Analyse the risks of ad hoc programming in web development and develop a metrics of appropriate tools

Today the World Wide Web has become one of the most powerful tools for business promotion and social networking. As the use of websites and web applications to promote the businesses has increased drastically over the past few years, the complexity of managing them and protecting them from security threats has become a complicated task for the organizations. On the other hand, most of the web projects are at risk and less secure due to lack of quality programming. Although there are plenty of frameworks available for free in the market to improve the quality of programming, most of the programmers use ad hoc programming rather than using frameworks which could save their time and repeated work. The research identifies the different frameworks in PHP and .NET programming, and evaluates their benefits and drawbacks in the web application development. The research aims to help web development companies to minimize the risks involved in developing large web projects and develop a metrics of appropriate frameworks to be used for the specific projects. The study examined the way web applications were developed in different software companies and the advantages of using frameworks while developing them. The findings of the results show that it was not only the experience of developers that motivated them to use frameworks. The major conclusions and recommendations drawn from this research were that the main reasons behind web developers avoiding frameworks are that they are difficult to learn and implement. Also, the motivations factors for programmers towards using frameworks were self-efficiency, habit of learning new things and awareness about the benefits of frameworks. The research recommended companies to use appropriate frameworks to protect their projects against security threats like SQL injection and RSS injectio

An overview of recent distributed algorithms for learning fuzzy models in Big Data classification

AbstractNowadays, a huge amount of data are generated, often in very short time intervals and in various formats, by a number of different heterogeneous sources such as social networks and media, mobile devices, internet transactions, networked devices and sensors. These data, identified as Big Data in the literature, are characterized by the popular Vs features, such as Value, Veracity, Variety, Velocity and Volume. In particular, Value focuses on the useful knowledge that may be mined from data. Thus, in the last years, a number of data mining and machine learning algorithms have been proposed to extract knowledge from Big Data. These algorithms have been generally implemented by using ad-hoc programming paradigms, such as MapReduce, on specific distributed computing frameworks, such as Apache Hadoop and Apache Spark. In the context of Big Data, fuzzy models are currently playing a significant role, thanks to their capability of handling vague and imprecise data and their innate characteristic to be interpretable. In this work, we give an overview of the most recent distributed learning algorithms for generating fuzzy classification models for Big Data. In particular, we first show some design and implementation details of these learning algorithms. Thereafter, we compare them in terms of accuracy and interpretability. Finally, we argue about their scalability

Keep on Connecting

The “Keep on Connecting” work group was formed in March 2020 to support library workers as they moved primarily to telework in response to the COVID-19 pandemic. This group was charged to develop professional development and community-building content and experiences to promote best practices for telework and maintain a culture of support and community. Programming was conducted via several online platforms ranging from blog posts on desk-seating ergonomics to presentations on balancing work with children home from school to trivia games over Zoom. Over the course of the largely ad hoc programming, it became clear that certain topics and activities were either more relevant or better suited to supporting engagement within our library community. As countless academic libraries found themselves with large portions of their faculty and staff working remotely due to the pandemic, we describe what we found worked best in our institution, which is composed of over 130 library employees at an urban research university. In addition to sample communications and media from sessions, results from a standard program evaluation survey are shared to highlight what participants found to be most helpful to them. The digital poster format will also be leveraged to share a timeline of how the work group formed, approached its tasks, and changed over time

Domain Objects and Microservices for Systems Development: a roadmap

This paper discusses a roadmap to investigate Domain Objects being an adequate formalism to capture the peculiarity of microservice architecture, and to support Software development since the early stages. It provides a survey of both Microservices and Domain Objects, and it discusses plans and reflections on how to investigate whether a modeling approach suited to adaptable service-based components can also be applied with success to the microservice scenario