Frequent Interaction and Fast Feedback Predict Perceived Social Support: Using Crawled and Self‐Reported Data of Facebook Users

The present study examines if Facebooking can contribute to psychological well‐being and if so, which aspects of Facebook use could play a significant role. Matching crawled data with self‐reported data from mobile Facebook users, we found that more social interactions with Facebook friends and faster friends' reactions to a user's post contributed toFacebook user's perceived social support and ultimately alleviated loneliness. We also found that the association between frequency as well as speed of Facebook social interactions and psychological well‐being were more significant among those who were sensitive to other's behaviors and feelings than those who were not. The theoretical and practical implications of the findings, particularly regarding mobile communication context, are also discussed.     

Managing a Very Connected World: A Report on APNOMS2015

This article presents a report on APNOMS2015, which was held August 19–21, 2015 in Busan, Korea. The theme of APNOMS2015 was “Managing a Very Connected World.”     

Rendering of HDR images to improve brightness discrimination

The impression of quality of images can be enhanced on a high dynamic range (HDR) displays. Generally, a conventional 8‐bit image can be processed to an HDR image by inverse tone mapping operators. Among the operators, brightness discrimination mapping by applying brightness adaptation model attempted to mimic the human visual system. In this paper, we use a brightness adaptation model to derive a brightness discrimination mapping algorithm for HDR displays. The proposed algorithm maximizes a function, which represents the local and global brightness discrimination range by exploiting characteristics of the human visual system. Enhancement of details is verified by visualizing HDR images from dark to bright regions. Improvement of dynamic range is quantified by measuring increased discrimination ratio.     

User Satisfaction with Tablet PC Features

Previous studies of tablet personal computers have concentrated on their use in education and healthcare. The current study focused instead on personal usage, investigating how satisfied users are with their own tablets after having used them in their daily lives. The objective was to identify the major features for tablets and to investigate how form factors affect the preference of functions by performing a comparison of iPad1 and Galaxy Tab. Also, gender and ethnicity were analyzed to determine whether they influence satisfaction with the devices. For e‐mail and web browsing functions, users’ ratings showed more satisfaction with the iPad1 since it has a larger display; for the e‐book reader function, users indicated higher satisfaction with the Galaxy Tab. Male users evaluated their devices by the function itself, whereas female users were mainly concerned with aesthetic aspects. Koreans indicated that they were less satisfied with their tablets than were other ethnic groups.     

Ergonomic Analysis of Mobile Cart–Assisted Stocking Activities Using Electromyography

Workers in grocery stores are exposed to numerous musculoskeletal risks that can be reduced using assistive devices while performing stocking tasks. A regional grocery store has recently deployed a mobile cart without comprehension of its ergonomic impact on workers, which this article investigates using normalized electromyography data (%MVC). This article studies not only ergonomic impact based on %MVC values but also work performance represented by a muscle force metric (MFM). The results from this study showed highest muscle groups in %MVC and MFM were the erector spinae and triceps. Interestingly, muscle activations on erector spinae were reduced when mobile cart is used. %MVC and MFM distribution for value‐added‐ and non‐value‐added subtasks were slightly different, with larger differences observed for non‐value‐added tasks. Video recordings revealed higher work performance when the mobile cart is used. In future research, the number of participants will be increased to further validate the results from this study.     

Object-aware image thumbnailing using image classification and enhanced detection of ROI

Thumbnail images are used to display a large collection of photos in various digital devices. It aims for people to browse and search the image collection effectively. The provided thumbnail images are expressed in a much lower resolution compared to the resolution of the original image. Thus, it faces a significant problem of how to represent the content of a given image effectively in a tiny thumbnail image. Many image thumbnailing methods have been presented in literature for this purpose. However, the existing thumbnailing methods are designed to use a single method to all kinds of images, regardless of image contents. On the other hand, the proposed method employs two different thumbnail generation methods either of which is applied according to corresponding image context. To achieve this, we first classify images into two groups by detecting the object existence. Then, an ROI cropping method using a saliency map is presented for images with objects, in order to represent the important region of images in the thumbnail. Images without any interesting objects, such as landscape images, are considered to be resized by using a simple scaling method to maintain the whole image context. Experimental results show that the proposed method yields comparable performance on a variety of datasets.     

Feature scalability for a low complexity face recognition with unconstrained spatial resolution

Automatic face recognition (FR) based applications in low computing power constrained systems, such as mobile and smart camera, have become particularly interesting topic in recent years. In this context, we present computationally efficient FR framework underpinning the so-called feature scalability algorithm. The proposed framework aims at implementing robust FR systems under low-computing power restriction and varying face resolution. Key beneficial property of our proposed FR framework based on feature scalability is to require low computational complexity without sacrificing a level of FR performance. To do this, using feature scalability algorithm enables to directly estimate the features (from pre-enrolled gallery images) that are well matched with the feature of an input probe image with different resolution (generally lower resolution) without any complex process. In addition, our method is helpful for relieving storage shortage problem as it does not require a large amount of training and gallery images with different face resolutions. Results show that our proposed feature scalability algorithm can be seamlessly embedded into state-of-the-art feature extraction methods extensively used for FR by achieving impressive recognition performance. Also, according to the results on computational complexity measurement, the proposed method is proven to be useful for substantially saving FR operation time.     

