Perceptual organization and curve partitioning

In this paper we offer a critical evaluation of the partitioning (perceptual organization) problem, noting the extent to which it has distinct formulations and parameterizations. We show that most partitioning techniques can be characterized as variations of four distinct paradigms, and argue that any effective technique must satisfy two general principles. We give concrete substance to our general discussion by introducing new partitioning techniques for planar geometric curves, and present experimental results demonstrating their effectiveness.     

Annotating affective dimensions in user-generated content

Language Resources and Evaluation - In an era where user-generated content becomes ever more prevalent, reliable methods to judge emotional properties of these kinds of complex texts are needed,...     

ViComp: composition of user-generated videos

We propose ViComp, an automatic audio-visual camera selection framework for composing uninterrupted recordings from multiple user-generated videos (UGVs) of the same event. We design an automatic audio-based cut-point selection method to segment the UGV. ViComp combines segments of UGVs using a rank-based camera selection strategy by considering audio-visual quality and camera selection history. We analyze the audio to maintain audio continuity. To filter video segments which contain visual degradations, we perform spatial and spatio-temporal quality assessment. We validate the proposed framework with subjective tests and compare it with state-of-the-art methods.     

An in-depth study of graph partitioning measures for perceptual organization

In recent years, one of the effective engines for perceptual organization of low-level image features is based on the partitioning of a graph representation that captures Gestalt inspired local structures, such as similarity, proximity, continuity, parallelism, and perpendicularity, over the low-level image features. Mainly motivated by computational efficiency considerations, this graph partitioning process is usually implemented as a recursive bipartitioning process, where, at each step, the graph is broken into two parts based on a partitioning measure. We focus on three such measures, namely, the minimum, average, and normalized cuts. The minimum cut partition seeks to minimize the total link weights cut. The average cut measure is proportional to the total link weight cut, normalized by the sizes of the partitions. The normalized cut measure is normalized by the product of the total connectivity (valencies) of the nodes in each partition. We provide theoretical and empirical insight into the nature of the three partitioning measures in terms of the underlying image statistics. In particular, we consider for what kinds of image statistics would optimizing a measure, irrespective of the particular algorithm used, result in correct partitioning. Are the quality of the groups significantly different for each cut measure? Are there classes of images for which grouping by partitioning does not work well? Also, can the recursive bipartitioning strategy separate out groups corresponding to K objects from each other? In the analysis, we draw from probability theory and the rich body of work on stochastic ordering of random variables. Our major conclusion is that optimization of none of the three measures is guaranteed to result in the correct partitioning of K objects, in the strict stochastic order sense, for all image statistics.     

Reasoning about memory layouts

Verification methods for memory-manipulating C programs need to address not only well-typed programs that respect invariants such as the split-heap memory model, but also programs that access through pointers arbitrary memory objects such as local variables, single struct fields, or array slices. We present a logic for memory layouts that covers these applications and show how proof obligations arising during the verification can be discharged automatically using the layouts. The framework developed in this way is also suitable for reasoning about data structures manipulated by algorithms, which we demonstrate by verifying the Schorr-Waite graph marking algorithm.     

Optimal three-dimensional VLSI layouts

In this note we propose VLSI layouts for the cube-connectedcycles (CCC) in a three-dimensional medium. The first type of layout is for the all-active-volume mode, where computing modules are placed anywhere within the solid chip; the second type is for the one-active-layer mode, where computing modules are placed on an external face of the solid chip. In both cases we present volume × time optimal realizations. In addition, by opening the cycles of the CCC, one obtains the well-known FFT network. Both realizations use minimum volume; an all-active-volume realization with the same volume performance was earlier proposed by Rosenberg, while the present one-active-layer realization is the only optimal one that is known.     

Social itinerary recommendation from user-generated digital trails

Planning travel to unfamiliar regions is a difficult task for novice travelers. The burden can be eased if the resident of the area offers to help. In this paper, we propose a social itinerary recommendation by learning from multiple user-generated digital trails, such as GPS trajectories of residents and travel experts. In order to recommend satisfying itinerary to users, we present an itinerary model in terms of attributes extracted from user-generated GPS trajectories. On top of this itinerary model, we present a social itinerary recommendation framework to find and rank itinerary candidates. We evaluated the efficiency of our recommendation method against baseline algorithms with a large set of user-generated GPS trajectories collected from Beijing, China. First, systematically generated user queries are used to compare the recommendation performance in the algorithmic level. Second, a user study involving current residents of Beijing is conducted to compare user perception and satisfaction on the recommended itinerary. Third, we compare mobile-only approach with Mobile+Cloud architecture for practical mobile recommender deployment. Lastly, we discuss personalization and adaptation factors in social itinerary recommendation throughout the paper.     

