Realizing Video Analytic Service in the Fog-Based Infrastructure-Less Environments

Deep learning has unleashed the great potential in many fields and now is the most significant facilitator for video analytics owing to its capability to providing more intelligent services in a complex scenario. Meanwhile, the emergence of fog computing has brought unprecedented opportunities to provision intelligence services in infrastructure-less environments like remote national parks and rural farms. However, most of the deep learning algorithms are computationally intensive and impossible to be executed in such environments due to the needed supports from the cloud. In this paper, we develop a video analytic framework, which is tailored particularly for the fog devices to realize video analytic service in a rapid manner. Also, the convolution neural networks are used as the core processing unit in the framework to facilitate the image analysing process

Multi-Modal Machine Learning for Assessing Gaming Skills in Online Streaming: A Case Study with CS:GO

Online streaming is an emerging market that address much attention. Assessing gaming skills from videos is an important task for streaming service providers to discover talented gamers. Service providers require the information to offer customized recommendation and service promotion to their customers. Meanwhile, this is also an important multi-modal machine learning tasks since online streaming combines vision, audio and text modalities. In this study we begin by identifying flaws in the dataset and proceed to clean it manually. Then we propose several variants of latest end-to-end models to learn joint representation of multiple modalities. Through our extensive experimentation, we demonstrate the efficacy of our proposals. Moreover, we identify that our proposed models is prone to identifying users instead of learning meaningful representations. We purpose future work to address the issue in the end

Multi-level virtual ring : a foundation network architecture to support peer-to-peer application in wireless sensor network

Two main problems prevent the deployment of peer-to-peer application in a wireless sensor network: the index table, which should be distributed stored rather than uses a central server as the director; the unique node identifier, which cannot use the global addresses. This paper presents a multi-level virtual ring (MVR) structure to solve these two problems.The index table in MVR is distributed stored by using the DHT technique. MVR is constructed decentralized and runs on mobile nodes themselves, requiring no central server or interruption. Naming system in MVR uses natural names rather than global addresses to identify sensor nodes. The MVR can route directly on the name identifiers of the sensor nodes without being aware the location. Some sensor nodes are selected as the backbone nodes by the backbone selection algorithm and are placed on the different levels of the virtual rings. MVR hashes nodes&rsquo; identifiers on the virtual ring, and stores them at the backbone nodes. Furthermore, MVR adopts cross-level routing to improve the routing efficiency.Experiments using ns2 simulator for up to 200 nodes show that the storage and bandwidth requirements of MVR grow slowly with the size of the network. Furthermore, MVR has demonstrated as self-administrating, fault-tolerant, and resilient under the different workloads.<br /