023

Many different complex systems display emergent behavior, and quite a few of these systems have been studied in the past. The science of complexity, popularly known as chaos theory, deals with emergent systems in other fields. Designing emergence is something of a paradoxical task because one of the defining aspects of emergent behavior is that it occurs only after a system is put into motion. In this paper, we begin with the definition of complex systems. Then, we describe the continuum between strictly ordered systems and entirely chaotic ones and show that emergence takes place somewhere between the two. After that, we survey and show how gameplay emerges from the complex system. Our survey points out that three structural features of complex systems contribute to emergence: (a) active and interconnected elements; (b) feedback loops; and (c) interaction at different scales. To show the active and interconnected elements, we explain cellular automata as an example of simple systems that can produce emergence in games. Moreover, we described how a system can be stabilized/destabilized by feedback loops and how different behaviors may emerge in a system at different scales, along with particular games. In this survey, we identified seven classes of emergence that can be considered in games. These classes are Simple, Weak, Multiple, Strong, Cluster, Hub, and Complex Emergence. These classes are produced by different combinations of feedback loops and interactions among the elements of a system at different scales.

Due to the increasing use of VANET networks and the use of smart systems in these types of networks, their challenges have been the focus of researchers. One of the important challenges of such networks is the security issues that threaten this category of networks. In this article, the Sybil attack, which is one of the security challenges in VANET networks, has been investigated and identified. In a Sybil attack, a node threatens VANET networks by stealing the identity of other nodes or creating a virtual identity, by making incorrect decisions and sending false information. In this article, the clustering method is used to avoid the overhead of identification nodes in centralized methods and avoid delay in distributed methods. RSU determines the cluster head with the help of fuzzy logic. The cluster head creates moving clusters by placing similar nodes in terms of direction, speed, and distance in separate clusters while moving. The cluster head performs malicious node detection using a directional antenna and a fuzzy system. The first fuzzy system places the cluster head in the best possible place of the cluster. The cluster head identifies the malicious nodes in each cluster locally, while the second fuzzy system interferes in determining the validity of the cluster members. In the proposed plan, in addition to optimizing the sending and receiving of messages, The simulation results show that the proposed method has improved by 1.2% in detecting the malicious node, 0.4% in the number of a false positive detection, 0.6% in the lost packet, and 0.1% in the delay compared to the previous methods.

Epileptic seizure prediction has been one of the interesting topics among researchers in recent years. Recent evidence suggests that, in many seizures, changes in the preictal signal begin minutes before the ictal begins, raising hopes of predicting the seizure onset before it occurs. Convolutional neural network (ConvNet) is a powerful computational tool with deep learning capacity which is able to detect complex structures in data. In this study, we employed a ConvNet and a set of techniques to make optimal use of the existing data for an end-to-end learning. Multi-channel non-invasive raw EEGs from the CHB-MIT database were used for training of the proposed model. The proposed method resulted in sensitivity of 92.05% and false prediction rate of 0.073/h with the cross-validation approach in distinguishing preictal and ictal. We obtained a 10-minute seizure prediction horizon that is relatively higher than the values obtained in other researches. This longer time period can give the patient more opportunity for preventive actions. Seizure occurrence period was computed nearly 20 minutes which lets the patient wait less for the seizure to occur and this in turn makes him have less anxiety. Furthermore, a feature map visualizing method was employed in the present work to decode the employed deep network and to understand how it learns and what it learns when trying to solve the seizure prediction task. By investigating feature maps of the used ConvNet’s middle layer, we observed that the proposed network retains most of the beta and gamma band properties in layers.

Intangible assets are defined as non-monetary assets that do not have physical substance but possess economic features that grant rights and advantages to their owner. The role of digital applications in this century can be compared with the function of oil in the past century with was the driving force for growth, wealth, and change. The Covid-19 pandemic has led to the rapid growth of digital services in Iran, and many companies have included digital development in their plans. However, the valuation of these companies poses many difficulties, and introducing the national information network in Iran will add to the importance of evaluation even further.This may lead to an underestimating of the book value of enterprises with extensive intangible assets. Intangible assets are usually difficult to evaluate, and the International Valuation Standard 210 recommends three approaches: (a) an income approach; (b) a market approach; and (c) a cost approach. However, generating accurate results can be challenging. This study innovatively apply traditional approaches to digital intangible assets and combines them with a customer-perspective value to provide more precise results for decision-making and suggest new valuation pattern. To this end, one of the large companies providing digital services in Iran was selected for evaluation, and the results are presented. This pattern is practical and can be implemented for all companies providing digital services in Iran.

Information security has become an important issue in the modern world due to its increasing popularity in Internet commerce and communication technologies such as the Internet of Things. Future media actors are considered a threat to security. Therefore, the need to use different levels of information security in different fields is more needed. Advanced information security methods are vital to prevent this type of threat. Cryptography is a valuable and efficient component for the safe transfer or storage of information in the cyber world. Familiarity with all types of encryption models is an essential need for cybersecurity experts. This paper separates Cryptographic algorithms into symmetric (SYM) and asymmetric (ASYM) categories based on the type of cryptographic structure. SYM algorithms mostly use the Feistel network (FN) structure, Substitution-Permutation Network (SPN), and the ASYM algorithms follow the mathematical structures. Based on this, we examined different encryption methods in terms of performance and detailed comparison of key size, block size, and the number of rounds. In continuation of the weakness of each algorithm against attacks and open challenges in each category, to study more is provided.

In different societies, buildings are considered one of the main energy consumers in the world, and accordingly, they are responsible for a significant percentage of greenhouse gas emissions. Due to the upward growth of the population, the demand for energy consumption is increasing day by day. In such a situation, the prediction of energy consumption has become a vital issue to control the efficiency of energy consumption. To obtain an effective solution to solve this problem, a number of machine learning methods were examined and Xgboost and MLP methods were selected as the best available methods. In order to obtain more suitable results in this research, a system based on stacking was proposed. In the proposed method based on stacking, XGBoost and MLP methods were used in the first level so that the advantages of both methods can be used. The predictions made by each of these methods, in the second level, were used as input to another XGBoost algorithm, which was used as a meta-learner. To obtain better results, the hyperparameters of the basic techniques were optimized using the successive halving search. For a better comparison, machine learning regression techniques were implemented to solve the problem of energy consumption intensity prediction, and the results obtained from them were analyzed on WiDS Datathon. The results showed that the proposed system has improved the MAE, MAPE, and R2 criteria by 0.6, 0.03, and 0.07, respectively, compared to the best existing method.Keywords: Energy Consumption, Stacking, Regression, XGBoost, MLP.