Abstract The iSchools represent an international community of over 130 university faculties. They have a shared interest in research and teaching about information. The findings reported here provide insight into the perspectives and visions of the deans and directors of 75 iSchools. Findings show that (1) attention to people, information, and technology represents shared values; and (2) the global identity of the community blends diversity and inclusiveness. The diversity and inclusiveness of the iSchools requires balancing academic traditions with innovations. The current leaders use this balance to the community's identity as well as Information Science scholarship as a discipline.

Abstract The documentary's core argument is that big data and targeted data have been used successfully to influence enough “persuadable” voters to influence the outcome of key elections such as the Brexit referendum and the 2016 US presidential race, and the film pushes this theme by picking just the right facts to make a case. This review will attempt to put The Great Hack in its historical, political, and technical context, and will try to answer the question: How dangerous was Cambridge Analytica? The documentary claims the technology is to some considerable degree dangerous, although perhaps less dangerous than those involved with Cambridge Analytica like to think. The film is certainly worth viewing, as long as the viewer remembers that it is good journalism, but not a scholarly analysis.

Advances in formulating spoken document retrieval for a new National Gallery of the Spoken Word (NGSW) are addressed. NGSW is the first large-scale repository of its kind, consisting of speeches, news broadcasts, and recordings from the 20th century. After presenting an overview of the audio stream content of the NGSW, with sample audio files from U.S. Presidents from 1893 to the present, an overall system diagram is proposed with a discussion of critical tasks associated with effective audio information retrieval. These include advanced audio segmentation, speech recognition model adaptation for acoustic background noise and speaker variability, and information retrieval using natural language processing for text query requests that include document and query expansion. For segmentation, a new evaluation criterion entitled fused error score (FES) is proposed, followed by application of the CompSeg segmentation scheme on DARPA Hub4 Broadcast News (30.5% relative improvement in FES) and NGSW data. Transcript generation is demonstrated for a six-decade portion of the NGSW corpus. Novel model adaptation using structure maximum likelihood eigenspace mapping shows a relative 21.7% improvement. Issues regarding copyright assessment and metadata construction are also addressed for the purposes of a sustainable audio collection of this magnitude. Advanced parameter-embedded watermarking is proposed with evaluations showing robustness to correlated noise attacks. Our experimental online system entitled "SpeechFind" is presented, which allows for audio retrieval from a portion of the NGSW corpus. Finally, a number of research challenges such as language modeling and lexicon for changing time periods, speaker trait and identification tracking, as well as new directions, are discussed in order to address the overall task of robust phrase searching in unrestricted audio corpora.

A general formulation for speech watermarking through parametric modeling is suggested, then the paper focuses on a watermarking technique based on linear-predictive (LP) modeling of speech. In the particular strategy employed here, information is embedded by modifying the autocorrelation values of the original speech. The amount of information that can be embedded is subject to fidelity constraints. The modified LP coe#cients derived from the new set of autocorrelation values are used for reconstructing the watermarked speech. The perceptual quality of the watermarked speech depends on the relative energy of the embedded watermark, the watermark sequence used, and the LP model order. Robustness of the technique to various signal processing operations and attacks like compression, cropping, and additive noise are studied via experiments on a small speech application. Factors a#ecting watermark robustness and related security issues are also discussed.