nils-herrmann/clean_scirepeval huggingface dataset:
Peer-Assisted Social Media Streaming with Social Reciprocity

Online video sharing and social networking are cross-pollinating rapidly in today's Internet: Online social network users are sharing more and more media contents among each other, while online video sharing sites are leveraging social connections among users to promote their videos. An intriguing development as it is, the operational challenge in previous video sharing systems persists, em i.e., the large server cost demanded for scaling of the systems. Peer-to-peer video sharing could be a rescue, only if the video viewers' mutual resource contribution has been fully incentivized and efficiently scheduled. Exploring the unique advantages of a social network based video sharing system, we advocate to utilize social reciprocities among peers with social relationships for efficient contribution incentivization and scheduling, so as to enable high-quality video streaming with low server cost. We exploit social reciprocity with two give-and-take ratios at each peer: (1) peer contribution ratio (em PCR), which evaluates the reciprocity level between a pair of social friends, and (2) system contribution ratio (em SCR), which records the give-and-take level of the user to and from the entire system. We design efficient peer-to-peer mechanisms for video streaming using the two ratios, where each user optimally decides which other users to seek relay help from and help in relaying video streams, respectively, based on combined evaluations of their social relationship and historical reciprocity levels. Our design achieves effective incentives for resource contribution, load balancing among relay peers, as well as efficient social-aware resource scheduling. We also discuss practical implementation and implement our design in a prototype social media sharing system. Our extensive evaluations based on PlanetLab experiments verify that high-quality large-scale social media sharing can be achieved with conservative server costs.
Data parallelization of Kd-tree ray tracing on the Cell Broadband Engine

Ray tracing is a widely used rendering technique in computer graphics, and its intense computational requirement prohibits real-time ray tracing applications wide use in consumer markets. One main feature of ray tracing is parallelism and the mainstream computer market is switching to systems with multi-core. In this paper, in order to accelerate ray tracing to achieve real-time processing, we propose a data parallel kd-tree ray tracing algorithm on Cell Broadband Engine ( which is a state-of-the-art multi-core processor. This paper expounds the feasibility and key issues of kd-tree ray tracing on the Cell/B.E. processor and introduces the implementation and Cell-specific in ray tracing program design. The results highlight that our parallel algorithm for kd-tree ray tracing is scalable with a matrix of cores, resolutions or different object models. The execution time reduces from several minutes to several seconds.
Who Is Earning? Understanding and Modeling the Virtual Gifts Behavior of Users in Live Streaming Economy

Recently, live streaming is gaining increasing en- thusiasm, like Twitch, Inke and YouTube. Online streamers can interact with viewers in real time in live platforms with audio, video and text. The virtual gift is one of the most important business models for these live streaming platforms. The viewers can purchase and send virtual gifts to the streamers during the live process. These behaviors have great market potential and economic value, but little previous research paid attention about this. In this paper, we measured the basic information and economic behavior of users on a large representative live streaming platform (Inke1). To be specific, we discovered 20% of viewers contributed 97% of all gifts, and 77% of viewers only present for top three streamers. Especially, we also found that most gifting behaviors broke out in short periods when barrage increased, and gifting behaviors could be inspired by others due to competitiveness. Moreover, we investigated the correlation of various factors, proposed a decision tree based prediction model to infer the future revenue of the streamers. The result will bring benefit for both industrial entrepreneurs (for improving the design of the system operating mechanism of live platforms) and researchers (for interesting problems in personalized recommendation, etc.).
Position prediction motion-compensated interpolation for frame rate up-conversion using temporal modeling

In this paper, a new motion-compensated interpolation method (MCI) for frame rate up-conversion is proposed. In conventional MCI algorithms using transmitted motion vectors (MVs), the block artifacts are caused by the unreliable MVs from the encoder. In our proposed method, motion vectors correction (MVC) is applied on the transmitted MVs before interpolation considering the spatial correlation. In consideration of the temporal correlation, generative model is used to generate the temporal-predicted MVs. Then position prediction method is applied on the transmitted MVs and the temporal-predicted MVs separately to predict the positions the interpolated blocks really move to, which makes the MVs used for interpolation nearer to the true motion. The final MVs are selected from the two candidates of position prediction by the minimum sum of absolute difference (SAD). Applied to the H.264 decoder, our proposed method achieves significant increase on PSNR and obvious decrease of the block artifacts.
Power-Rate-Distortion Optimization for Multi-Source Video Streaming under Energy Constraints over Ad Hoc Networks

