ELVIS: Entertainment-led video summaries

© ACM, 2010. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in ACM Transactions on Multimedia Computing, Communications, and Applications, 6(3): Article no. 17 (2010) http://doi.acm.org/10.1145/1823746.1823751Video summaries present the user with a condensed and succinct representation of the content of a video stream. Usually this is achieved by attaching degrees of importance to low-level image, audio and text features. However, video content elicits strong and measurable physiological responses in the user, which are potentially rich indicators of what video content is memorable to or emotionally engaging for an individual user. This article proposes a technique that exploits such physiological responses to a given video stream by a given user to produce Entertainment-Led VIdeo Summaries (ELVIS). ELVIS is made up of five analysis phases which correspond to the analyses of five physiological response measures: electro-dermal response (EDR), heart rate (HR), blood volume pulse (BVP), respiration rate (RR), and respiration amplitude (RA). Through these analyses, the temporal locations of the most entertaining video subsegments, as they occur within the video stream as a whole, are automatically identified. The effectiveness of the ELVIS technique is verified through a statistical analysis of data collected during a set of user trials. Our results show that ELVIS is more consistent than RANDOM, EDR, HR, BVP, RR and RA selections in identifying the most entertaining video subsegments for content in the comedy, horror/comedy, and horror genres. Subjective user reports also reveal that ELVIS video summaries are comparatively easy to understand, enjoyable, and informative

A modified flower pollination algorithm and carnivorous plant algorithm for solving engineering optimization problem

Optimization in an essential element in mechanical engineering and has never been an easy task. Hence, using an effective optimiser to solve these problems with high complexity is important. In this study, two metaheuristic algorithms, namely, modified flower pollination algorithm (MFPA) and carnivorous plant algorithm (CPA), were proposed. Flower pollination algorithm (FPA) is a biomimicry optimisation algorithm inspired by natural pollination. Although FPA has shown better convergence than particle swarm optimisation and genetic algorithm in the pioneering study, improving the convergence characteristic of FPA still needs more work. To speed up the convergence, modifications of: (i) employing chaos theory in the initialisation of initial population to enhance the diversity of the initial population in the search space, (ii) replacing FPA’s local search strategy with frog leaping algorithm to improve intensification, and (iii) integrating inertia weight into FPA’s global search strategy to adjust the searching ability of the global strategy, were presented. CPA, on the other hand, was developed based on the inspiration from how carnivorous plants adapt to survive in harsh environments. Both MFPA and CPA were first evaluated using twenty-five well-known benchmark functions with different characteristics and seven Congress on Evolutionary Computation (CEC) 2017 test functions. Their convergence characteristic and computational efficiency were analysed and compared with eight widely used metaheuristic algorithms, with the superiority validated using the Wilcoxon signed-rank test. The applicability of MFPA and CPA were further examined on eighteen mechanical engineering design problems and two challenging real-world applications of controlling the orientation of a five-degrees-of-freedom robotic arm and moving-object tracking in a complicated environment. For the optimisation of classical benchmark functions, CPA was ranked first. It also obtained the first rank in CEC04 and CEC07 modern test functions. Both CPA and MFPA showed promising results on the mechanical engineering design problems. CPA improved over the particle swarm optimisation algorithm in terms of the best fitness value by 69.40-95.99% in the optimisation of the robotic arm. Meanwhile, MFPA demonstrated a better tracking performance in the considered case studies by at least 52.99% better fitness function evaluation and fewer number of function evaluations as compared with the competitors

Improving Listening and Speaking Achievements of the Eighth Grade Students of SMP Negeri 6 Metro by Using Videos Plus Discussion Method

Listening and speaking are the important skills that have to be mastered by the students. By having these skills, the students can communicate with others easily. However, the teaching and learning of listening and speaking skills are still problematical. The objectives of this study were to find out whether or not videos plus discussion method was effective in improving the students\u27 listening and speaking achievements.  This study involved one hundred and ninety six of the eighth grade students of SMP Negeri 6 Metro and forty of them were selected as the sample by using purposive sampling technique. The results of this study showed that the videos plus discussion method was considered as one of the effective ways to improve the students\u27 listening and speaking achievements

Group influence on blogs design behaviour

Issues of national culture influence on web design behaviour have been rampant and stimulating on static web pages across the globe. The emergence of a new breed of publication-type web page brought about by the advancement of web technology however, saw a different species of online communication groups. Bloggers as these groups are called; used blogs as their communication and publication tool to distinguish themselves from other websites and online social media users. Since bloggers are groups that are recognised and credited to cultivate their own culture, the idea that national culture has an influence on blogs design behaviour and preferences may have been weakened. Bloggers groups themselves would be the influential factor that determines design preferences of bloggers in a network of blogs. To address the issue, this paper has conducted an assessment on blogs from six countries using content analysis method, national culture traits and SIDE model to ascertain design features characteristics and behaviour. Results from both the global and local blogs in each country showed that blogs design preferences in one country differ between both the global and local bloggers. Furthermore, global bloggers design preferences in countries under observation are found to be similar to one another

Automatic fine-grained area detection for thin client systems

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Enhanced visualisation of dance performance from automatically synchronised multimodal recordings

The Huawei/3DLife Grand Challenge Dataset provides multimodal recordings of Salsa dancing, consisting of audiovisual streams along with depth maps and inertial measurements. In this paper, we propose a system for augmented reality-based evaluations of Salsa dancer performances. An essential step for such a system is the automatic temporal synchronisation of the multiple modalities captured from different sensors, for which we propose efficient solutions. Furthermore, we contribute modules for the automatic analysis of dance performances and present an original software application, specifically designed for the evaluation scenario considered, which enables an enhanced dance visualisation experience, through the augmentation of the original media with the results of our automatic analyses

Boosting Image Forgery Detection using Resampling Features and Copy-move analysis

Realistic image forgeries involve a combination of splicing, resampling, cloning, region removal and other methods. While resampling detection algorithms are effective in detecting splicing and resampling, copy-move detection algorithms excel in detecting cloning and region removal. In this paper, we combine these complementary approaches in a way that boosts the overall accuracy of image manipulation detection. We use the copy-move detection method as a pre-filtering step and pass those images that are classified as untampered to a deep learning based resampling detection framework. Experimental results on various datasets including the 2017 NIST Nimble Challenge Evaluation dataset comprising nearly 10,000 pristine and tampered images shows that there is a consistent increase of 8%-10% in detection rates, when copy-move algorithm is combined with different resampling detection algorithms