abstract: Relate North 2014: Engagement, Art and Representation Jokela, T., & Coutts, G. (Eds.). (2014). Relate North 2014: Engagement, Art and Representation. Rovaniemi, Finland: Lapland University Press. 171 pp. ISBN: 978-952-310-989-6.Sustainability dominates the discourse on the future of the planet Earth, particularly its polar areas. During the last decades, the University of Lapland in Finland has taken an active role in organizing global collaboration in art and art education among countries that meet similar challenges due to their circumpolar location and harsh living conditions. The book at hand is the first international, peerreviewed collection of articles published by the Arctic Sustainable Arts and Design (ASAD) network, which consists of 26 universities from eight countries, mostly in northern Europe. The collection features artists, teachers, and researchers from Canada, Finland, Iceland, Norway, Russia, and Scotland, each exploring different aspects of contemporary art, design, and education from the perspective of their specific geographic locations.The preface of the publication emphasizes the unique opportunities for artistic and educational experimentation created by Arctic environments. Drawing from postcolonial theoretical thinking, the editors argue that the flow of innovation and creativity can emanate not only from cultural centers but also from sparsely populated peripheral areas. The issues discussed by the authors show similar patterns in the Arctic to those which characterize the colonial and neo-colonial histories of North America: the extraction of natural resources and the loss of land, the plight of indigenous societies, the mixing of people and cultures, border schisms, and tourism. Art, the authors demonstrate, plays an essential role in the representations of northern heritage and identity through stimulating debate about these issues, creating places for dialogue, and chronicling the histories and lived experiences of the people in the Arctic areas.The challenges and prospects of sustainable development constitute the main theme running through the articles, some focusing on the artists' work among local communities in the Arctic, others reporting on educational projects and case studies that reach out from traditional learning environments in order to enable students to collaborate and form partnerships across national, professional, and disciplinary borders. As climate change is rapidly transforming the Northern Sea Route into a more trafficable one, international competition for economic gains and political influence in the area has escalated with unpredictable consequences. Thus, many of the authors perceive sustainability in a broad sense, taking into consideration its exceedingly volatile nature. In their articles, the sociocultural dimensions of agency, self-definition, and knowledge production become negotiated along with the issues of the power struggles related to ownership, ecology, and economy.The collection opens with Ruth Beer'sdescription of a historically informed and site-determined art project, Trading Routes: Grease Trails, Oil Futures (2013), which examines the complex role of the global petroleum trade in Canada against the background of national identity formation through art, particularly landscape painting. In an array of art exhibits and community projects, the artists in charge studied the imposing presence of the oil industries' infrastructure and its effects on the everyday life of local people, indigenous and others alike. The article seeks to answer the question of whether the imperial stereotyping of the North as terra nullius,an empty land, can be fractured by the strangeness of everyday life made visible by art practices that reclaim the marginalized histories and voices along the route of the pipeline.In an eloquent and well-informed essay about the theoretical and philosophical underpinnings of her quest, the Scottish artist Gina Wall relates her brief but utterly unsettling encounter with the "terrible beauty" of the Icelandic landscape. …


title: Open data for competitive advantage: insights from open data use by companies

abstract: Politicians have high expectations for commercial open data use. Yet, companies appear to challenge the assumption that open data can be used to create competitive advantage, since any company can access open data and since open data use requires scarce resources. In this paper we examine commercial open data use for creating competitive advantage from the perspective of Resource Based Theory (RBT) and Resource Dependency Theory (RDT). Based on insights from a scenario, interviews and a survey and from RBT and RDT as a reference theory, we derive seven propositions. Our study suggests that the generation of competitive advantage with open data requires a company to have in-house capabilities and resources for open data use. The actual creation of competitive advantage might not be simple. The propositions also draw attention to the accomplishment of unique benefits for a company through the combination of internal and external resources. Recommendations for further research include testing the propositions.


title: Chemotypical variation in Vanilla planifolia Jack. (Orchidaceae) from the Puebla-Veracruz Totonacapan region

abstract: One of the threats in the diversity loss of the primary gene pool of Vanilla planifolia is the lack of information on existing level of polymorphism in cultivated germplasm, and the different expressions of this polymorphism. For this reason, it is proposed to study the chemical polymorphism of the four phytochemicals that define the vanilla aroma quality in fruits (vanillin, vanillic acid, p-hydroxybenzaldehyde, p-hydroxybenzoic acid) by HPLC analysis (High Performance Liquid Chromatography) of 25 collections of unknown genotype, grown in the region Totonacapan Puebla-Veracruz, Mexico. The results identified a selection process, domestication in fruit aroma of vanilla, during which increased the participation of vanillin and reduced the presence of three minor compounds (vanillic acid, p-hydroxybenzaldehyde and p-hydroxybenzoic acid) in the global aroma. We distinguished a total of six chemotypes of V. planifolia in the Totonacapan region, some chemotypes with wild aromatic characteristics (low participation of vanillin) related to the material less cultivated in the region and domesticated chemotypes with high participation of vanillin, for the most cultivated material. The results show that the diversification of the chemotypes of V. planifolia is not related to environmental variation. The data indicate that in the possible center of origin of vanilla, there is phytochemical polymorphism, which indirectly suggests the existence of genetic polymorphism, essential for the design of a breeding program for optimizing the use and conservation of diversity of the primary gene pool of Vanilla planifolia.


title: Die Attach Materials for High Temperature Applications: A Review

abstract: The need for high power density and high temperature capabilities in today's electronic devices continues to grow. More robust devices with reliable and stable functioning capabilities are needed, for example in aerospace and automotive industries as well as sensor technology. These devices need to perform under extreme temperature conditions, and not show any deterioration in terms of switching speeds, junction temperatures, and power density, and so on. While the bulk of research is performed to source and manufacture these high temperature devices, the device interconnect technology remains under high focus for packaging. The die attach material has to withstand high temperatures generated during device functioning and also cope with external conditions which will directly determine how well the device performs in the field. This literature work seeks to review the numerous research attempts thus far for high temperature die attach materials on wide band gap materials of silicon carbide, gallium nitride and diamond, document their successes, concerns and application possibilities, all of which are essential for high temperature reliability.


Polish Mathematicians Finding Patterns in Enigma Messages


How reuse influences productivity in object-oriented systems


title: Bayesian face revisited: A joint formulation

abstract: In this paper, we revisit the classical Bayesian face recognition method by Baback Moghaddam et al. and propose a new joint formulation. The classical Bayesian method models the appearance difference between two faces. We observe that this "difference" formulation may reduce the separability between classes. Instead, we model two faces jointly with an appropriate prior on the face representation. Our joint formulation leads to an EM-like model learning at the training time and an efficient, closed-formed computation at the test time. On extensive experimental evaluations, our method is superior to the classical Bayesian face and many other supervised approaches. Our method achieved 92.4% test accuracy on the challenging Labeled Face in Wild (LFW) dataset. Comparing with current best commercial system, we reduced the error rate by 10%.


title: AutoBash: improving configuration management with operating system causality analysis

abstract: AutoBash is a set of interactive tools that helps users and system administrators manage configurations. AutoBash leverages causal tracking support implemented within our modified Linux kernel to understand the inputs (causal dependencies) and outputs (causal effects) of configuration actions. It uses OS-level speculative execution to try possible actions, examine their effects, and roll them back when necessary. AutoBash automates many of the tedious parts of trying to fix a misconfiguration, including searching through possible solutions, testing whether a particular solution fixes a problem, and undoing changes to persistent and transient state when a solution fails. Our results show that AutoBash correctly identifies the solution to several CVS, gcc cross-compiler, and Apache configuration errors. We also show that causal analysis reduces AutoBash's search time by an average of 35% and solution verification time by an average of 70%.


title: Improving Statistical MT Through Morphological Analysis

abstract: In statistical machine translation, estimating word-to-word alignment probabilities for the translation model can be difficult due to the problem of sparse data: most words in a given corpus occur at most a handful of times. With a highly inflected language such as Czech, this problem can be particularly severe. In addition, much of the morphological variation seen in Czech words is not reflected in either the morphology or syntax of a language like English. In this work, we show that using morphological analysis to modify the Czech input can improve a Czech-English machine translation system. We investigate several different methods of incorporating morphological information, and show that a system that combines these methods yields the best results. Our final system achieves a BLEU score of .333, as compared to .270 for the baseline word-to-word system.


title: Advances in Information Science The Effect of Social Network Sites on Adolescents’ Social and Academic Development: Current Theories and Controversies

abstract: Teenagers are among the most prolific users of social network sites (SNS). Emerging studies find that youth spend a considerable portion of their daily life interacting through social media. Subsequently, questions and controversies emerge about the effects SNS have on adolescent development. This review outlines the theoretical frameworks researchers have used to understand adolescents and SNS. It brings together work from disparate fields that examine the relationship between SNS and social capital, privacy, youth safety, psychological well-being, and educational achievement. These research strands speak to high-profile concerns and controversies that surround youth participation in these online communities, and offer ripe areas for future research. © 2011 Wiley Periodicals, Inc.


title: Quantifying the Impact of Edge Computing on Mobile Applications

abstract: Computational offloading services at the edge of the Internet for mobile devices are becoming a reality. Using a wide range of mobile applications, we explore how such infrastructure improves latency and energy consumption relative to the cloud. We present experimental results from WiFi and 4G LTE networks that confirm substantial wins from edge computing for highly interactive mobile applications.


title: Real-Time Cloud Simulation Using Lennard-Jones Approximation

abstract: Cloud simulation is important for creating images of outdoor scenes. However, the complexity of this natural phenomenon makes the simulation of large-scale clouds difficult in real time. In this paper, we present a new method for 3D cloud simulation in which cloud animation is simplified and simulated by approximating Lennard-Jones Potential. To solve the N-body problem in Lennard-Jones Potential, we minimized the interaction between particles by dividing the simulation space into many cells and we defined a cutoff distance to perform calculation between neighboring particles. Additionally, a separate distance is introduced between particles to maintain the stability in the Lennard-Jones system. Our experimental results demonstrate that our method is computationally inexpensive and suitable for real time applications where large-scale simulation of clouds is required.


