Using the Earth Mover's Distance for perceptually meaningful visual saliency

Visual saliency is one of the mechanisms that guide our visual attention, or where we look. This topic has seen a lot of research in recent years, starting with biologicallyinspired models, followed by the information-theoretic and recently statistical-based models. This dissertation looks at a state-of-the-art statistical model and studies what effects the histogram construction method and histogram distance measures have on detecting saliency. Equi-width histograms, which have constant bin size, equi-depth histograms, which have constant density per bin, and diagonal histograms, whose bin widths are determined from constant diagonal portions of the empirical cumulative distribution function (ecdf), are used to calculate saliency scores on a publicly available dataset. Crossbin distances are introduced and compared with the currently employed bin-to-bin distances by calculating saliency scores on the same dataset. An exhaustive experiment with combinations of all histogram construction methods and histogram distance measures is performed. It was discovered that using the equi-depth histogram is able to improve various saliency metrics. It is also shown that employing cross-bin histogram distances improves the contrast of the resulting saliency maps, making them more perceptually meaningful but lowering their saliency scores in the process. A novel improvement is made to the model which removes the implicit center bias, which also generates more perceptually meaningful saliency maps but lowers saliency scores. A new scoring method is proposed which aims to deal with the perceptual and scoring disparities

Business to Customer (B2C) E-Marketplace for Small and Medium Enterprise in UUM

E-Marketplaces can provide significant value to buying and selling organizations of all sizes. They facilitate more efficient and effective trade of goods and services, and eliminate inefficiencies inherent in the trading process. The development of business to customer e-Commerce has brought significant changes in recent years in Malaysia. Malaysian businesses, Small and Medium Enterprises (SME) have been relatively slow in web adoption. In UUM there are many Small and Medium Enterprises (SME) working at the mall of university, they needs to develop a trade methods to selling product and selling effectively, awareness of the problem which arises because the understanding of the electronic environment of the interaction of SMEs with customers. Moreover, during the holiday there are no any customers, which that mean cannot maintain the business. On other side, the students find it difficult to provide the daily needs such as fresh foods and deliver without damage. This study is to develop e-Marketplace within the University Utara Malaysia (UUM) and its surroundings, the prototype was develop by using C# language, and the research design adopted the general methodology. The prototype was evaluated by use questionnaire technique based on usability testing with the System Usability Scale (SUS). The prototype was assessed by a sample consists of sixty-three respondents. The results have been positive

Timely Classification of Encrypted or ProtocolObfuscated Internet Traffic Using Statistical Methods

