Event Based Retrieval From Digital Libraries Containing Data Streams

The objective of this research is to study the issues involved in building a digital library that contains data streams and allows event-based retrieval. “Digital Libraries are storehouses of information available through the Internet that provide ways to collect, store, and organize data and make it accessible for search, retrieval, and processing” [29]. Data streams are sources of information for applications such as news-on-demand, weather services, and scientific research, to name a few. A data stream is a sequence of data units produced over a period of time. Examples of data streams are video streams, audio stream, and sensor readings. Saving data streams in digital libraries is advantageous because of the services provided by digital libraries such as archiving, preservation, administration, and access control. Events are noteworthy occurrences that happen during data streams. Events are easier to remember than specific time instances at which they occur; hence using them for retrieval is more commensurate with human behavior and can be more efficient via direct accessing instead of scanning. The focus of this research is not only on storing data streams in a digital library and using event-based retrieval, but also on relating streams and playing them back at the same time, possibly in a synchronized manner, to facilitate better understanding in research or other working situations. Our approach for this research starts by considering digital libraries for: stock market, news streams, census bureau statistics, weather, sports games, and the educational environment. For each of these applications, we form categories of possible users and the basic requirements for each of them. As a result, we identify a list of design goals that we take into consideration in developing the architecture of the library. To illustrate and validate our approach we implement a medical digital library containing actual Computed Tomography (CT) scan streams. It also contains sample medical text and audio streams to show the heterogeneity of the library. Streams are displayed in a concise, yet complete, way that makes it unproblematic for users to decide whether or not to playback a stream and to set playback options. The playback interface itself is organized in a way that accommodates synchronous and asynchronous streams and enables users to control the playback of these streams. We study the performance of the specialized search and retrieval processes in comparison to traditional search and retrieval processes. We conclude with a discussion on how to adapt the library to additional stream types in addition to suggesting other future efforts in this area

The UPS Prototype: An Experimental End-User Service Across E-Print Archives

A meeting was held in Santa Fe, New Mexico, October 21-22, 1999, to generate discussion and consensus about interoperability of publicly available scholarly information archives. The invitees represented several well known e-print and report archive initiatives, as well as organizations with interests in digital libraries and the transformation of scholarly communication. The central goal of the meeting was to agree on recommendations that would make the creation of end-user services -- such as scientific search engines and linking systems -- for data originating from distributed and dissimilar archives easier. The Universal Preprint Service (UPS) Prototype was developed in preparation for this meeting. As a proof-of-concept of a multi-discipline digital library of publicly available scholarly material, the Prototype harvested nearly 200,000 records from several different archives and created an attractive end-user environment. This paper describes the results of the project. This is done in two ways. On the one hand, the experimental end-user service that was created during the project is illustrated. On the other hand, the lessons that the project team drew from the experience of creating the Prototype are presented

An approach for optimizing multi-objective problems using hybrid genetic algorithms

© 2020, The Author(s). Optimization problems can be found in many aspects of our lives. An optimization problem can be approached as searching problem where an algorithm is proposed to search for the value of one or more variables that minimizes or maximizes an optimization function depending on an optimization goal. Multi-objective optimization problems are also abundant in many aspects of our lives with various applications in different fields in applied science. To solve such problems, evolutionary algorithms have been utilized including genetic algorithms that can achieve decent search space exploration. Things became even harder for multi-objective optimization problems when the algorithm attempts to optimize more than one objective function. In this paper, we propose a hybrid genetic algorithm (HGA) that utilizes a genetic algorithm (GA) to perform a global search supported by the particle swarm optimization algorithm (PSO) to perform a local search. The proposed HGA achieved the concept of rehabilitation of rejected individuals. The proposed HGA was supported by a modified selection mechanism based on the K-means clustering algorithm that succeeded to restrict the selection process to promising solutions only and assured a balanced distribution of both the selected to survive and selected for rehabilitation individuals. The proposed algorithm was tested against 4 benchmark multi-objective optimization functions where it succeeded to achieve maximum balance between search space exploration and search space exploitation. The algorithm also succeeded in improving the HGA’s overall performance by limiting the average number of iterations until convergence

Energy efficient path planning techniques for UAV-based systems with space discretization

Unmanned Aerial Vehicles are miniature air-crafts that have proliferated in many military and civil applications. Their affordability allows for tasks to be held with not just one but a fleet of UAVs. One of the problems that arise with the use of multi-UAVs is the multi-UAV path planning and assignment problem. We propose three algorithms that aim at assigning energy efficient trajectories for a fleet of UAVs. Our optimal path planning solution (OPP) is formulated using a Mixed Integer Linear Programming model (MILP). We also propose two other heuristic solutions that are greedy in nature; namely, Greedy Least Cost (GLC) and First Detect First Reserve (FDFR). To aid with collision avoidance, we adopt the concept of space discretization, and present a more realistic view of the space a UAV occupies. The comparative study of our proposed solutions reveals insightful trade-offs between energy consumption and complexity. 2016 IEEE.Scopu