A Large-Scale , Open , Multilingual NLP Training


title: Deep Fisher Kernels -- End to End Learning of the Fisher Kernel GMM Parameters

abstract: Fisher Kernels and Deep Learning were two developments with significant impact on large-scale object categorization in the last years. Both approaches were shown to achieve state-of-the-art results on large-scale object categorization datasets, such as ImageNet. Conceptually, however, they are perceived as very different and it is not uncommon for heated debates to spring up when advocates of both paradigms meet at conferences or workshops. In this work, we emphasize the similarities between both architectures rather than their differences and we argue that such a unified view allows us to transfer ideas from one domain to the other. As a concrete example we introduce a method for learning a support vector machine classifier with Fisher kernel at the same time as a task-specific data representation. We reinterpret the setting as a multi-layer feed forward network. Its final layer is the classifier, parameterized by a weight vector, and the two previous layers compute Fisher vectors, parameterized by the coefficients of a Gaussian mixture model. We introduce a gradient descent based learning algorithm that, in contrast to other feature learning techniques, is not just derived from intuition or biological analogy, but has a theoretical justification in the framework of statistical learning theory. Our experiments show that the new training procedure leads to significant improvements in classification accuracy while preserving the modularity and geometric interpretability of a support vector machine setup.


title: Software-Defined LANs for Interconnected Smart Environment

abstract: In this paper, we propose a solution to delegate the control and the management of the network connecting the many devices of a smart environment to a software entity, while keeping end-users in control of what is happening in their networks. For this, we rely on the logical manipulation of all connected devices through device abstraction and network programmability. Applying Software Defined Networking (SDN) principles, we propose a software-based solution that we call Software-Defined LANs in order to interconnect devices of smart environments according to the services the users are requesting or expecting.We define the adequate virtualization framework based on Virtual Objects and Communities of Virtual Objects. Using these virtual entities, we apply the SDN architectural principles to define a generic architecture that can be applied to any smart environment. Then we describe a prototype implementing these concepts in the home networking context, through a scenario in which users of two different homes can easily interconnect two private but shareable DLNA devices in a dedicated videodelivery SD-LAN. Finally we provide a discussion of the benefits and challenges of our approach regarding the generalization of SDN principles, autonomic features, Internet of Things scalability, security and privacy aspects enabled by SD-LANs intrinsic properties.


Personality as a predictor of Business Social Media Usage: an Empirical Investigation of Xing Usage Patterns


title: Adoption factors associated with electronic health record among long-term care facilities: a systematic review

abstract: OBJECTIVES ::: The Health Information Technology for Economic and Clinical Health (HITECH) Act created incentives for adopting electronic health records (EHRs) for some healthcare organisations, but long-term care (LTC) facilities are excluded from those incentives. There are realisable benefits of EHR adoption in LTC facilities; however, there is limited research about this topic. The purpose of this systematic literature review is to identify EHR adoption factors for LTC facilities that are ineligible for the HITECH Act incentives. ::: ::: ::: SETTING ::: We conducted systematic searches of Cumulative Index of Nursing and Allied Health Literature (CINAHL) Complete via Ebson B. Stephens Company (EBSCO Host), Google Scholar and the university library search engine to collect data about EHR adoption factors in LTC facilities since 2009. ::: ::: ::: PARTICIPANTS ::: Search results were filtered by date range, full text, English language and academic journals (n=22). ::: ::: ::: INTERVENTIONS ::: Multiple members of the research team read each article to confirm applicability and study conclusions. ::: ::: ::: PRIMARY AND SECONDARY OUTCOME MEASURES ::: Researchers identified common themes across the literature: specifically facilitators and barriers to adoption of the EHR in LTC. ::: ::: ::: RESULTS ::: Results identify facilitators and barriers associated with EHR adoption in LTC facilities. The most common facilitators include access to information and error reduction. The most prevalent barriers include initial costs, user perceptions and implementation problems. ::: ::: ::: CONCLUSIONS ::: Similarities span the system selection phases and implementation process; of those, cost was the most common mentioned. These commonalities should help leaders in LTC facilities align strategic decisions to EHR adoption. This review may be useful for decision-makers attempting successful EHR adoption, policymakers trying to increase adoption rates without expanding incentives and vendors that produce EHRs.


title: Traffic Sign Recognition based on multi-block LBP features using SVM with normalization

abstract: The automatic traffic sign detection and recognition has been converted to a real challenge for high performance of computer vision and machine learning techniques. It is an important issue, in particular for vehicle safety applications. It is usually tackled in three stages: detection, feature extraction and classification. We focus in this work on the second stage of the process, namely traffic sign feature extraction with applying the Block-Based Local Binary Pattern (LBP) method. This paper investigates the use of two normalization techniques: min-max and z-score. These techniques are incorporated in the SVM prediction model. For comparison purposes we also implemented the block-Based LBP with the K-Nearest Neighbor (K-NN) classifier. The evaluation of the proposed approach on the German Traffic Sign Recognition Benchmark Dataset (GTSRD) proved that the normalization of SVM input space can significantly influence the higher accuracy performance of the classification procedure. Extensive and comparative experiments have been conducted to evaluate our proposed method.


title: Knowledge Representation with Ontology Tools & Methodology

abstract: As a backbone of the Semantic Web, Ontologies provide a shared understanding of a domain of text. Ontologies, with their appearance, usage, and classification address for concrete ontology language which is important for the Semantic Web. They can be used to support a great variety of tasks in different domains such as knowledge representation, natural language processing, information retrieval, information exchange, collaborative systems, databases, knowledge management, database integration, digital libraries, information retrieval, or multi agent systems. Thus a fast and efficient ontology development is a requirement for the success of many knowledge based systems and for the Semantic Web itself. This paper provides discussion on existing ontology tools and methodologies and the state of the art of the field.


title: Polarities, quasi-symmetric designs, and Hamada’s conjecture

abstract: We prove that every polarity of PG(2k − 1,q), where k≥ 2, gives rise to a design with the same parameters and the same intersection numbers as, but not isomorphic to, PG k (2k,q). In particular, the case k = 2 yields a new family of quasi-symmetric designs. We also show that our construction provides an infinite family of counterexamples to Hamada’s conjecture, for any field of prime order p. Previously, only a handful of counterexamples were known.


title: DDoS Attack Protection in the Era of Cloud Computing and Software-Defined Networking

abstract: Cloud computing has become the real trend of enterprise IT service model that offers cost-effective and scalable processing. Meanwhile, Software-Defined Networking (SDN) is gaining popularity in enterprise networks for flexibility in network management service and reduced operational cost. There seems a trend for the two technologies to go hand-in-hand in providing an enterprise's IT services. However, the new challenges brought by the marriage of cloud computing and SDN, particularly the implications on enterprise network security, have not been well understood. This paper sets to address this important problem. We start by examining the security impact, in particular, the impact on DDoS attack defense mechanisms, in an enterprise network where both technologies are adopted. We find that SDN technology can actually help enterprises to defend against DDoS attacks if the defense architecture is designed properly. To that end, we propose a DDoS attack mitigation architecture that integrates a highly programmable network monitoring to enable attack detection and a flexible control structure to allow fast and specific attack reaction. The simulation results show that our architecture can effectively and efficiently address the security challenges brought by the new network paradigm.


title: A generic approach to the theory of superregenerative reception

abstract: The superregenerative receiver has been widely used for many decades in short-range wireless communications because of its relative simplicity, reduced cost, and low power consumption. However, the theory that describes the behavior of this type of receiver, which was mainly developed prior to 1950, is of limited scope, since it applies to particular implementations, usually operating under continuous-wave signal or narrow-band modulation. As a novelty, we present the theory of superregenerative reception from a generic point of view. We develop an analytic study based on a generic block diagram of the receiver and consider not only narrow-band, but a wider variety of input signals. The study allows general results and conclusions that can be easily particularized to specific implementations to be obtained. Starting from the proposed model, the differential equation that describes the operation of the receiver in the linear mode is deducted and solved. Normalized parameters and functions characterizing the performance of the receiver are presented, as well as the requirements for proper operation. Several characteristic phenomena, such as hangover and multiple resonance, are described. The nonlinear behavior of the active device is also modeled to obtain a solution of the differential equation in the logarithmic mode of operation. The study is completed with a practical example operating at 2.4 GHz and illustrating the typical performance of a superregenerative receiver.


