International Journal of Web Research
Volume:6 Issue: 1, Spring-Summer 2023

Content interpretation is a cognitive ability which is mostly concerned with understanding the intention of the person who has created or narrated the content.  Narrator is also important since his/ her specific intention, which is inevitable in media like a social network, may change the reality of a created content. Here, the focal point is a sort of transformation with the aim of yielding the class of a message behind the content. In this paper, we propose a rule-based framework for interpreting contents in a social network that has the ability to perform such a transformation through using some generic rules with propositions at high abstraction level. The reason for selecting abstract propositions is their ability in covering wide range of facts occurring in the real world situation. Our suggested framework is in reality able to determine the class of a message indicating the possible intention of either a content’s creator or its narrator, such as whether a narrator is seeking honesty/justice toward the others, is after respect for the people, cares for compassion/ mercy, emphasizes a significance of knowing/ thinking in life, or is after self-upgradation to conduct a healthy life. These classes of message are determined according to both philosophical and psychological aspects which do exist behind the cognitive, emotional and ethical faculties in human being. Results of some experiments show that the generic rules proposed in this paper, which are structured on the ground of abstract propositions, have enough ability to respond successfully to the issue of interpretation in a social network with the characteristics already mentioned. Also, these results approve the fact that such an abstraction is able enough to handle the possible facts hidden in the contents showing up in social network.

Apprehending the personality types of software engineers is essential for both individuals and organizations, especially in software engineering, which heavily relies on teamwork and soft skills. This paper explores various aspects and dimensions of personality exhibited by software engineers, focusing on Iranian culture. To achieve this, we conducted a comprehensive study that involved analyzing existing research on a global scale and a case study specifically targeting professional software engineers in Iran. The Myers-Briggs Type Indicator test was utilized to gather data, and the responses were carefully filtered, resulting in 102 valid datasets for analysis, representing both the private and public sectors. Our methodology included the development of a comprehensive questionnaire comprised of personal and standardized MBTI questions in Persian. The findings of our study indicate that software engineers in Iran predominantly exhibit a thinking personality rather than a feeling one. Moreover, personality types such as ISTJ, INTJ, ESTJ, and ENTJ were observed to be more prevalent among software engineers, while ISFJ, ISFP, ESFP, ENFP, and ESFJ were less common. While these results align with global trends, there are also noteworthy distinctions among Iranian software engineers. The implications of our research extend to practical applications for managers, human resources specialists, and recruiters. By understanding software engineers' personality types and traits, employers can optimize talent acquisition strategies, improve job placements, and tailor career development programs accordingly. This knowledge is helpful for students and those interested in a career in software engineering. It aids in making informed decisions that meet the field's requirements.

Accurate traffic classification is important for various network activities such as accurate network management and proper resource utilization. Port-based approaches, deep packet inspection, and machine learning are widely used techniques for classifying and analyzing network traffic flows. Most classification methods are suitable for small-scale datasets and cannot achieve a high classification accuracy owing to their shallow learning structure and limited learning ability. The emergence of deep learning technology and software-driven networks has enabled the application of classification methods for processing large-scale data.In this study, a two-step classification method based on deep learning algorithms is presented, which can achieve high classification accuracy without manually selecting and extracting features. In the proposed method, an Autoencoder was used to extract features and remove unnecessary and redundant features. In the second step, the proposed method uses the features extracted by the autoencoder from a hybrid deep-learning model based on the CNN and LSTM algorithms to classify network traffic.To evaluate the proposed method, the results of the proposed two-stage hybrid method is compared with comparative algorithms including decision tree, Naïve Bayes, random forest. The proposed combined CNN+LSTM method obtains the best results by obtaining values of 0.997, 0.972, 0.959, and 0.964, respectively, for the evaluation criteria of, accuracy, precision, recall, and F1 score.The proposed method is a practical and operational method with high accuracy, which can be applied in the real world and used in the detection of security anomalies in networks using traffic classification and network data.

Sentiment analysis is the process of identifying and categorizing people’s emotions or opinions regarding various topics. The analysis of Twitter sentiment has become an increasingly popular topic in recent years. In this paper, we present several machine learning and a deep learning model to analysis sentiment of Persian political tweets. Our analysis was conducted using Bag of Words and ParsBERT for word representation. We applied Gaussian Naive Bayes, Gradient Boosting, Logistic Regression, Decision Trees, Random Forests, as well as a combination of CNN and LSTM to classify the polarities of tweets. The results of this study indicate that deep learning with ParsBERT embedding performs better than machine learning. The CNN-LSTM model had the highest classification accuracy with 89 percent on the first dataset and 71 percent on the second dataset. Due to the complexity of Persian, it was a difficult task to achieve this level of efficiency. The main objective of our research was to reduce the training time while maintaining the model's performance. As a result, several adjustments were made to the model architecture and parameters. In addition to achieving the objective, the performance was slightly improved as well.

