Innovative Document Summarization Techniques: Revolutionizing Knowledge Understanding


Book Description

The prevalence of digital documentation presents some pressing concerns for efficient information retrieval in the modern age. Readers want to be able to access the information they desire without having to search through a mountain of unrelated data, so algorithms and methods for effectively seeking out pertinent information are of critical importance. Innovative Document Summarization Techniques: Revolutionizing Knowledge Understanding evaluates some of the existing approaches to information retrieval and summarization of digital documents, as well as current research and future developments. This book serves as a sounding board for students, educators, researchers, and practitioners of information technology, advancing the ongoing discussion of communication in the digital age.




Dynamic Information Retrieval Modeling


Book Description

Big data and human-computer information retrieval (HCIR) are changing IR. They capture the dynamic changes in the data and dynamic interactions of users with IR systems. A dynamic system is one which changes or adapts over time or a sequence of events. Many modern IR systems and data exhibit these characteristics which are largely ignored by conventional techniques. What is missing is an ability for the model to change over time and be responsive to stimulus. Documents, relevance, users and tasks all exhibit dynamic behavior that is captured in data sets typically collected over long time spans and models need to respond to these changes. Additionally, the size of modern datasets enforces limits on the amount of learning a system can achieve. Further to this, advances in IR interface, personalization and ad display demand models that can react to users in real time and in an intelligent, contextual way. In this book we provide a comprehensive and up-to-date introduction to Dynamic Information Retrieval Modeling, the statistical modeling of IR systems that can adapt to change. We define dynamics, what it means within the context of IR and highlight examples of problems where dynamics play an important role. We cover techniques ranging from classic relevance feedback to the latest applications of partially observable Markov decision processes (POMDPs) and a handful of useful algorithms and tools for solving IR problems incorporating dynamics. The theoretical component is based around the Markov Decision Process (MDP), a mathematical framework taken from the field of Artificial Intelligence (AI) that enables us to construct models that change according to sequential inputs. We define the framework and the algorithms commonly used to optimize over it and generalize it to the case where the inputs aren't reliable. We explore the topic of reinforcement learning more broadly and introduce another tool known as a Multi-Armed Bandit which is useful for cases where exploring model parameters is beneficial. Following this we introduce theories and algorithms which can be used to incorporate dynamics into an IR model before presenting an array of state-of-the-art research that already does, such as in the areas of session search and online advertising. Change is at the heart of modern Information Retrieval systems and this book will help equip the reader with the tools and knowledge needed to understand Dynamic Information Retrieval Modeling.




The 1st International Conference on Advanced Intelligent System and Informatics (AISI2015), November 28-30, 2015, Beni Suef, Egypt


Book Description

The conference topics address different theoretical and practical aspects, and implementing solutions for intelligent systems and informatics disciplines including bioinformatics, computer science, medical informatics, biology, social studies, as well as robotics research. The conference also discuss and present solutions to the cloud computing and big data mining which are considered hot research topics. The conference papers discussed different topics – techniques, models, methods, architectures, as well as multi aspect, domain-specific, and new solutions for the above disciplines. The accepted papers have been grouped into five parts: Part I—Intelligent Systems and Informatics, addressing topics including, but not limited to, medical application, predicting student performance, action classification, and detection of dead stained microscopic cells, optical character recognition, plant identification, rehabilitation of disabled people. Part II—Hybrid Intelligent Systems, addressing topics including, but not limited to, EMG signals, text classification, geomagnetic inverse problem, email filtering. Part III—Multimedia Computing and Social Networks, addressing topics including, but not limited to, augmented reality, telepresence robot, video flash matting, community detection, quality images, face thermal image extraction, MRI tumor segmentation. Part V—Cloud Computing and Big Data Mining, discussing topics including, but not limited to, mining on microblogs, query optimization, big data classification, access control, friendsourcing, and assistive technology. Part VI—Swarm Optimization and Its Applications, addressing topics including, but not limited to, solving set covering problem, adaptive PSO for CT liver segmentation, water quality assessment, attribute reduction, fish detection, solving manufacturing cell design problem.




Billboard


Book Description

In its 114th year, Billboard remains the world's premier weekly music publication and a diverse digital, events, brand, content and data licensing platform. Billboard publishes the most trusted charts and offers unrivaled reporting about the latest music, video, gaming, media, digital and mobile entertainment issues and trends.




Text Mining and Visualization


Book Description

Text Mining and Visualization: Case Studies Using Open-Source Tools provides an introduction to text mining using some of the most popular and powerful open-source tools: KNIME, RapidMiner, Weka, R, and Python. The contributors-all highly experienced with text mining and open-source software-explain how text data are gathered and processed from a w




Social Media and Networking: Concepts, Methodologies, Tools, and Applications


Book Description

In the digital era, users from around the world are constantly connected over a global network, where they have the ability to connect, share, and collaborate like never before. To make the most of this new environment, researchers and software developers must understand users’ needs and expectations. Social Media and Networking: Concepts, Methodologies, Tools, and Applications explores the burgeoning global community made possible by Web 2.0 technologies and a universal, interconnected society. With four volumes of chapters related to digital media, online engagement, and virtual environments, this multi-volume reference is an essential source for software developers, web designers, researchers, students, and IT specialists interested in the growing field of digital media and engagement. This four-volume reference includes various chapters covering topics related to Web 2.0, e-governance, social media activism, internet privacy, digital and virtual communities, e-business, customer relationship management, and more.