title: Contrast-limited adaptive histogram equalization: speed and effectiveness

abstract: An experiment intended to evaluate the clinical application of contrast-limited adaptive histogram equalization (CLAHE) to chest computer tomography (CT) images is reported. A machine especially designed to compute CLAHE in a few seconds is discussed. It is shown that CLAHE can be computed in 4 s after 5-s loading time using the specially designed parallel engine made from a few thousand dollars worth of off-the-shelf components. The processing appears to be useful for a wide range of medical images, but the limitations of observer calibration make it impossible to demonstrate such usefulness by agreement experiments. >


title: Automated LGE Myocardial Scar Segmentation Using MaskSLIC Supervoxels - Replicating the Clinical Method

abstract: Cardiovascular diseases (CVD) are one of the major killers in modern society. In the UK alone 70,000 people have died from CVD last year according to the 2016 British Heart Foundation Annual Report [2]. Furthermore, the number of patients suffering from chronic CVDs is likely to rise due to an ageing population, as well as, better survival rates after cardiac events. In the case of a heart attack, accurate quantification of the formed scar is essential for improving and deciding the treatment plan, and therefore improving the patient outcomes. In this work we present an automated method for segmenting the scar from late gadolinium enhancement magnetic resonance images using maskSLIC clustering method and Otsu thresholding to divide the myocardium into regions based on differences in intensity. Our method is fast and simple to use, and is consistent across cases and eliminates the spatial inconsistencies previously reported in the literature. The validation is performed using visual assessment from \(cmr^{42}\) clinical software.


title: Robustness of the routing protocol for low-power and lossy networks (RPL) in smart grid's neighbor-area networks

abstract: Neighbor-area network (NAN), also known as smart meter communication network, is one of the most important constitutive segments of smart grid communication network. Since almost all smart meters are deployed in hash outdoor environment, they could fail or wireless links between them could be fluctuating over time. These dynamics could hinder the network connectivity and degrade the reliability of data communications. However, the robustness of NANs in the case of network element failures has not received sufficient attention in existing work. This paper therefore proposes a cross-layer scheme that adaptively switches preferred parent nodes in order to help the routing protocol for low-power and lossy networks (RPL), the state-of-the-art implementation of self-organizing routing class, quickly deflect network traffic from points of failures in the NAN scenario. Operation and performance of the proposed scheme in IEEE 802.11-based wireless mesh NANs are studied by simulations.


title: Broadcasting information via display names in instant messaging

abstract: Many instant messenger (IM) clients let a person specify the identifying name that appears in another person's contact list. We have noticed that many people add extra information to this name as a way to broadcast information to their contacts. Twelve IM contact lists comprising 444 individuals were monitored over three weeks to observe how these individuals used and altered their display names. Almost half of them changed their display names at varying frequencies, where the new information fell into seventeen different categories of communication supplied to others. Three themes encompass these categories: Identification ("who am I"?), Information About Self ("this is what is going on with me") and Broadcast Message ("I am directing information to the community"). The design implication is that systems supporting person to person casual interaction, such as IM, should explicitly include facilities that allow people to broadcast these types of information to their community of contacts.


title: A Post-failure Analysis of Mobile Payment Platforms

abstract: Despite on-going efforts over the past decade, mobile payments are yet to take off successfully. Repeated failures show that mobile payment platforms are complex to launch. The objective of this paper is to unveil factors explaining the failure of past mobile payment platforms. The use of a multi-level framework aims at enriching the variety of issues investigated. In order to explore the reasons of failure, we selected four cases from different countries happening at different times. Our results show that these cases share many of the same failure factors: lack of collaboration between the stakeholders, no technology standard, and low value-added for consumers and merchants compared to existing payment solutions. Even though the list is not exhaustive, the uncovered factors in our study seem to be necessary (but not sufficient) for the further developments of mobile payment platforms.


title: FALKON: An Optimal Large Scale Kernel Method

abstract: Kernel methods provide a principled way to perform non linear, nonparametric learning. They rely on solid functional analytic foundations and enjoy optimal statistical properties. However, at least in their basic form, they have limited applicability in large scale scenarios because of stringent computational requirements in terms of time and especially memory. In this paper, we take a substantial step in scaling up kernel methods, proposing FALKON, a novel algorithm that allows to efficiently process millions of points. FALKON is derived combining several algorithmic principles, namely stochastic subsampling, iterative solvers and preconditioning. Our theoretical analysis shows that optimal statistical accuracy is achieved requiring essentially $O(n)$ memory and $O(n\sqrt{n})$ time. An extensive experimental analysis on large scale datasets shows that, even with a single machine, FALKON outperforms previous state of the art solutions, which exploit parallel/distributed architectures.


title: RSSI-based indoor positioning using diversity and Inertial Navigation

abstract: A substantial criterion with the use of wireless communication is the missing location information of the mobile participants. The RSSI (Received Signal Strength Indicator)-based localization technique is an easy and well known method to predict the position of an unknown node in indoor environments whereas additional measures are required for a sufficient accuracy. The distance-pending path loss is affected by strong variations, especially appearing as frequency specific signal dropouts. A diversity concept with redundant data transmission in different frequency bands can reduce the dropout probability. Not only the availability of the communication and the positioning, but also the accuracy of the localization can be increased by the diversity concept. Another improvement can be reached by a sensor fusion of the RSSI-based position data with an Inertial Navigation System. First experimental results with miniaturized transceiver prototypes show that a good performance for precision and availability can also be reached with low infrastructural costs.


title: Fracture detection in x-ray images through stacked random forests feature fusion

abstract: Bone fractures are among the most common traumas in musculoskeletal injuries. They are also frequently missed during the radiological examination. Thus, there is a need for assistive technologies for radiologists in this field. Previous automatic bone fracture detection work has focused on detection of specific fracture types in a single anatomical region. In this paper, we present a generalized bone fracture detection method that is applicable to multiple bone fracture types and multiple bone structures throughout the body. The method uses features extracted from candidate patches in X-ray images in a novel discriminative learning framework called the Stacked Random Forests Feature Fusion. This is a multilayer learning formulation in which the class probability labels, produced by random forests learners at a lower level, are used to derive the refined class distribution labels at the next level. The candidate patches themselves are selected using an efficient subwindow search algorithm. The outcome of the method is a number of fracture bounding-boxes ranked from the most likely to the least likely to contain a fracture. We evaluate the proposed method on a set of 145 X-rays images. When the top ranking seven fracture bounding-boxes are considered, we are able to capture 81.2% of the fracture findings reported by a radiologist. The proposed method outperforms other fracture detection frameworks that use local features, and single layer random forests and support vector machine classification.


title: Separating Fact from Fear: Tracking Flu Infections on Twitter

abstract: Twitter has been shown to be a fast and reliable method for disease surveillance of common illnesses like influenza. However, previous work has relied on simple content analysis, which conflates flu tweets that report infection with those that express concerned awareness of the flu. By discriminating these categories, as well as tweets about the authors versus about others, we demonstrate significant improvements on influenza surveillance using Twitter.


title: The use of Twitter hashtags in the formation of ad hoc publics

abstract: As the use of Twitter has become more commonplace throughout many nations, its role in political discussion has also increased. This has been evident in contexts ranging from general political discussion through local, state, and national elections (such as in the 2010 Australian elections) to protests and other activist mobilisation (for example in the current uprisings in Tunisia, Egypt, and Yemen, as well as in the controversy around Wikileaks). ::: ::: Research into the use of Twitter in such political contexts has also developed rapidly, aided by substantial advancements in quantitative and qualitative methodologies for capturing, processing, analysing, and visualising Twitter updates by large groups of users. Recent work has especially highlighted the role of the Twitter hashtag – a short keyword, prefixed with the hash symbol ‘#’ – as a means of coordinating a distributed discussion between more or less large groups of users, who do not need to be connected through existing ‘follower’ networks. ::: ::: Twitter hashtags – such as ‘#ausvotes’ for the 2010 Australian elections, ‘#londonriots’ for the coordination of information and political debates around the recent unrest in London, or ‘#wikileaks’ for the controversy around Wikileaks thus aid the formation of ad hoc publics around specific themes and topics. They emerge from within the Twitter community – sometimes as a result of pre-planning or quickly reached consensus, sometimes through protracted debate about what the appropriate hashtag for an event or topic should be (which may also lead to the formation of competing publics using different hashtags). ::: ::: Drawing on innovative methodologies for the study of Twitter content, this paper examines the use of hashtags in political debate in the context of a number of major case studies.


title: Defending Against Voice Spoofing: A Robust Software-Based Liveness Detection System

abstract: The recent proliferation of smartphones has been the primary driving factor behind the booming of voice-based mobile applications. However, the human voice is often exposed to the public in many different scenarios, and an adversary can easily "steal" a person's voice and attack voice-based applications with the help of state-of-the-art voice synthesis/conversion softwares. In this paper, we propose a robust software-based voice liveness detection system for defending against voice spoofing attack. The proposed system is tailored for mobile platforms and can be easily integrated with existing mobile applications. We propose three approaches based on leveraging the vibration of human vocal cords, the motion of the human vocal system, and the functionality of vibration motor inside the smartphone. Experimental results show that our system can detect a live speaker with a mean accuracy of 94.38% and detect an attacker with a mean accuracy of 88.89% by combining three approaches we proposed.


