nils-herrmann/clean_scirepeval huggingface dataset:
The Hidden Potential of Movie Genome Communities: Analyzing Fine-Grained Semantic Information in Motion Pictures

The paper discusses the existence of distinct communities of fine-grained semantic features in movies, which may result in some movies being popular at box office, others winning Oscars while some receiving high ratings from users/critics. We study a phenomenon related to the Movie Genome Project, which aims to categorize movies by user taste, mood, story, plot development and other semantic meta-data. More specifically, our research reveals that four unique communities of semantic genomes appear in every successful movie. Similarly, five unique gene communities describe very poorly rated movies. Using this community structure of genomes representing the network of semantic features in movies, we develop an optimization function that attempts to identify the genetic algorithm powering successful or profitable movies. Our results indicate that utilizing movie genome communities in genetic optimization perform better than standard classifiers such as decision trees in predicting movie profitability.
Empowering Cross-Domain Internet Media with Real-Time Topic Learning from Social Streams

This paper aims to connect social media from disparate sources on the Internet by building a common topic space in-between, using which cross domain media recommendations can be realized on the web. The topic space is built and updated in real time by extending the Latent Dirichlet Allocation (LDA) model to cater to streaming online data. Our topical model, named Online Streaming LDA (OSLDA), is able to extract, learn, populate, and update the topic space in real time, scaling with streaming tweets. Based on the proposed topic space learned in real time, we present media recommendation applications that cannot be achieved by conventional media analysis techniques: (1) tweet enrichment by recommending related videos, and (2) popular video recommendation for featuring socially trending topical videos. We conduct experiments over a collection of 3.6 million tweets and 1.2 million click-through data from a video search engine. Our results show that the learned topic model plays a natural role connecting cross-domain social media, leading to a better user experience consuming social media.
Lossless ROI Privacy Protection of H.264/AVC Compressed Surveillance Videos

Privacy becomes one of the major concerns of video surveillance systems, especially in cloud-based systems. Privacy protection of surveillance videos aims to protect privacy information without hampering normal video surveillance tasks. Region-of-interest (ROI) privacy protection is more practical compared with the whole video encryption approaches. However, one common drawback of virtually all current ROI privacy protection methods is that the original compressed surveillance video recorded in the camera is permanently distorted by the privacy protection process, due to the quantization in the re-encoding process. Thus, the integrity of the original compressed surveillance video captured by the camera is destroyed. This is unacceptable for some application scenarios, such as video forensics for investigations and video authentication for law enforcement. In this paper, we introduce a new paradigm for privacy protection in surveillance videos, referred to as lossless privacy region protection, which has the property that the distortion introduced by the protection of the privacy data can be completely removed from the protected videos by authorized users. We demonstrate the concept of lossless privacy region protection through a proposed scheme applied on H.264/Advanced Video Coding compressed videos.
Progressive pseudo-analog transmission for mobile video live streaming

Mobile video live streaming is facing great challenges in offering high quality of experience (QoE) under varying channel conditions. In this paper, we propose a progressive pseudo-analog transmission scheme in which the received video quality gracefully adapts to both SNR and bandwidth variations. Building upon the emerging pseudoanalog video transmission, the proposed scheme further adopts a greedy approach to improve the received video quality with each allocated bandwidth share. The optimal scheduling and power allocation are derived under the mean squared error (MSE) criterion. Testbed evaluations show that the proposed scheme outperforms the state-of-the-art digital and analog transmission schemes by a notable margin.
Proactive caching of online video by mining mainstream media

Online video sharing is becoming more and more popular and demands a large amount of network resources. Caching content has been an effective method for providing better quality of service for video applications. In the age of social network, information sharing and consumption has significantly changed. More and more viewers of online videos are influenced by the trends in social and mainstream media. Given the rich information about the trends in the media, we propose a cross-platform, proactive video caching scheme in this paper. We employ a combination of the topic modeling tool Latent Dirichlet Allocation (LDA) and frequent pattern mining algorithm Apriori to effectively detect and connect trending topics from mainstream media to online videos. Furthermore, we design a reputation-based video-ranking algorithm to select candidates for caching at geographically relevant proxy nodes to reduce the delay and improve the overall traffic in the network. Simulation results show that our cross-domain proactive caching method performs significantly better than classical caching methods that are based on the historical popularity.
Scalable Non-Binary Distributed Source Coding Using Gray Codes