An approach for evolving transformation sequences using hybrid genetic algorithms

© 2020 The Authors. The digital transformation revolution has been crawling toward almost all aspects of our lives. One form of the digital transformation revolution appears in the transformation of our routine everyday tasks into computer executable programs in the form of web, desktop and mobile applications. The vast field of software engineering that has witnessed a significant progress in the past years is responsible for this form of digital transformation. Software development as well as other branches of software engineering has been affected by this progress. Developing applications that run on top of mobile devices requires the software developer to consider the limited resources of these devices, which on one side give them their mobile advantages, however, on the other side, if an application is developed without the consideration of these limited resources then the mobile application will neither work properly nor allow the device to run smoothly. In this paper, we introduce a hybrid approach for program optimization. It succeeded in optimizing the search process for the optimal program transformation sequence that targets a specific optimization goal. In this research we targeted the program size, to reach the lowest possible decline rate of the number of Lines of Code (LoC) of a targeted program. The experimental results from applying the hybrid approach on synthetic program transformation problems show a significant improve in the optimized output on which the hybrid approach achieved an LoC decline rate of 50.51% over the application of basic genetic algorithm only where 17.34% LoC decline rate was reached

Justifying Arabic Text Sentiment Analysis Using Explainable AI (XAI): LASIK Surgeries Case Study

With the increasing use of machine learning across various fields to address several aims and goals, the complexity of the ML and Deep Learning (DL) approaches used to provide solutions has also increased. In the last few years, Explainable AI (XAI) methods to further justify and interpret deep learning models have been introduced across several domains and fields. While most papers have applied XAI to English and other Latin-based languages, this paper aims to explain attention-based long short-term memory (LSTM) results across Arabic Sentiment Analysis (ASA), which is considered an uncharted area in previous research. With the use of Local Interpretable Model-agnostic Explanation (LIME), we intend to further justify and demonstrate how the LSTM leads to the prediction of sentiment polarity within ASA in domain-specific Arabic texts regarding medical insights on LASIK surgery across Twitter users. In our research, the LSTM reached an accuracy of 79.1% on the proposed data set. Throughout the representation of sentiments using LIME, it demonstrated accurate results regarding how specific words contributed to the overall sentiment polarity classification. Furthermore, we compared the word count with the probability weights given across the examples, in order to further validate the LIME results in the context of ASA

An approach for optimizing multi-objective problems using hybrid genetic algorithms

Optimization problems can be found in many aspects of our lives. An optimization problem can be approached as searching problem where an algorithm is proposed to search for the value of one or more variables that minimizes or maximizes an optimization function depending on an optimization goal. Multi-objective optimization problems are also abundant in many aspects of our lives with various applications in different fields in applied science. To solve such problems, evolutionary algorithms have been utilized including genetic algorithms that can achieve decent search space exploration. Things became even harder for multi-objective optimization problems when the algorithm attempts to optimize more than one objective function. In this paper, we propose a hybrid genetic algorithm (HGA) that utilizes a genetic algorithm (GA) to perform a global search supported by the particle swarm optimization algorithm (PSO) to perform a local search. The proposed HGA achieved the concept of rehabilitation of rejected individuals. The proposed HGA was supported by a modified selection mechanism based on the K-means clustering algorithm that succeeded to restrict the selection process to promising solutions only and assured a balanced distribution of both the selected to survive and selected for rehabilitation individuals. The proposed algorithm was tested against 4 benchmark multi-objective optimization functions where it succeeded to achieve maximum balance between search space exploration and search space exploitation. The algorithm also succeeded in improving the HGA’s overall performance by limiting the average number of iterations until convergence

Agent-Based Mobile Event Notification System

In recent years, the noticeable move towards using mobile devices (mobile phones and PDAs) and wireless technologies have made information available in the context of "anytime, anywhere using any mobile device" experience. Delivering information to mobile devices needs some sort of communication means such as Push, Pull, or mixed (Push and Pull) technologies to deliver any chunk of information (events, ads, advisory tips, learning materials, etc.). Events are the most important pieces of information that should be delivered timely wherever the user is. Agent-based technology offers autonomous, flexible, adaptable, and reliable way of delivering events to any device, anywhere, and on time. Publish/subscribe communication model is the basic infrastructure for event-based communication. In this paper, we define the need to mobilize the event notification process in educational environment and the possible categories of event notifications that students can receive from their educational institution. This paper also proposes a framework for agent-based mobile event notification system. The proposed framework is derived from the concept of push–based publish/subscribe communication model but taking advantage from software agents to serve in the mobile environment. Finally, the paper provides a detailed analysis for the proposed system