title: Unifying the role of IT in hyperturbulence and competitive advantage via a multilevel perspective of IS strategy

abstract: While information technology (IT) serves as a new source of sustainable competitive advantage for firms, it also induces hyperturbulent environments (or hyperturbulence) that erode that sustainable competitive advantage. In this paper, we posit that these contradictions might be due to cross-level nonlinear causality between firm-level IT-based strategic actions and collective-level IT-induced hyperturbulence. We develop a multilevel perspective of IS strategy for theorizing this causality, and unifying novel with established research. Complex adaptive systems theory is employed as the overarching framework for its strength in formalizing cross-level nonlinear causal paths. Using literature-based theorization and agent-based modeling, we establish two bottom-up nonlinear causal paths by which IT drives hyperturbulence: IT can act as an external force (i.e., component IT innovation) to locally instigate firm strategic actions that aggregate to temporary hyper-turbulence or as an internal force (i.e., architectural IT innovation) to drive pervasive firm strategic interactions that aggregate to persistent hyperturbulence. Each causal path produces varied amounts of reducible and irreducible uncertainties and thereby renders a top-down nonlinear effect that reshapes the opportunity for IT to contribute to competitive advantage. This multilevel theorization paves the way for new, IS-specific theory regarding IT's unique role in inducing nonlinear dynamics and in affording new business strategies in today's competitive environments.


title: Mixed infection caused byLecythophora caninasp. nov. andPlectosphaerella cucumerinain a German shepherd dog

abstract: AbstractWe describe an opportunistic, disseminated infection in a German shepherd dog associated with two fungal organisms not previously reported to cause disease. Lecythophora canina, a new species here described, was isolated from an osteolytic bone lesion. A fine needle aspirate of the lesion demonstrated septate hyphae. Plectospharella cucumerina (anamorph Plectosporium tabacinum) was isolated from a urine sample. Clinical manifestations were blindness, altered mentation, and osteomyelitis. Treatment with itraconazole and terbinafine for greater than one year resulted in stable clinical disease.


title: An overview of assistive technology for persons with multiple sclerosis.

abstract: Multiple sclerosis (MS) is a progressive neurologic disease clinically characterized by episodes of focal disorder of the cranial nerves, spinal cord, and the brain. MS affects a significant number of young adults, and they most often face a future of progressive functional losses as more of their central nervous system and cranial nerves are affected. As the disease progresses, they have new impairments with accompanying limitations in activities, restrictions to their participation in life, and compromised quality of life. Assistive technology includes any item that is used to maintain or improve functional capabilities. The rehabilitation healthcare provider has many opportunities to intervene with assistive technologies to decrease activity limitations and participation restrictions. The purpose of this article is to (1) review the impairments and associated activity limitations and participations restrictions experienced by persons with MS, (2) provide an overview of high- and low-tech assistive technologies appropriate for persons with MS, (3) discuss funding opportunities for assistive technologies, (4) review current studies of assistive technology used for persons with MS and discuss future research directions, and (5) consider assistive technology as an intervention for disability prevention.


abstract: Summary ::: This European guideline for the diagnosis and treatment of insomnia was developed by a task force of the European Sleep Research Society, with the aim of providing clinical recommendations for the management of adult patients with insomnia. The guideline is based on a systematic review of relevant meta-analyses published till June 2016. The target audience for this guideline includes all clinicians involved in the management of insomnia, and the target patient population includes adults with chronic insomnia disorder. The GRADE (Grading of Recommendations Assessment, Development and Evaluation) system was used to grade the evidence and guide recommendations. The diagnostic procedure for insomnia, and its co-morbidities, should include a clinical interview consisting of a sleep history (sleep habits, sleep environment, work schedules, circadian factors), the use of sleep questionnaires and sleep diaries, questions about somatic and mental health, a physical examination and additional measures if indicated (i.e. blood tests, electrocardiogram, electroencephalogram; strong recommendation, moderate- to high-quality evidence). Polysomnography can be used to evaluate other sleep disorders if suspected (i.e. periodic limb movement disorder, sleep-related breathing disorders), in treatment-resistant insomnia, for professional at-risk populations and when substantial sleep state misperception is suspected (strong recommendation, high-quality evidence). Cognitive behavioural therapy for insomnia is recommended as the first-line treatment for chronic insomnia in adults of any age (strong recommendation, high-quality evidence). A pharmacological intervention can be offered if cognitive behavioural therapy for insomnia is not sufficiently effective or not available. Benzodiazepines, benzodiazepine receptor agonists and some antidepressants are effective in the short-term treatment of insomnia (≤4 weeks; weak recommendation, moderate-quality evidence). Antihistamines, antipsychotics, melatonin and phytotherapeutics are not recommended for insomnia treatment (strong to weak recommendations, low- to very-low-quality evidence). Light therapy and exercise need to be further evaluated to judge their usefulness in the treatment of insomnia (weak recommendation, low-quality evidence). Complementary and alternative treatments (e.g. homeopathy, acupuncture) are not recommended for insomnia treatment (weak recommendation, very-low-quality evidence).


title: Learning Relation Representations from Word Representations

abstract: Identifying the relations that connect words is an important step towards understanding human languages and is useful for various NLP tasks such as knowledge base completion and analogical reasoning. Simple unsupervised operators such as vector offset between two-word embeddings have shown to recover some specific relationships between those words, if any. Despite this, how to accurately learn generic relation representations from word representations remains unclear. We model relation representation as a supervised learning problem and learn parametrised operators that map pre-trained word embeddings to relation representations. We propose a method for learning relation representations using a feed-forward neural network that performs relation prediction. Our evaluations on two benchmark datasets reveal that the penultimate layer of the trained neural network-based relational predictor acts as a good representation for the relations between words.


title: A RFID case-based logistics resource management system for managing order-picking operations in warehouses

abstract: In the supply chain, a warehouse is an essential component for linking the chain partners. It is necessary to allocate warehouse resources efficiently and effectively to enhance the productivity and reduce the operation costs of the warehouse. Therefore, warehouse management systems (WMSs) have been developed for handling warehouse resources and monitoring warehouse operations. However, it is difficult to update daily operations of inventory level, locations of forklifts and stock keeping units (SKUs) in real-time by using the bar-code-based or manual-based warehouse management systems. In this paper, RFID technology is adopted to facilitate the collection and sharing of data in a warehouse. Tests are performed for evaluating the reading performance of both the active and passive RFID apparatus. With the help of the testing results, the efficient radio frequency cover ranges of the readers are examined for formulating a radio frequency identification case-based logistics resource management system (R-LRMS). The capabilities of R-LRMS are demonstrated in GSL Limited. Three objectives are achieved: (i) a simplification of RFID adoption procedure, (ii) an improvement in the visibility of warehouse operations and (iii) an enhancement of the productivity of the warehouse. The successful case example proved the feasibility of R-LRMS in real working practice.


title: Product development decisions: a review of the literature

abstract: This paper is a review of research in product development, which we define as the transformation of a market opportunity into a product available for sale. Our review is broad, encompassing work in the academic fields of marketing, operations management, and engineering design. The value of this breadth is in conveying the shape of the entire research landscape. We focus on product development projects within a single firm. We also devote our attention to the development of physical goods, although much of the work we describe applies to products of all kinds. We look inside the "black box" of product development at the fundamentaldecisions that are made by intention or default. In doing so, we adopt the perspective of product development as a deliberate business process involving hundreds of decisions, many of which can be usefully supported by knowledge and tools. We contrast this approach to prior reviews of the literature, which tend to examine the importance of environmental and contextual variables, such as market growth rate, the competitive environment, or the level of top-management support.


title: A 2D particle velocity sensor with minimal flow-disturbance

abstract: A 2D sound particle velocity sensor, consisting of a cross of two connected, heated wires is presented. We developed a fabrication process by which the wires become freely suspended 350 μm above the chip surface. This largely eliminates the influence of boundary layer effects and increases the temperature gradient along the wires, both due to the large distance to the silicon substrate. As a result, the sensor has increased sensitivity and reduced power consumption compared to an earlier design [1]. Furthermore, due to the fully symmetrical structure of the sensor the sensitive directions are exactly orthogonal to each other and have near identical sensitivity, thus requiring no individual calibration.


title: Circularly polarized GPS antenna for simultaneous LHCP and RHCP reception with high isolation

abstract: In this paper a compact dual-feed Global Positioning System (GPS) antenna for simultaneous left- and right hand circularly polarized wave reception is presented. The antenna structure consists of a single patch antenna on top of a printed circuit board and two aperture coupled feeds in the ground plane which are used to generate both left- and right-hand (LH and RH) circular polarizations for the patch. The feed network is implemented as a 2-section branch-line coupler underneath the ground plane. It is able to generate adequate phase shifts for both LH and RH circular polarizations simultaneously via two feed ports. Due to the aperture coupling and coupler implementation three layer printed circuit board approach is used. The measured axial ratio is < 1.35 for both polarizations over the 24 MHz bandwidth at the frequency of 1.575 GHz. The isolation between the LH and RH feed ports in reception mode is −35… −26 dB in the broadside direction. The antenna enables simultaneous direct and multipath signal measurement with equal spatial coordinates for both polarizations.


