Inappropriate placement of methods causes Feature Envy (FE) code smell and makes classes coupled with each other. To achieve cohesion among classes, FE code smell can be removed using automated Move Method Refactoring (MMR) suggestions. However, challenges arise when existing techniques provide multiple MMR suggestions for a single FE instance. The developers need to manually find an appropriate target classes for applying MMR as an FE instance cannot be moved to multiple classes. In this paper, a technique is proposed named MultiMMRSReducer, to reduce multiple MMR suggestions by considering the Total Call-Frequencies of Distinct Entities (TCFDE). Experimental results show that TCFDE can reduce the multiple MMR suggestions of an FE instance and performs 77.92% better than an existing approach, namely, JDeodorant. Moreover, it can ensure minimum future changes in the dependent classes of an FE instance.

A Cyber-Physical System strongly depends on the sensor data to understand the current condition of the environment and act on that. Due to network faults, insufficient power supply, and rough environment, sensor data become noisy and the system may perform unwanted operations causing severe damage. In this paper, a technique has been proposed to analyze the trustworthiness of a sensor reading before performing operation based on the record. The technique employs regression analysis to select nearby sensors and develops a linear model for a target sensor. Using the linear model, target sensor reading is predicted in a particular time stamp with respect to each nearby sensor’s reading. If the difference between the predicted and actual value is within a given limit, the reading is considered as trustworthy for the corresponding nearby sensor. At last, majority consensus is taken to consider the reading as trustworthy. To evaluate the proposed technique, a data set containing temperature reading of 8 sensors for 24 hours was used where first 90% data was used for nearby sensor selection and linear model construction, and rest 10% for testing. The result analysis shows that the proposed technique detects 19, 69, and 73 trustworthy data from 73 records with respect to 3%, 4% and 5% deviation from actual reading.

In today's data driven, automated and digitized world, a significant stage of information extraction is to look for special keywords, more formally known as 'Named Entity'. This has been an active research topic for more than two decades and significant progresses have been made. Today we have models powered by deep learning that, although not perfect, have near human level accuracy on certain occasions. Unfortunately these algorithms require a lot of annotated training data, which we hardly have for Bengali language. This paper proposes a partial string matching approach to identify a named entity from an unstructured text corpus in Bengali. The algorithm is a partial string matching technique, based on Breadth First Search (BFS) search on a Trie data structure, augmented with dynamic programming. This technique is capable of not only identifying named-entities present on a text, but also estimating the actual named-entities from erroneous data. To evaluate the proposed technique, we conducted experiments in a closed domain where we employed this approach on a text corpus with some predefined named entities. The texts experimented on was both structured and unstructured, and our algorithm managed to succeed in both the cases.

To cope up with the pace of digitalization all over the world, like developed countries, developing countries are also offering services to its citizens through various online portals, web applications and web sites. Unfortunately, due to the lack of consideration on vulnerability issues during the development phase, many of those web based services are suffering from serious security threats. For these developing countries, vulnerability statistics are required to have insight about the current security status of the provided web services. That statistical data can assist the stakeholders to take appropriate actions against cyberattacks. In this work, we conduct a survey to observe the responses of web based services against four most commonly found web attacks called Man in the Middle, SQL Injection, Cross Site Scripting and Denial of Service. We carry out the survey for 30 websites (applications) of Bangladesh as the country has been focusing on digitalization of government services for the last few years and has already been offering various online services to its citizens. Among the 30 websites of several categories, result shows that approximately 77% sites are vulnerable to Man in the Middle attack whereas 3% are vulnerable to SQL Injection and Cross Site Scripting.