Internet traffic classification aims to identify the type of application or protocol that generated a particular packet or stream of packets on the network. Through traffic classification, Internet Service Providers (ISPs), governments, and network administrators can access basic functions and several solutions, including network management, advanced network monitoring, network auditing, and anomaly detection. Traffic classification is essential as it ensures the Quality of Service (QoS) of the network, as well as allowing efficient resource planning. With the increase of encrypted or obfuscated protocol traffic on the Internet and multilayer data encapsulation, some classical classification methods have lost interest from the scientific community. The limitations of traditional classification methods based on port numbers and payload inspection to classify encrypted or obfuscated Internet traffic have led to significant research efforts focused on Machine Learning (ML) based classification approaches using statistical features from the transport layer. In an attempt to increase classification performance, Machine Learning strategies have gained interest from the scientific community and have shown promise in the future of traffic classification, specially to recognize encrypted traffic. However, ML approach also has its own limitations, as some of these methods have a high computational resource consumption, which limits their application when classifying large traffic or realtime flows. Limitations of ML application have led to the investigation of alternative approaches, including featurebased procedures and statistical methods. In this sense, statistical analysis methods, such as distances and divergences, have been used to classify traffic in large flows and in realtime. The main objective of statistical distance is to differentiate flows and find a pattern in traffic characteristics through statistical properties, which enable classification. Divergences are functional expressions often related to information theory, which measure the degree of discrepancy between any two distributions. This thesis focuses on proposing a new methodological approach to classify encrypted or obfuscated Internet traffic based on statistical methods that enable the evaluation of network traffic classification performance, including the use of computational resources in terms of CPU and memory. A set of traffic classifiers based on KullbackLeibler and JensenShannon divergences, and Euclidean, Hellinger, Bhattacharyya, and Wootters distances were proposed. The following are the four main contributions to the advancement of scientific knowledge reported in this thesis. First, an extensive literature review on the classification of encrypted and obfuscated Internet traffic was conducted. The results suggest that portbased and payloadbased methods are becoming obsolete due to the increasing use of traffic encryption and multilayer data encapsulation. MLbased methods are also becoming limited due to their computational complexity. As an alternative, Support Vector Machine (SVM), which is also an ML method, and the KolmogorovSmirnov and Chisquared tests can be used as reference for statistical classification. In parallel, the possibility of using statistical methods for Internet traffic classification has emerged in the literature, with the potential of good results in classification without the need of large computational resources. The potential statistical methods are Euclidean Distance, Hellinger Distance, Bhattacharyya Distance, Wootters Distance, as well as KullbackLeibler (KL) and JensenShannon divergences. Second, we present a proposal and implementation of a classifier based on SVM for P2P multimedia traffic, comparing the results with KolmogorovSmirnov (KS) and Chisquare tests. The results suggest that SVM classification with Linear kernel leads to a better classification performance than KS and Chisquare tests, depending on the value assigned to the Self C parameter. The SVM method with Linear kernel and suitable values for the Self C parameter may be a good choice to identify encrypted P2P multimedia traffic on the Internet. Third, we present a proposal and implementation of two classifiers based on KL Divergence and Euclidean Distance, which are compared to SVM with Linear kernel, configured with the standard Self C parameter, showing a reduced ability to classify flows based solely on packet sizes compared to KL and Euclidean Distance methods. KL and Euclidean methods were able to classify all tested applications, particularly streaming and P2P, where for almost all cases they efficiently identified them with high accuracy, with reduced consumption of computational resources. Based on the obtained results, it can be concluded that KL and Euclidean Distance methods are an alternative to SVM, as these statistical approaches can operate in realtime and do not require retraining every time a new type of traffic emerges. Fourth, we present a proposal and implementation of a set of classifiers for encrypted Internet traffic, based on JensenShannon Divergence and Hellinger, Bhattacharyya, and Wootters Distances, with their respective results compared to those obtained with methods based on Euclidean Distance, KL, KS, and ChiSquare. Additionally, we present a comparative qualitative analysis of the tested methods based on Kappa values and Receiver Operating Characteristic (ROC) curves. The results suggest average accuracy values above 90% for all statistical methods, classified as ”almost perfect reliability” in terms of Kappa values, with the exception of KS. This result indicates that these methods are viable options to classify encrypted Internet traffic, especially Hellinger Distance, which showed the best Kappa values compared to other classifiers. We conclude that the considered statistical methods can be accurate and costeffective in terms of computational resource consumption to classify network traffic. Our approach was based on the classification of Internet network traffic, focusing on statistical distances and divergences. We have shown that it is possible to classify and obtain good results with statistical methods, balancing classification performance and the use of computational resources in terms of CPU and memory. The validation of the proposal supports the argument of this thesis, which proposes the implementation of statistical methods as a viable alternative to Internet traffic classification compared to methods based on port numbers, payload inspection, and ML.A classificação de tráfego Internet visa identificar o tipo de aplicação ou protocolo que gerou um determinado pacote ou fluxo de pacotes na rede. Através da classificação de tráfego, Fornecedores de Serviços de Internet (ISP), governos e administradores de rede podem ter acesso às funções básicas e várias soluções, incluindo gestão da rede, monitoramento avançado de rede, auditoria de rede e deteção de anomalias. Classificar o tráfego é essencial, pois assegura a Qualidade de Serviço (QoS) da rede, além de permitir planear com eficiência o uso de recursos. Com o aumento de tráfego cifrado ou protocolo ofuscado na Internet e do encapsulamento de dados multicamadas, alguns métodos clássicos da classificação perderam interesse de investigação da