abstract: The classification of different tumor types is of great importance in cancer diagnosis and drug discovery. However, most previous cancer classification studies are clinical based and have limited diagnostic ability. Cancer classification using gene expression data is known to contain the keys for addressing the fundamental problems relating to cancer diagnosis and drug discovery. The recent advent of DNA microarray technique has made simultaneous monitoring of thousands of gene expressions possible. With this abundance of gene expression data, researchers have started to explore the possibilities of cancer classification using gene expression data. Quite a number of methods have been proposed in recent years with promising results. But there are still a lot of issues which need to be addressed and understood.In order to gain a deep insight into the cancer classification problem, it is necessary to take a closer look at the problem, the proposed solutions and the related issues all together. In this survey paper, we present a comprehensive overview of various proposed cancer classification methods and evaluate them based on their computation time, classification accuracy and ability to reveal biologically meaningful gene information. We also introduce and evaluate various proposed gene selection methods which we believe should be an integral preprocessing step for cancer classification. In order to obtain a full picture of cancer classification, we also discuss several issues related to cancer classification, including the biological significance vs. statistical significance of a cancer classifier, the asymmetrical classification errors for cancer classifiers, and the gene contamination problem.


title: PHYSICAL AND NUMERICAL MODELLING OF THE DYNAMIC BEHAVIOR OF A FLY LINE

abstract: The planar equations of motion for a tapered fly line subjected to tension, bending, aerodynamic drag, and weight are derived. The resulting theory describes the large non-linear deformation of the line as it forms a propagating loop during fly casting. A cast is initiated by the motion of the tip of the fly rod that represents the boundary condition at one end of the fly line. At the opposite end, the boundary condition describes the equations of motion of a small attached fly (point mass with air drag). An efficient numerical algorithm is reviewed that captures the initiation and propagation of a non-linear wave that describes the loop. The algorithm is composed of three major steps. First, the non-linear initial-boundary-value problem is transformed into a two-point boundary-value problem, using finite differencing in time. The resulting non-linear boundary-value problem is linearized and then transformed into an initial-value problem in space. Example results are provided that illustrate how an overhead cast develops from initial conditions describing a perfectly laid out back cast. The numerical solutions are used to explore the influence of two sample effects in fly casting, namely, the drag created by the attached fly and the shape of the rod tip path.


title: cGAN Based Facial Expression Recognition for Human-Robot Interaction

abstract: As an emerging research topic for proximity service (ProSe), automatic emotion recognition enables the machines to understand the emotional changes of human beings which can not only facilitate natural, effective, seamless, and advanced human-robot interaction or human-computer interface but also promote emotional health. Facial expression recognition (FER) is a vital task for emotion recognition. However, significant gap between human and machine exists in FER task. In this paper, we present a conditional generative adversarial network-based approach to alleviate the intra-class variations by individually controlling the facial expressions and learning the generative and discriminative representations simultaneously. The proposed framework consists of a generator G and three discriminators (Di, Da, and Dexp). The generator G transforms any query face image into another prototypic facial expression image with other factors preserved. Armed with action units condition, the generator G pays more attention to information relevant to facial expression. Three loss functions ($L_{I}$ , La, and Lexp) corresponding to the three discriminators (Di, Da, and Dexp) were designed to learn generative and discriminative representations. Moreover, after rendering the generated expression back to its original facial expression, cycle consistency loss is also applied to guarantee the identity and produce more constrained visual representations. Optimized by combining both synthesis and classification loss functions, the learnt representation is explicitly disentangled from other variations such as identity, head pose, and illumination. Qualitative and quantitative experimental results demonstrate the proposed FER system is effective for expression recognition.


Evaluating entity linking: An analysis of current benchmark datasets and a roadmap for doing a better job


title: A Literature Review and Classification of Recommender Systems on Academic Journals

abstract: Recommender systems have become an important research field since the emergence of the first paper on collaborative filtering in the mid?1990s. In general, recommender systems are defined as the supporting systems which help users to find information, products, or services (such as books, movies, music, digital products, web sites, and TV programs) by aggregating and analyzing suggestions from other users, which mean reviews from various authorities, and user attributes. However, as academic researches on recommender systems have increased significantly over the last ten years, more researches are required to be applicable in the real world situation. Because research field on recommender systems is still wide and less mature than other research fields. Accordingly, the existing articles on recommender systems need to be reviewed toward the next generation of recommender systems. However, it would be not easy to confine the recommender system researches to specific disciplines, considering the nature of the recommender system researches. So, we reviewed all articles on recommender systems from 37 journals which were published from 2001 to 2010. The 37 journals are selected from top 125 journals of the MIS Journal Rankings. Also, the literature search was based on the descriptors “Recommender system”, “Recommendation system”, “Personalization system”, “Collaborative filtering” and “Contents filtering”. The full text of each article was reviewed to eliminate the article that was not actually related to recommender systems. Many of articles were excluded because the articles such as Conference papers, master’s and doctoral dissertations, textbook, unpublished working papers, non?English publication papers and news were unfit for our research. We classified articles by year of publication, journals, recommendation fields, and data mining techniques. The recommendation fields and data mining techniques of 187 articles are reviewed and classified into eight recommendation fields (book, document, image, movie, music, shopping, TV program, and others) and eight data mining techniques (association rule, clustering, decision tree, k?nearest neighbor, link analysis, neural network, regression, and other heuristic methods). The results represented in this paper have several significant implications. First, based on previous publication rates, the interest in the recommender system related research will grow significantly in the future. Second, 49 articles are related to movie recommendation whereas image and TV program recommendation are identified in only 6 articles. This result has been caused by the easy use of MovieLens data set. So, it is necessary to prepare data set of other fields. Third, recently social network analysis has been used in the various applications. However studies on recommender systems using social network analysis are deficient. Henceforth, we expect that new recommendation approaches using social network analysis will be developed in the recommender systems. So, it will be an interesting and further research area to evaluate the recommendation system researches using social method analysis. This result provides trend of recommender system researches by examining the published literature, and provides practitioners and researchers with insight and future direction on recommender systems. We hope that this research helps anyone who is interested in recommender systems research to gain insight for future research.


title: Estimating the simulator sickness in immersive virtual reality with optical flow analysis

abstract: Recently, Virtual Reality (VR) has shown the ability of providing immersive experience to users and taking them to travel around the virtual environment. Sometimes when traveling in a large-scale virtual environment, the tracking space may not be large enough to fit the virtual environment. Several moving approaches such as virtual jumping and flying are proposed to overcome this limitation. Nevertheless, inappropriate design of moving approaches will cause serious simulator sickness. Intensive visual flow can be one of the major factors that cause the simulator sickness. According to Oculus Best Practices [Oculus 2017], it is necessary to avoid an intensive display of visual flow while developing VR application. There are several researches trying to combat simulator sickness through reducing optical flow. FOV restrictor [Fernandes and Feiner 2016] is one of the successful ways. By decreasing the FOV of the HMD, it can effectively reduce optical flow from peripheral vision and make users feel more comfortable. However, the immersiveness in VR will be declined due to the limited size of FOV.


title: Strategic Brand Management of the Luxury Brand GUCCI

abstract: In recent years, on the one hand, Japanese apparel companies are suffering badly from decreases in profits under the variable markets, but on the other hand, many luxury brand companies are advancing into Japan or overseas markets and achieving market share with high profit ratios. This paper reveals which strategy and brand management is effective for luxury branding through the case of the luxury brand GUCCI. It shows that the luxury brand companies have to manage and control the quality of products, prices, distribution channels and promotions to establish and maintain their position as a luxury brand. That is to say, they takeLuxury strategy�. It also shows that sustaining innovation and disruptive innovation are necessary to establish and maintain their position not only in the HDD industry, but also in luxury brand industry.


Sustainable Business Model: A Review


title: Pipelined CPU Design With FPGA in Teaching Computer Architecture

abstract: This paper presents a pipelined CPU design project with a field programmable gate array (FPGA) system in a computer architecture course. The class project is a five-stage pipelined 32-bit MIPS design with experiments on the Altera DE2 board. For proper scheduling, milestones were set every one or two weeks to help students complete the project on time. The goal of the project is to educate students effectively via hands-on learning, rather than having them achieve a complete and flawless CPU design. This study reveals that 21 MIPS instructions are enough to achieve the purpose. With the addition in 2010 of the properly enforced scheduling and the FPGA system, many more students successfully completed the class project than was the case in 2009. A student survey and the independent samples t-test reveal the effectiveness of the methodology with the FPGA system. This work differs from previous work in that the devised project requires the implementation of a real CPU instead of utilizing simulators or just experimenting with ready-made complete CPU models.


title: Visual Question Answering: A Survey of Methods and Datasets

abstract: Visual Question Answering (VQA) is a challenging task that has received increasing attention from both the computer vision and the natural language processing communities. Given an image and a question in natural language, it requires reasoning over visual elements of the image and general knowledge to infer the correct answer. In the first part of this survey, we examine the state of the art by comparing modern approaches to the problem. We classify methods by their mechanism to connect the visual and textual modalities. In particular, we examine the common approach of combining convolutional and recurrent neural networks to map images and questions to a common feature space. We also discuss memory-augmented and modular architectures that interface with structured knowledge bases. In the second part of this survey, we review the datasets available for training and evaluating VQA systems. The various datatsets contain questions at different levels of complexity, which require different capabilities and types of reasoning. We examine in depth the question/answer pairs from the Visual Genome project, and evaluate the relevance of the structured annotations of images with scene graphs for VQA. Finally, we discuss promising future directions for the field, in particular the connection to structured knowledge bases and the use of natural language processing models.