Comparison of two scaling approaches for the development of biomechanical multi-body human models

Dimensional scaling approaches are widely used to develop multi-body human models in injury biomechanics research. Given the limited experimental data for any particular anthropometry, a validated model can be scaled to different sizes to reflect the biological variance of population and used to characterize the human response. This paper compares two scaling approaches at the whole-body level: one is the conventional mass-based scaling approach which assumes geometric similarity; the other is the structure-based approach which assumes additional structural similarity by using idealized mechanical models to account for the specific anatomy and expected loading conditions. Given the use of exterior body dimensions and a uniform Young’s modulus, the two approaches showed close values of the scaling factors for most body regions, with 1.5 % difference on force scaling factors and 13.5 % difference on moment scaling factors, on average. One exception was on the thoracic modeling, with 19.3 % difference on the scaling factor of the deflection. Two 6-year-old child models were generated from a baseline adult model as application example and were evaluated using recent biomechanical data from cadaveric pediatric experiments. The scaled models predicted similar impact responses of the thorax and lower extremity, which were within the experimental corridors; and suggested further consideration of age-specific structural change of the pelvis. Towards improved scaling methods to develop biofidelic human models, this comparative analysis suggests further investigation on interior anatomical geometry and detailed biological material properties associated with the demographic range of the population.     

Location K-anonymity in indoor spaces

With the expansion of wireless-communication infrastructure and the evolution of indoor positioning technologies, the demand for location-based services (LBS) has been increasing in indoor as well as outdoor spaces. However, we should consider a significant challenge regarding the location privacy for realizing indoor LBS. To avoid violations of location privacy, much research has been performed, and location \(\mathcal {K}\)-anonymity has been intensively studied to blur a user location with a cloaking region involving at least \(\mathcal {K}-1\) locations of other persons. Owing to the differences between indoor and outdoor spaces, it is, however, difficult to apply this approach directly in an indoor space. First, the definition of the distance metric in indoor space is different from that in Euclidean and road-network spaces. Second, a bounding region, which is a general form of an anonymizing spatial region (ASR) in Euclidean space, does not respect the locality property in indoor space, where movement is constrained by building components. Therefore, we introduce the concept of indoor location \(\mathcal {K}\)-anonymity in this paper. Then, we investigate the requirements of ASR in indoor spaces and propose novel methods to determine the ASR, considering hierarchical structures of the indoor space. While indoor ASRs are determined at the anonymizer, we also propose processing methods for r-range queries and k-nearest-neighbor queries at a location-based service provider. We validate our methods with experimental analysis of query-processing performance and resilience against attacks in indoor spaces.     

Efficient indexing and retrieval of large-scale geo-tagged video databases

We are witnessing a significant growth in the number of smartphone users and advances in phone hardware and sensor technology. In conjunction with the popularity of video applications such as YouTube, an unprecedented number of user-generated videos (UGVs) are being generated and consumed by the public, which leads to a Big Data challenge in social media. In a very large video repository, it is difficult to index and search videos in their unstructured form. However, due to recent development, videos can be geo-tagged (e.g., locations from GPS receiver and viewing directions from digital compass) at the acquisition time, which can provide potential for efficient management of video data. Ideally, each video frame can be tagged by the spatial extent of its coverage area, termed Field-Of-View (FOV). This effectively converts a challenging video management problem into a spatial database problem. This paper attacks the challenges of large-scale video data management using spatial indexing and querying of FOVs, especially maximally harnessing the geographical properties of FOVs. Since FOVs are shaped similar to slices of pie and contain both location and orientation information, conventional spatial indexes, such as R-tree, cannot index them efficiently. The distribution of UGVs’ locations is non-uniform (e.g., more FOVs in popular locations). Consequently, even multilevel grid-based indexes, which can handle both location and orientation, have limitations in managing the skewed distribution. Additionally, since UGVs are usually captured in a casual way with diverse setups and movements, no a priori assumption can be made to condense them in an index structure. To overcome the challenges, we propose a class of new R-tree-based index structures that effectively harness FOVs’ camera locations, orientations and view-distances, in tandem, for both filtering and optimization. We also present novel search strategies and algorithms for efficient range and directional queries on our indexes. Our experiments using both real-world and large synthetic video datasets (over 30 years’ worth of videos) demonstrate the scalability and efficiency of our proposed indexes and search algorithms.