nils-herrmann/clean_scirepeval huggingface dataset:
Network Assisted Media Streaming in Multi-Hop Wireless Networks

Delivery of high-quality streaming services over multi-hop wireless mesh networks (WMNs) is a challenging research problem because of quality fluctuation and interference of wireless links in WMNs, as well as strict throughput, delay, and reliability requirements of streaming applications. In this paper, we propose a Network Assisted Peer-to-Peer (NAP2P) system for file-based media streaming services such as video-on-demand in WMNs. In NAP2P, mesh routers dynamically cache content and form a P2P network with end user devices. This architecture enables several efficient and scalable communication mechanisms, for example, automatic caching and multi-source multi-path streaming for optimizing system performance. We address the design issues in NAP2P. Especially we design a multi-source multi-path routing mechanism to meet the QoS requirements of streaming sessions. A mathematical formulation for such source selection and routing optimization problem is presented, which determines the optimal content sources and streaming paths for multiple concurrent flows in the presence of wireless interference and subject to flow QoS constraints. By leveraging the unique architecture of NAP2P, we investigate the performance gain by applying mesh cache routers as network peers in P2P networks, as well as the benefits of allowing end user devices to provide content to their peers.
Computational Depth Sensing : Toward high-performance commodity depth cameras

Depth information plays an important role in a variety of applications, including manufacturing, medical imaging, computer vision, graphics, and virtual/augmented reality (VR/AR). Depth sensing has thus attracted sustained attention from both academia and industry communities for decades. Mainstream depth cameras can be divided into three categories: stereo, time of flight (ToF), and structured light. Stereo cameras require no active illumination and can be used outdoors, but they are fragile for homogeneous surfaces. Recently, off-the-shelf light field cameras have demonstrated improved depth estimation capability with a multiview stereo configuration. ToF cameras operate at a high frame rate and fit time-critical scenarios well, but they are susceptible to noise and limited to low resolution . Structured light cameras can produce high-resolution, high-accuracy depth, provided that a number of patterns are sequentially used. Due to its promising and reliable performance, the structured light approach has been widely adopted for three-dimensional (3-D) scanning purposes. However, achieving real-time depth with structured light either requires highspeed (and thus expensive) hardware or sacrifices depth resolution and accuracy by using a single pattern instead.
Sequence-based rate control for constant quality video

Most model based rate control solutions have the generally questionable assumption that video sequence is stationary, and also suffer from the fundamental problems of model parameter mis-estimation. In this paper, we propose a sequence based bit allocation solution with the capability of tracking the nonstationary characteristics in the video source without look-ahead encoding and thus the stationarity assumption is no longer needed. In addition, a new model parameter estimation approach is provided to solve the problems in the existing model parameter estimation schemes. Moreover, a general concept of bit allocation guarantee is presented to achieve the allocated bits in a deterministic way. The proposed rate control solution can achieve constant quality video with less quality flicker and motion jerkiness.
Challenges and opportunities in supporting video streaming over infrastructure wireless mesh networks

Multi-hop wireless mesh networks (WMNs) are emerging as a promising technology with applications in last mile Internet access, public safety, disaster response, battlefield, etc. The unique characteristics of WMNs such as limited and dynamic bandwidth, error prone nature, interferences, and user mobility, however, pose a number of significant challenges for high quality video streaming. In this paper, we discuss the major challenges, review some general approaches, and present a Unified Cache and Peer-to-Peer (UNICAP) framework we developed recently to support high quality streaming over WMNs. Issues addressed in UNICAP include the architecture, cache server/peer/route selection with cross-layer optimization, and admission control. Some future research directions are also discussed.
HTTP streaming of JPEG2000 images

JPEG2000 is a new image compression standard. One of the goals of JPEG2000 is to support large images. Since even the compressed image file can be very big for large images, downloading the entire image can take a long time depending upon the user's connection speed. Thus, we propose to use streaming of JPEG2000 images. We use the Hypertext Transfer Protocol (HTTP) for streaming. The use of HTTP to stream JPEG2000 images will ease its deployment. Thus, JPEG2000 images can be hosted by a Web server and can be streamed to the client helper application. The solution is scalable, so client devices with different capabilities, a variety of screen resolutions, and heterogeneous bandwidths can all achieve a scalable viewing of the same content stored in a single file. It is also possible to stream only selected regions of the image at a particular resolution and decode this partial stream and display it at the client. Thus, the proposed approach can handle resolution, quality and region of interest scalability feature of JPEG2000 images.
Format-compliant selective scrambling for multimedia access control

