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Traditional knowledge, biological resources and intellectual property rights in Asia : the example of the Philippines

The relationship between traditional knowledge and intellectual property rights has become a topic for intensive debates at the national level, in various international settings and within and among different UN agencies, including the World Intellectual Property Organisation (WIPO), the UN Food and Agriculture Organisation (FAO), UNESCO, UNCTAD and the United Nations Environment Programme (UNEP). However, a consensus on a definition of traditional knowledge has yet to emerge due to persistent differences in perception. On the one hand, indigenous communities hold locally specific and holistic views of traditional knowledge, which are difficult to place within the framework of current intellectual property rights. Governments of developing countries, on the other hand, mostly focus on clearly defined aspects of traditional knowledge and their interpretation in the national interest and as expressions of national culture. Asian governments, in particular, have advocated the latter view. The Philippines provide an exception due to a tradition of recognising indigenous people as separate "cultural communities". However, the practical implementation of so-called "community intellectual rights" thus far is largely confined to access and benefit sharing rules, compensation requirements for traditional farmers and defensive protection measures such as digital libraries documenting traditional knowledge.

Regularized nonnegative shared subspace learning

Joint modeling of related data sources has the potential to improve various data mining tasks such as transfer learning, multitask clustering, information retrieval etc. However, diversity among various data sources might outweigh the advantages of the joint modeling, and thus may result in performance degradations. To this end, we propose a regularized shared subspace learning framework, which can exploit the mutual strengths of related data sources while being immune to the effects of the variabilities of each source. This is achieved by further imposing a mutual orthogonality constraint on the constituent subspaces which segregates the common patterns from the source specific patterns, and thus, avoids performance degradations. Our approach is rooted in nonnegative matrix factorization and extends it further to enable joint analysis of related data sources. Experiments performed using three real world data sets for both retrieval and clustering applications demonstrate the benefits of regularization and validate the effectiveness of the model. Our proposed solution provides a formal framework appropriate for jointly analyzing related data sources and therefore, it is applicable to a wider context in data mining.

Affect computing in film through sound energy dynamics

We develop an algorithm for the detection and classification of affective sound events underscored by specific patterns of sound energy dynamics. We relate the portrayal of these events to proposed high level affect or emotional coloring of the events. In this paper, four possible characteristic sound energy events are identified that convey well established meanings through their dynamics to portray and deliver certain affect, sentiment related to the horror film genre. Our algorithm is developed with the ultimate aim of automatically structuring sections of films that contain distinct shades of emotion related to horror themes for nonlinear media access and navigation. An average of 82% of the energy events, obtained from the analysis of the audio tracks of sections of four sample films corresponded correctly to the proposed affect. While the discrimination between certain sound energy event types was low, the algorithm correctly detected 71% of the occurrences of the sound energy events within audio tracks of the films analyzed, and thus forms a useful basis for determining affective scenes characteristic of horror in movies.

Automatic genre identification for content-based video categorization

This paper presents a set of computational features originating from our study of editing effects, motion, and color used in videos, for the task of automatic video categorization. These features besides representing human understanding of typical attributes of different video genres, are also inspired by the techniques and rules used by many directors to endow specific characteristics to a genre-program which lead to certain emotional impact on viewers. We propose new features whilst also employing traditionally used ones for classification. This research, goes beyond the existing work with a systematic analysis of trends exhibited by each of our features in genres such as cartoons, commercials, music, news, and sports, and it enables an understanding of the similarities, dissimilarities, and also likely confusion between genres. Classification results from our experiments on several hours of video establish the usefulness of this feature set. We also explore the issue of video clip duration required to achieve reliable genre identification and demonstrate its impact on classification accuracy.

Eventscapes : visualizing events over time with emotive facets

The scale and dynamicity of social media, and interaction between traditional news sources and online communities, has created challenges to information retrieval approaches. Users may have no clear information need or be unable to express it in the appropriate idiom, requiring instead to be oriented in an unfamiliar domain, to explore and learn. We present a novel data-driven visualization, termed Eventscape, that combines time, visual media, mood, and controversy. Formative evaluation highlights the value of emotive facets for rapid evaluation of mixed news and social media topics, and a role for such visualizations as pre-cursors to deeper search. Copyright 2011 ACM.