title: Fingerprints of global warming on wild animals and plants

abstract: Over the past 100 years, the global average temperature has increased by approximately 0.6 °C and is projected to continue to rise at a rapid rate1. Although species have responded to climatic changes throughout their evolutionary history2, a primary concern for wild species and their ecosystems is this rapid rate of change3. We gathered information on species and global warming from 143 studies for our meta-analyses. These analyses reveal a consistent temperature-related shift, or ‘fingerprint’, in species ranging from molluscs to mammals and from grasses to trees. Indeed, more than 80% of the species that show changes are shifting in the direction expected on the basis of known physiological constraints of species. Consequently, the balance of evidence from these studies strongly suggests that a significant impact of global warming is already discernible in animal and plant populations. The synergism of rapid temperature rise and other stresses, in particular habitat destruction, could easily disrupt the connectedness among species and lead to a reformulation of species communities, reflecting differential changes in species, and to numerous extirpations and possibly extinctions.


title: Translinear Circuits Using Subthreshold Floating-Gate MOS Transistors

abstract: We describe a family of current-mode circuits with multiple inputs and multiple outputs whose output currents are products and/or quotients of powers of the input currents. These circuits are made up of multipleinput floating-gate MOS (FGMOS) transistors operating in the subthreshold regime. The powers are set by capacitor ratios; hence, they can be quite accurate. We analyze the general family of such circuits and present experimental data from several members that we fabricated in a standard 2μm double-poly p-well process through MOSIS.


title: Multicommodity flows in planar undirected graphs and shortest paths

abstract: This paper deals with the multicommodity flow problems for two classes of planar undirected graphs. The first class C 12 consists of graphs in which each source-sink pair is located on one of two specified face boundaries. The second class C 01 consists of graphs in which some of the source-sink pairs are located on a specified face boundary and all the other pairs share a common sink located on the boundary. We show that the multicommodity flow problem for a graph in C 12 (resp. C 01 ) can be reduced to the shortest path problem for an undirected (resp. a directed) graph obtained from the dual of the original undirected graph.


title: Peripheral neuropathy: differential diagnosis and management.

abstract: Peripheral neuropathy has a variety of systemic, metabolic, and toxic causes. The most common treatable causes include diabetes mellitus, hypothyroidism, and nutritional deficiencies. The diagnosis requires careful clinical assessment, judicious laboratory testing, and electrodiagnostic studies or nerve biopsy if the diagnosis remains unclear. A systematic approach begins with localization of the lesion to the peripheral nerves, identification of the underlying etiology, and exclusion of potentially treatable causes. Initial blood tests should include a complete blood count, comprehensive metabolic profile, and measurement of erythrocyte sedimentation rate and fasting blood glucose, vitamin B12, and thyroid-stimulating hormone levels; specialized tests should be ordered if clinically indicated. Lumbar puncture and cerebrospinal fluid analysis may be helpful in the diagnosis of Guillain-Barré syndrome and chronic inflammatory demyelinating neuropathy. Electrodiagnostic studies, including nerve conduction studies and electromyography, can help in the differentiation of axonal versus demyelinating or mixed neuropathy. Treatment should address the underlying disease process, correct any nutritional deficiencies, and provide symptomatic treatment.


title: Creating AI Characters for Fighting Games Using Genetic Programming

abstract: This paper proposes a character generation approach for the M.U.G.E.N. fighting game that can create engaging AI characters using a computationally cheap process without the intervention of the expert developer. The approach uses a genetic programming algorithm that refines randomly generated character strategies into better ones using tournament selection. The generated AI characters were tested by 27 human players and were rated according to results, perceived difficulty and how engaging the gameplay was. The main advantages of this procedure are that no prior knowledge of how to code the strategies of the AI character is needed and there is no need to interact with the internal code of the game. In addition, the procedure is capable of creating a wide diversity of players with different strategic skills, which could be potentially used as a starting point to a further adaptive process.


title: Detection of unhealthy plant leaves using image processing and genetic algorithm with Arduino

abstract: India is an agricultural country and about seventy percent of our population depends on agriculture. One-third of our national income comes from agriculture. So the disease detection of plants plays an important role in the agricultural field. Majority of the plant diseases are caused by the attack of bacteria, fungi, virus etc. If proper care is not taken in this area, it may lead to serious effects on plants and adversely affects the productivity and quality. To detect, the plant diseases we need a fast automatic way. The main approach adopted in practice for detection and identification of plant diseases is naked eye observation through experts. The decision making capability of an expert also depends on his/her physical condition, such as fatigue and eye sight, work pressure, climate etc. So this method is time consuming and less efficient. Here, a project is proposed with an idea of detecting plant diseases using image processing. Image processing toolbox of Matlab is used for measuring affected area of disease and to determine the difference in the color of the disease affected area. This concept can be extended to detect the symptoms of any type of plant diseases that is affected on different horticulture crops. The algorithm can be used to classify the leaves and the classified outcomes are separated using Arduino based conveyor belt system. This reduces an important task of monitoring of farms crops at very early stage itself to detect the symptom of diseases appear on plant leaves.


title: On the Critical Role of Divergent Selection in Evolvability

abstract: An ambitious goal in evolutionary robotics is to evolve increasingly complex robotic behaviors with minimal human design effort. Reaching this goal requires evolutionary algorithms that can unlock from genetic encodings their latent potential for evolvability. One issue clouding this goal is conceptual confusion about evolvability, which often obscures the aspects of evolvability that are important or desirable. The danger from such confusion is that it may establish unrealistic goals for evolvability that prove unproductive in practice. An important issue separate from conceptual confusion is the common misalignment between selection and evolvability in evolutionary robotics. While more expressive encodings can represent higher-level adaptations (e.g. sexual reproduction or developmental systems) that increase long-term evolutionary potential (i.e. evolvability), realizing such potential requires gradients of fitness and evolvability to align. In other words, selection is often a critical factor limiting increasing evolvability. Thus, drawing from a series of recent papers, this article seeks to both (1) clarify and focus the ways in which the term evolvability is used within artificial evolution, and (2) argue for the importance of one type of selection, i.e. divergent selection, for enabling evolvability. The main argument is that there is a fundamental connection between divergent selection and evolvability (on both the individual and population level) that does not hold for typical goal-oriented selection. The conclusion is that selection pressure plays a critical role in realizing the potential for evolvability, and that divergent selection in particular provides a principled mechanism for encouraging evolvability in artificial evolution.


title: A review on buildings energy consumption information

abstract: The rapidly growing world energy use has already raised concerns over supply difficulties, exhaustion of energy resources and heavy environmental impacts (ozone layer depletion, global warming, climate change, etc.). The global contribution from buildings towards energy consumption, both residential and commercial, has steadily increased reaching figures between 20% and 40% in developed countries, and has exceeded the other major sectors: industrial and transportation. Growth in population, increasing demand for building services and comfort levels, together with the rise in time spent inside buildings, assure the upward trend in energy demand will continue in the future. For this reason, energy efficiency in buildings is today a prime objective for energy policy at regional, national and international levels. Among building services, the growth in HVAC systems energy use is particularly significant (50% of building consumption and 20% of total consumption in the USA). This paper analyses available information concerning energy consumption in buildings, and particularly related to HVAC systems. Many questions arise: Is the necessary information available? Which are the main building types? What end uses should be considered in the breakdown? Comparisons between different countries are presented specially for commercial buildings. The case of offices is analysed in deeper detail.


title: Binocular automatic particle inspection machine for bottled medical liquid examination

abstract: Automatic Particle Inspection Machine (APIM) is capable of detecting visible impurities in ampoules and penicillin bottles. However, for Large Volume Parenteral (LVP), Dynamic Background Interference and Narrow Depth of Field problems mostly lead to failure using traditional monocular inter-frame difference algorithm. In this paper a binocular inspection algorithm and system based on monocular machine is proposed to overcome those disturbances. The binocular inspection system only changes the pose of cameras without increasing hardware costs and the algorithm employs an efficient sequential-image-stereo matching method to achieve real-time online inspection. Experimental results of inspection comparison between monocular and binocular for alarm rate and omission rate show detection validity even if the blurred parts due to the disturbance cannot be distinguished from its own background by monocular method.


title: CloudVMI: Virtual Machine Introspection as a Cloud Service

abstract: Virtual machine introspection (VMI) is a mechanism that allows indirect inspection and manipulation of the state of virtual machines. The indirection of this approach offers attractive isolation properties that has resulted in a variety of VMI-based applications dealing with security, performance, and debugging in virtual machine environments. Because it requires privileged access to the virtual machine monitor, VMI functionality is unfortunately not available to cloud users on public cloud platforms. In this paper, we present our work on the CloudVMI architecture to address this concern. CloudVMI virtualizes the VMI interface and makes it available as-a-service in a cloud environment. Because it allows introspection of users' VMs running on arbitrary physical machines in a cloud environment, our VMI-as-a-service abstraction allows a new class of cloud-centric VMI applications to be developed. We present the design and implementation of CloudVMI in the Xen hypervisor environment. We evaluate our implementation using a number of VMI applications, including a simple application that illustrates the cross-physical machine capabilities of CloudVMI.


