In the complicated world of legal law, Question Answering (QA) systems only work if they can give correct, situation-aware, and logically sound answers. Traditional evaluation methods, which rely on superficial similarity measures, can’t catch the complex accuracy and reasoning needed in legal answers. This means that evaluation methods need to change completely. To fix the problems with current methods, this study presents a new model-based evaluation metric that is designed to work well with legal QA systems. We are looking into the basic ideas that are needed for this kind of metric, as well as the problems of putting it into practice in the real world, finding the right technological frameworks, creating good evaluation methods. We talk about a theory framework that is based on legal standards and computational linguistics. We also talk about how the metric was created and how it can be used in real life. Our results, which come from thorough tests, show that our suggested measure is better than existing ones. It is more reliable, accurate, and useful for judging legal quality assurance systems.

The rise of the Internet of Things (IoT) and Cyber-Physical Systems (CPS) has brought about a new era of connectivity, building intelligence into the very structure of our society. There are both challenges and possibilities that come with this change, especially when it comes to maintaining industrial machinery. When it comes to big, important machines like electrical transformers, predictive maintenance is very important because the costs of unplanned downtimes can be too high to bear. This essay looks at how the IoT can be used to actively keep an eye on these tools, with a focus on how sensor data can be collected and used to keep operations running smoothly. We look at how machine learning (ML) and deep learning (DL) can be used to look at this data and guess how much useful life a piece of machinery still has. This lets the machine be shut down for repair and keeps it from breaking down without warning. Even though these technologies have clear benefits, there isn’t much written about how to combine them into a unified business process for predictive analytics. This study fills in the blanks by proposing a business process made just for analyzing sensor data and predicting Remaining Useful Life (RUL) of industrial machinery. To facilitate testing of the proposed business process, we provide a prototype implementation and discuss its details. We also present a way to evaluate this business process by applying the prototype implementation to a real-world dataset. The evaluation results show that the proposed business process is promising.

In a variety of sectors, we observe the emerging need for responding to user questions in a fast and efficient manner. We argue that addressing this need by developing question answering reader system applications will lead to several benefits: a) the density of call centers is reduced, and b) time is saved by getting answers through the application instead of going to the company itself. Examples of these applications, such as search engines help users find answers to their questions on documents containing important information, such as legal documents. These applications can be applied in digital banking, electronic commerce, and legal documents. In this study, we investigate the design of a business workflow that can provide answers to questions through documents containing important information, such as legal documents. In this study, we examine open-domain reader systems and propose a business workflow for open-domain reader systems. We are implementing a prototype application on the dataset to investigate the usability of the proposed business workflow. We discuss the prototype’s implementation details and share its evaluation results. The results show that the T5-based model provides better results in open-domain reader systems.

Fast data delivery to users is a big challenge in client-server architecture-based data-intensive systems. Here, prefetching is a widely used technique to increase data-intensive mobile or web applications’ operational performance and data delivery speed. This study proposes a methodology that can address this challenge using deep-learning-enabled prefetching approaches. The proposed methodology minimizes the data-access latency by adopting an approach that models the customer navigational browsing data and predicts the following user actions. The proposed approach utilizes two different recurrent neural network methods to model the clickstream data. These methods include LSTM and bi-directional LSTM. To show the usability of the proposed methodology, we provide a prototype implementation. To this end, we use a public dataset obtained from log files of a coffee store mobile application. In the prototype implementation, because the small number of users accounted for the majority of the load on the system, we segmented users as active and cold users. To facilitate testing of the prototype implementation, we conducted an experimental study. Here, we investigated whether the system can predict users’ near-future requests. In the experimental study, we record the cache hit rates. The results show that the proposed prefetching is promising and can be utilized in client-server-based data-intensive applications.

The Internet of Things (IoT) usually consists of many fully synchronized devices. Since these devices sense data from outside, they are usually outside and sometimes in places that are difficult to access in case of failure. Sometimes, late-recognized errors cause the system not to work and cause significant damage to the system. For this reason, it is crucial to avoid faulty situations with minor damage or even prevent these situations before they occur. Although there are studies on the topic in the literature, there is an emerging need to examine this problem comprehensively and target predictive maintenance and self-healing systems utilizing IoT systems. In this study, we propose a business workflow architecture using streaming-based machine learning algorithms to provide predictive maintenance utilizing IoT systems. In the proposed predictive maintenance workflow, we utilized various machine learning algorithms such as Adaptive Random Forest, Hoeffding Tree, Leveraging Bagging, SPegasos, and Single Drift Classifier classification algorithms. To show the usability of the proposed business workflow, we provide a prototype implementation. We provide an experimental study on the prototype to investigate the prediction success. We also investigate how fast the system can learn from the streaming data. We conduct this evaluation with unbalanced and balanced data. In this manuscript, we report the results of our experimental study.

