Empirical observations on video editing in the mobile context

Grid computing provides environment that can executes job efficiently by sharing resources in heterogeneous environment. Grid computing environment is evolving into wireless grid and mobile grid environments that consider user mobility and integrate mobile devices into wired grid. Recently, mobile devices are increasing by much in performance, and users also increasing. So researches on utilizing mobile devices have being studied but most of them don't use mobile devices as resources and don't consider characteristics of mobile devices. Therefore, in this paper, we propose a group-based resource selection algorithm that supports fault-tolerance in mobile grid and considers various characteristics of mobile devices. Our proposed algorithm 1) considers remaining battery power, mobility, and performance of mobile devices, 2) calculates rank of mobile devices and classifies to K groups, and then, 3) assigns a job to top-N groups concurrently for fault-tolerance. Ranking and grouping of mobile devices are performed to consider dynamic characteristics of mobile devices whenever a job is submitted.

Mobile device is an important interactive platform. Due to the limitation of computation, memory, display area and energy, how to realize the efficient and real-time interaction of 3D models based on mobile devices is an important research topic. Considering features of mobile devices, this paper adopts remote rendering mode and point models, and then, proposes a transmission and rendering approach that could interact in real time. First, improved simplification algorithm based on MLS and display resolution of mobile devices is proposed. Then, a hierarchy selection of point models and a QoS transmission control strategy are given based on interest area of operator, interest degree of object in the virtual environment and rendering error. They can save the energy consumption. Finally, the rendering and interaction of point models are completed on mobile devices. The experiments show that our method is efficient.

As a result of the growth of sensor-enabled mobile devices, in recent years, users can utilize diverse digital contents everywhere and anytime. However, the interfaces of mobile applications are often unnatural due to limited computational capability, miniaturized input/output controls, and so on. To complement the poor user interface (UI) and fully utilize mobility as feature of mobile devices, we explore possibilities for a new UI of mobile devices. This paper describes the method for recognizing and analyzing a user's continuous action including the user's various gestures and postures. The application example we created is mobile game called AM-Fishing game on mobile devices that employ the accelerometer as the main interaction modality. The demonstration shows the evaluation for the system usability.

Inference latency prediction for CNNs on heterogeneous mobile devices and ML frameworks

The boom in the capabilities and features of mobile devices, like smartphones, tablets, and wearables, combined with the ubiquitous and affordable Internet access and the advances in the areas of cooperative networking, computer vision, and mobile cloud computing transformed mobile augmented reality (MAR) from science fiction to a reality. Although mobile devices are more constrained computationalwise from traditional computers, they have a multitude of sensors that can be used to the development of more sophisticated MAR applications and can be assisted from remote servers for the execution of their intensive parts. In this paper, after introducing the reader to the basics of MAR, we present a categorization of the application fields together with some representative examples. Next, we introduce the reader to the user interface and experience in MAR applications and continue with the core system components of the MAR systems. After that, we discuss advances in tracking and registration, since their functionality is crucial to any MAR application and the network connectivity of the devices that run MAR applications together with its importance to the performance of the application. We continue with the importance of data management in MAR systems and the systems performance and sustainability, and before we conclude this survey, we present existing challenging problems.

Mobile Information Retrieval (Mobile IR) is a relatively recent branch of Information Retrieval (IR) that is concerned with enabling users to carry out, using a mobile device, all the classical IR operations that they were used to carry out on a desktop. This includes finding content available on local repositories or on the web in response to a user query, interacting with the system in an explicit or implicit way, reformulate the query and/or visualise the content of the retrieved documents, as well as providing relevance judgments to improve the retrieval process. This book is structured as follows. Chapter 2 provides a very brief overview of IR and of Mobile IR, briefly outlining what in Mobile IR is different from IR. Chapter 3 provides the foundations of Mobile IR, looking at the characteristics of mobile devices and what they bring to IR, but also looking at how the concept of relevance changed from standard IR to Mobile IR. Chapter 4 presents an overview of the document collections that are searchable by a Mobile IR system, and that are somehow different from classical IR ones; available for experimentation, including collections of data that have become complementary to Mobile IR. Similarly, Chapter 5 reviews mobile information needs studies and users log analysis. Chapter 6 reviews studies aimed at adapting and improving the users interface to the needs of Mobile IR. Chapter 7, instead, reviews work on context awareness, which studies the many aspects of the user context that Mobile IR employs. Chapter 8 reviews some of evaluation work done in Mobile IR, highlighting the distinctions with classical IR from the perspectives of two main IR evaluation methodologies: users studies and test collections. Finally, Chapter 9 reports the conclusions of this review, highlighting briefly some trends in Mobile IR that we believe will drive research in the next few years.

In recent years, health care technologies already became matured such as electronic medical records that can be easily stored. However, how to get medical resources more convenient is currently concern issue. In spite of many literatures discussed about medical systems, but these literatures should face many security challenges. The most important issue is patients' privacy. Therefore, we propose a secure medical data exchange protocol based on cloud environment. In our scheme, we use mobile device's characteristics, allowing peoples use medical resources on the cloud environment to seek medical advice conveniently.

