The instruments of advertising on the Internet are increasing. Advertisements may be a source of inconvenience and threats. This paper presents a comparison among three different tools for blocking advertisements, namely parental control tools, AdBlock Plus, and Pi-hole. This comparison aims to evaluate the effectiveness of these tools in blocking advertisements. The result showed that the Pi-hole is the most effective tool to block advertisements on the entire home network, while the other tools vary in the level of blocking advertisements on the network.
Participatory Budget (PB) is a process that distributes part of the city budgets among projects submitted and selected by the dwellers. The key challenge for IT-supported e-PB is the projects’ comparison and ranking. This paper focuses on an empirical test of a hybrid method for comparing BP projects. In this study, we investigate two difficult to measure dimensions: beneficiaries and categories. We use ontology to describe and map distances between concept and then generate ranking based on the fuzzy TOPSIS method. The method is validated through experiments with annotators and working methods. The results surpass semantic measure, but also show the space for further development.
In this paper, we have focused on the implementation of a mechanism for detecting malicious nodes in the MP-OLSR routing protocol through the integration of trust reasoning. The study carried out has enabled the emergence of trust rules between nodes based on local information and messages exchanged during the neighborhood discovery process and network topology to make the MP-OLSR protocol more trusted. The correlation between the received control messages allows nodes to validate the neighborhood at one hop and two hops. When the information received is consistent with the MP-OLSR protocol specifications, this reasoning allows the creation of a trust relationship. Otherwise, it allows nodes to be wary of the sources of its inconsistencies. When an attack is detected, the countermeasure proposed allows the attacking node to be isolated from the network and to guarantee its non-participation in the routing mechanism by simply integrating this node in the HELLO message with a modified Link Code, so that it is declared by all the nodes of the network as an attacker.
In cryptography, chaotic systems are commonly used for image encryption due to the complex properties of chaos such as sensitivity to initial conditions, unpredictability, and ergodicity. An image encryption method using one-dimensional chaotic maps and Josephus permutation sequence is depicted in this paper. The keystreams required for the encryption process are generated from one-dimensional chaotic maps. Double scrambling operation is proposed in the pixel permutation phase to enhance security. Josephus permutation sequence is used in the first level scrambling. For the second level of scrambling, a novel method is implemented using the keystream generated from a chaotic tent map. Also, the keystream generated from the logistic map is used in the substitution phase. Experimental results and security analysis shows that the proposed encryption scheme can resist various attacks and ensures high security.
In an educational environment, classifying the cognitive aspect of students is critical. It is because an accurate classification is needed by a lecturer to take the right decision for enhancing a better educational environment. To the best of our knowledge, there is no previous research that focuses on this classification process. In this paper, we propose discretization and feature selection methods before the classification. For this purpose, we adopt the equal frequency for the discretization whose result is evaluated by using logistic regression with two regularizations: lasso and ridge. The experimental result shows that four-intervals on the ridge achieve the highest accuracy. It is to be the base to determine the level of the student’s performance: excellent, good, fair, and poor. Next, we remove unnecessary features, by using the Gain Ratio and Gini Index. Also, we build classifiers to evaluate our proposed methods by using k-Nearest Neighbors (k-NN), Neural Network (NN), and CN2 Rule Induction. The experimental result indicates that both discretization and feature selection can enhance the performance of the classification process. Concerning the accuracy level, there is an increase of about 35%, 2.14%, and 3.8% on average of k-NN, NN, and CN2 Rule Induction respectively, from those with original features.