Characterizing and modelling popularity of user-generated videos

This paper develops a framework for studying the popularity dynamics of user-generated videos, presents a characterization of the popularity dynamics, and proposes a model that captures the key properties of these dynamics. We illustrate the biases that may be introduced in the analysis for some choices of the sampling technique used for collecting data; however, sampling from recently-uploaded videos provides a dataset that is seemingly unbiased. Using a dataset that tracks the views to a sample of recently-uploaded YouTube videos over the first eight months of their lifetime, we study the popularity dynamics. We find that the relative popularities of the videos within our dataset are highly non-stationary, owing primarily to large differences in the required time since upload until peak popularity is finally achieved, and secondly to popularity oscillation. We propose a model that can accurately capture the popularity dynamics of collections of recently-uploaded videos as they age, including key measures such as hot set churn statistics, and the evolution of the viewing rate and total views distributions over time.     

Enabling broadcast of user-generated live video without servers

We are witnessing the unprecedented popularity of User-Generated-Content (UGC) on the Internet. While YouTube hosts pre-recorded video clips, in near future, we expect to see the emergence of User-Generated Live Video, for which any user can create its own temporary live video channel from a webcam or a hand-held wireless device. Hosting a large number of UG live channels on commercial servers can be very expensive. Server-based solutions also involve various economic, copyright and content control issues between users and the companies hosting their content. In this paper, leveraging on the recent success of P2P video streaming, we study the strategies for end users to directly broadcast their own live channels to a large number of audiences without resorting to any server support. The key challenge is that end users are normally bandwidth constrained and can barely send out one complete video stream to the rest of the world. Existing P2P streaming solutions cannot maintain a high level of user Quality-of-Experience (QoE) with such a highly constrained video source. We propose a novel Layered P2P Streaming (LPS) architecture, to address this challenge. LPS introduces playback delay differentiation and constructs virtual servers out of peers to boost end users’ capability of driving large-scale video streaming. Through detailed packet-level simulations and PlanetLab experiments, we show that LPS enables a source with upload bandwidth slightly higher than the video streaming rate to stream video to tens of thousands of peers with premium quality of experience.     

Mesh layouts for block-based caches

Current computer architectures employ caching to improve the performance of a wide variety of applications. One of the main characteristics of such cache schemes is the use of block fetching whenever an uncached data element is accessed. To maximize the benefit of the block fetching mechanism, we present novel cache-aware and cache-oblivious layouts of surface and volume meshes that improve the performance of interactive visualization and geometric processing algorithms. Based on a general I/O model, we derive new cache-aware and cache-oblivious metrics that have high correlations with the number of cache misses when accessing a mesh. In addition to guiding the layout process, our metrics can be used to quantify the quality of a layout, e.g. for comparing different layouts of the same mesh and for determining whether a given layout is amenable to significant improvement. We show that layouts of unstructured meshes optimized for our metrics result in improvements over conventional layouts in the performance of visualization applications such as isosurface extraction and view-dependent rendering. Moreover, we improve upon recent cache-oblivious mesh layouts in terms of performance, applicability, and accuracy.     

Optimal layouts of midimew networks

Midimew networks are mesh-connected networks derived from a subset of degree-4 circulant graphs. They have minimum diameter and average distance among all degree-4 circulant graphs, and are better than some of the most common topologies for parallel computers in terms of various cost measures. Among the many midimew networks, the rectangular ones appear to be most suitable for practical implementation. Unfortunately, with the normal way of laying out these networks on a 2D plane, long cross wires that grow with the size of the network exist. In this paper, we propose ways to lay out rectangular midimew networks in a 2D grid so that the length of the longest wire is at most a small constant. We prove that these constants are optimal under the assumption that rows and columns are moved as a whole during the layout process     

Conflict-free container routing in mesh yard layouts

Container terminals play an important role in global cargo transportation and they have become an essential intermodal interface between the sea and the land. In the container terminal, the service area is often arranged into rectangular blocks, which leads to a mesh-like path topology. We present a mathematical model for general container routing in mesh yard layouts. Based on this model, a simple container routing algorithm guaranteeing freedom of conflicts is then presented. The algorithm works by carefully choosing suitable containers’ speeds such that the containers using the same junction will arrive at different points in time, and hence incur no conflicts; meanwhile, high routing performance can be achieved. The task completion time and the requirements on timing control during the container routing are also presented. Numerical results verify that our routing scheme has good performance and is free of conflicts.     

