10680010
Automatic sports genre categorization and view-type classification over large-scale dataset
This paper presents a framework with two automatic tasks targeting large-scale and low quality sports video archives collected from online video streams. The framework is based on the bag of visual-words model using speeded-up robust features (SURF). The first task is sports genre categorization based on hierarchical structure. Following on the second task which is based on automatically obtained genre, views are classified using support vector machines (SVMs). As a consequence, the views classification result can be used in video parsing and highlight extraction. As compared with state-of-the-art methods, our approach is fully automatic as well as domain knowledge free and thus provides a better extensibility. Furthermore, our dataset consists of 14 sport genres with 6850 minutes in total. Both sport genre categorization and view type classification have more than 80% accuracy rates, which validate this framework's robustness and potential in web-based applications.
11989987
A comprehensive simulation study of SWAN QoS model in MANETs with proactive and reactive routing
In this paper, one of the QoS models for MANETs - SWAN is studied under varying conditions to provide both quantitative and qualitative assessments of the applicability of the model in different scenarios. Results and analysis from NS-2 based simulation indicate that (i) real-time (RT) applications in SWAN-enabled mobile ad hoc networks experience low and stable delays under various traffic and mobility environment at the expense of best-effort (BE) traffic, (ii) SWAN model works compatibly with proactive as well as reactive routing protocols. A careful choice of parameters is important to balance the throughput of the BE traffic and RT traffic and QoS provisions in MANETs.
36024332
L-∞ constrained micronoise filtering for high-fidelity image compression
Imaging apparatus inevitably impose undesirable noises onto acquired images during real imaging process. Usually these noises are too faint to cause unpleasing visual effects, however, they degrade image fidelity and significantly lower the compression ratio of lossless coding. More baffling, in this case, there leaves little room for traditional noise filtering methods to work. This paper will introduce some of our efforts trying to weaken the effect of such Micro Noise during near-lossless compression. Experimental results on ISO test images and micro Gaussian noises demonstrate that with potentiality of filtering micro noise, an improved near-lossless coder can not only achieve obviously higher compression ratio but also provide better image fidelity (measured by mean squared error) than lossless coding.
39605083
RSA-based Verifiable and Recoverable Encryption of Signatures and its application in certified e-mail delivery
This paper presents two variant protocols RSA-CEMDI and RDA-CEMD2 for certified e-mail delivery with RSA receipts. The protocols provide non-repudiation of origin and non-repudiation of receipt security services to protect communicating parties from each other's false denials that the e-mail message has been sent and received. The protocols also provide strong fairness to ensure that the recipient receives the e-mail if and only if the sender receives the receipt. Services of an off-time and transparent third party are invoked in the protocols only in exceptional circumstances, i.e.. when the communicating parties fail to complete the e-mail for receipt exchange due to a network failure or a party's misbehaviour. Protocol RSA-CEMDI also offers confidentiality protection for the e-mail message and the corresponding receipt from the third party, achieved at the cost of some additional computations. The protocols are based on a cryptographic primitive called Verifiable and Recoverable Encryption of a Signature (VRES). The novel design of the VRES allows efficiency improvements in comparison with the related certified e-mail delivery protocols based on similar primitives.
23948133
Building Domain Ontology Based on Web Data and Generic Ontology
The automatic or semi-automatic construction of ontology has become a research topic of interest in recent years. This paper describes a mechanism for constructing domain specific ontologies automatically based on web data and generic ontology. Firstly, we employ the hierarchical agglomerative clustering(HAC) algorithm, clustering web pages hierarchically and resulting in a binary tree. Then an algorithm is proposed, which selectd from the binary tree the significative nodes as topics implying concepts of domain interests. Lastly, the Chinese generic ontology, HowNet, is introduced to evolve the topics (together with their hierarchical structures) into domain ontology. We experiment our method in the field of computer hardware based on web pages collected from Chinese BtoC web sites. An in-depth discussion on the experiment results is also given.
18987585
Efficient elliptic curve scalar multiplication algorithms resistant to power analysis
This paper presents four algorithms for securing elliptic curve scalar multiplication against power analysis. The highest-weight binary form (HBF) of scalars and randomization are applied to resist power analysis. By using a special method to recode the scalars, the proposed algorithms do not suffer from simple power analysis (SPA). With the randomization of the secret scalar or base point, three of the four algorithms are secure against differential power analysis (DPA), refined power analysis (RPA) and zero-value point attacks (ZPA). The countermeasures are also immune to the doubling attack. Fast Shamir's method is used in order to improve the efficiency of parallel scalar multiplication. Compared with previous countermeasures, the new countermeasures achieve higher security and do not impact overall performance.
15488270
Segmentation of Spectrum Map for HFSWR Based on Feature Extraction
When targets fall in different types of clutter and noise background in High Frequency Surface Wave Radar (HFSWR), uniform detection algorithms always result in poor detection and tracking performances. To solve the problem, two methods are proposed in this paper by extracting the background features according to the probability distribution and the image characteristic of the spectrum map. The first method uses Parzen window for estimating probability density function and Kullback-Leibler convergence for evaluating the difference between the Azimuth-Range-Doppler cells in spectrum map which can be used to distinguish the homogenous detection background from the non-homogenous. The second method is proposed based on the image edge feature in multi-scale space. The real data results about HFSWR show that the proposed methods can not only identify noise/clutter but also help choose appropriate detection/tracking algorithms to improve performances in such complex environments.