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Social audio features for advanced music retrieval interfaces

The size of personal music collections has constantly increased over the past years. As a result, the traditional metadata based lists to browse these collections have reached their limits. Interfaces that are based on music similarity offer an alternative and thus are increasingly gaining attention. Music similarity is typically either derived from audio-features (objective approach) or from user driven information sources, such as collaborative filtering or social tags (subjective approach). Studies show that the latter techniques outperform audio-based approaches when it comes to describe the perceived music similarity. However, subjective approaches typically only define pairwise relations as opposed to the global notion of similarity given by audio-feature spaces. Many of the proposed interfaces for similarity based music access inherently depend on this global notion and are thus not applicable to user driven music similarity measures. The first contribution of this paper is a high dimensional music space that is based on user driven similarity measures. It combines the advantages of audio-feature spaces (global view) with the advantages of subjective sources that better reflect the users' perception. The proposed space compactly represents similarity and therefore is well suited for offline use, such as in mobile applications. To demonstrate the practical applicability, the second contribution is a comprehensive mobile music player that incorporates several smart interfaces to access the user's music collection. Based on this application, we finally present a large-scale user study that underlines the benefits of the introduced interfaces and shows their great user acceptance.

https://dl.acm.org/ft_gateway.cfm?id=1874007&dwn=1

Finding Probabilistic k-Skyline Sets on Uncertain Data

Skyline is a set of points that are not dominated by any other point. Given uncertain objects, probabilistic skyline has been studied which computes objects with high probability of being skyline. While useful for selecting individual objects, it is not sufficient for scenarios where we wish to compute a subset of skyline objects, i.e., a skyline set. In this paper, we generalize the notion of probabilistic skyline to probabilistic k-skyline sets (Pk-SkylineSets) which computes k-object sets with high probability of being skyline set. We present an efficient algorithm for computing probabilistic k-skyline sets. It uses two heuristic pruning strategies and a novel data structure based on the classic layered range tree to compute the skyline set probability for each instance set with a worst-case time bound. The experimental results on the real NBA dataset and the synthetic datasets show that Pk-SkylineSets is interesting and useful, and our algorithms are efficient and scalable.

https://dl.acm.org/ft_gateway.cfm?id=2806452&dwn=1

Sampling Big Trajectory Data

The increasing prevalence of sensors and mobile devices has led to an explosive increase of the scale of spatio-temporal data in the form of trajectories. A trajectory aggregate query, as a fundamental functionality for measuring trajectory data, aims to retrieve the statistics of trajectories passing a user-specified spatio-temporal region. A large-scale spatio-temporal database with big disk-resident data takes very long time to produce exact answers to such queries. Hence, approximate query processing with a guaranteed error bound is a promising solution in many scenarios with stringent response-time requirements. In this paper, we study the problem of approximate query processing for trajectory aggregate queries. We show that it boils down to the distinct value estimation problem, which has been proven to be very hard with powerful negative results given that no index is built. By utilizing the well-established spatio-temporal index and introducing an inverted index to trajectory data, we are able to design random index sampling (RIS) algorithm to estimate the answers with a guaranteed error bound. To further improve system scalability, we extend RIS algorithm to concurrent random index sampling (CRIS) algorithm to process a number of trajectory aggregate queries arriving concurrently with overlapping spatio-temporal query regions. To demonstrate the efficacy and efficiency of our sampling and estimation methods, we applied them in a real large-scale user trajectory database collected from a cellular service provider in China. Our extensive evaluation results indicate that both RIS and CRIS outperform exhaustive search for single and concurrent trajectory aggregate queries by two orders of magnitude in terms of the query processing time, while preserving a relative error ratio lower than 10\%, with only 1% search cost of the exhaustive search method.

https://dl.acm.org/ft_gateway.cfm?id=2806422&dwn=1

CHI '07: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

Welcome to the CHI 2007 proceedings. We believe the technical papers and notes herein present some of the best current work in the diverse and dynamic field of human-computer interaction (HCI).

