New Media In Malaysia In Projecting Collective Action

New media has significantly lowered the cost of publication, transcending geographical boundaries making spatial and temporal impediments redundant. Due to technological advancements, new media has also become more pervasive in society and it has started to compete with traditional media. This thesis explores the relationship between the use of new media by the Lifeworld (society) and its accompanying Web 2.0 technologies, and the System (structure) in Malaysia, with a long history of regulating the press and free speech, using the Argumentative Discourse Analysis (ADA) framework. The study adopted the ‘Web Crawling’ and snowball blog-seeding method of gathering an initial list of bloggers and online activists for online and offline semi-structured interviews added with the content analysis (blogs) and secondary method for the purpose of this study. Results show that with the rise of a ‘New’ middle class, collective action through new media activists have given birth to New Social Movements such as Bersih and online activism (Hactavism) in Malaysia. Most importantly, through new media (independent newportals, blogs cybertroopers) the opposition with the assistance of coalitional capital of CSOs and CSAs in their agenda setting have employed discursive formations that have led to resistance or contentious politics (resistance of Colonisation by the System) in the context of Malaysia. These strategies and tactics of resistance politics through collective actions such as NSMs and hactavism have resulted in different policy implications by the Malaysian government

Nutritional status and socioeconomic change among Toba and Wichí populations of the Argentinean Chaco

The prevalence of overweight and obesity is growing at an accelerated pace in disadvantaged populations. Indigenous populations all over the world, whose lifestyle is changing rapidly and drastically, seem to be particularly prone to show an increased prevalence of overweight and its co-morbidities among adults. The aim of this study was to evaluate the association between socioeconomic and nutritional statuses in adults of two indigenous populations of the Argentine Gran Chaco: the Toba and Wichí of the province of Formosa. Originally hunter-gatherers, they are now more settled and engage in temporary wage labor and local political positions. A total of 541 adults (>20 years old) participated in the study. Almost 50% of the adult Toba and 34% of the adult Wichí were overweight and 10% of adults in both populations were obese. Socioeconomic status was positively associated with body mass index in both populations. Furthermore, political connectedness with the non-indigenous sector, as in the case of community leaders, was highly correlated with obesity. Differences within and between groups can be explained by biocultural factors that include gender, diet (foraged vs store-bought), lifestyle (sedentary vs more active), and history of political power. Our study highlights the interactions among social, cultural, and political economic variables, such as political hierarchies within the group or degree of social connectedness with community leaders. By making these variables an integral part of our analysis and interpretation, we hope to improve our understanding of the situation of indigenous populations in transition. © 2009 Elsevier B.V. All rights reserved.Fil: Valeggia, Claudia Rita. University of Pennsylvania; Estados UnidosFil: Burke, Kevin M.. University of Pennsylvania; Estados UnidosFil: Fernandez Duque, Eduardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Centro de Ecología Aplicada del Litoral. Universidad Nacional del Nordeste. Centro de Ecología Aplicada del Litoral; Argentina. University of Pennsylvania; Estados Unido

Modeling temporal networks using random itineraries

We propose a procedure to generate dynamical networks with bursty, possibly repetitive and correlated temporal behaviors. Regarding any weighted directed graph as being composed of the accumulation of paths between its nodes, our construction uses random walks of variable length to produce time-extended structures with adjustable features. The procedure is first described in a general framework. It is then illustrated in a case study inspired by a transportation system for which the resulting synthetic network is shown to accurately mimic the empirical phenomenology

Distributed data mining in grid computing environments

The official published version of this article can be found at the link below.The computing-intensive data mining for inherently Internet-wide distributed data, referred to as Distributed Data Mining (DDM), calls for the support of a powerful Grid with an effective scheduling framework. DDM often shares the computing paradigm of local processing and global synthesizing. It involves every phase of Data Mining (DM) processes, which makes the workflow of DDM very complex and can be modelled only by a Directed Acyclic Graph (DAG) with multiple data entries. Motivated by the need for a practical solution of the Grid scheduling problem for the DDM workflow, this paper proposes a novel two-phase scheduling framework, including External Scheduling and Internal Scheduling, on a two-level Grid architecture (InterGrid, IntraGrid). Currently a DM IntraGrid, named DMGCE (Data Mining Grid Computing Environment), has been developed with a dynamic scheduling framework for competitive DAGs in a heterogeneous computing environment. This system is implemented in an established Multi-Agent System (MAS) environment, in which the reuse of existing DM algorithms is achieved by encapsulating them into agents. Practical classification problems from oil well logging analysis are used to measure the system performance. The detailed experiment procedure and result analysis are also discussed in this paper