The popularity of watching video clips on mobile devices is rapidly increasing. The providers of such video services have developed mobile capabilities and have worked to increase their video selections. This study investigates the effect of the factors of preview content (the thumbnail and the title) and context (the popularity cue and the serial position) on video selection in a mobile context by adopting dual process theory and the model of attention capture and transfer. We performed a logit transformation on the dependent variable, and then applied generalized least squares (GLS) regression to analyze 206,221 logs and 323 thumbnails and titles of a video service. Image and text- mining techniques were used to ascertain the level of valence and response to content. This study has four main findings: (1) low valence but high arousal of a thumbnail has a positive effect on video selection; (2) high valence and arousal by a title has a positive effect on video selection; (3) the upper serial position of a video clip and a high popularity cue have a positive effect on the video selection; and (4) the length and recency of a video have a positive effect on the video selection. The results of this study suggest practical implications to help the programming and marketing strategy of the video service as well.

The increasing popularity of applications such as video streaming in today's mobile devices introduces higher demand for throughput, and puts a strain especially on cellular links. Cooperation among mobile devices by exploiting both cellular and local area connections is a promising approach to meet the increasing demand. In this paper, we consider that a group of cooperative mobile devices, exploiting both cellular and local area links and within proximity of each other, are interested in the same video content. Traditional network control algorithms introduce high overhead and delay in this setup as the control and cooperation decisions are made in a source- centric manner. Instead, we develop a device-centric stochastic cooperation scheme. Our device-centric scheme; DcC allows mobile devices to make control decisions such as flow control, scheduling, and cooperation without loss of optimality. Thanks to being device-centric, DcC reduces; (i) overhead; i.e., the number of control packets that should be transmitted over cellular links, so cellular links are used more efficiently, and (ii) the amount of delay that each packet experiences, which improves quality of service. The simulation results demonstrate the benefits of DcC. Index Terms—Cooperative mobile devices, network utility maximization, stochastic network control. I. INTRODUCTION The increasing popularity of applications such as video streaming in today's mobile devices introduces higher demand for throughput, and puts a strain especially on cellular links. In fact, cellular traffic is growing exponentially and it is expected to remain so for the foreseeable future (1), (2).

The increased popularity of feature-rich mobile devices in recent years has enabled widespread consumption and production of social media content via mobile devices. Because mobile devices and mobile applications change context within which an individual generates and consumes microblog content, we might expect microblogging behavior to differ depending on whether the user is using a mobile device. To our knowledge, little has been established about what, if any, effects such mobile interfaces have on microblogging. In this paper, we investigate this question within the context of Twitter, among the most popular microblogging platforms. This work makes three specific contributions. First, we quantify the ways in which user profiles are effected by the mobile context: (1) the extent to which users tend to be either fully non-mobile or mobile and (2) the relative activity of the mobile Twitter community. Second, we assess the differences in content between mobile and non-mobile tweets (posts to the Twitter platform). Our results show that mobile platforms produce very different patterns of Twitter usage. As part of our analysis, we propose and apply a classification system for tweets. We consider this to be the third contribution of this work. While other classification systems have been proposed, ours is the first to permit the independent encoding of a tweet’s form, content, and intended audience. In this paper we apply this system to show how tweets differ between mobile and non-mobile contexts. However, because of its flexibility and breadth, the schema may be useful to researchers studying Twitter content in other contexts as well.

Scalable Cloudlet-based Mobile Computing Model

Mobile phones have evolved from voice-centric communication devices to powerful personal multimedia devices. Among other multimedia features, they enable the users to capture video clips with built-in digital cameras. However, due to the continuous nature of video, it is often difficult to capture a video clip exactly as intended - in many cases the possibility to edit the clip after capture would be useful. We describe the design of a video editor application for mobile devices. We present the main user goals for video editing in the mobile context based on a Contextual Inquiry study and an application design that supports these goals. We demonstrate that video editing on mobile devices is feasible and report a usability evaluation with encouraging results.

Every person's goal is to be both physically and psychologically healthy. Existing applications offer personalized exercise and workouts to meet the varied user requirements. People who lack the time to visit fitness centers have found the apps to be a great relief. Also, it offers totally free plans and workout recommendations. Many people have begun using these applications after realizing how important they are to their everyday lives. This study tries to comprehend how users feel about fitness programs that are available for mobile devices. This study mainly focuses on the efficiency of fitness applications in terms of many aspects including time, cost, and accessibility. It also explores the benefits of mobile applications over workout facilities. The study also makes suggestions for ways to boost and enhance user engagement with app usage.

Mobile devices have evolved rapidly in recent years and have become an everyday commodity. The young mobile PLM market is still in its infancy. At present, companies have covered their business processes with stationary workstations while mobile business applications have limited relevance and have been used to a limited extent. Companies can cover their overall business processes more time-efficiently and cost-effectively when they integrate mobile users in workflows. Mobile device features have the potential to significantly support the product development process through interactive user interactions and make them more effective. Moreover, entirely new workflows can be defined and established by considering mobile situations in business processes, which were excluded from the outset in the stationary context. This paper presents a novel approach for the usage of mobile device features in PDM systems to enhance user experience through novel interaction methods.

Although smartphones are increasingly becoming more and more powerful, enabling pervasiveness is severely hindered by the resource limitations of mobile devices. The combination of social interactions and mobile devices in the form of ‘crowd computing’ has the potential to surpass these limitations. In this paper, we introduce Honeybee; a crowd computing framework for mobile devices. Honeybee enables mobile devices to share work, utilize local resources and human collaboration in the mobile context. It employs ‘work stealing’ to effectively load balance tasks across nodes that are a priori unknown. We describe the design of Honeybee, and report initial experimental data from applications implemented using Honeybee.