L-Atur is a web application with the essence of human and machine collaboration that assists users who don't have coding or design skills to generate their customized products. We noticed that our previous version only supported small-scale designs properly, and the generated shapes didn't provide enough visual complexity for large-scale results. In this paper, we improved the supported grammatical properties in order to expand design variations. We offered random and specified color choices, variable line thickness, and multi-rule L-systems grammars as the new options for the current version of L-Atur. We also provided an in-depth analysis of how these new suggested properties can have fundamental impacts on the generated designs visually. We also discussed grammatical evolution as a solution to improve grammatical diversity by using machine-generated ones. As a result, the current version can cover broadened design aspects ranging from small-scale products to large ones with diverse levels of complexity and a high level of visual novelty.

The rise of the Internet and the exponential increase in data have made manual data summarization and analysis a challenging task. Instagram social network is a prominent social network widely utilized in Iran for information sharing and communication across various age groups. The inherent structure of Instagram, characterized by its text-rich content and graph-like data representation, enables the utilization of text and graph processing techniques for data analysis purposes. The degree distributions of these networks exhibit scale-free characteristics, indicating non-random growth patterns. Recently, word co-occurrence has gained attention from researchers across multiple disciplines due to its simplicity and practicality. Keyword extraction is a crucial task in natural language processing. In this study, we demonstrated that high-precision extraction of keywords from Instagram posts in the Persian language can be achieved using unsupervised word co-occurrence methods without resorting to conventional techniques such as clustering or pre-trained models. After graph visualization and community detection, it was observed that the top topics covered by news agencies are represented by these graphs. This approach is generalizable to new and diverse datasets and can provide acceptable outputs for new data. To the author's knowledge, this method has not been employed in the Persian language before on Instagram social network. The new crawled data has been publicly released on GitHub for exploration by other researchers. By employing this method, it is possible to use other graph-based algorithms, such as community detections. The results help us to identify the key role of different news agencies in information diffusion among the public, identify hidden communities, and discover latent patterns among a massive amount of data.

Trees provide a wide range of benefits to humans and other living organisms. An accurate method tree species identification will improve their management and conservation.  Also, tree identification and description are crucial for genetic study, biodiversity conservation, management and regeneration strategies. The conventional methods of tree identification are time-consuming and requires a high level of expertise, necessitating development of a more efficient tree identification means. In this research, a QR code system for tree identification was developed. Tree data were collected from campuses of two tertiary institutions in Akure, Nigeria: Federal University of Technology and Federal College of Agriculture. System design was built around a three-tier architectural model. PostgresSQL was used as the Database System, the lowest tier. The Middle tier is the Web Server, Apache HTTP Server.  Php 8.1 was the scripting language that communicates with the database. For the Client tier, HTML, CSS and Javascript were used. The QR code generator was developed using PHP 8.1. The PHP script used a QR code library to generate the QR code image. The QR code is linked to the website database containing all tree species information. The generated QR codes were attached to trees, and when scanned, the website is automatically launched and the tree information is retrieved. A survey was conducted to get end-users’ feedback within the study sites. The results obtained revealed that the QR codes are easy to use, and can make tree identification more interesting, thus increasing people’s knowledge about trees and improving Trees management.

Among the common crimes and deviations in virtual social networks, the crime of verbal abuse (insult) against women is the most prevalent and common. Some characteristics of social networks, such as the remoteness of communication, being virtual and intangible, the possibility of anonymity, are effective in explaining the high number of these crimes. These features give criminals the opportunity to excuse their actions with ease and lessen the twinge in their conscience. However, the main source of women's victimization should be found in the human interactions of cyber space and not its structure. Criminals sometimes justify their behavior by denying responsibility or denying injury and sometimes with the thought that the victim deserves the crime. This article tries to reveal the cause and manner of verbal abuse against women in Telegram social network. For this purpose, the analysis has been built upon the methods of a criminological theory known as the theory of "neutralization techniques". The data was collected through observation and indirect interview and the method used to analyze this data is descriptive-analytical. The findings of this research indicate that in the case of the slightest unusual behavior of a female user, she faces more and more severe reactions than in the same case for a man. Regarding their abusive behavior, they consider the virtual and intangible nature of the injuries or the guilt of the victim to be enough to neutralize their conscience.