We present a framework for access control of standard-compliant compressed video and audio bitstreams for entertainment purposes. This includes format compliant selective encryption and compliance-preserving secure spatial shuffling. The approach leverages well-known encryption algorithms, has low complexity, and maintains standard-compliance of the encrypted bitstream. The standard compliance feature guarantees the inheritance of many error resiliency properties of the video and audio compression standards, and works with many existing network bandwidth adaptation and error control techniques that have been developed for standard-compliant compressed video/audio, thus making it especially suitable for Internet commerce and wireless multimedia applications. Standard compliance also allows subsequent signal processing techniques to be applied to the encrypted bitstream. The approach is also capable of providing layered tradeoffs between security, complexity, delay, bit overhead, etc. We demonstrate the effectiveness of the proposed framework by applying it to MPEG-4 video bitstreams and MPEG-4 AAC audio bitstreams. The proposed framework has been adopted into MPEG-4 IPMP (Intellectual Property Management and Protection) extension Committee Draft.
On the rate-distortion performance of dynamic bitstream switching mechanisms

Bitstream switching is an effective way to deal with bandwidth variation in transmitting multimedia over time-varying channels. A number of bitstream switching mechanisms have been proposed to reduce the potential error drift introduced by bitstream switching, often without explicitly taking into account the impact of the switching on the bandwidth and delay requirements in their performance analysis. This paper studies the relative rate-distortion (R-D) performance of some of these bitstream streaming mechanisms by extending a delay-aware R-D optimized dynamic bitstream switching framework we previously proposed. By considering different switching mechanisms, we show that the proposed extended R-D based framework can adaptively choose the right switching mechanism and effectively switch at the right time to the right bitstream to achieve optimized rate-distortion performance.
Mobile Media in Action: Remote Target Localization and Tracking

Smartphones have come a long way over the past 10 years&#x2014;from containing a simple database of calendar entries and contacts to including a digital camera, digital compass, GPS, and thousands of apps available with the touch of a button on the screen. People like smartphones for many reasons, particularly because of their mobility, friendly user interface, large number of useful applications, interactive games, and higher bandwidth with the help of 3G networks. From a technical point of view, the fundamental reason behind the popularity of smartphones is that they are not only a communications device, but also provide a powerful sensing and computing platform. A growing number of computing tasks previously executed on PCs are now being performed on smartphones. Because of all this, smartphones are increasingly attracting attention from researchers, with research efforts in mobile media streaming, video retargeting, location-based systems, and mobile vision systems, among others. PositionIt, a mobile media-based remote target-localization and tracking system, is one part of a comprehensive system that uses networked smartphones to localize a remote target of interest by fusing data sensed by microphones, cameras, and other embedded sensors.
Directional spatial interpolation for DCT-based low bit rate coding

A novel approach which combines directional spatial interpolation at the receiver and block-based coding is proposed for low bit rate coding. The directional spatial interpolation at the receiver can reconstruct faithfully a large percentage of the blocks that are intentionally not sent. Experimental results show that the new proposed coding system can provide significant subjective and objective gains over conventional approaches for still image compression, and has the potential to be extended for video coding.
Social Multimedia Signals: Sense, Process, and Put Them to Work

Social media gives ordinary people the power to be content creators and information disseminators. This information is embedded in multimedia shared across social networks, containing valuable indications about various facets of human life-about what captures our attention, our sharing biases, and the digital traces we abdicate. Social multimedia signal processing aims to transform the noise-like phenomena in social media into signals useful for building novel, socially aware multimedia applications and targeted advertising techniques as well as exploring new marketing methods. With a fresh way to look at the existence of multimedia in online social networks, we can also explore new marketing methods and targeted advertising techniques.
Throughput and delay analysis of the IEEE 802.15.3 CSMA/CA mechanism considering the suspending events in unsaturated traffic conditions

Unlike in IEEE 802.11, the CSMA/CA traffic conditions in IEEE 802.15.3 are typically unsaturated. This paper presents an extended analytical model based on Bianchi's model in IEEE 802.11, by taking into account the device suspending events, unsaturated traffic conditions, as well as the effects of error-prone channels. Based on this model we re-derive a closed form expression of the average service time. The accuracy of the model is validated through extensive simulations. The analysis is also instructional for IEEE 802.11 networks under limited load.
Geometric-structure-based error concealment with novel applications in block-based low-bit-rate coding

