On the potentials of object group localization in the internet of things

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It’s not all about the music:online fan communities and collecting Hard Rock Café pins

Previous studies of music fan culture have largely centered on the diverse range of subcultures devoted to particular genres, groups, and stars. Where studies have moved beyond the actual music and examined the fashion, concerts, and collecting ephemera such as vinyl records and posters, they have tended to remain closely allied to notions of subcultural distinction, emphasizing hierarchies of taste. This paper shifts the focus in music fan studies beyond the appreciation of the music and discusses the popular fan practice of collecting souvenir pins produced and sold by the Hard Rock Café (HRC) within a framework of fan tourism. Traveling to and collecting unique pins from locations across the globe creates a fan dialogue that centers on tourism and the collecting practices associated with souvenir consumption. Collectors engage in practices such as blogging, travel writing, and administration that become important indicators of their particular expression of fandom: pin collecting. Membership requires both time and money; recording visits around the world and collecting unique pins from every café builds fans' cultural capital. This indicates an internationalization of popular fandom, with the Internet acting as a connective virtual space between local and national, personal and public physical space. The study of HRC pin collecting and its fan community suggests that HRC enthusiasts are not so because they enjoy rock music or follow any particular artist but due to the physical ephemera that they collect and the places and spaces they visit

Semantic-Aware Image Analysis

Extracting and utilizing high-level semantic information from images is one of the important goals of computer vision. The ultimate objective of image analysis is to be able to understand each pixel of an image with regard to high-level semantics, e.g. the objects, the stuff, and their spatial, functional and semantic relations. In recent years, thanks to large labeled datasets and deep learning, great progress has been made to solve image analysis problems, such as image classification, object detection, and object pose estimation. In this work, we explore several aspects of semantic-aware image analysis. First, we explore semantic segmentation of man-made scenes using fully connected conditional random fields which can model long-range connections within the image of man-made scenes and make use of contextual information of scene structures. Second, we introduce a semantic smoothing method by exploiting the semantic information to accomplish semantic structure-preserving image smoothing. Semantic segmentation has achieved significant progress recently and has been widely used in many computer vision tasks. We observe that high-level semantic image labeling information can provide a meaningful structure prior to image smoothing naturally. Third, we present a deep object co-segmentation approach for segmenting common objects of the same class within a pair of images. To address this task, we propose a CNN-based Siamese encoder-decoder architecture. The encoder extracts high-level semantic features of the foreground objects, a mutual correlation layer detects the common objects, and finally, the decoder generates the output foreground masks for each image. Finally, we propose an approach to localize common objects from novel object categories in a set of images. We solve this problem using a new common component activation map in which we treat the class-specific activation maps as components to discover the common components in the image set. We show that our approach can generalize on novel object categories in our experiments

Causality and dispersion relations and the role of the S-matrix in the ongoing research

The adaptation of the Kramers-Kronig dispersion relations to the causal localization structure of QFT led to an important project in particle physics, the only one with a successful closure. The same cannot be said about the subsequent attempts to formulate particle physics as a pure S-matrix project. The feasibility of a pure S-matrix approach are critically analyzed and their serious shortcomings are highlighted. Whereas the conceptual/mathematical demands of renormalized perturbation theory are modest and misunderstandings could easily be corrected, the correct understanding about the origin of the crossing property requires the use of the mathematical theory of modular localization and its relation to the thermal KMS condition. These new concepts, which combine localization, vacuum polarization and thermal properties under the roof of modular theory, will be explained and their potential use in a new constructive (nonperturbative) approach to QFT will be indicated. The S-matrix still plays a predominant role but, different from Heisenberg's and Mandelstam's proposals, the new project is not a pure S-matrix approach. The S-matrix plays a new role as a "relative modular invariant"..Comment: 47 pages expansion of arguments and addition of references, corrections of misprints and bad formulation

A Review of Verbal and Non-Verbal Human-Robot Interactive Communication

In this paper, an overview of human-robot interactive communication is presented, covering verbal as well as non-verbal aspects of human-robot interaction. Following a historical introduction, and motivation towards fluid human-robot communication, ten desiderata are proposed, which provide an organizational axis both of recent as well as of future research on human-robot communication. Then, the ten desiderata are examined in detail, culminating to a unifying discussion, and a forward-looking conclusion

S-COL: A Copernican turn for the development of flexibly reusable collaboration scripts

Collaboration scripts are usually implemented as parts of a particular collaborative-learning platform. Therefore, scripts of demonstrated effectiveness are hardly used with learning platforms at other sites, and replication studies are rare. The approach of a platform-independent description language for scripts that allows for easy implementation of the same script on different platforms has not succeeded yet in making the transfer of scripts feasible. We present an alternative solution that treats the problem as a special case of providing support on top of diverse Web pages: In this case, the challenge is to trigger support based on the recognition of a Web page as belonging to a specific type of functionally equivalent pages such as the search query form or the results page of a search engine. The solution suggested has been implemented by means of a tool called S-COL (Scripting for Collaborative Online Learning) and allows for the sustainable development of scripts and scaffolds that can be used with a broad variety of content and platforms. The tool’s functions are described. In order to demonstrate the feasibility and ease of script reuse with S-COL, we describe the flexible re-implementation of a collaboration script for argumentation in S-COL and its adaptation to different learning platforms. To demonstrate that a collaboration script implemented in S-COL can actually foster learning, an empirical study about the effects of a specific script for collaborative online search on learning activities is presented. The further potentials and the limitations of the S-COL approach are discussed