We propose a dynamic rate allocation scheme based on power-rate-distortion (PRD) optimization model among multiple video sources over ad hoc networks. This work is an extension of the PRD model for single source video streaming. With a total rate constraint and different power consumption constraints for each node, our optimization algorithm minimizes the average video distortion for all sources. The optimization is performed at the receiver of the video streams. Experimental results for a video surveillance scenario demonstrate that the proposed scheme outperforms a simple fixed-QP scheme. The proposed scheme promotes the average PSNR by 0.32-0.45 dB without shortening the system lifetime, or prolongs system lifetime by more than 20% without cutting down the overall PSNR.
Joint Social and Content Recommendation for User-Generated Videos in Online Social Network

Online social network is emerging as a promising alternative for users to directly access video contents. By allowing users to import videos and re-share them through the social connections, a large number of videos are available to users in the online social network. The rapid growth of the user-generated videos provides enormous potential for users to find the ones that interest them; while the convergence of online social network service and online video sharing service makes it possible to perform recommendation using social factors and content factors jointly. In this paper, we design a joint social-content recommendation framework to suggest users which videos to import or re-share in the online social network. In this framework, we first propose a user-content matrix update approach which updates and fills in cold user-video entries to provide the foundations for the recommendation. Then, based on the updated user-content matrix, we construct a joint social-content space to measure the relevance between users and videos, which can provide a high accuracy for video importing and re-sharing recommendation. We conduct experiments using real traces from Tencent Weibo and Youku to verify our algorithm and evaluate its performance. The results demonstrate the effectiveness of our approach and show that our approach can substantially improve the recommendation accuracy.
Improving the stability of spanning trees for application-layer multicast

Currently, a large number of ALM algorithms have been proposed to build the overlay networks and transmit data through spanning trees over the overlay networks. However, few works focus on the congestion of spanning tree, the key to the performance of application. This paper studies the impact of spanning tree congestion on ALM performance and proposes a model to quantify and evaluate the congestion. A statistical link model is introduced to describe congestion occurring in ALM networks and a dependency-degree factor is presented to express the dependence between adjacent links. Furthermore, two schemes, the inter-level redundant links (INTER-RL) scheme and the intra-level redundant links (INTRA-RL) scheme, are proposed to enhance the resilience of ALM spanning trees against congestion. Simulation results indicate that the proposed statistical model well describes the congestion in ALM networks and two redundant link schemes both significantly boost the robustness of ALM even in tough network environments.
A cascade SVM approach for head-shoulder detection using histograms of oriented gradients

This paper presents a head-shoulder detection approach using cascade SVM and Histograms of Oriented Gradients (HOG). The HOG features which are extracted from variable-size blocks can capture salient features of head-shoulder automatically. A two stage cascade using SVM approach is designed to be the classifier. During detection, the majority of negative windows are rejected at the first stage, leaving a relatively small number of windows to be classified at the second stage, which improves the speed and precision of the detector. Due to the large number of possible target locations in an image, we applied camera self-calibration approach to facilitate the estimation for the size and location of the detection window. The experiments on surveillance videos from Trecvid 2008  proved that our approach can achieve fast and accurate head-shoulder detection.
Beyond 1 Million Nodes—Crowdsourced Video CDN: Architecture, Technology, and Economy

The video service has already become one of the Internet killer applications. To deliver the ever growing video traffic with the Quality of Experience (QoE) guarantee, deploying the infrastructure of Content Delivery Network (CDN) closer and closer to users has been a noticeable trend. And this trend becomes more promising when recently CDNs providers start utilizing the storage and network resources of the edge network devices (e.g., the Wi-Fi Access Points) for content delivery [13], i.e., crowdsourced CDN. In this article, we envision the crowdsourced CDN and propose a set of practical strategies to guide the implementation of such new paradigm. Firstly, we perform a large-scale measurement to explore the geo-content popularity and the geo-heterogeneity of the resource distribution. Benefiting from the measurement observations, we design the key solutions for the system operation, including regional content popularity prediction based on Markov Decision Process (MDP), region partition for the edge device organization and the collaborative content replication. Moreover, we consider the economic issues and propose a sequential auction mechanism to cope with the resource allocation when multiple Content Providers (CPs) compete for the edge resources for their content caching. We also demonstrate the performance gain of our design with data-driven simulation.
Spatial and Temporal Data Parallelization of Multi-view Video Encoding Algorithm

Multi-view video coding technology is proposed to resolve the problem of huge data storage and transmission for free-view and 3D interactive video. How to support real time multi-view video encoding which has high computing complexity with sharply increased multi-view video data is essential In this paper, we proposed a solution of spatial and temporal data parallelization for multi-view video encoding algorithm based on IBM cell multiprocessor system using selections of optimal theories & methods. The performance of our tasks distributing scheme is eight times faster than the serial algorithm, speedup is notable.
Optimizing the Throughput of Data-Driven Peer-to-Peer Streaming