comunidade científica. As limitações dos métodos tradicionais da classificação com base no número da porta e na inspeção de carga útil payload para classificar o tráfego de Internet cifrado ou ofuscado levaram a esforços significativos de investigação com foco em abordagens da classificação baseadas em técnicas de Aprendizagem Automática (ML) usando recursos estatísticos da camada de transporte. Na tentativa de aumentar o desempenho da classificação, as estratégias de Aprendizagem Automática ganharam o interesse da comunidade científica e se mostraram promissoras no futuro da classificação de tráfego, principalmente no reconhecimento de tráfego cifrado. No entanto, a abordagem em ML também têm as suas próprias limitações, pois alguns desses métodos possuem um elevado consumo de recursos computacionais, o que limita a sua aplicação para classificação de grandes fluxos de tráfego ou em tempo real. As limitações no âmbito da aplicação de ML levaram à investigação de abordagens alternativas, incluindo procedimentos baseados em características e métodos estatísticos. Neste sentido, os métodos de análise estatística, tais como distâncias e divergências, têm sido utilizados para classificar tráfego em grandes fluxos e em tempo real. A distância estatística possui como objetivo principal diferenciar os fluxos e permite encontrar um padrão nas características de tráfego através de propriedades estatísticas, que possibilitam a classificação. As divergências são expressões funcionais frequentemente relacionadas com a teoria da informação, que mede o grau de discrepância entre duas distribuições quaisquer. Esta tese focase na proposta de uma nova abordagem metodológica para classificação de tráfego cifrado ou ofuscado da Internet com base em métodos estatísticos que possibilite avaliar o desempenho da classificação de tráfego de rede, incluindo a utilização de recursos computacionais, em termos de CPU e memória. Foi proposto um conjunto de classificadores de tráfego baseados nas Divergências de KullbackLeibler e JensenShannon e Distâncias Euclidiana, Hellinger, Bhattacharyya e Wootters. A seguir resumemse os tese. Primeiro, realizámos uma ampla revisão de literatura sobre classificação de tráfego cifrado e ofuscado de Internet. Os resultados sugerem que os métodos baseados em porta e baseados em carga útil estão se tornando obsoletos em função do crescimento da utilização de cifragem de tráfego e encapsulamento de dados multicamada. O tipo de métodos baseados em ML também está se tornando limitado em função da complexidade computacional. Como alternativa, podese utilizar a Máquina de Vetor de Suporte (SVM), que também é um método de ML, e os testes de KolmogorovSmirnov e Quiquadrado como referência de comparação da classificação estatística. Em paralelo, surgiu na literatura a possibilidade de utilização de métodos estatísticos para classificação de tráfego de Internet, com potencial de bons resultados na classificação sem aporte de grandes recursos computacionais. Os métodos estatísticos potenciais são as Distâncias Euclidiana, Hellinger, Bhattacharyya e Wootters, além das Divergências de Kullback–Leibler (KL) e JensenShannon. Segundo, apresentamos uma proposta e implementação de um classificador baseado na Máquina de Vetor de Suporte (SVM) para o tráfego multimédia P2P (PeertoPeer), comparando os resultados com os testes de KolmogorovSmirnov (KS) e Quiquadrado. Os resultados sugerem que a classificação da SVM com kernel Linear conduz a um melhor desempenho da classificação do que os testes KS e Quiquadrado, dependente do valor atribuído ao parâmetro Self C. O método SVM com kernel Linear e com valores adequados para o parâmetro Self C pode ser uma boa escolha para identificar o tráfego Par a Par (P2P) multimédia cifrado na Internet. Terceiro, apresentamos uma proposta e implementação de dois classificadores baseados na Divergência de KullbackLeibler (KL) e na Distância Euclidiana, sendo comparados com a SVM com kernel Linear, configurado para o parâmestro Self C padrão, apresenta reduzida capacidade de classificar fluxos com base apenas nos tamanhos dos pacotes em relação aos métodos KL e Distância Euclidiana. Os métodos KL e Euclidiano foram capazes de classificar todas as aplicações testadas, destacandose streaming e P2P, onde para quase todos os casos foi eficiente identificálas com alta precisão, com reduzido consumo de recursos computacionais.Com base nos resultados obtidos, podese concluir que os métodos KL e Distância Euclidiana são uma alternativa à SVM, porque essas abordagens estatísticas podem operar em tempo real e não precisam de retreinamento cada vez que surge um novo tipo de tráfego. Quarto, apresentamos uma proposta e implementação de um conjunto de classificadores para o tráfego de Internet cifrado, baseados na Divergência de JensenShannon e nas Distâncias de Hellinger, Bhattacharyya e Wootters, sendo os respetivos resultados comparados com os resultados obtidos com os métodos baseados na Distância Euclidiana, KL, KS e Quiquadrado. Além disso, apresentamos uma análise qualitativa comparativa dos métodos testados com base nos valores de Kappa e Curvas Característica de Operação do Receptor (ROC). Os resultados sugerem valores médios de precisão acima de 90% para todos os métodos estatísticos, classificados como “confiabilidade quase perfeita” em valores de Kappa, com exceçãode KS. Esse resultado indica que esses métodos são opções viáveis para a classificação de tráfego cifrado da Internet, em especial a Distância de Hellinger, que apresentou os melhores resultados do valor de Kappa em comparaçãocom os demais classificadores. Concluise que os métodos estatísticos considerados podem ser precisos e económicos em termos de consumo de recursos computacionais para classificar o tráfego da rede. A nossa abordagem baseouse na classificação de tráfego de rede Internet, focando em distâncias e divergências estatísticas. Nós mostramos que é possível classificar e obter bons resultados com métodos estatísticos, equilibrando desempenho de classificação e uso de recursos computacionais em termos de CPU e memória. A validação da proposta sustenta o argumento desta tese, que propõe a implementação de métodos estatísticos como alternativa viável à classificação de tráfego da Internet em relação aos métodos com base no número da porta, na inspeção de carga útil e de ML.Thesis prepared at Instituto de Telecomunicações Delegação da Covilhã and at the Department of Computer Science of the University of Beira Interior, and submitted to the University of Beira Interior for discussion in public session to obtain the Ph.D. Degree in Computer Science and Engineering. This work has been funded by Portuguese FCT/MCTES through national funds and, when applicable, cofunded by EU funds under the project UIDB/50008/2020, and by operation Centro010145FEDER000019 C4 Centro de Competências em Cloud Computing, cofunded by the European Regional Development Fund (ERDF/FEDER) through the Programa Operacional Regional do Centro (Centro 2020). This work has also been funded by CAPES (Brazilian Federal Agency for Support and Evaluation of Graduate Education) within the Ministry of Education of Brazil under a scholarship supported by the International Cooperation Program CAPES/COFECUB Project 9090134/ 2013 at the University of Beira Interior

