Video Classification Using Spatial-Temporal Features and PCA

We investigate the problem of automated video classification by analysing the low-level audio-visual signal patterns along the time course in a holistic manner. Five popular TV broadcast genre are studied including sports, cartoon, news, commercial and music. A novel statistically based approach is proposed comprising two important ingredients designed for implicit semantic content characterisation and class identities modelling. First, a spatial-temporal audio-visual "super" feature vector is computed, capturing crucial clip-level video structure information inherent in a video genre. Second, the feature vector is further processed using Principal Component Analysis to reduce the spatial-temporal redundancy while exploiting the correlations between feature elements, which give rise to a compact representation for effective probabilistic modelling of each video genre. Extensive experiments are conducted assessing various aspects of the approach and their influence on the overall system performance

Power Allocation for Energy-Harvesting-based Fading Cognitive Multiple Access Channels: with or without Successive Interference Cancellation

This paper considers a fading cognitive multiple access channel (CMAC), where multiple secondary users (SUs), who share the spectrum with a primary user (PU), transmit to a cognitive base station (CBS). A power station is assumed to harvest energy from the nature and then provide power to the SUs. We investigate the power allocation problems for such a CMAC to maximize the SU sum rate under the interference power constraint, the sum transmit power constraint and the peak transmit power constraint of each individual SU. In particular, two scenarios are considered: with successive interference cancellation (SIC) and without SIC. For the first scenario, the optimal power allocation algorithm is derived. For the second scenario, a heuristic algorithm is proposed. We show that the proposed algorithm with SIC outperforms the algorithm without SIC in terms of the SU sum rate, while the algorithm without SIC outperforms the algorithm with SIC in terms of the number of admitted SUs for a high sum transmit power limit and a low peak transmit power limit of each individual SU

An interactive and multi-level framework for summarising user generated videos

We present an interactive and multi-level abstraction framework for user-generated video (UGV) summarisation, allowing a user the flexibility to select a summarisation criterion out of a number of methods provided by the system. First, a given raw video is segmented into shots, and each shot is further decomposed into sub-shots in line with the change in dominant camera motion. Secondly, principal component analysis (PCA) is applied to the colour representation of the collection of sub-shots, and a content map is created using the first few components. Each sub-shot is represented with a ``footprint'' on the content map, which reveals its content significance (coverage) and the most dynamic segment. The final stage of abstraction is devised in a user-assisted manner whereby a user is able to specify a desired summary length, with options to interactively perform abstraction at different granularity of visual comprehension. The results obtained show the potential benefit in significantly alleviating the burden of laborious user intervention associated with conventional video editing/browsing

Anti-social behavior detection in audio-visual surveillance systems

In this paper we propose a general purpose framework for detection of unusual events. The proposed system is based on the unsupervised method for unusual scene detection in web{cam images that was introduced in [1]. We extend their algorithm to accommodate data from different modalities and introduce the concept of time-space blocks. In addition, we evaluate early and late fusion techniques for our audio-visual data features. The experimental results on 192 hours of data show that data fusion of audio and video outperforms using a single modality

A Method for Filling up the Missed Data in Information Table

Almost algorithms based on the rough sets, such as mean value method, maximum frequency method, mode method are week in supporting the hidden rules in the information tablel. By the breaking point sets in the information system, a new method for packing the missed attribute value is provided in the paper,. The method is more efficient for indicating the decision rules

SRPK1/2 and PP1α exert opposite functions by modulating SRSF1-guided MKNK2 alternative splicing in colon adenocarcinoma.

BACKGROUND: The Mnk2 kinase, encoded by MKNK2 gene, plays critical roles in MAPK signaling and was involved in oncogenesis. Human MKNK2 pre-mRNA can be alternatively spliced into two splicing isoforms, the MKNK2a and MKNK2b, thus yielding Mnk2a and Mnk2b proteins with different domains. The involvement of Mnk2 alternative splicing in colon cancer has been implicated based on RNA-sequencing data from TCGA database. This study aimed at investigating the upstream modulators and clinical relevance of Mnk2 alternative splicing in colon adenocarcinoma (CAC). METHODS: PCR, western blotting and immunohistochemistry (IHC) were performed to assess the expression of Mnk2 and upstream proteins in CAC. The function of Mnk2 and its regulators were demonstrated in different CAC cell lines as well as in xenograft models. Two independent cohorts of CAC patients were used to reveal the clinical significance of MKNK2 alternative splicing. RESULTS: Comparing with adjacent nontumorous tissue, CAC specimen showed a decreased MKNK2a level and an increased MKNK2b level, which were correlated with KRAS mutation and tumor size. The SRSF1 (serine/arginine-rich splicing factor 1) was further confirmed to be the major splicing factor targeting MKNK2 in CAC cells. Higher expression of SRPK1/2 or decreased activity of PP1α were responsible for enhancing SRSF1 phosphorylation and nucleus translocation, subsequently resulted in a switch of MKNK2 alternative splicing. CONCLUSIONS: Our data showed that phosphorylation and subcellular localization of SRSF1 were balanced by SRPK1/2 and PP1α in CAC cells. High nucleus SRSF1 promoted MKNK2 splicing into MKNK2b instead of MNK2a, consequently enhanced tumor proliferation