In line with increasing attention to the scope of Business Process Management (BPM) over the past two decades, many techniques and tools have been introduced. Finding the proper technique and tool in each phase of the business process management life cycle takes time and effort. This study aims to design and develop an ontology to facilitate the selection of suitable techniques and tools at each step of the BPM life cycle. This ontology provides a common understanding of concepts of this domain for computers. The study results showed that two taxonomies for techniques and software tools for business process management were created based on BPM life cycle steps. Then, an ontology was developed for them. Noy & McGuinness methodology was applied to implement this ontology, and Protégé 5.2 and owl language were used. Also, the quality criteria-based approach was used for the evaluation of ontology. All the main concepts in the domain of BPM techniques and tools were extracted from previous studies. There are 298 terms. 58 of them are domain concepts or classes, 2 are about taxonomic relations, 2 are related to data property, and 224 are instances. This research used these terms, and the deployed ontology with the quality criteria-based approach was evaluated.

Federated learning is a distributed data analysis approach used in many IoT applications, including IoMT, due to its ability to provide acceptable accuracy and privacy. However, a critical issue with Federated learning is the poisoning attack, which has severe consequences on the accuracy of the global model caused by the server's lack of access to raw data. To deal with this problem effectively, a distributed federated learning approach involving blockchain technology is proposed. Using the consensus mechanism based on reputation-based verifier selection, verifiers are selected based on their honest participation in identifying compromised clients. This approach ensures that these clients are correctly identified and their attack is ineffective. The proposed detection mechanism can efficiently resist the data poisoning attack, which significantly improves the accuracy of the global model. Based on evaluation, the accuracy of the global model is compared with and without the proposed detection mechanism that varies with the percentage of poisonous clients and different values for the fraction of poisonous data. In addition to the stable accuracy range of nearly 93%, the accuracy of our proposed detection mechanism is not affected by the increase of α in different values of β. </span>

The Internet of Things (IoT) has gained significant attention in recent years, with the proliferation of connected devices and the need for efficient data transfer in IoT networks. Software-Defined Networking (SDN) has emerged as a promising solution to address the challenges of network management and optimization in IoT environments. This paper presents a comparative study of data transfer in SDN network architecture in IoT, focusing on the benefits, challenges, and future perspectives of integrating SDN and IoT. Given the crucial role of security in IoT, this paper seeks to access a secure architecture for computer networks to provide a solution for security challenges. To achieve this, a comparative analysis of two SDN architectures is conducted in this research. We have utilized the Miniedit software, which serves as a laboratory for software-defined networks, to implement and simulate these SDN architectures. The results of this study are based on a comparison of the two secure architectures using DITG tables. This comparative study offers valuable insights into the integration of SDN in IoT network architecture and its influence on data transfer.

During the COVID-19 pandemic, universities worldwide favored distance learning systems. Although e-learning provides numerous benefits, it also poses challenges for learners, so learners’ satisfaction is a significant concern to be assessed. The present study aimed to assess learners’ satisfaction with the University of Tehran's LMS during the pandemic. It applied a model based on the Technology Acceptance Model (TAM) and supplemented it with factor analysis. Research validity was assessed by using confirmatory factor analysis, while Cronbach's alpha was employed to measure the reliability of the research instrument. The study used the Partial Least Squares (PLS) analytical method to construct structural equations. In order to do so, data was collected from a comprehensive questionnaire distributed to 334 students at the University of Tehran, in which 143 participated. The findings reaffirmed the TAM's effectiveness in understanding the factors influencing learners’ satisfaction with the LMS. Consequently, these insights empower the technical team to enhance system functionality and infrastructure, aiming at improving service quality in the first semester of the academic year 2021. The Results showed that: 1) The TAM-based proposed scale has successfully explained factors predicting learners’ satisfaction. 2) Technical knowledge contributes to the perceived ease of use and influences perceived usefulness with a coefficient of 0.89, and 3) The significant relationships between technical knowledge and attitude were met. The paper's contribution is finding that learners’ technical knowledge significantly impacts their satisfaction, which helps the ICT team develop a user-friendly environment and increases the flexibility of templates. The study focused on creating training videos to improve learners’ technical knowledge. These results can change and improve the software development strategy for the next semester.