Networking for Philanthropy: Increasing Volunteer Behavior via Social Networking Sites

Social networking sites (SNSs) provide a unique social venue to engage the young generation in philanthropy through their networking capabilities. An integrated model that incorporates social capital into the Theory of Reasoned Action is developed to explain volunteer behavior through social networks. As expected, volunteer behavior was predicted by volunteer intention, which was influenced by attitudes and subjective norms. In addition, social capital, an outcome of the extensive use of SNSs, was as an important driver of users' attitude and subjective norms toward volunteering via SNSs.Advertisin

Intra-organizational integration and innovation: organizational structure, environmental contingency and R&D performance

It is widely thought that intra-firm integration has a positive effect on organizational performance, especially in environments characterized by complex and uncertain information. However, counter arguments suggest that integration may limit flexibility and thereby reduce performance in the face of uncertainty. Research and development activities of a firm are especially likely to face complex and uncertain information environments. Following prior work in contingency theory, this paper analyzes the effects of intra-organizational integration on manufacturing firms’ innovative performance. Based on a survey of R&D units in US manufacturing firms and patent data from the NBER patent database, we examine the relation between mechanisms for linking R&D to other units of the firm and the relative innovativeness of the firm. Furthermore, we argue that the impact of integration may vary by the importance of secrecy in protecting firms’ innovation advantages. We find that intra-firm integration is associated with higher self-reported innovativeness and more patents. We also find some evidence that this effect is moderated by the appropriability regime the firm faces, with the benefits of cross-functional integration being weaker in industries where secrecy is especially important. These results both support and develop the contingency model of organizational performance.Innovation; Organizations; Contingency theory;

Improving Term Frequency Normalization for Multi-topical Documents, and Application to Language Modeling Approaches

Term frequency normalization is a serious issue since lengths of documents are various. Generally, documents become long due to two different reasons - verbosity and multi-topicality. First, verbosity means that the same topic is repeatedly mentioned by terms related to the topic, so that term frequency is more increased than the well-summarized one. Second, multi-topicality indicates that a document has a broad discussion of multi-topics, rather than single topic. Although these document characteristics should be differently handled, all previous methods of term frequency normalization have ignored these differences and have used a simplified length-driven approach which decreases the term frequency by only the length of a document, causing an unreasonable penalization. To attack this problem, we propose a novel TF normalization method which is a type of partially-axiomatic approach. We first formulate two formal constraints that the retrieval model should satisfy for documents having verbose and multi-topicality characteristic, respectively. Then, we modify language modeling approaches to better satisfy these two constraints, and derive novel smoothing methods. Experimental results show that the proposed method increases significantly the precision for keyword queries, and substantially improves MAP (Mean Average Precision) for verbose queries.Comment: 8 pages, conference paper, published in ECIR '0

A Deep Ranking Model for Spatio-Temporal Highlight Detection from a 360 Video

We address the problem of highlight detection from a 360 degree video by summarizing it both spatially and temporally. Given a long 360 degree video, we spatially select pleasantly-looking normal field-of-view (NFOV) segments from unlimited field of views (FOV) of the 360 degree video, and temporally summarize it into a concise and informative highlight as a selected subset of subshots. We propose a novel deep ranking model named as Composition View Score (CVS) model, which produces a spherical score map of composition per video segment, and determines which view is suitable for highlight via a sliding window kernel at inference. To evaluate the proposed framework, we perform experiments on the Pano2Vid benchmark dataset and our newly collected 360 degree video highlight dataset from YouTube and Vimeo. Through evaluation using both quantitative summarization metrics and user studies via Amazon Mechanical Turk, we demonstrate that our approach outperforms several state-of-the-art highlight detection methods. We also show that our model is 16 times faster at inference than AutoCam, which is one of the first summarization algorithms of 360 degree videosComment: In AAAI 2018, 9 page