User interface (UI) testing is a necessary process to evaluate whether the developed web applications meet the software requirements defined by the end-users. This study proposes an e-business workflow that automates UI testing utilizing Hidden Markov Models. The proposed workflow is designed to generate test scripts based on user browsing behaviors. In particular, it is able to generate browsing patterns that are unseen in the historical web usage data. To demonstrate the usability of the proposed testing framework, we provide a prototype implementation. Furthermore, to facilitate testing of the prototype implementation, we conduct experimental studies that investigate the quality of newly generated test scripts. In order to achieve this, we analyze the similarity between the actual browsing behaviors and the newly generated browsing behaviors. The results show that the proposed testing workflow is able to learn from the user-browsing behaviors extracted from the historical web usage log data. The results also show that the workflow is able to generate new and unseen user-browsing behaviors that have high similarities with the actual user-browsing behaviors. In turn, this indicates that the proposed system is able to generate high-quality new test scripts that can provide comprehensive testing of web applications.

Software testing is an important process to evaluate whether the developed software applications meet the required specifications. There is an emerging need for testing frameworks for big data software projects to ensure the quality of the big data applications and satisfy the user requirements. In this study, we propose a software testing framework that can be utilized in big data projects both in e-science and e-commerce. In particular, we design the proposed framework to test big data-based recommendation applications. To show the usability of the proposed framework, we provide a reference prototype implementation and use the prototype to test a big data recommendation application. We apply the prototype implementation to test both functional and non-functional methods of the recommendation application. The results indicate that the proposed testing framework is usable and efficient for testing the recommendation systems that use big data processing techniques.

User behavior can be identified by clustering the web-site navigational patterns expressed by clickstream sequences. Representation of clickstream sequences is yet a challenging problem. The main difficulty is related to the fact that one needs to represent clickstream sequences, which mainly consists of dynamic URLs, in such a way that traditional clustering algorithms are applicable to group together these sequences. In this study, we present the application of embedding approaches to represent click-stream data sequences, to enable machine learning algorithms learn the users' navigational behaviors on web-sites. By utilizing embedding representation, we propose an algorithm that takes clickstream data as input and creates clustered sequential patterns. We discuss the details of different representation algorithms and present its evaluations. We investigate the accuracy in finding the hidden clustered data sequences within the clickstream data. The results show that Word2Vec, representation method that can lead to high quality clustering of user navigational patterns.

In functional User Interface testing, test scenarios are written with respect to the requirements that are specified by test analysts. Usually, a test analyst focuses on base URLs and HTML components while collecting requirements of User Interface test scenarios. A base URL is essentially a unit segment of large scale graph data. It has mostly dynamic shape and is used to navigate pages amongst application's pages. We argue that even though dynamic URLs have additional important information about the content of the page, they are not being utilized in generating User Interface test scenarios. In this study, we address this lack of capability and focus on the development of a methodology that can support the usage of large-scale dynamic URL datasets in UI test script generation. Our proposed methodology is designed as an add-on tool that can be used on the top of the existing UI test automation tools to improve testing quality. We introduce a higher quality testing methodology to make the results more accurate, and we discuss the proposed methodology and give an overview of the implementation details followed by the evaluation results. We perform various performance evaluations to investigate how well the proposed algorithms scale under increasing data sizes. The results are promising and show the usability of the proposed methodology.

Today, application virtualization environments are used in many different fields. With the spread of application virtualization environments, solutions to manage these environments have become an important necessity today. Within the scope of this research, we propose a design of a RESTful web service for managing application virtualization environments. In this manuscript, we discuss the programming interface of the proposed service in detail. We describe our approach when designing “semantics” for the RESTful web service. We overview application usage scenarios in which our design has successfully been used. To show the usability of the proposed design, a prototype implementation is developed. The performance of the prototype implementation for different functionalities is evaluated. The evaluation results indicate that the proposed service can be utilized in the management of application virtualization environments.