During recent years, the Internet has witnessed a rapid growth in deployment of data-driven (or swarming based) peer-to-peer (P2P) media streaming. In these applications, each node independently selects some other nodes as its neighbors (i.e. gossip-style overlay construction), and exchanges streaming data with the neighbors (i.e. data scheduling). To improve the performance of such protocol, many existing works focus on the gossip-style overlay construction issue. However, few of them concentrate on optimizing the streaming data scheduling to maximize the throughput of a constructed overlay. In this paper, we analytically study the scheduling problem in data-driven streaming system and model it as a classical min-cost network flow problem. We then propose both the global optimal scheduling scheme and distributed heuristic algorithm to optimize the system throughput. Furthermore, we introduce layered video coding into data-driven protocol and extend our algorithm to deal with the end-host heterogeneity. The results of simulation with the real world traces indicate that our distributed algorithm significantly outperforms conventional ad hoc scheduling strategies especially in stringent buffer and bandwidth constraints.
Flexible video coding scheme for content-based video storage and retrieval

In this paper, we proposed a flexible video coding scheme for content-based video storage and retrieval. The proposed flexible video coding scheme applies shot clustering on the uncompressed video in the first pass and encodes the video in multi-clusters based on different video contents in the second pass. The compressed video can be flexibly decoding to meet the different requirements such as video indexing, video summary and structure extraction without fully decoding and re-analyzing. Also the flexible encoder can achieve global coding optimization by encoding similarity video shots continuously. It is very important to reduce the process complexity at the decoder and provide the flexible video content access on the compressed videos in many off-line video storage and retrieval applications. The experiments on sport and film videos illustrate the coding performance increase and the effect of the proposed coding scheme for content-based video storage
SAM: Cache space allocation in collaborative edge-caching network

Edge caching technique is becoming more and more important in 5G network architecture, which shows great performance in terms of mitigating the backbone network congestion and reducing the delivery latency. The key idea of this technique is to cache popular contents in the small base stations(SBSs) which locate at the edge of the wireless network, and deliver these contents to nearby mobile users(MUs) from the cache directly. The collaborative in-cluster prefetching is improving the network performance even more. In this paper, we study the case where one monopolistic state-owned mobile network operator (MNO) offers edge caching service to multiple Content Providers(CPs), and focus on the mechanism design for the cache space allocation in this market. As the caching policy on each edge node is coupled with each other due to in-cluster collaboration, one time auction or flat pricing will both harm the social welfare. We propose a 2-optimal sequential auction mechanism (SAM) to solve this problem, which improves the social welfare by 20%∼70% compared to the traditional Vickery-Clarke-Groves(VCG) mechanism.
A multi-dimensional auction mechanism for mobile crowdsourced video streaming

Adaptive bitrate video streaming is a widely-used technology for mobile video streaming over HTTP. In this work, we study a crowdsourced video streaming framework, which enables nearby mobile users to crowdsource their radio resources for cooperatively adaptive bitrate video streaming. We propose a multi-dimensional auction based incentive mechanism to promote the user cooperation, supporting the asynchronous downloading and the bitrate adapting of video users. In this mechanism, each user initiates an auction whenever he is ready to download a new data segment in an asynchronous fashion, and all nearby users compete for the downloading opportunity by submitting a multidimensional bid consisting of the intended segment bitrate and the associated value. Design of such a multi-dimensional auction is very challenging, as we need to guarantee the user's truthful reporting on the information on multiple dependent dimensions. We first propose a truthful second-score (multi-dimensional) auction framework, within which we further derive the efficient mechanism that maximizes the social welfare (of each segment downloading) and the sub-optimal mechanism that approximately maximizes the auctioneer payoff. Experiment results show that our proposed crowdsourced streaming can achieve 60% ˜ 76% of the maximum social welfare even when 80 percentage of users lose their direct network connections.
Joint Example-Based Depth Map Super-Resolution

The fast development of time-of-flight (ToF) cameras in recent years enables capture of high frame-rate 3D depth maps of moving objects. However, the resolution of depth map captured by ToF is rather limited, and thus it cannot be directly used to build a high quality 3D model. In order to handle this problem, we propose a novel joint example-based depth map super-resolution method, which converts a low resolution depth map to a high resolution depth map, using a registered high resolution color image as a reference. Different from previous depth map SR methods without training stage, we learn a mapping function from a set of training samples and enhance the resolution of the depth map via sparse coding algorithm. We further use a reconstruction constraint to make object edges sharper. Experimental results show that our method outperforms state-of-the-art methods for depth map super-resolution.
Generation of Layered Depth Images from Multi-View Video