In this paper we propose a bit-plane based, scalable distributed source coding (DSC) scheme for non-binary correlated sources. In the source coding part of DSC, the symbols are quantized and the quantization indices are converted into binary forms using Gray codes. The channel coding part encodes each bit-plane independently using turbo codes. The turbo decoder that works in binary domain is facilitated by an outer loop that captures the symbol-domain correlation. Simulation results show that, in asymmetric DSC of joint Gaussian sources, the overall system performance is greatly improved by using Gray codes. It is also demonstrated that progressive transmission is well supported
Reactive power-voltage integrated control method based on MCR

In power system, the voltage pulsation and deficiency or surplus of reactive power will have a great effect on the quality of electric power supply. Dynamicly local reactive power compensation is one of the best methods to reduce the power network loss and improve the efficiency of transmission and distribution. Through the effective methods of compensation, we can keep the balance of reactive power in the maximum degree by the maintenance of acceptable voltage. This paper firstly describes the principles of synthetic compensation of reactive power and voltage then analyzes the principle of magnetically controlled reactor-MCR in detail and derives the characteristics, secondly analyses the disadvantages of the conventional reactive power compensation devices and the advantages of MCR, thirdly on this basis designs a control strategy of reactive power and voltage which is based on MCR, so as to achieve the goal of dynamic reactive power compensation. At the end of paper, simulator and experimental research are carried out for the model of compensation method; the results accord with the theoretical analyses done before. So the compensation method is proved to be accurate in theory and feasible in practice.
Joint Design of Source Rate Control and QoS-Aware Congestion Control for Video Streaming Over the Internet

Multimedia streaming over the Internet has been a very challenging issue due to the dynamic uncertain nature of the channels. This paper proposes an algorithm for the joint design of source rate control and congestion control for video streaming over the Internet. With the incorporation of a virtual network buffer management mechanism (VB), the quality of service (QoS) requirements of the application can be translated into the constraints of the source rate and the sending rate. Then at the application layer, the source rate control is implemented based on the derived constraints, and at the transport layer, a QoS-aware congestion control mechanism is proposed that strives to meet the send rate constraint derived from VB, by allowing temporary violation of transport control protocol (TCP)-friendliness when necessary. Long-term TCP-friendliness, nevertheless, is preserved by introducing a rate-compensation algorithm. Simulation results show that compared with traditional source rate/congestion control algorithms, this cross-layer design approach can better support the QoS requirements of the application, and significantly improve the playback quality by reducing the overflow and underflow of the decoder buffer, and improving quality smoothness, while maintaining good long-term TCP-friendliness
Boundary matching detection for progressive transmission of VQ indices over noisy channels

An important issue associated with employing vector quantization (VQ) for image coding is that of reliability regarding the actual performance in the presence of channel noise. This paper presents a novel, approach to addressing such issues. The novelty of the proposed approach is that it renders much better reconstruction image quality with greatly reduced complexity, comparing to existing schemes. The proposed approach takes advantages of the spatial contiguity property of images, instead of treating the transmitted data as general information data like many other joint source-channel coding schemes do.<<ETX>>
Integrated image and speech analysis for content-based video indexing

We study an important problem in multimedia database, namely the automatic extraction of indexing information from raw data based on video contents. The goal of our research project is to develop a prototype system for automatic indexing of sports videos. The novelty of our work is that we propose to integrate speech understanding and image analysis algorithms for extracting information. The main thrust of this work comes from the observation that in news or sports video indexing, usually speech analysis is more efficient in detecting events than image analysis. Therefore, in our system, the audio processing modules are first applied to locate candidates in the whole data. This information is passed to the video processing modules, which further analyze the video. The final products of video analysis are in the form of pointers to the locations of interesting events in a video. Our algorithms have been tested extensively with real TV programs, and results are presented and discussed.
Feature-oriented rate shaping of pre-compressed image/video

Rate shaping plays an important role in transmitting compressed video on a network with dynamic bandwidth. We present a scheme for rate shaping JPEG/MPEG-pre-compressed image/video with feature analysis in scale space. The proposed scheme draws its strengths from a technique for identifying perceptually important features based on geometry-driven diffusion, an interpolation scheme allowing many intentionally dropped blocks to be recovered faithfully from neighboring transmitted blocks, and some careful observations about the close interactions between block dropping and feature analysis. Compared with a successful rate shaping scheme based on joint block dropping and uniform coefficient truncation, the new scheme not only produces images of significantly higher visual quality, but also better PSNR at low bit rates.
Path-Diversity Overlay Retransmission Architecture for Reliable Multicast

