Brief of Digital Humanities And Law Scholars as Amici Curiae In Support Of Defendant-Appellees And Affirmance, (The Authors Guild, Inc., et al., v. Google, Inc., et al.)

Amici are over 150 professors and scholars who teach, write, and research in computer science, the digital humanities, linguistics or law, and two associations that represent Digital Humanities scholars generally.2 Amici have an interest in this case because of its potential impact on their ability to discover and understand, through automated means, the data in and relationships among textual works. Legal Scholar Amici also have an interest in the sound development of intellectual property law. Resolution of the legal issue of copying for non-expressive uses has far-reaching implications for the scope of copyright protection, a subject germane to Amici’s professional interests and one about which they have great expertise. Amici speak only to the issue of copying for non-expressive uses. A complete list of individual Amici is attached as Appendix A. Mass digitization is a key enabler of socially valuable computational and statistical research (often called “data mining” or “text mining”). While the practice of data mining has been used for several decades in traditional scientific disciplines such as astrophysics and in social sciences such as economics, it has only recently become technologically and economically feasible within the humanities. This has led to a revolution, dubbed “Digital Humanities,” ranging across subjects such as literature and linguistics to history and philosophy. New scholarly endeavors enabled by Digital Humanities advancements are still in their infancy but have enormous potential to contribute to our collective understanding of the cultural, political, and economic relationships among various collections (or corpora) of works—including copyrighted works—and with society. The Court’s ruling in this case on the legality of mass digitization could dramatically affect the future of work in the Digital Humanities

Brief of Digital Humanities And Law Scholars as Amici Curiae In Support Of Defendant-Appellees And Affirmance, (The Authors Guild, Inc., et al., v. Google, Inc., et al.)

Amici are over 150 professors and scholars who teach, write, and research in computer science, the digital humanities, linguistics or law, and two associations that represent Digital Humanities scholars generally.2 Amici have an interest in this case because of its potential impact on their ability to discover and understand, through automated means, the data in and relationships among textual works. Legal Scholar Amici also have an interest in the sound development of intellectual property law. Resolution of the legal issue of copying for non-expressive uses has far-reaching implications for the scope of copyright protection, a subject germane to Amici’s professional interests and one about which they have great expertise. Amici speak only to the issue of copying for non-expressive uses. A complete list of individual Amici is attached as Appendix A. Mass digitization is a key enabler of socially valuable computational and statistical research (often called “data mining” or “text mining”). While the practice of data mining has been used for several decades in traditional scientific disciplines such as astrophysics and in social sciences such as economics, it has only recently become technologically and economically feasible within the humanities. This has led to a revolution, dubbed “Digital Humanities,” ranging across subjects such as literature and linguistics to history and philosophy. New scholarly endeavors enabled by Digital Humanities advancements are still in their infancy but have enormous potential to contribute to our collective understanding of the cultural, political, and economic relationships among various collections (or corpora) of works—including copyrighted works—and with society. The Court’s ruling in this case on the legality of mass digitization could dramatically affect the future of work in the Digital Humanities

Data centric trust evaluation and prediction framework for IOT

© 2017 ITU. Application of trust principals in internet of things (IoT) has allowed to provide more trustworthy services among the corresponding stakeholders. The most common method of assessing trust in IoT applications is to estimate trust level of the end entities (entity-centric) relative to the trustor. In these systems, trust level of the data is assumed to be the same as the trust level of the data source. However, most of the IoT based systems are data centric and operate in dynamic environments, which need immediate actions without waiting for a trust report from end entities. We address this challenge by extending our previous proposals on trust establishment for entities based on their reputation, experience and knowledge, to trust estimation of data items [1-3]. First, we present a hybrid trust framework for evaluating both data trust and entity trust, which will be enhanced as a standardization for future data driven society. The modules including data trust metric extraction, data trust aggregation, evaluation and prediction are elaborated inside the proposed framework. Finally, a possible design model is described to implement the proposed ideas

MOSDEN: A Scalable Mobile Collaborative Platform for Opportunistic Sensing Applications

Mobile smartphones along with embedded sensors have become an efficient enabler for various mobile applications including opportunistic sensing. The hi-tech advances in smartphones are opening up a world of possibilities. This paper proposes a mobile collaborative platform called MOSDEN that enables and supports opportunistic sensing at run time. MOSDEN captures and shares sensor data across multiple apps, smartphones and users. MOSDEN supports the emerging trend of separating sensors from application-specific processing, storing and sharing. MOSDEN promotes reuse and re-purposing of sensor data hence reducing the efforts in developing novel opportunistic sensing applications. MOSDEN has been implemented on Android-based smartphones and tablets. Experimental evaluations validate the scalability and energy efficiency of MOSDEN and its suitability towards real world applications. The results of evaluation and lessons learned are presented and discussed in this paper.Comment: Accepted to be published in Transactions on Collaborative Computing, 2014. arXiv admin note: substantial text overlap with arXiv:1310.405

Big Data and the Internet of Things

Advances in sensing and computing capabilities are making it possible to embed increasing computing power in small devices. This has enabled the sensing devices not just to passively capture data at very high resolution but also to take sophisticated actions in response. Combined with advances in communication, this is resulting in an ecosystem of highly interconnected devices referred to as the Internet of Things - IoT. In conjunction, the advances in machine learning have allowed building models on this ever increasing amounts of data. Consequently, devices all the way from heavy assets such as aircraft engines to wearables such as health monitors can all now not only generate massive amounts of data but can draw back on aggregate analytics to "improve" their performance over time. Big data analytics has been identified as a key enabler for the IoT. In this chapter, we discuss various avenues of the IoT where big data analytics either is already making a significant impact or is on the cusp of doing so. We also discuss social implications and areas of concern.Comment: 33 pages. draft of upcoming book chapter in Japkowicz and Stefanowski (eds.) Big Data Analysis: New algorithms for a new society, Springer Series on Studies in Big Data, to appea