Between context-aware media capture and multimedia content analysis - where do we find the promised land?

Various issues related to the multimedia information retrieval and media access are discussed. The feasible solutions for automatic signal-based analysis of media content are analyzed. The extent of user involvement in the content creation process is emphasized. The applications driving the creation and usage of context and metadata are also elaborated.

An efficient and distributed file search in unstructured peer-to-peer networks

Peer-to-peer (P2P) networks are gaining increased attention from both the scientific community and the larger Internet user community. Data retrieval algorithms lie at the center of P2P networks, and this paper addresses the problem of efficiently searching for files in unstructured P2P systems. We propose an Improved Adaptive Probabilistic Search (IAPS) algorithm that is fully distributed and bandwidth efficient. IAPS uses ant-colony optimization and takes file types into consideration in order to search for file container nodes with a high probability of success. We have performed extensive simulations to study the performance of IAPS, and we compare it with the Random Walk and Adaptive Probabilistic Search algorithms. Our experimental results show that IAPS achieves high success rates, high response rates, and significant message reduction.

Privacy protected data forwarding in human associated delay tolerant networks

Human associated delay-tolerant networks (HDTNs) are new networks for DTNs, where mobile devices are associated with humans and demonstrate social related communication characteristics. As most of recent works use real social trace files to study the date forwarding in HDTNs, the privacy protection becomes a serious issue. Traditional privacy protections need to keep the attributes semantics, such as data mining and information retrieval. However, in HDTNs, it is not necessary to keep these meaningful semantics. In this paper, instead, we propose to anonymize the original data by coding to preserve individual's privacy and apply Privacy Protected Data Forwarding (PPDF) model to select the top N nodes to perform the multicast. We use both MIT Reality and Infocom 06 datasets, which are human associated mobile network trace file, to simulate our model. The results of our simulations show that this method can achieve a high data forwarding performance while protect the nodes' privacy as well.

An intelligent content discovery technique for health portal content management

Background: Continuous content management of health information portals is a feature vital for its sustainability and widespread acceptance. Knowledge and experience of a domain expert is essential for content management in the health domain. The rate of generation of online health resources is exponential and thereby manual examination for relevance to a specific topic and audience is a formidable challenge for domain experts. Intelligent content discovery for effective content management is a less researched topic. An existing expert-endorsed content repository can provide the necessary leverage to automatically identify relevant resources and evaluate qualitative metrics.Objective: This paper reports on the design research towards an intelligent technique for automated content discovery and ranking for health information portals. The proposed technique aims to improve efficiency of the current mostly manual process of portal content management by utilising an existing expert-endorsed content repository as a supporting base and a benchmark to evaluate the suitability of new contentMethods: A model for content management was established based on a field study of potential users. The proposed technique is integral to this content management model and executes in several phases (ie, query construction, content search, text analytics and fuzzy multi-criteria ranking). The construction of multi-dimensional search queries with input from Wordnet, the use of multi-word and single-word terms as representative semantics for text analytics and the use of fuzzy multi-criteria ranking for subjective evaluation of quality metrics are original contributions reported in this paper.Results: The feasibility of the proposed technique was examined with experiments conducted on an actual health information portal, the BCKOnline portal. Both intermediary and final results generated by the technique are presented in the paper and these help to establish benefits of the technique and its contribution towards effective content management.Conclusions: The prevalence of large numbers of online health resources is a key obstacle for domain experts involved in content management of health information portals and websites. The proposed technique has proven successful at search and identification of resources and the measurement of their relevance. It can be used to support the domain expert in content management and thereby ensure the health portal is up-to-date and current.

vPELS : An e-learning social environment for VLSI design with content security using DRM

This article provides a proposal for personal e-learning system (vPELS [where 'v' stands for VLSI: very large scale integrated circuit])) architecture in the context of social network environment for VLSI Design. The main objective of vPELS is to develop individual skills on a specific subject - say, VLSI - and share resources with peers. The authors' system architecture defines the organisation and management of the personal learning environment in such a way as to aid in creating, verifying and sharing learning artefacts and making money at the same time. The authors also focus in their research on one of the most interesting arenas in digital content or document management - Digital Rights Management (DRM) - and its application to e-learning.