This paper first proposes a computationally efficient spatial directional interpolation scheme, which makes use of the local geometric information extracted from the surrounding blocks. The proposed error-concealment scheme produces results that are superior to those of other approaches, in terms of both peak signal-to-noise ratio and visual quality. Then a novel approach that incorporates this directional spatial interpolation at the receiver is proposed for block-based low-bit-rate coding. The key observation is that the directional spatial interpolation at the receiver can reconstruct faithfully a large percentage of the blocks that are intentionally not sent. A rate-distortion optimal way to drop the blocks is shown. The new approach can be made compatible with standard JPEG and MPEG decoders. The block-dropping approach also has an important application for dynamic rate shaping in transmitting precompressed videos over channels of dynamic bandwidth. Experimental results show that the proposed coding and rate-shaping systems can provide significant subjective and objective gains over conventional approaches.
Integrated secure watermark detection and privacy preserving storage in the compressive sensing domain

Secure watermark detection techniques are important to protect the secrecy of the watermark pattern. In this paper, we identify an application scenario that requires performing secure watermark detection in the cloud, and in the meantime, supporting privacy preserving multimedia data storage using the cloud. We then propose a compressive sensing (CS) based framework using secure multiparty computation (MPC) protocols to address such a requirement. In our framework, the multimedia data and secret watermark pattern are presented to the cloud for secure watermark detection in a compressive sensing domain to protect the privacy. The compressive sensing transformation is executed by a MPC protocol under the semi-honest security model to protect the privacy of the compressive sensing matrix and the watermark pattern. We derive the lower bound of the expected watermark detection performance in the compressive sensing domain, given the original image, watermark pattern and only the size of the compressive sensing matrix. The lower bound has been validated by our experiments. Our theoretical analysis and experimental results show that secure watermark detection in the compressive sensing domain is feasible. Our framework can also be extended to other collaborative secure signal processing and data-mining applications in the cloud.
A New Data-Mining Based Approach for Network Intrusion Detection

Nowadays, as information systems are more open to the Internet, the importance of secure networks is tremendously increased. New intelligent Intrusion Detection Systems (IDSs) which are based on sophisticated algorithms rather than current signature-base detections are in demand. In this paper, we propose a new data-mining based technique for intrusion detection using an ensemble of binary classifiers with feature selection and multiboosting simultaneously. Our model employs feature selection so that the binary classifier for each type of attack can be more accurate, which improves the detection of attacks that occur less frequently in the training data. Based on the accurate binary classifiers, our model applies a new ensemble approach which aggregates each binary classifier’s decisions for the same input and decides which class is most suitable for a given input. During this process, the potential bias of certain binary classifier could be alleviated by other binary classifiers’ decision. Our model also makes use of multiboosting for reducing both variance and bias. The experimental results show that our approach provides better performance in terms of accuracy and cost than the winner entry of the ‘Knowledge Development and Data mining’ (KDD) ’99 cup challenge. Future works will extend our analysis to a new ‘Protected Repository for the Defense of Infrastructure against Cyber Threats’ (PREDICT) dataset as well as real network data.
Cross-Site Request Forgery: Attack and Defense

Cross Site Request Forgery (CSRF) has emerged as a potent threat to Web 2.0 applications. Because of the stateless nature of the HTTP protocol, a malicious website can force the user's browser to send unauthorized requests to a trusted site. This demo provides hands on exposure of the various ways in which some popular web applications are exploited using CSRF, in addition to demonstrating techniques by which CSRF signatures can be detected and attacks effectively resisted even before initiation. The user needs only to install a simple extension to get notified about potential CSRF vulnerabilities. Because validating the Referer Header is a common CSRF prevention method, a novel solution to the Referer Privacy issue will also be demonstrated.
Kinect-like depth denoising

Accuracy and stability of Kinect-like depth data is limited by its generating principle. In order to serve further applications with high quality depth, the preprocessing on depth data is essential. In this paper, we analyze the characteristics of the Kinect-like depth data by examing its generation principle and propose a spatial-temporal denoising algorithm taking into account its special properties. Both the intra-frame spatial correlation and the inter-frame temporal correlation are exploited to fill the depth hole and suppress the depth noise. Moreover, a divisive normalization approach is proposed to assist the noise filtering process. The 3D rendering results of the processed depth demonstrates that the lost depth is recovered in some hole regions and the noise is suppressed with depth features preserved.
Adaptive wavelet transforms with spatially varying filters for scalable image coding

An adaptive wavelet transform algorithm for scalable image coding is proposed. A quadtree segmentation scheme based on an entropy criterion is proposed to segment the image into several regions to allow for adaptive filtering using different types of prototype wavelet filters. A joint bit allocation and coding method is presented to efficiently code the segmented image in an embedding fashion. Experimental results show that the proposed scheme generally provides higher peak signal-to-noise ratios (PSNRs) at higher bit rates, and significantly better visual quality at lower bit rates, compared to fixed-filter wavelet transform based coding schemes.
A compressive sensing based secure watermark detection and privacy preserving storage framework.

