International Journal Information and Communication Technology Research
Volume:13 Issue: 3, Summer 2021

This work presents a novel microwave sensor that is specially designed for the retrieval of complex permittivity. The proposed sensor is designed to operate in the C band (4.54 GHz). By implementing a novel feeding structure, the proposed SIW cavity design improves the coupling and achieves a better quality factor. Several techniques are used to enhance sensitivity, including a Photonic Band Gap (PBG), corner cut, and slow-wave vias. These techniques increase the interaction between the material under test and the electric field. By utilizing slow-wave vias, 35% size reduction is achieved. Achieving simultaneous miniaturization and sensitivity enhancement in this study introduces a new possibility and application for sensor design. The values of complex permittivities are extracted from scattering parameters obtained from simulation of the structure in CST Microwave Studio (MWS) using a machine learning approaches. Our sensor has 0.8% sensitivity, which is better than that of other sensors. Moreover, the maximum error rate in our method is lower than other existing methods. This ratio for the proposed method is 2.31% while for curve fitting and analytical solutions are 26% and 16%, respectively.

 Efficient monitoring and quick feedback control are the main requirements of smart cities to guarantee the stability and safety of urban infrastructures. Real-time monitoring in order to detect anomalies leads to the intensive data processing and hence requires a new computing scheme to offer large-scale and low latency services. Fog architecture by extending computing to the edge of the network, provides the ability to accurate and fast detection of abnormal patterns. A hierarchical fog computing architecture and an efficient hyperellipsoidal clustering algorithm presented in previous studies have been applied to identify anomalous behaviors in water distribution grids. We created an urban water distribution grid dataset using Epanet2w simulator software by measuring grid features: pressure and head for several scenarios. We created 12 distinct events (unexpected behavior) with different scales during the simulation time. To evaluate the effectiveness of the hierarchical anomaly detection model in water distribution grids, the data and computing nodes at different layers were executed as docker containers.  The evaluation results proved the efficiency of the proposed hierarchical anomaly detection model with a significant reduction in latency compared to the centralized scheme, while reaching a significant detection accuracy compared to the centralized one.

Self-organizing software generation teams are increasing due to the more innovation and convivence these networks provide. This article focuses on the security evaluation of the open-source software development (OSSD) team. A newly generated formula for assessing the network security of a collaborative network is proposed and evaluated. For this purpose, we take advantage of graph theory criteria. Using this measure, if the developer’s network is vulnerable in some parts, the guideline can be used for attracting/strengthening the ties in OSSD.

The diagnostic process is based on the fact that malicious activity is different from the activity of a normal system. Detection of intrusion is a very complex process. In this paper, we propose Feature Selection to improve the velocity support vector machines (SVM) based intrusion detection system (IDS). The new model has used a feature selection method based on Fisher Score with an innovation in fitness function reduce the dimension of the data, increase true positive detection and simultaneously decrease false positive detection. In addition, the computation time for training will also have a remarkable reduction. We demonstrate the feasibility of our method by performing several experiments on NSLKDD intrusion detection system competition dataset. Results show that the proposed method can reach high accuracy and low false positive rate (FPR) simultaneously. Numeric Results and comparison to other models have been presented.

Telegram is a cloud-based instant messenger with more than 500 million monthly active users. This messenger is very popular among Iranians, as more than 50 million Telegram users are Iranians. Telegram is used as a social network in Iran because it offers features beyond a simple messenger, but does not offer all the features of social networks, including user recommendation. In this paper, investigating a real dataset crawled from Telegram, we have provided a hybrid method using the user membership graph and group characteristics to recommend the user in Telegram. The membership graph connects users based on membership in the same groups. Also, the characteristics for each group are indicated by the name and description of that group in Telegram. We created a bag of words for each group using natural language processing methods, then combined the bag of words for each group with the results of the membership graph processing. Finally, users are recommended based on the list of groups obtained by the combination. The data used in this paper include more than 900,000 groups and 120 million users. Evaluation of the proposed method separately on two categories of Telegram specialized groups shows the model integration and error reduction for the first category to 0.009 and the second category to 0.016 in RMSE.

We proposed a model of learning and belief formation in which a group of agents tries to learn the true underlying state of the world and make the best possible decisions. Agents with limited computational ability, in addition to receiving noisy private signals, observe the decisions of their neighbors. It is well known that Bayesian inference is very complex in social observations, especially when agents are unaware of the structure of the social network. In our model, the role of knowledge derived from the social observations of each agent is separated from that’s of her private observations in the formation of her belief. Thus, to reduce the complexity of Bayesian inference, the processing of social observations is approximated using the inferential naivety assumption. With this assumption, agents naively believe that each neighbor's decisions are based solely on his or her private observations and that their social interactions are ignored. Another important initiative in the proposed model is to eliminate herd behavior by introducing an exponential bias and reducing the weight of early social observations compared to recent observations. A number of Monte Carlo simulation experiments confirm the features of the proposed model. This includes asymptotic learning of all agents and increased learning efficiency in social networks.