Learning to Rank for Information Retrieval and Natural Language Processing


Book Description

Learning to rank refers to machine learning techniques for training the model in a ranking task. Learning to rank is useful for many applications in information retrieval, natural language processing, and data mining. Intensive studies have been conducted on the problem recently and significant progress has been made. This lecture gives an introduction to the area including the fundamental problems, existing approaches, theories, applications, and future work. The author begins by showing that various ranking problems in information retrieval and natural language processing can be formalized as two basic ranking tasks, namely ranking creation (or simply ranking) and ranking aggregation. In ranking creation, given a request, one wants to generate a ranking list of offerings based on the features derived from the request and the offerings. In ranking aggregation, given a request, as well as a number of ranking lists of offerings, one wants to generate a new ranking list of the offerings. Ranking creation (or ranking) is the major problem in learning to rank. It is usually formalized as a supervised learning task. The author gives detailed explanations on learning for ranking creation and ranking aggregation, including training and testing, evaluation, feature creation, and major approaches. Many methods have been proposed for ranking creation. The methods can be categorized as the pointwise, pairwise, and listwise approaches according to the loss functions they employ. They can also be categorized according to the techniques they employ, such as the SVM based, Boosting SVM, Neural Network based approaches. The author also introduces some popular learning to rank methods in details. These include PRank, OC SVM, Ranking SVM, IR SVM, GBRank, RankNet, LambdaRank, ListNet & ListMLE, AdaRank, SVM MAP, SoftRank, Borda Count, Markov Chain, and CRanking. The author explains several example applications of learning to rank including web search, collaborative filtering, definition search, keyphrase extraction, query dependent summarization, and re-ranking in machine translation. A formulation of learning for ranking creation is given in the statistical learning framework. Ongoing and future research directions for learning to rank are also discussed. Table of Contents: Introduction / Learning for Ranking Creation / Learning for Ranking Aggregation / Methods of Learning to Rank / Applications of Learning to Rank / Theory of Learning to Rank / Ongoing and Future Work




Measuring User Engagement


Book Description

User engagement refers to the quality of the user experience that emphasizes the positive aspects of interacting with an online application and, in particular, the desire to use that application longer and repeatedly. User engagement is a key concept in the design of online applications (whether for desktop, tablet or mobile), motivated by the observation that successful applications are not just used, but are engaged with. Users invest time, attention, and emotion in their use of technology, and seek to satisfy pragmatic and hedonic needs. Measurement is critical for evaluating whether online applications are able to successfully engage users, and may inform the design of and use of applications. User engagement is a multifaceted, complex phenomenon; this gives rise to a number of potential measurement approaches. Common ways to evaluate user engagement include using self-report measures, e.g., questionnaires; observational methods, e.g. facial expression analysis, speech analysis; neuro-physiological signal processing methods, e.g., respiratory and cardiovascular accelerations and decelerations, muscle spasms; and web analytics, e.g., number of site visits, click depth. These methods represent various trade-offs in terms of the setting (laboratory versus ``in the wild''), object of measurement (user behaviour, affect or cognition) and scale of data collected. For instance, small-scale user studies are deep and rich, but limited in terms of generalizability, whereas large-scale web analytic studies are powerful but negate users' motivation and context. The focus of this book is how user engagement is currently being measured and various considerations for its measurement. Our goal is to leave readers with an appreciation of the various ways in which to measure user engagement, and their associated strengths and weaknesses. We emphasize the multifaceted nature of user engagement and the unique contextual constraints that come to bear upon attempts to measure engagement in different settings, and across different user groups and web domains. At the same time, this book advocates for the development of ``good'' measures and good measurement practices that will advance the study of user engagement and improve our understanding of this construct, which has become so vital in our wired world. Table of Contents: Preface / Acknowledgments / Introduction and Scope / Approaches Based on Self-Report Methods / Approaches Based on Physiological Measurements / Approaches Based on Web Analytics / Beyond Desktop, Single Site, and Single Task / Enhancing the Rigor of User Engagement Methods and Measures / Conclusions and Future Research Directions / Bibliography / Authors' Biographies / Index




Mobile Computing and Wireless Networks: Concepts, Methodologies, Tools, and Applications


Book Description

We live in a wireless society, one where convenience and accessibility determine the efficacy of the latest electronic gadgets and mobile devices. Making the most of these technologies—and ensuring their security against potential attackers—requires increased diligence in mobile technology research and development. Mobile Computing and Wireless Networks: Concepts, Methodologies, Tools, and Applications brings together a comprehensive range of voices and research in the area of mobile and wireless technologies, exploring the successes and failures, advantages and drawbacks, and benefits and limitations of the technology. With applications in a plethora of different research and topic areas, this multi-volume reference work benefits researchers, service providers, end-users, and information technology professionals. This four-volume reference work includes a diverse array of chapters and authors covering topics such as m-commerce, network ethics, mobile agent systems, mobile learning, communications infrastructure, and applications in fields such as business, healthcare, government, tourism, and more.




Web Services Foundations


Book Description

Web services and Service-Oriented Computing (SOC) have become thriving areas of academic research, joint university/industry research projects, and novel IT products on the market. SOC is the computing paradigm that uses Web services as building blocks for the engineering of composite, distributed applications out of the reusable application logic encapsulated by Web services. Web services could be considered the best-known and most standardized technology in use today for distributed computing over the Internet. Web Services Foundations is the first installment of a two-book collection covering the state-of-the-art of both theoretical and practical aspects of Web services and SOC research. This book specifically focuses on the foundations of Web services and SOC and covers - among others - Web service composition, non-functional aspects of Web services, Web service selection and recommendation, and assisted Web service composition. The editors collect advanced topics in the second book of the collection, Advanced Web Services, (Springer, 2013). Both books together comprise approximately 1400 pages and are the result of an enormous community effort that involved more than 100 authors, comprising the world’s leading experts in this field.