I Spot a Bot : Building a binary classifier to detect bots on Twitter


title: Clique Community Persistence: A Topological Visual Analysis Approach for Complex Networks

abstract: Complex networks require effective tools and visualizations for their analysis and comparison. Clique communities have been recognized as a powerful concept for describing cohesive structures in networks. We propose an approach that extends the computation of clique communities by considering persistent homology, a topological paradigm originally introduced to characterize and compare the global structure of shapes. Our persistence-based algorithm is able to detect clique communities and to keep track of their evolution according to different edge weight thresholds. We use this information to define comparison metrics and a new centrality measure, both reflecting the relevance of the clique communities inherent to the network. Moreover, we propose an interactive visualization tool based on nested graphs that is capable of compactly representing the evolving relationships between communities for different thresholds and clique degrees. We demonstrate the effectiveness of our approach on various network types.


title: A unity power factor three phase PWM SCR rectifier for high power applications in the metal industry

abstract: A new thyristor current-source rectifier that achieves unity power factor, low-current total harmonic distortion (THD), and DC-bus current and voltage control is presented. The rectifier is suitable for high-power applications such as induction heating and DC arc furnaces. It combines a traditional six-pulse thyristor bridge and a DC chopper that together solve power quality problems such as poor power factor and flicker generation. This topology achieves low input current THD and DC power control without additional power-factor-correction equipment, harmonic trap filters, use of multiple pulse rectifiers or high-K-factor transformers.


title: Anaphoric Annotation of Wikipedia and Blogs in the Live Memories Corpus

abstract: The Live Memories corpus is an Italian corpus annotated for anaphoric relations. This annotation effort aims to contribute to two significant issues for the CL research: the lack of annotated anaphoric resources for Italian and the increasing interest for the social Web. The Live Memories Corpus contains texts from the Italian Wikipedia about the region Trentino/Sd Tirol and from blog sites with users’ comments. It is planned to add a set of articles of local news papers. The corpus includes manual annotated information about morphosyntactic agreement, anaphoricity, and semantic class of the NPs. The anaphoric annotation includes discourse deixis, bridging relations and markes cases of ambiguity with the annotation of alternative interpretations. For the annotation of the anaphoric links the corpus takes into account specific phenomena of the Italian language like incorporated clitics and phonetically non realized pronouns. Reliability studies for the annotation of the mentioned phenomena and for annotation of anaphoric links in general offer satisfactory results. The Wikipedia and blogs dataset will be distributed under Creative Commons Attributions licence.


title: Synchronous Demodulator for Autonomous Sensors

abstract: In this paper, a novel differential synchronous demodulator is presented that uses the on-off switching characteristic of the power supply in autonomous systems as the control signal for demodulation. The proposed circuit, which uses only an op amp and a double-pole double-throw switch, can be useful in sensor nodes for sensor networks or distributed data-acquisition systems involving ac sensors or impedance measurements. Three prototype demodulators have been implemented using op amps with different power-bandwidth characteristics, and their common-mode rejection ratio (CMRR), nonlinearity, and initial transient were characterized. The working of the circuit when supplied by a piezoelectric energy converter is also verified.


title: DeepFlow: a deep learning framework for software-defined measurement

abstract: Providing fine grained traffic measurement is crucial for many network management tasks such as traffic engineering, anomaly detection, traffic accounting, and load balancing. Software-defined networks can potentially enable fine-grained measurement by providing statistics for each forwarding rule of an OpenFlow-enabled switch. However, providing fine grained traffic measurement in a scalable fashion in hardware switches poses significant challenges due to the limitations in the size of the TCAMs that fit only a relatively small number of rules compared to the number of active flows in the network. In this paper, we present DeepFlow, a framework for scalable software-defined measurement that relies on an efficient mechanism that a) adaptively detect the most active source and destination prefixes in the network, b) collects fine-grained flow-size measurements for the most active prefixes and coarse grained for the less active ones, and c) uses historical measurements in order to train a cloud-based Deep Learning model that can be used to provide short-term predictions whenever exact flow counters cannot be placed at a switch due to its limited resources. Thus the number of fine-grained flows measured can increase significantly without the need to use other flow sampling solutions that loose accuracy. An extensive experimental evaluation using a prototype implementation and real network traces shows that DeepFlow can provide very high accuracy for estimating flow sizes at various aggregation levels.


title: Position sensing in brake-by-wire callipers using resolvers

abstract: Recent designs for brake-by-wire systems use "resolvers" to provide accurate and continuous measurements for the absolute position and speed of the rotor of the electric actuators in brake callipers (permanent magnet DC motors). Resolvers are absolute-angle transducers that are integrated with estimator modules called "angle tracking observer" and together they provide position and speed measurements. Current designs for angle-tracking observers are unstable in applications with high acceleration and/or speed. In this paper, we introduce a new angle-tracking observer in which a closed-loop linear time-invariant (LTI) observer is integrated with a quadrature encoder. Finite-gain stability of the proposed design and its robustness to three different kinds of parameter variations are proven based on theorems of input-output stability in nonlinear control theory. In our experiments, we examined the performance of our observer and two other methods (a well-known LTI observer and an extended Kalman filter) to estimate the position and speed of a brake-by-wire actuator. The results show that because of the very high speed and acceleration of the actuator in this application, the LTI observer and Kalman filter cannot track the rotor position and diverge. In contrast, with a properly designed open-loop transfer function and selecting a suitable switching threshold, our proposed angle-tracking observer is stable and highly accurate in a brake-by-wire application


title: Innovation im Netz : Neue Zeiten für technische Innovationen: heterogen verteilt und interaktiv vernetzt

abstract: The relationship between technological innovation and its institutional embeddedness is analysed within the theoretical framework of reflexive modernization and under the aspect of time in order to unterstand the actual frictions of innovation in Westem societies. The author hints to the paradox of innovation being at the same time creative and destructive, and argues that it is the particular embeddedness of innovative action that sets the tone and pace of technological change. The institutional split of technological development into science on the one hand and economy on the other has accelerated the speed of innovation in modernity, but at the same time has created new problems of synchronization between different times. It is illustrated that the up to now predominant modes of coordination, innovation via markets and innovation by organization, are losing ground not in spite of, but because of their success. A new type, innovation in the network, is emerging as a reaction to the shortcomings of its predecessors. Networks of innovation between heterogeneous actors are argued to become the basis of a post-Schumpeterian innovation regime.


title: A Wearable Hip Assist Robot Can Improve Gait Function and Cardiopulmonary Metabolic Efficiency in Elderly Adults

abstract: The aims of this paper were to investigate the effectiveness of a newly developed wearable hip assist robot, that uses an active assist algorithm to improve gait function, muscle effort, and cardiopulmonary metabolic efficiency in elderly adults. Thirty elderly adults (15 males/ 15 females) participated in thispaper. The experimental protocol consisted of overground gait at comfortable speed under three different conditions: free gait without robot assistance, robot-assisted gait with zero torque (RAG-Z), and full RAG. Under all conditions, muscle effort was analyzed using a 12-channel surface electromyography system. Spatio-temporal data were collected at 120 Hz using a 3-D motion capture system with six infrared cameras. Metabolic cost parameters were collected as oxygen consumption per unit (ml/min/kg) and aerobic energy expenditure (Kcal/min). In the RAG condition, participants demonstrated improved gait function, decreased muscle effort, and reduced metabolic cost. Although the hip assist robot only provides assistance at the hip joint, our results demonstrated a clear reduction in knee and ankle muscle activity in addition to decreased hip flexor and extensor activity. Our findings suggest that this robot has the potential to improve stabilization of the trunk during walking in elderly adults.


title: Hybrid ARIMA-BPNN model for time series prediction of the Chinese stock market

abstract: Stock price prediction is a challenging task owing to the complexity patterns behind time series. Autoregressive integrated moving average (ARIMA) model and back propagation neural network (BPNN) model are popular linear and nonlinear models for time series forecasting respectively. The integration of two models can effectively capture the linear and nonlinear patterns hidden in a time series and improve forecast accuracy. In this paper, a new hybrid ARIMA-BPNN model containing technical indicators is proposed to forecast four individual stocks consisting of both main board market and growth enterprise market in software and information services sector. Experiment results show that the proposed method achieves the better one-step-ahead forecasting accuracies namely 78.79%, 72.73%, 59.09% and 66.67% respectively for each series than those of ARIMA, BPNN, and Khashei and Bijari's hybrid models.


title: Fast image search for learned metrics

abstract: We introduce a method that enables scalable image search for learned metrics. Given pairwise similarity and dissimilarity constraints between some images, we learn a Mahalanobis distance function that captures the imagespsila underlying relationships well. To allow sub-linear time similarity search under the learned metric, we show how to encode the learned metric parameterization into randomized locality-sensitive hash functions. We further formulate an indirect solution that enables metric learning and hashing for vector spaces whose high dimensionality make it infeasible to learn an explicit weighting over the feature dimensions. We demonstrate the approach applied to a variety of image datasets. Our learned metrics improve accuracy relative to commonly-used metric baselines, while our hashing construction enables efficient indexing with learned distances and very large databases.


title: Self-esteem and perceived regard: how I see myself affects my relationship satisfaction.