IP-multicast is a bandwidth efficient transmission mechanism for group communications. Reliability in IP-multicast, however, poses a set of significant challenges. To address the reliability and scalability issues in IP-multicast, this paper proposes a novel overlay retransmission architecture that exploits path-diversity by taking advantages of both IP multicast and an overlay network. We show that the proposed path diversity overlay retransmission architecture has the potential to significantly improve the reliability, delay, playback quality, and scalability of IP-multicast based multimedia applications. The general concept of using P2P overlay networks to help improve the QoS performance of multimedia applications as illustrated in this paper is expected to have significant impact on the deployment of next generation multimedia services
iImage: An Image Based Information Retrieval Application for the iPhone

Image processing is a powerful technology that can be used to analyze an image for many useful purposes. However, software like this is often out ofthe typical user's reach. iImage directly confronts this problem. The iImage application takes the sophisticated technologies of image analysis and identification based on MPEG-7 image feature tools and makes them readily available on the go. With the use of the iPhone Source Development Kit, iImage has become an application that is available on the world's most popular mobile device. The user simply takes a photo using the iPhone's built in camera, and in seconds appear similar images with relevant information from within our database.
Two greedy tree growing algorithms for designing variable rate vector quantizers

This paper presents two variable rate vector quantization schemes for image compression. The idea is to designate more bits to regions of an image that are active or difficult to code, and fewer bits to less active regions. The proposed schemes design variable rate tree-structured vector quantizers by growing the tree one node at a time. Basing the decision to split on some min-max-like criteria other than the conventional mean-squared-error (MSE) measure, these two schemes aim to provide reconstructed images with less noticeable edge degradation. A comparison with a previously proposed scheme of Riskin and (see  1991) shows that the proposed schemes have better performance in terms of image visual quality with reduced implementational complexity.
prTorrent: On establishment of piece rarity in the BitTorrent unchoking algorithm

BitTorrent is an extensively adopted p2p Content Distribution System on the Internet. In spite of its proincentive approach and ease of implementation, recent research has empirically shown BitTorrent to be vulnerable to strategic manipulation by its constituent peers in a swarm. Moreover, Honest Piece Revelation and Free-Riding is becoming an increasing concern. Our findings indicate that till date, it is the orthogonal treatment of piece rarity and unchoking, that has encouraged strategic manipulation enabling unfair maximization of incentives in p2p systems. In this paper, we propose that solution to such concerns lies in unifying a Piece Rarity factor with the BitTorrent Unchoking Algorithm. We also discuss a new Discount Parameter attack that compromises most Tit-for-Tat mechanisms. Our analysis demonstrates how underreporting, as a Piece Revelation Strategy in auction based choking algorithms, could result in Starvation. prTorrent, a novel approach based on BitTorrent shows how strategic formulation of the Piece Rarity parameter can optimize incentives in a swarm, and help its constituent peers in achieving the equilibrium facilitating truly co-operative behavior.
Overview of the OMA Secure Content IDentification Mechanism

With the wide availability of digital content and proliferation of Web2.0 applications, supporting secure and efficient identification of digital content becomes a more and more important issue. In early 2010, the Open Mobile Alliance (OMA) ratified a new standard known as Secure Content IDentification Mechanism (SCIDM) to facilitate the management of digital content. As the main contributors to the SCIDM standard, the authors provide an overview of this standard as well as a use case in this paper.
A Practical Hybrid Digital-Analog Scheme for Wireless Video Transmission

We propose a hybrid digital-analog framework for wireless video transmission, which benefits from both the high distortion-power performance of digital systems and the graceful performance degradation of analog systems. The proposed framework models video frames as a parallel Gaussian source, which is separated into digital and analog parts through scalar quantization. It features entropy coding and channel coding in digital transmission and power scaling in analog transmission. The key challenge in this framework is how to allocate the constrained power and bandwidth resources between and among digital and analog components to achieve minimal distortion at the receiver. Given the worst-case channel signal-to-noise ratio, we are able to derive a closed-form expression of the overall distortion. However, minimizing it is a mixed-integer non-linear programming problem, which is generally non-deterministic polynomial-time hard. By making reasonable and justified simplifications, we approach the optimal solution through a practical scheme. Evaluations show that the proposed scheme outperforms the state-of-the-art analog scheme SoftCast by a large margin. The gain in received video peak signal-to-noise ratio is up to 5.0 dB for various types of videos.
Gabor feature based ionospheric clutter region extraction in Range-Doppler map