Privacy is a critical issue when the data owners outsource data storage or processing to a third party computing service, such as the cloud. In this paper, we identify a cloud computing application scenario that requires simultaneously performing secure watermark detection and privacy preserving multimedia data storage. We then propose a compressive sensing (CS)-based framework using secure multiparty computation (MPC) protocols to address such a requirement. In our framework, the multimedia data and secret watermark pattern are presented to the cloud for secure watermark detection in a CS domain to protect the privacy. During CS transformation, the privacy of the CS matrix and the watermark pattern is protected by the MPC protocols under the semi-honest security model. We derive the expected watermark detection performance in the CS domain, given the target image, watermark pattern, and the size of the CS matrix (but without the CS matrix itself). The correctness of the derived performance has been validated by our experiments. Our theoretical analysis and experimental results show that secure watermark detection in the CS domain is feasible. Our framework can also be extended to other collaborative secure signal processing and data-mining applications in the cloud.
Scalable Lossy Compression for Pixel-Value Encrypted Images

Compression of encrypted data draws much attention in recent years due to the security concerns in a service oriented environment such as cloud computing. We propose a scalable lossy compression scheme for images having their pixel value encrypted with a standard stream cipher. The encrypted data are simply compressed by transmitting a uniformly sub sampled portion of the encrypted data and some bit-planes of another uniformly sub sampled portion of the encrypted data. With a proposed content adaptive interpolation prediction method with side information, at the receiver side, a decoder performs content adaptive interpolation based on the decrypted partial information, where the received bit-plane information serves as the side information that reflects the image edge information, making the image reconstruction more precise. When more bit-planes are transmitted, higher quality of the decompressed image can be achieved. The experimental results show that our proposed scheme achieves much better performance than the existing lossy compression scheme for pixel value encrypted images, and also similar performance as the state-of-the-art lossy compression for pixel permutation based encrypted images. In addition, our proposed scheme has the following advantages: at the decoder side, no computationally intensive iteration and no additional public orthogonal matrix is needed. It works well for both smooth and texture-rich images.
Efficient Compression of Encrypted Grayscale Images

Lossless compression of encrypted sources can be achieved through Slepian-Wolf coding. For encrypted real-world sources, such as images, the key to improve the compression efficiency is how the source dependency is exploited. Approaches in the literature that make use of Markov properties in the Slepian-Wolf decoder do not work well for grayscale images. In this correspondence, we propose a resolution progressive compression scheme which compresses an encrypted image progressively in resolution, such that the decoder can observe a low-resolution version of the image, study local statistics based on it, and use the statistics to decode the next resolution level. Good performance is observed both theoretically and experimentally.
Single-pass frame-level constant distortion bit allocation for smooth video quality

Quality fluctuation has a major negative effect on subjective video quality. A desirable single-pass frame-level constant-distortion bit allocation scheme is proposed in the paper for smooth video quality throughout the video sequence. The average distortion of previous coded frames is taken as the target distortion for the current frame. According to the linear rate control algorithm, the distortion and rate are exponential and linear function of the number of zero coefficients, respectively. Based on the target distortion and the distribution of the absolute DCT coefficients, we then derive the closed-form formulas for estimating the number of zero coefficients and the slope /spl theta/ in the linear rate control function as well as the bit budget for the current frame. Experimental results show that the proposed constant-distortion (but variable rate) bit allocation scheme provides much smoother video quality on all testing video sequences than constant bit allocation scheme.
Delta interpolation for upsampling imaging solutions

The advances in imaging optics, sensors, and camera processing capabilities in today's digital cameras have resulted in photos with over 10 megapixels. The high resolution may pose a challenge to the computational complexity of many imaging solutions, such as color enhancement. To reduce the complexity of these imaging solutions and enable real time operations, it has been proposed to employ an upsampling imaging solution approach, such as the celebrated joint bilateral upsampling algorithm. In this paper we present a delta interpolation scheme, which reconstructs a high resolution solution by explicitly employing knowledge from both the high and low resolution images and operating on a delta image. This method achieves substantially enhanced visual quality on color enhancement, and its performance is validated by experimental results.