In the modern world, people face an explosion of information and difficulty to find the right choice of their interest. Nowadays, people show interest in online shopping to meet their demands increasingly. For researchers and students, finding and buying the desired books from online shops is very tedious work. Recently Recommender System is an excellent tool to deal with such problems, but the Recommender System is suffering from multiple problems such as data sparsity, cold-start, and inaccuracy. To address these problems, we propose Deep Edu a novel Deep Neural Collaborative Filtering for educational services recommendation. A Deep Edu architecture consists of three parts of a Deep Neural Network model (such as input layer, a multilayered perceptron, and an output layer). The Deep Edu provides the following contributions: first, the users’ identifier and books identifier features are mapped into N-dimensional dense embedding vectors, second, the Multi-Layer-Perceptron (MLP) takes the N-dimensional and non-linear features. To increase the performance of Deep Edu in all metrics, we proposed the advance Loss function. Equipped with the following, Deep Edu not only capable of learning the N-dimensional and non-linear interactions between users’ identifier and books identifier, but moreover, it also considerably mitigates the cold-start, data sparsity, and inaccuracy problem. Over significant experiments performed on real-world good books dataset, the results show that Deep Edu’s recommendation performance obviously outperforms existing Educational services recommendation methods.

Steganography aims to hide the messages from unauthorized persons for various purposes, e.g., military correspondence, financial transaction data. Securing the data during transmission is of utmost importance these days. The confidentiality, integrity, and availability of the data are at risk because of the emerging technologies and complexity in software applications, and therefore, there is a need to secure such systems and data. There are various methodologies to deal with security issues when utilizing an open system like the Internet. This research proposes a new technique in steganography within RGB shading space to achieve enhanced security compared with existing systems. We evaluate our approach with the help of diverse image quality evaluation techniques including MSE (Mean Square Error), RMSE (Root Mean Square Error), PSNR (Peak Signal-to-Noise Ratio), MAE (Mean Absolute Error), NCC (Normalized Cross-Correlation) and SSIM (Structural Similarity Index). Our experimental results demonstrate improved strength, intangibility, and security when contrasted with existing techniques and vindicate the effectiveness of this exploration work. The proposed approach achieved a 3.6701% average higher score for PSNR Correlation than the next best existing approach. Moreover, in PSNR with a variable amount of cipher embedded in the same images of the same dimensions, the proposed approach attained a 5.22% better score. Embedding the same size of cipher in images of different size resulted a 3.56% better score.

Modern enterprise systems can be composed of many web services (e.g., SOAP and RESTful). Users of such systems might not have direct access to those services, and rather interact with them through a single entry point which provides a GUI (e.g., a web page or a mobile app). Although the interactions with such entry point might be secure, a hacker could trick such systems to send malicious inputs to those internal web services. A typical example is XML injection targeting SOAP communications. Previous work has shown that it is possible to automatically generate such kind of attacks using search-based techniques. In this paper, we improve upon previous results by providing more efficient techniques to generate such attacks. In particular, we investigate four different algorithms and two different fitness functions. A large empirical study, involving also two industrial systems, shows that our technique is effective at automatically generating XML injection attacks.

The autonomous navigation of an Unmanned Aerial Vehicle (UAV) relies heavily on the navigation sensors. The UAV’s level of autonomy depends upon the various navigation systems, such as state measurement, mapping, and obstacle avoidance. Selecting the correct components is a critical part of the design process. However, this can be a particularly difficult task, especially for novices as there are several technologies and components available on the market, each with their own individual advantages and disadvantages. For example, satellite-based navigation components should be avoided when designing indoor UAVs. Incorporating them in the design brings no added value to the final product and will simply lead to increased cost and power consumption. Another issue is the number of vendors on the market, each trying to sell their hardware solutions which often incorporate similar technologies. The aim of this paper is to serve as a guide, proposing various methods to support the selection of fit-for-purpose technologies and components whilst avoiding system layout conflicts. The paper presents a study of the various navigation technologies and supports engineers in the selection of specific hardware solutions based on given requirements. The selection methods are based on easy-to-follow flow charts. A comparison of the various hardware components specifications is also included as part of this work.

Computing students face the problem with time and quality of the work while managing their graduation/senior projects. Rapid Application Development (RAD) model is based on continual user involvement for the process of requirement gathering via prototyping. After each iteration, the developers can validate the requirements that are completed in the iteration. Managing a project with RAD is easier but not flexible. On the other hand, Agile project management techniques focus on flexibility, agility, teamwork and quality based on user stories. Continual user involvement is avoided, which requires extensive maintenance time for fixing iteration and release of the story points. This also makes it necessary to provide onsite training to the users of the application. This research provides the pros and cons of RAD and Agile project management techniques, to help students in deciding the best approach for managing their graduation projects. For the evaluation of these techniques, similar case studies were given to the senior project students (having similar CGPAs) for building similar functionality-based applications. The two projects “Life Organizer” developed and managed using RAD and “Smart Patient Assistant (SPA)” developed and managed through Agile methodology were evaluated against the quality assurance criteria for senior projects. The study found that the project developed with RAD methodology performed 13.33% better in providing extensive and elaborated documentation than the students following the Agile technique. On the other hand, SPA-Agile based project, due to teamwork had 2.5% better implementation than Life Organizer-RAD based project.

Modern web applications often interact with internal web services, which are not directly accessible to users. However, malicious user inputs can be used to exploit security vulnerabilities in web services through the application front-ends. Therefore, testing techniques have been proposed to reveal security flaws in the interactions with back-end web services, e.g., XML Injections (XMLi). Given a potentially malicious message between a web application and web services, search-based techniques have been used to find input data to mislead the web application into sending such a message, possibly compromising the target web service. However, state-of-the-art techniques focus on (search for) one single malicious message at a time. Since, in practice, there can be many different kinds of malicious messages, with only a few of them which can possibly be generated by a given front-end, searching for one single message at a time is ineffective and may not scale. To overcome these limitations, we propose a novel co-evolutionary algorithm (COMIX) that is tailored to our problem and uncover multiple vulnerabilities at the same time. Our experiments show that COMIX outperforms a single-target search approach for XMLi and other multi-target search algorithms originally defined for white-box unit testing.