This paper proposes a Gabor feature based ionospheric clutter region extraction method in RD map for its good performance of picking up the orientation information from such clutter in a Range-Doppler map. The better performance of the proposed method is shown by comparing with the statistical information based and adaptive threshold based methods. Such results can be used for ionospheric clutter sounding, interference mitigation with STAP, system performance evaluation, and carrier frequency selection.
View Adaptive Recurrent Neural Networks for High Performance Human Action Recognition from Skeleton Data

Skeleton-based human action recognition has recently attracted increasing attention due to the popularity of 3D skeleton data. One main challenge lies in the large view variations in captured human actions. We propose a novel view adaptation scheme to automatically regulate observation viewpoints during the occurrence of an action. Rather than re-positioning the skeletons based on a human defined prior criterion, we design a view adaptive recurrent neural network (RNN) with LSTM architecture, which enables the network itself to adapt to the most suitable observation viewpoints from end to end. Extensive experiment analyses show that the proposed view adaptive RNN model strives to (1) transform the skeletons of various views to much more consistent viewpoints and (2) maintain the continuity of the action rather than transforming every frame to the same position with the same body orientation. Our model achieves significant improvement over the state-of-the-art approaches on three benchmark datasets.
An efficient color re-indexing scheme for palette-based compression

This paper presents a fast and efficient way for color re-indexing that tends to maximize the compression performance of a palette-based compression system. The proposed scheme relates the index difference of neighboring pixels to the potential cost of bits. It optimizes the assignment of index values to colors in a one-step look-ahead greedy fashion. Experimental results suggest that the proposed re-indexing scheme can reduce the bit rate by up to 43%, when compared to a previously proposed intensity-based color-indexing scheme. Furthermore, we show that with the proposed color re-indexing scheme, the palette-based JPEG-LS and palette-based JPEG-2000 can often outperform the graphical interchange format (GIF) significantly.
Network friendly media security: rationales, solutions, and open issues

Network friendly media security refers to the security technologies that are specifically designed to cope with existing and future multimedia networking infrastructures and technologies so as to ease the deployment and maintain or improve the quality of service performance of multimedia applications. It is especially useful for streaming and mobile multimedia applications where content adaptation is a necessity. In this paper, we analyze the various motivations behind network-friendly security solutions, review some of the most recent approaches, discuss some open issues, and suggest some potential solutions.
Progressive Pseudo-analog Transmission for Mobile Video Streaming

We propose a progressive pseudo-analog video transmission scheme that simultaneously handles SNR and bandwidth variations with graceful quality degradation for mobile video streaming. With the inherited SNR-adaptability from pseudo-analog transmission, the proposed progressive solution acquires bandwidth adaptability through an innovative scheduling algorithm with optimal power allocation. The basic idea is to aggressively transmit or retransmit important coefficients so that distortion is minimized at the receiver after each received packet. We derive the closed-form expression of reduced distortion for each packet under given transmission power and known channel conditions, and show that the optimal solution can be obtained with a water-filling algorithm. We also illustrate through analyses and simulations that a near-optimal solution can be found through approximation when only statistical channel information is available. Simulations show that our solution approaches the performance upper bound of pseudo-analog transmission in an additive white Gaussian noise channel and significantly outperforms existing pseudo-analog solutions in a fast Rayleigh fading channel. Trace-driven emulations are also carried out to demonstrate the advantage of the proposed solution over the state-of-the-art digital and pseudo-analog solutions under a real dramatically varying wireless environment.
Path-Diversity P2P Overlay Retransmission for Reliable IP-Multicast

IP-multicast is a bandwidth efficient transmission mechanism for group communications. Reliability in IP-multicast, however, poses a set of significant challenges. To address the reliability and scalability issues in IP-multicast, this paper proposes a novel, highly distributed, and lightweight overlay peer-to-peer retransmission architecture that exploits path-diversity by taking advantages of both IP-multicast and an overlay network. An approach that leverages both disjoint-path-finding and periodic selective probing to take into account peer's recent packet loss probability, retransmission delay and recent retransmission success rate is proposed to effectively construct an efficient and dynamic overlay retransmission network. We show that the proposed path diversity overlay retransmission architecture has the potential to significantly reduce the retransmission delay, improve the reliability, playback quality, and scalability of IP-multicast based multimedia applications. Given a deployed IP-multicast network, the proposed overlay retransmission architecture is practical, scalable, and easy to deploy, requiring no change to the existing network infrastructure.