The feature of rendering arbitrary view make layered depth image (LDI) be suitable to present multi-view video data and provide interactivity form such as free view video(FVV). However, visual artifacts occurred in the rendered result limit the practicality of LDI. In this paper, we proposed an approach to deal with this problem during generating of LDI, which refines colors of the depth pixels by choosing proper candidate depth pixels, removes matting effects by projecting LDI backward to reference views, and eliminate the gaps or holes due to disocclusion and undersample by local background interpolation. Experimental results show that our approach is practicable and efficient.
Multi-User Cooperative Mobile Video Streaming: Performance Analysis and Online Mechanism Design

Adaptive bitrate streaming enables video users to <italic>adapt</italic> their playing bitrates to the real-time network conditions, hence achieving the desirable quality-of-experience (QoE). In a multi-user wireless scenario, however, existing single-user based bitrate adaptation methods may fail to provide the desirable QoE, due to lack of consideration of multi-user interactions (such as the multi-user interferences and network congestion). In this work, we propose a novel user cooperation framework based on <italic>user-provided networking</italic> for multi-user mobile video streaming over wireless cellular networks. The framework enables nearby mobile video users to crowdsource their cellular links and resources for cooperative video streaming. We first analyze the social welfare performance bound of the proposed cooperative streaming system by introducing a virtual time-slotted system. Then, we design a low complexity Lyapunov-based online algorithm, which can be implemented in an online and distributed manner without the complete future and global network information. Numerical results show that the proposed online algorithm achieves an average 97 percent of the theoretical maximum social welfare. We further conduct experiments with real data traces, to compare our proposed online algorithm with the existing online algorithms in the literature. Experiment results show that our algorithm outperforms the existing algorithms in terms of both the achievable bitrate (with an average gain of 20 <inline-formula><tex-math notation="LaTeX">$\sim$</tex-math><alternatives><inline-graphic xlink:href="huang-ieq1-2834358.gif"/></alternatives></inline-formula> 30 percent) and social welfare (with an average gain of 10 <inline-formula><tex-math notation="LaTeX">$\sim$</tex-math><alternatives><inline-graphic xlink:href="huang-ieq2-2834358.gif"/></alternatives></inline-formula> 50 percent).
Experiences on peer to peer based live video steaming

In this paper, we emphasize salient experiences on peer to peer based live video streaming. First, we describe a two-layer overlay structure and a multi-sender based transmission algorithm to broadcast video programs. Although both numerical analysis and practical data reveal feasibility to support nearly 600 concurrent users in 500kbps streaming delivery, the tremendous workload imposed upon structure maintenance server limits its scalability. Therefore, in the second implementation, a gossip-based overlay protocol is deployed to provide distributed membership management, and an efficient scheduling mechanism is proposed to fetch video contents with lower delay. The experimental data from PlanetLab indicates that it ensures continuous playback, demanding bandwidth and low latency. More interestingly, the maximum concurrent users 15,239 with one common streaming server in 300kbps distribution sessions was tracked , which demonstrates that it offers a scalable and reliable streaming system for a large and highly dynamic population of end hosts
Overcoming view switching dynamic in multi-view video streaming over P2P network

Multi-view video streaming over P2P network has emerged as a scalable solution for providing multi-view video service on Internet, as it utilizes users' bandwidth resource to reduce streaming server's bandwidth cost when delivering the dataintensive multi-view video. However, the view switching behavior in multi-view video introduces excessive dynamic to the system, which brings two performance issues to existing solutions: large view switching delay and poor streaming quality for views with small number of audience. To address the problems, we propose a novel P2P streaming framework for multi-view video that organizes viewers of different views together to cooperate in view switching and content delivery, achieving reduced view switching delay. Further, we propose a heuristic method to balance resources across all views and improve the streaming quality for views with small number of viewers. The experiments show that our proposed method achieves lower view switching delay and better streaming quality compared with existing solutions.
Virtual support window for adaptive-weight stereo matching

It attracts many researchers' attention to find a stereo matching algorithm both accurate and fast. Yoon and Kweon's Adaptive Support-Weight(ASW) method is supposed to be a very successful algorithm in both accuracy and speed. However, it is very time consuming for ASW to take a large number of pixels into consideration for computing a disparity. In this paper, we present a stereo matching algorithm based on the ASW method to solve this problem. We introduce a time efficient technique, partial sum, to reduce the information of pixels in a large window into a small one so that disparities can be computed using the large window of pixels with low time complexity. Our method can obtain better representation of local properties of a pixel than the ASW method. Experimental results show that our method produces more accurate disparity maps than ASW method.