Evolutionary Clustering in Indonesian Ethnic Textile Motifs \ud

The wide varieties of Indonesian textiles could reflect the varsity that has been living with the diversity of Indonesian ethnic groups. Meme as an evolutionary modeling technique promises some conjectures to capture the innovative process of the cultural objects production in the particular collective patterns acquainted can be regarded as fitness in the large evolutionary landscape of cultural life. We have presented the correlations between memeplexes that is transformed into distances has generated the phylomemetic tree, both among some samples from Indonesian textile handicrafts and batik, the designs that have been living through generations with Javanese people, the largest ethnic group in Indonesian archipelago. The memeplexes is extracted from the geometrical shape, i.e.: fractal dimensions and the histogram analysis of the employed colorization. We draw some interesting findings from the tree and open the future anthropological development that might catch the attention further observation

Analysis of Swine Movements in a Province in Northern Vietnam and Application in the Design of Surveillance Strategies for Infectious Diseases

While swine production is rapidly growing in South-East Asia, the structure of the swine industry and the dynamic of pig movements have not been well-studied. However, this knowledge is a prerequisite for understanding the dynamic of disease transmission in swine populations and designing cost-effective surveillance strategies for infectious diseases. In this study, we assessed the farming and trading practices in the Vietnamese swine familial farming sector, which accounts for most pigs in Vietnam, and for which disease surveillance is a major challenge. Farmers from two communes of a Red River Delta Province (northern Vietnam) were interviewed, along with traders involved in pig transactions. Major differences in the trade structure were observed between the two communes. One commune had mainly transversal trades, that is between farms of equivalent sizes, whereas the other had pyramidal trades, that is from larger to smaller farms. Companies and large familial farrow-to-finish farms were likely to act as major sources of disease spread through pig sales, demonstrating their importance for disease control. Familial fattening farms with high pig purchases were at greater risk of disease introduction and should be targeted for disease detection as part of a risk-based surveillance. In contrast, many other familial farms were isolated or weakly connected to the swine trade network limiting their relevance for surveillance activities. However, some of these farms used boar hiring for breeding, increasing the risk of disease spread. Most familial farms were slaughtering pigs at the farm or in small local slaughterhouses, making the surveillance at the slaughterhouse inefficient. In terms of spatial distribution of the trades, the results suggested that northern provinces were highly connected and showed some connection with central and southern provinces. These results are useful to develop risk-based surveillance protocols for disease detection in the swine familial sector and to make recommendations for disease control. (Résumé d'auteur

Understanding Visualization: A formal approach using category theory and semiotics

This article combines the vocabulary of semiotics and category theory to provide a formal analysis of visualization. It shows how familiar processes of visualization fit the semiotic frameworks of both Saussure and Peirce, and extends these structures using the tools of category theory to provide a general framework for understanding visualization in practice, including: relationships between systems, data collected from those systems, renderings of those data in the form of representations, the reading of those representations to create visualizations, and the use of those visualizations to create knowledge and understanding of the system under inspection. The resulting framework is validated by demonstrating how familiar information visualization concepts (such as literalness, sensitivity, redundancy, ambiguity, generalizability, and chart junk) arise naturally from it and can be defined formally and precisely. This article generalizes previous work on the formal characterization of visualization by, inter alia, Ziemkiewicz and Kosara and allows us to formally distinguish properties of the visualization process that previous work does not

Obfuscation-resilient Android Malware Analysis Based on Contrastive Learning

Due to its open-source nature, Android operating system has been the main target of attackers to exploit. Malware creators always perform different code obfuscations on their apps to hide malicious activities. Features extracted from these obfuscated samples through program analysis contain many useless and disguised features, which leads to many false negatives. To address the issue, in this paper, we demonstrate that obfuscation-resilient malware analysis can be achieved through contrastive learning. We take the Android malware classification as an example to demonstrate our analysis. The key insight behind our analysis is that contrastive learning can be used to reduce the difference introduced by obfuscation while amplifying the difference between malware and benign apps (or other types of malware). Based on the proposed analysis, we design a system that can achieve robust and interpretable classification of Android malware. To achieve robust classification, we perform contrastive learning on malware samples to learn an encoder that can automatically extract robust features from malware samples. To achieve interpretable classification, we transform the function call graph of a sample into an image by centrality analysis. Then the corresponding heatmaps are obtained by visualization techniques. These heatmaps can help users understand why the malware is classified as this family. We implement IFDroid and perform extensive evaluations on two widely used datasets. Experimental results show that IFDroid is superior to state-of-the-art Android malware familial classification systems. Moreover, IFDroid is capable of maintaining 98.2% true positive rate on classifying 8,112 obfuscated malware samples

Make Research Data Public? -- Not Always so Simple: A Dialogue for Statisticians and Science Editors

Putting data into the public domain is not the same thing as making those data accessible for intelligent analysis. A distinguished group of editors and experts who were already engaged in one way or another with the issues inherent in making research data public came together with statisticians to initiate a dialogue about policies and practicalities of requiring published research to be accompanied by publication of the research data. This dialogue carried beyond the broad issues of the advisability, the intellectual integrity, the scientific exigencies to the relevance of these issues to statistics as a discipline and the relevance of statistics, from inference to modeling to data exploration, to science and social science policies on these issues.Comment: Published in at http://dx.doi.org/10.1214/10-STS320 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Emerging Religious Marketplace in Nigeria: A Quest for Interpretation

In contemporary Nigerian society, the evolving trends in Christian religious culture suggest that neoliberal (social) mind-set is influencing certain practices in many Churches. The objective of this paper is to examine how the above-mentioned contemporary culture influences current religious landscape. The sociological concept of commodification was adopted as a way of ‘reading’ this religious context. The research methodology combines theoretical and ethnographic approaches to this study. The research findings show that neoliberal mind-set is influencing how religious commodification shapes the characteristics of Nigerian Christian marketplace

An Emergentist Account of Collective Cognition in Collaborative Problem Solving

As a first step toward an emergentist theory of collective cognition in collaborative problem solving, we present a proto-theoretical account of how one might conceive and model the intersubjective processes that organize collective cognition into one or another--convergent, divergent, or tensive--cognitive regime. To explore the sufficiency of our emergentist proposal we instantiate a minimalist model of intersubjective convergence and simulate the tuning of collective cognition using data from an empirical study of small-group, collaborative problem solving. Using the results of this empirical simulation, we test a number of preliminary hypotheses with regard to patterns of interaction, how those patterns affect a cognitive regime, and how that cognitive regime affects the efficacy of a problem-solving group