Domain-and species-specific monoclonal antibodies recognize the Von Willebrand Factor-C domain of CCN5

The CCN family of proteins typically consists of four distinct peptide domains: an insulin-like growth factor binding protein-type (IGFBP) domain, a Von Willebrand Factor C (VWC) domain, a thrombospondin type 1 repeat (TSP1) domain, and a carboxy-terminal (CT) domain. The six family members participate in many processes, including proliferation, motility, cell-matrix signaling, angiogenesis, and wound healing. Accumulating evidence suggests that truncated and alternatively spliced isoforms are responsible for the diverse functions of CCN proteins in both normal and pathophysiologic states. Analysis of the properties and functions of individual CCN domains further corroborates this idea. CCN5 is unique among the CCN family members because it lacks the CT-domain. To dissect the domain functions of CCN5, we are developing domain-specific mouse monoclonal antibodies. Monoclonal antibodies have the advantages of great specificity, reproducibility, and ease of long-term storage and production. In this communication, we injected mixtures of GST-fused rat CCN5 domains into mice to generate monoclonal antibodies. To identify the domains recognized by the antibodies, we constructed serial expression plasmids that express dual-tagged rat CCN5 domains. All of the monoclonal antibodies generated to date recognize the VWC domain, indicating it is the most highly immunogenic of the CCN5 domains. We characterized one particular clone, 22H10, and found that it recognizes mouse and rat CCN5, but not human recombinant CCN5. Purified 22H10 was successfully applied in Western Blot analysis, immunofluorescence of cultured cells and tissues, and immunoprecipitation, indicating that it will be a useful tool for domain analysis and studies of mouse-human tumor models

Organizational Social Media: A Literature Review and Research Agenda

Social media refers to online tools that make it possible for users to create content, publish, share and communicate online. Social media use by and in organizations is a developing research field still in its infancy. The present paper presents a literature review on the subject of Organizational Social Media (OSM), starting and proceeding from van Osch and Coursaris’s literature review extending to 2011. The review contributes to the IS research field by describing how the IS research field defines and categorizes social media, identifying what topics are currently interesting and suggesting future research topics. The findings suggest that to a great extent the IS research field focuses on internal activities e.g. communication and knowledge sharing made possible by social media and that a common definition of social media is lacking

Modeling heat and mass transfer in reacting gas-solid flow using particle-resolved direct numerical simulation