Affordable Earth Return On-Demand (AERO) Vehicle Design

This presentation will summarize the key design and fabrication work done for our senior design project, Affordable Earth Return On-Demand (AERO). This cost effective, readily deployable, small reentry vehicle is a self-contained system that de-orbits, reenters, and parachutes its 1 kilogram of high-priority payload safely to Earth’s surface

One-Shot Learning for Periocular Recognition: Exploring the Effect of Domain Adaptation and Data Bias on Deep Representations

One weakness of machine-learning algorithms is the need to train the models for a new task. This presents a specific challenge for biometric recognition due to the dynamic nature of databases and, in some instances, the reliance on subject collaboration for data collection. In this paper, we investigate the behavior of deep representations in widely used CNN models under extreme data scarcity for One-Shot periocular recognition, a biometric recognition task. We analyze the outputs of CNN layers as identity-representing feature vectors. We examine the impact of Domain Adaptation on the network layers' output for unseen data and evaluate the method's robustness concerning data normalization and generalization of the best-performing layer. We improved state-of-the-art results that made use of networks trained with biometric datasets with millions of images and fine-tuned for the target periocular dataset by utilizing out-of-the-box CNNs trained for the ImageNet Recognition Challenge and standard computer vision algorithms. For example, for the Cross-Eyed dataset, we could reduce the EER by 67% and 79% (from 1.70% and 3.41% to 0.56% and 0.71%) in the Close-World and Open-World protocols, respectively, for the periocular case. We also demonstrate that traditional algorithms like SIFT can outperform CNNs in situations with limited data or scenarios where the network has not been trained with the test classes like the Open-World mode. SIFT alone was able to reduce the EER by 64% and 71.6% (from 1.7% and 3.41% to 0.6% and 0.97%) for Cross-Eyed in the Close-World and Open-World protocols, respectively, and a reduction of 4.6% (from 3.94% to 3.76%) in the PolyU database for the Open-World and single biometric case.Comment: Submitted preprint to IEE Acces

COLLABORATIVE RESEARCH: Interactive Effects of Chronic N Deposition, Acidification, and Phosphorus Limitation on Coupled Element Cycling in Streams

Human activity has doubled the amount of nitrogen on the landscape, creating a pollution problem and changing the balance among multiple nutrients that limit biological activity in ecosystems. At the same time, other disturbances, such as acidification, interact with nitrogen enrichment in ways that strongly influence the productivity and health of terrestrial and aquatic ecosystems. This project examines the interactions among multiple elements and disturbances (nitrogen, phosphorus, metals, and acidification) along a continuum from the atmosphere through soils to streams. This project takes advantage of two unique experiments in which entire watersheds have been experimentally enriched with nitrogen and acid for nearly two decades. A series of new studies in those watersheds examine how chemical and biological changes in soils alter the ability of streams to take up, use, and retain nitrogen and phosphorus. These nutrient interactions are then related to important biological processes that affect the productivity and health of streams.This research addresses an important pollution problem that requires an approach that integrates biology and geochemistry along flow paths that link the terrestrial and aquatic ecosystems. This type of integration is a challenge, but needed for effective environmental management, environmental research, and science teaching. Results from this project and interactions between university and US Forest Service researchers will inform effective management of watersheds faced with multiple pollution problems. A series of collaborative workshops in which high school, undergraduate, and graduate students work with researchers and teachers will promote multidisciplinary learning. The collaboration will seek to develop a computer simulation model for use in teaching integrated biology and chemistry in high school and college science curricula

Cross-Spectral Periocular Recognition with Conditional Adversarial Networks

This work addresses the challenge of comparing periocular images captured in different spectra, which is known to produce significant drops in performance in comparison to operating in the same spectrum. We propose the use of Conditional Generative Adversarial Networks, trained to con-vert periocular images between visible and near-infrared spectra, so that biometric verification is carried out in the same spectrum. The proposed setup allows the use of existing feature methods typically optimized to operate in a single spectrum. Recognition experiments are done using a number of off-the-shelf periocular comparators based both on hand-crafted features and CNN descriptors. Using the Hong Kong Polytechnic University Cross-Spectral Iris Images Database (PolyU) as benchmark dataset, our experiments show that cross-spectral performance is substantially improved if both images are converted to the same spectrum, in comparison to matching features extracted from images in different spectra. In addition to this, we fine-tune a CNN based on the ResNet50 architecture, obtaining a cross-spectral periocular performance of EER=1%, and GAR>99% @ FAR=1%, which is comparable to the state-of-the-art with the PolyU database.Comment: Accepted for publication at 2020 International Joint Conference on Biometrics (IJCB 2020