abstract: The authors explored the relations among self-esteem, perceived regard, and satisfaction in dating relationships. On the basis of the dependency regulation model (T. DeHart, B. Pelham, & S. Murray, 2004), the authors hypothesized that self-esteem would influence individuals' self-perceptions and views of how their partners perceive them (metaperception). They also hypothesized that perceived regard (self-perception minus metaperception) would predict relationship satisfaction. Regression analyses indicated that for moderate relationship-relevant traits (i.e., caring, loving), high self-esteem was associated with self-enhancement (idealization), whereas low self-esteem was associated with self-deprecation. For low relationship-relevant traits (i.e., quiet, reserved), both low and high self-esteem individuals self-enhanced. Hierarchical regression analyses indicated that self-esteem and perceived regard for moderate relationship-relevant traits predicted satisfaction. The authors discuss the implications of idealization, self-verification, and self-deprecation for the perceivers and their relationships.


title: Adaptive Neural Tracking Control for a Class of Nonstrict-Feedback Stochastic Nonlinear Systems With Unknown Backlash-Like Hysteresis

abstract: This paper considers the problem of adaptive neural control of stochastic nonlinear systems in nonstrict-feedback form with unknown backlash-like hysteresis nonlinearities. To overcome the design difficulty of nonstrict-feedback structure, variable separation technique is used to decompose the unknown functions of all state variables into a sum of smooth functions of each error dynamic. By combining radial basis function neural networks' universal approximation capability with an adaptive backstepping technique, an adaptive neural control algorithm is proposed. It is shown that the proposed controller guarantees that all the signals in the closed-loop system are four-moment semiglobally uniformly ultimately bounded, and the tracking error eventually converges to a small neighborhood of the origin in the sense of mean quartic value. Simulation results further show the effectiveness of the presented control scheme.


title: Assessing IOT Projects in University Education - A Framework for Problem-Based Learning

abstract: Internet of Things (IoT) provides a thematic umbrella that allows educators to combine various theoretical aspects of computer science with substantial problems in everyday life. As such, building IoT device prototypes has been suggested by many as a means for teaching computer science and software engineering. However, how assessment should be conducted in these exploratory courses is often left vague, and thus, there is a need for applicable assessment methodologies. This article reports results from three years of action research in teaching prototyping of Internet of Things devices in a practical, problem-based setting. We present an example course outline for arranging learning experiences in IoT prototyping and provide a general assessment framework, along with recommendations for best practices for facilitating personalized learning in similar contexts. The results highlight that a general evaluation criteria can be composed, despite the versatile nature and varying complexity of the students' project outcomes. Also, we underline that students' learning can be evaluated in detail, despite large variations in their prior knowledge of IoT technologies or new product development as they enter the classroom for the first time.


title: Abstract The Pyro toolkit for AI and robotics

abstract: This article introduces Pyro, an open-source Python robotics toolkit for exploring topics in AI and robotics. We present key abstractions that allow Pyro controllers to run unchanged on a variety of real and simulated robots. We demonstrate Pyro's use in a set of curricular modules. We then describe how Pyro can provide a smooth transition for the student from symbolic agents to real-world robots, which significantly reduces the cost of learning to use robots. Finally we show how Pyro has been successfully integrated into existing Al and robotics courses.


title: Composite Task-Completion Dialogue Policy Learning via Hierarchical Deep Reinforcement Learning

abstract: The majority of NLG evaluation relies on automatic metrics, such as BLEU . In this paper, we motivate the need for novel, system- and data-independent automatic evaluation methods: We investigate a wide range of metrics, including state-of-the-art word-based and novel grammar-based ones, and demonstrate that they only weakly reflect human judgements of system outputs as generated by data-driven, end-to-end NLG. We also show that metric performance is data- and system-specific. Nevertheless, our results also suggest that automatic metrics perform reliably at system-level and can support system development by finding cases where a system performs poorly.


Party, Class, and Mobility: An Empirical Investigation of Parents' Chinese Communist Party Membership's Effects on Children's Social Class in Contemporary China


title: Short-Run Assessment of French Economic Activity Using Optim

abstract: This paper describes a short-term projection model for French economic activity, OPTIM, the aim of which is twofold. First it gives an early estimate of real GDP growth for the previous quarter, when no figure has yet been released by Insee, the French National Statistical Institute, along with flash estimates for main GDP components (consumption, investment, inventories and external trade) together with a breakdown by sectors (services, manufacturing, construction, equipment, agri-food). This appears particularly useful for the short-run analysis. In this respect OPTIM may be considered as a traditional bridge equation model since it links a particular indicator available generally ahead of the release of the quarterly national accounts with a quarterly aggregate like GDP, consumption…. Second, this tool supplies also estimates for GDP growth and its main components for the current quarter and for the next quarter (i.e two and three quarters respectively following the latest reference period of Insee's GDP data release). A pool of (mainly) monthly variables is used, which are, sometimes, directly introduced in the specification but, more often, summarised by the implementation of a principal component analysis (PCA). The largest part of the set of indicators comprises survey data together with monthly traditional indicators (industrial production, consumption in manufactured goods…). But other data (in particular financial data) are also introduced. The outcomes of OPTIM rely on a relatively complex procedure involving about twenty equations and mixing two alternative approaches: a supply approach consisting in a direct modelling of GDP and a demand approach where GDP is the sum of consumption, investment, changes in stocks and net trade (exports minus imports). The discrepancy between these two estimates is distributed according to an original method, yielding a unique GDP estimation. The paper is organised as follows. Section 1 presents the main features of OPTIM. Section 2 deals with data description while section 3 addresses the data assessment's issue. In section 4, the main equations are described. Section 5 presents a general assessment of OPTIM in terms of forecasting record. Finally section 6 concludes and proposes some avenues for further developments.


title: Imagined Communities: Awareness, Information Sharing, and Privacy on the Facebook

abstract: Online social networks such as Friendster, MySpace, or the Facebook have experienced exponential growth in membership in recent years. These networks offer attractive means for interaction and communication, but also raise privacy and security concerns. In this study we survey a representative sample of the members of the Facebook (a social network for colleges and high schools) at a US academic institution, and compare the survey data to information retrieved from the network itself. We look for underlying demographic or behavioral differences between the communities of the network's members and non-members; we analyze the impact of privacy concerns on members' behavior; we compare members' stated attitudes with actual behavior; and we document the changes in behavior subsequent to privacy-related information exposure. We find that an individual's privacy concerns are only a weak predictor of his membership to the network. Also privacy concerned individuals join the network and reveal great amounts of personal information. Some manage their privacy concerns by trusting their ability to control the information they provide and the external access to it. However, we also find evidence of members' misconceptions about the online community's actual size and composition, and about the visibility of members' profiles.


title: Frictional adhesion: a new angle on gecko attachment

abstract: SUMMARY Directional arrays of branched microscopic setae constitute a dry adhesive ::: on the toes of pad-bearing geckos, nature9s supreme climbers. Geckos are ::: easily and rapidly able to detach their toes as they climb. There are two ::: known mechanisms of detachment: (1) on the microscale, the seta detaches when ::: the shaft reaches a critical angle with the substrate, and (2) on the ::: macroscale, geckos hyperextend their toes, apparently peeling like tape. This ::: raises the question of how geckos prevent detachment while inverted on the ::: ceiling, where body weight should cause toes to peel and setal angles to ::: increase. Geckos use opposing feet and toes while inverted, possibly to ::: maintain shear forces that prevent detachment of setae or peeling of toes. If ::: detachment occurs by macroscale peeling of toes, the peel angle should ::: monotonically decrease with applied force. In contrast, if adhesive force is ::: limited by microscale detachment of setae at a critical angle, the toe ::: detachment angle should be independent of applied force. We tested the ::: hypothesis that adhesion is increased by shear force in isolated setal arrays ::: and live gecko toes. We also tested the corollary hypotheses that (1) adhesion ::: in toes and arrays is limited as on the microscale by a critical angle, or (2) ::: on the macroscale by adhesive strength as predicted for adhesive tapes. We ::: found that adhesion depended directly on shear force, and was independent of ::: detachment angle. Therefore we reject the hypothesis that gecko toes peel like ::: tape. The linear relation between adhesion and shear force is consistent with ::: a critical angle of release in live gecko toes and isolated setal arrays, and ::: also with our prior observations of single setae. We introduced a new model, ::: frictional adhesion, for gecko pad attachment and compared it to existing ::: models of adhesive contacts. In an analysis of clinging stability of a gecko ::: on an inclined plane each adhesive model predicted a different force control ::: strategy. The frictional adhesion model provides an explanation for the very ::: low detachment forces observed in climbing geckos that does not depend on toe ::: peeling.


What Can Behavioral Economics Teach Us About Privacy?


title: Improved elimination of motion artifacts from a photoplethysmographic signal using a Kalman smoother with simultaneous accelerometry

abstract: A photoplethysmography (PPG) signal provides very useful information about a subject's hemodynamic status in a hospital or ubiquitous environment. However, PPG is very vulnerable to motion artifacts, which can significantly distort the information belonging to the PPG signal itself. Thus, the reduction of the effects of motion artifacts is an important issue when monitoring the cardiovascular system by PPG. There have been many adaptive techniques to reduce motion artifacts from PPG signals. In the present study, we compared a method based on the fixed-interval Kalman smoother with the usual adaptive filtering algorithms, e.g. the normalized least mean squares, recursive least squares and the conventional Kalman filter. We found that the fixed-interval Kalman smoother reduced motion artifacts from the PPG signal most effectively. Therefore, the use of the fixed-interval Kalman smoother can reduce motion artifacts in PPG, thus providing the most reliable information that can be deduced from the reconstructed PPG signals.