Reacting gas-solid flows occur in nature and many industrial applications. Emerging carbon-neutral and sustainable energy generation technologies such as CO2 capture and biofuel production from fast pyrolysis of biomass are examples of reacting gas-solid flows in industry. Fundamental scientific understanding of reacting gas-solid flows is needed to overcome technological barriers for the successful development of these technologies. Multiphase computational fluid dynamics (CFD) simulations are increasingly being used for scale-up of reactors from laboratory to pilot to full-scale plants, and also for evaluation of different design options. Device-scale CFD simulations of reacting gas-solid flow are based on statistical descriptions that require closure models for interphase exchange of momentum, heat, and species. The predictive capability of multiphase CFD simulations depends on the accuracy of the models for the interphase exchange terms. Therefore, multiphase CFD simulations require accurate physics-based multiphase flow models of heat and mass transfer as well as chemical reaction rates. Particle-resolved direct numerical simulation (PR-DNS) is a first-principles approach to provided transformative insights into multiphase flow physics for model development. PR-DNS of reacting gas-solid flows can provide accurate quantification of gas-solid interactions. The primary objective of this work is to develop improved closure models for CFD simulations in reacting gas-solid flows using the PR-DNS approach. A computational tool called particle-resolved uncontaminated-fluid reconcilable immersed boundary method (PUReIBM) has been developed as a part of this work to perform PR-DNS of heat and mass transfer in reacting gas-solid flows. A pseudo-spectral (PS) version of the PUReIBM simulation of flow past a fixed homogeneous particle assembly and freely evolving suspension of particles with heat transfer has provided PR-DNS data that are used to develop closure models in the Eulerian-Eulerian two-fluid average fluid temperature equation and probability density function transport equation, and validate the assumptions in multiphase flow statistical theories. A fully finite-difference (FFD) version of PUReIBM is also developed to account for wall-bounded flow. The FFD PR-DNS is validated by a suite of test cases and used to perform a detailed comparison with experimental data by using the same setup. In order to extend unclosed models to account for wall effect, wall effect on drag and heat transfer of particle assemblies are studied using FFD PR-DNS. In order to validate the assumption of the isothermal particle in the case of flow past a fixed bed of particles, a preliminary study of the transient heat transfer from a single particle is performed by FFD PR-DNS. A better understanding of the role of heat and mass transfer in reacting gas-solid flow is gained by using FFD PR-DNS to simulate mass transfer in flow past a sphere with a first-order chemical reaction on the particle surface for low and high Reynolds number. These capabilities of the PR-DNS approach provide insight into flow physics and have provided data that has been used to develop improved heat transfer models for gas-solid flow

Theoretical study of the elasticity, mechanical behavior, electronic structure, interatomic bonding, and dielectric function of an intergranular glassy film model in prismatic β-Si3N4

This is the published version. Copyright © 2010 The American Physical SocietyMicrostructures such as intergranular glassy films (IGFs) are ubiquitous in many structural ceramics. They control many of the important physical properties of polycrystalline ceramics and can be influenced during processing to modify the performance of devices that contain them. In recent years, there has been intense research, both experimentally and computationally, on the structure and properties of IGFs. Unlike grain boundaries or dislocations with well-defined crystalline planes, the atomic scale structure of IGFs, their fundamental electronic interactions, and their bonding characteristics are far more complicated and not well known. In this paper, we present the results of theoretical simulations using ab initio methods on an IGF model in β-Si3N4 with prismatic crystalline planes. The 907-atom model has a dimension of 14.533 Å×15.225 Å×47.420 Å. The IGF layer is perpendicular to the z axis, 16.4 Å wide, and contains 72 Si, 32 N, and 124 O atoms. Based on this model, the mechanical and elastic properties, the electronic structure, the interatomic bonding, the localization of defective states, the distribution of electrostatic potential, and the optical dielectric function are evaluated and compared with crystalline β-Si3N4. We have also performed a theoretical tensile experiment on this model by incrementally extending the structure in the direction perpendicular to the IGF plane until the model fully separated. It is shown that fracture occurs at a strain of 9.42% with a maximum stress of 13.9 GPa. The fractured segments show plastic behavior and the formation of surfacial films on the β-Si3N4. These results are very different from those of a previously studied basal plane model [J. Chen et al., Phys. Rev. Lett. 95, 256103 (2005)] and add insights to the structure and behavior of IGFs in polycrystalline ceramics. The implications of these results and the need for further investigations are discussed

Frontiers of antifibrotic therapy in systemic sclerosis

Although fibrosis is becoming increasingly recognized as a major cause of morbidity and mortality in modern societies, targeted anti-fibrotic therapies are still not approved for most fibrotic disorders. However, intense research over the last decade has improved our understanding of the underlying pathogenesis of fibrotic diseases. We now appreciate fibrosis as the consequence of a persistent tissue repair responses, which, in contrast to normal wound healing, fails to be effectively terminated. Profibrotic mediators released from infiltrating leukocytes, activated endothelial cells and degranulated platelets may predominantly drive fibroblast activation and collagen release in early stages, whereas endogenous activation of fibroblasts due epigenetic modifications and biomechanical or physical factors such as stiffening of the extracellular matrix and hypoxia may play pivotal role for disease progression in later stages. In the present review, we discuss novel insights into the pathogenesis of fibrotic diseases using systemic sclerosis (SSc) as example for an idiopathic, multisystem disorder. We set a strong translational focus and predominantly discuss approaches with very high potential for rapid transfer from bench-to-bedside. We highlight the molecular basis for ongoing clinical trials in SSc and also provide an outlook on upcoming trials. This article is protected by copyright. All rights reserved