Challenges and solutions in the opinion summarization of user-generated content

The present is marked by the influence of the Social Web on societies and people worldwide. In this context, users generate large amounts of data, especially containing opinion, which has been proven useful for many real-world applications. In order to extract knowledge from user-generated content, automatic methods must be developed. In this paper, we present different approaches to multi-document summarization of opinion from blogs and reviews. We apply these approaches to: (a) identify positive and negative opinions in blog threads in order to produce a list of arguments in favor and against a given topic and (b) summarize the opinion expressed in reviews. Subsequently, we evaluate the proposed methods on two distinct datasets and analyze the quality of the obtained results, as well as discuss the errors produced. Although much remains to be done, the approaches we propose obtain encouraging results and point to clear directions in which further improvements can be made.     

Broadcasting and personalization of user-generated contents in DVB-H mobile networks

During the last years, we have witnessed the boom of the digital market due to the proliferation of emergent audiovisual services and the increasing number of broadband networks. In this scenario, users insistently demand innovative services for exchanging and sharing their own audiovisual contents. In order to meet these needs, in this paper, we propose a system that broadcasts user-generated audiovisual contents for handheld devices in a mobile network based on the DVB-H broadcasting standard. Besides, our system offers diverse value-added services to these new active users, such as: (1) annotation, sharing and personalized distribution of audiovisual contents, (2) multi modal access, via Web or by client applications running locally in the handheld device, and (3) exploitation of return channels to transmit interactive contents that enhance the user’s experience. To achieve these goals, our system adopts well-known technologies for broadcasting and semantic annotation of audiovisual contents, as well as emergent technology from Web 2.0 and Semantic Web. We have carried out tests involving a group of students from the University of Vigo, who were satisfied with the personalization capabilities offered by our TV system for mobile settings.     

An effective coherence measure to determine topical consistency in user-generated content

When searching for blogs on a specific topic, information seekers prefer blogs that place a central focus on that topic over blogs whose mention of the topic is diffuse or incidental. In order to present users with better blog feed search results, we developed a measure of topical consistency that is able to capture whether or not a blog is topically focused. The measure, called the coherence score, is inspired by the genetics literature and captures the tightness of the clustering structure of a data set relative to a background collection. In a set of experiments on synthetic data, the coherence score is shown to provide a faithful reflection of topic clustering structure. The properties that make the coherence score more appropriate than lexical cohesion, a common measure of topical structure, are discussed. Retrieval experiments show that integrating the coherence score as a prior in a language modeling-based approach to blog feed search improves retrieval effectiveness. The coherence score must, however, be used judiciously in order to avoid boosting the ranking of irrelevant but topically focused blogs. To this end, we experiment with a series of weighting schemes that adjust the contribution of the coherence score according to the relevance of a blog to the user query. An appropriate weighting scheme is able to improve retrieval performance. Finally, we show that the coherence score can be reliably estimated with a sample exceeding 20 posts in size. Consistent with this finding, experiments show that the best retrieval performance is achieved if coherence scores are used when a blog contains more than 20 posts.     

On the area of hypercube layouts

This paper precisely analyzes the wire density and required area in standard layout styles for the hypercube. It shows that the most natural, regular layout of a hypercube of N² nodes in the plane, in an N×N grid arrangement, uses ⌊2N/3⌋+1 horizontal wiring tracks for each row of nodes. (In the process, we see that the number of tracks per row can be reduced by 1 with a less regular design, as can also be seen from an independent argument of Bezrukov et al.) This paper also gives a simple formula for the wire density at any cut position and a full characterization of all places where the wire density is maximized (which does not occur at the bisection).     

Tighter layouts of the cube-connected cycles

F.P. Preparata and J. Vuillemin (1981) proposed the cube-connected cycles (CCC) and its compact layout. We give a new layout of the CCC which uses less than half the area of the Preparata-Vuillemin layout. We also give a lower bound on the layout area of the CCC. The area of the new layout deviates from this bound by a small constant factor. If we “unfold” the cycles in the CCC, the resulting structure can be laid out in optimal area     

An ANN-based approach of interpreting user-generated comments from social media

The IT advancement facilitates growth of social media networks, which allow consumers to exchange information online. As a result, a vast amount of user-generated data is freely available via Internet. These data, in the raw format, are qualitative, unstructured and highly subjective thus they do not generate any direct value for the business. Given this potentially useful database it is beneficial to unlock knowledge it contains. This however is a challenge, which this study aims to address. This paper proposes an ANN-based approach to analyse user-generated comments from social media. The first mechanism of the approach is to map comments against predefined product attributes. The second mechanism is to generate input-output models which are used to statistically address the significant relationship between attributes and comment length. The last mechanism employs Artificial Neural Networks to formulate such a relationship, and determine the constitution of rich comments. The application of proposed approach is demonstrated with a case study, which reveals the effectiveness of the proposed approach for assessing product performance. Recommendations are provided and direction for future studies in social media data mining is marked.