Search

Nowadays, semantic interoperability is a new keyword in the Internet of Things (IoT) for the exchange of information between sources. The constant need for interaction and cooperation has resulted in the creation of the Semantic Web with the help of tools and reasoners which manage personal information. Given the significance of the IoT and the increasing use of semantic techniques in this field, the present bibliometric investigation was conducted in the domain of semantic reasoning in the IoT. Bibliometrics involves analyzing bibliographic data of scientific sources, and it can be employed to arrive at an analysis of the status quo in a scientific field. In this study, through the analysis of 799 articles retrieved from the Web of Science database, distribution of topic categories, prolific and influential authors, language of articles, publishers of articles and their geographical distribution, the most debated/researched and the most frequently cited articles, and keyword trends were studied. The results of this study indicate that the number of articles published in the domain of semantic reasoning in the IoT has increased considerably in recent years. Of the articles analyzed, it was revealed that 10 countries produced 84% of the total documents, with China being in the lead. Moreover, as a result of keyword analysis, it can be maintained that the words fog computing, edge computing, Semantic Web, and wireless sensor network are among the most important keywords in this domain. As well, ontology has the highest average number of citations among the specialized keywords.

The popularity and amazing attractiveness of cryptocurrencies, and especially Bitcoin, absorb countless enthusiasts daily. Although Blockchain technology prevents fraudulent behavior, it cannot detect fraud on its own. There are always unimaginable ways to commit fraud, and the need to use anomaly detection methods to identify abnormal and fraudulent behaviors has become a necessity. The main purpose of this study is to present a new method for detecting anomalies in Bitcoin with more appropriate efficiency. For this purpose, in this study, the diagnosis of the collective anomaly was used, and instead of diagnosing the anomaly of individual addresses and wallets, the anomaly of users was examined, and the anomaly was more visible among users who had multiple wallets. In addition to using the collective anomaly detection method in this study, the Trimmed_Kmeans algorithm was used for clustering and the proposed method succeeded in identifying 14 users who had committed theft, fraud, and hack with 26 addresses in 9 cases. Compared to previous works, which detected a maximum of 7 addresses in 5 cases of fraud, the proposed method has performed well. Therefore, the proposed method, by presenting a new approach, in addition to reducing the processing power to extract features, succeeded in detecting abnormal users and also was able to find more transactions and addresses committed a scam.

The financial industry is a pioneer in Blockchain technology. One of the most popular platforms in Token-based banking is the flexible Stellar platform. This platform is open-source, and today, its wide range of features makes it possible for many countries and companies to use it in cryptocurrency and Token-based modern banking. This network charges a fee for each transaction. As well, a percentage of the net amount is generated as the inflation rate of the network due to the increased number of tokens. These fees and inflationary amounts are aggregated into a general account and ultimately distributed among members of the network on a collective vote basis. In this mechanism, network users select an account as the destination for which they wish to transfer assets using their user interface, which is generally a wallet. This account could be the account of charities that need this help. It is then determined the target distribution network based on the voting results of all members. One of the challenges in this network is the purposeful and fair distribution of these funds between accounts. In this paper, the first step is a complete infrastructure of a Stellar financial network that will consist of three network-based segments of the core network, off-chain server, and wallet interface. In the second step, a context-aware recommendation system will be explored and implemented as a solution for the purposeful management of payroll account selection. The results of this study concerning the importance of the purposeful division of collective assets and showing a context-aware recommendation system as a solution to improve the process of stellar users' participation in the voting process by effectively helping them in choosing an eligible destination

Today's world is struggling with the COVID-19 pandemic, as one of the greatest challenges of the 21st century. During the lockdown caused by this disease, many financial losses have been inflicted on people and all industries. One of the fastest ways to save these industries from the COVID-19 is to provide a reliable solution for people's health assessment. In this article, blockchain technology is used to propose a model which provides and validates the health certificates for people who travel or present in society. For this purpose, we take advantage of blockchain features in protecting people's privacy. Since a variety of antibody and human health proving tests against the virus are developing, this study tries simultaneously to design an integrated and secure system to meet the authenticity and accuracy of different people's health certificates for the companies requiring these certifications. In this system, on the one hand, there are qualified laboratories that are responsible for performing standard testing and also providing results to the system controller. Finally, people are considered as the end-user of the system. To provide test information for the entities, the mechanism of KYC tokens will be used based on the Stellar private blockchain network. In this mechanism, the user will buy a certain amount of KYC tokens from the system controller. These tokens are charged in the user's wallet, and the user can send these tokens from his wallet to any destination company, to exchange the encrypted health certificate information. Finally, considering the appropriate platform provided by blockchain technology and the requirement of a reliable and accurate solution for issuing health certificates during the Covid-19 pandemic or any other disease, this article offers a solution to meet the requirements., Comment: This paper has been published in the International Journal of Web Research, Volume 3, Issue 1, 2020

In recent years, people spend a lot of time on social networks. They use social networks as a place to comment on personal or public events. Thus, a large amount of information is generated and shared daily in these networks. Using such a massive amount of information can help authorities to react to events accurately and timely. In this study, the social network investigated is Twitter. The main idea of this research is to differentiate among tweets based on some of their features. This study aimed at investigating the performance of event detection by weighting three attributes of tweets; including the followers count, the retweets count, and the user location. The results show that the average execution time and the precision of event detection in the presented method improved 27% and 31%, respectively, than the base method. Another result of this research is the ability to detect all events (including hot events and less important ones) in the presented method.

Accessing people's personality traits has always been a challenging task. On the other hand, acquiring personality traits based on behavioral data is one of the growing interest of human beings. Numerous researches showed that people spend a large amount of time on social networks and show behaviors that create some personality patterns in cyberspace. One of these social networks that have been widely welcomed in some countries, including Iran, is Telegram. The basis of this research is automatically identifying users' personalities based on their behavior on Telegram. For this purpose, messages from Telegram group users are extracted, and then the personality traits of each member according to the NEO Personality Inventory are identified. For personality analysis, the study is employed three approaches, including; Cosine Similarity, Bayes, and MLP algorithms. Finally, this study provides a recommender system that uses the Cosine similarity algorithm to explore and recommend relevant Telegram channels to members according to the extracted personalities. The results show a 65.42% satisfaction rate for the recommender system based on the proposed personality analysis.

Today, ranking systems in universities have been considered by the academic community, and there is a tight competition between world universities to achieve higher ranks. In the meantime, the ranking of university websites is also in the spotlight, and the Webometric research center announces the ranks of university websites twice a year. Examining university rankings indicators and the Webometric ranks of the university indicates that some of these indicators, directly and indirectly, affect each other. On the other hand, a preliminary study of Webometric indicators shows that some Search Engine Optimization (SEO) indicators can affect Webometric ranks. The purpose of this research is to show how far the SEO metrics can affect the website rank of the university. To do this, after extracting 38 points of the significant SEO metrics of the extracted universities using various tools, data analysis was conducted along with applying association rules on the data. The results of the research show that some of the SEO metrics, such as the number of backlinks, Alexa Rank, and Page Rank have a direct and significant impact on the website rank of universities, and in this regard, interesting rules have been extracted., Comment: A Persian version of this paper has been published