CHI is the leading HCI conference. Creating the technical program requires a huge investment of time and effort from members of the research community. 840 submissions were processed (571 papers, 269 notes), requiring over 3000 reviews. We thank all the reviewers for the dedication with which they undertook this task. We are particularly indebted to the papers and notes program committee members, also known as the Associate Chairs (ACs). Balancing areas of expertise, ACs were selected from the field's leading researchers. The AC role included recruiting all reviewers, moderating and supervising the review process to ensure a high-quality set of reviews was obtained, initiating and organizing author rebuttal and reviewer discussions and approving final submissions. The estimated time expenditure to serve as an AC was 11 days of full-time work; many committee members spent more time than that. Papers ACs came to San Jose in December 2006 from around the world for two intense days of review, debates, and deliberation; Notes ACs who could not attend the parallel notes meeting in San Jose engaged in a virtual conference. The committee was extremely serious and careful in making CHI paper and note decisions, with many submissions receiving multiple discussions, before and during the program committee meetings. No review process can guarantee perfect decisions, but we are confident that every possible effort was made to ensure fair process and high quality decision-making. This year's program committee certainly has our respect and gratitude, and deserves the sincere appreciation of the entire HCI community. We would also like to thank the ACs and their organizations for underwriting the travel expenses for meeting.

CHI is both a journal-quality archival forum and a community-building conference. To encourage quality in the written presentation of accepted work, all of the 142 full paper and 40 note acceptances were provisional. As a result, authors actively responded and incorporated feedback from the reviews into the final versions of the papers that appear here.

Twenty-eight accepted papers and four accepted notes (5% of submissions) deemed to make an especially noteworthy contribution to human-computer interaction research were nominated by the program committee for Best Paper and Best Note Awards; these nominated papers and notes are identified in the Final Program. At the conference, up to six of these will be announced as winners of a CHI Best Paper Award (1% of submissions), and one note will be selected as an exemplary note. While all papers accepted into the CHI technical papers program have passed a rigorous examination of their quality, the Best Paper and Best Notes Awards signal and reward particularly outstanding contributions in each year.

https://dl.acm.org/ft_gateway.cfm?id=1240624&dwn=1

Exact L nearest neighbor search in high dimensions

We present an algorithm for solving the nearest neighbor problem with respect to $L_{\infty}$-distance. It requires no preprocessing and storage only for the point set $P$. Its average runtime assuming that the set $P$ of $n$ points is drawn randomly from the unit cube $[0,1]^{d}$ under uniform distribution is essentially $\Theta (nd/ln\; n)$ thereby improving the brute-force method by a factor of $\Theta (1/ln\; n)$. Several generalizations of the method are also presented, in particular to other “well-behaved” probability distributions and to the important problem of finding the $k$ nearest neighbors to a query point.

https://dl.acm.org/ft_gateway.cfm?id=378655&dwn=1

Multiresolution sphere packing tree: a hierarchical multiresolution 3D data structure

Sphere packing arrangements are frequently found in nature, exhibiting efficient space-filling and energy minimization properties. Close sphere packings provide a tight, uniform, and highly symmetric spatial sampling at a single resolution. We introduce the Multiresolution Sphere Packing Tree (MSP-tree): a hierarchical spatial data structure based on sphere packing arrangements suitable for 3D space representation and selective refinement. Compared to the commonly used octree, MSP-tree offers three advantages: a lower fanout (a factor of four compared to eight), denser packing (about 24% denser), and persistence (sphere centers at coarse resolutions persist at finer resolutions). We present MSP-tree both as a region-based approach that describes the refinement mechanism succintly and intuitively, and as a lattice-based approach better suited for implementation. The MSP-tree offers a robust, highly symmetric tessellation of 3D space with favorable image processing properties.

https://dl.acm.org/ft_gateway.cfm?id=1364954&dwn=1

Program evolvability under environmental variations and neutrality

Biological organisms employ various mechanisms to cope with the dynamic environments they live in. One recent research reported that depending on the rates of environmental variation, populations evolve toward genotypes in different regions of the neutral networks to adapt to the changes. Inspired by that work, we used a genetic programming system to study the evolution of computer programs under environmental variation. Similar to biological evolution, the genetic programming populations exploit neutrality to cope with environmental fluctuations and evolve evolvability. We hope this work sheds new light on the design of open-ended evolutionary systems which are able to provide consistent evolvability under variable conditions.

https://dl.acm.org/ft_gateway.cfm?id=1274041&dwn=1

Streaming matching of events

Streaming matching of events based on its content has many applications including customer subscriptions or recommendations for new ads. Customer subscriptions system requires minimal delay between event appearance and corresponding notifications. Such system should be horizontally scalable with regard to events and customers. There are many approaches to solving this task ranging from brute-force ones to utilizing some complex data structures. We present our approach integrated into several high-loaded production services at Yandex.Classifieds.

https://dl.acm.org/ft_gateway.cfm?id=3022218&dwn=1