Query: Technology as an operant resource in service (eco)systems

Relevant passages:
From goods to service ( s ) : Divergences and convergences of logics <=SEP=> There are two logics or mindsets from which to consider and motivate a transition from goods to service(s). The first, “goods-dominant (G-D) logic”, views services in terms of a type of (e.g., intangible) good and implies that goods production and distribution practices should be modified to deal with the differences between tangible goods and services. The second logic, “service-dominant (S-D) logic”, considers service – a process of using ones resources for the benefit of and in conjunction with another party – as the fundamental purpose of economic exchange and implies the need for a revised, service-driven framework for all of marketing. This transition to a service-centered logic is consistent with and partially derived from a similar transition found in the business-marketing literature — for example, its shift to understanding exchange in terms value rather than products and networks rather than dyads. It also parallels transitions in other sub-disciplines, such as service marketing. These parallels and the implications for marketing theory and practice of a full transition to a service-logic are explored. © 2008 Elsevier Inc. All rights reserved. Over the last several decades, leading-edge firms, as well as many business scholars and consultants, have advocated the need for refocusing substantial firm activity or transforming the entire firm orientation from producing output, primarily manufactured goods, to a concern with service(s) (see, e.g., Davies, Brady, & Hobday, 2007; Gebauer& Fleisch, 2007). These initiatives can be found in both business-to-business (e.g., IBM, GE) and businessto-consumer enterprises (e.g. Lowe's, Kodak, Apple) and in some cases entire industries (e.g., software-as-a-service). The common justification is that these initiatives are analogous with the shift from a manufacturing to a service economy in developed countries, if not globally. That is, it is based on the idea that virtually all economies are producing and exchanging more services than they are goods; thus, services require increased attention. This perception suggests that firms need to redirect the production and marketing strategy that they have adopted for manufactured goods by adjusting them for the distinguishing characteristics of services. ⁎ Corresponding author. Tel.: +1 808 956 8167; fax: +1 808 956 9886. E-mail addresses: svargo@hawaii.edu (S.L. Vargo), rlusch@eller.arizona.edu (R.F. Lusch). 1 Tel.: +1 520 621 7480. 0019-8501/$ see front matter © 2008 Elsevier Inc. All rights reserved. doi:10.1016/j.indmarman.2007.07.004 Please cite this article as: Vargo, S. L., & Lusch, R. F., From goods to service(s): (2008), doi:10.1016/j.indmarman.2007.07.004 This logic of the need for a shift in the activities of the enterprise and/or industry to match the analogous shift in the economy is so intuitively compelling that it is an apparent truism. It is a logic that follows naturally from marketing's traditional foundational thought. But is it the only logic; is it the correct logic? Does it move business-to-business (B2B) firms and/or academic marketing thought in a desirable and enhanced direction? While we agree that a shift to a service focus is desirable, if not essential to a firm's well being and the advancement of academic thought, we question the conventional, underlying rationale and the associated, implied approach. The purpose of this commentary is to explore this traditional logical foundation with its roots in the manufacturing and provision of tangible output and to propose an alternative logic, one grounded in a revised understanding of the meaning of service as a process and its central role in economic exchange. It is a logic that represents the convergence and extension of divergent marketing thought by sub-disciplines and other research initiatives. We argue that this more service-centric logic not only amplifies the necessity for the development of a service focus but it also provides a stronger foundation for theory development and, consequently, application. It is a logic that provides a framework for elevating knowledge discovery in business marketing (as well as other “sub-disciplines”) beyond the Divergences and convergences of logics, Industrial Marketing Management 2 S.L. Vargo, R.F. Lusch / Industrial Marketing Management xx (2008) xxx–xxx ARTICLE IN PRESS identification and explanation of B2B marketing differences from other forms of marketing to a level capable of informing not only the business-marketing firm but “mainstream” marketing in general. Thus, we argue a service-centered focus is enriching and unifying. 1. Alternative logics Broadly speaking, there are two perspectives for the consideration of service(s). One views goods (tangible output embedded with value) as the primary focus of economic exchange and “services” (usually plural) as either (1) a restricted type of (intangible) good (i.e., as units of output) or (2) an add-on that enhances the value of a good. We (Vargo & Lusch, 2004a; Lusch & Vargo, 2006a) call this logic goods-dominant (G-D) logic. Others have referred to it as the “neoclassical economics research tradition” (e.g., Hunt, 2000), “manufacturing logic” (e.g., Normann, 2001), “old enterprise logic” (Zuboff & Maxmin, 2002), and “marketing management” (Webster, 1992). Regardless of the label, G-D logic points toward using principles developed to manage goods production to manage services “production” and “delivery”. The second logic considers “service” (singular) – a process of doing something for another party – in its own right, without reference to goods and identifies service as the primary focus of exchange activity. We (Vargo & Lusch, 2004a, 2006) call this logic service-dominant (S-D) logic. In S-D logic, goods continue to play an important, service-delivery role, at least in a subset of economic exchange. In contrast to implying the modification of goods-based models of exchange to fit a transition to service, S-D logic provides a service-based foundation centered on service-driven principles. We show that this transition is highly consistent with many contemporary business-marketing models. 2. Goods-dominant logic As the label implies, G-D logic is centered on the good – or more recently, the “product”, to include both tangible (goods) and intangible (services) units of output – as archetypical units of exchange. The essence of G-D logic is that economic exchange is fundamentally concerned with units of output (products) that are embedded with value during the manufacturing (or farming, or extraction) process. For efficiency, this production ideally takes place in isolation from the customer and results in standardized, inventoriable goods. The roots of G-D logic are found in the work of Smith (1776) and took firmer, paradigmatic grasp in the context of the Industrial Revolution during the quest for a science of economics, at a time when “science” meant Newtonian mechanics, a paradigm for which the idea of goods embedded with value was particularly amenable. Management and mainstream academic marketing, as well as society in general, inherited this logic from economics (see Vargo, Lusch, & Morgan, 2006; Vargo & Morgan, 2005). However, since formal marketing thought emerged over 100 years ago, G-D logic and its associated concept of embedded value (or utility) have caused problems for marketers. For exPlease cite this article as: Vargo, S. L., & Lusch, R. F., From goods to service(s): (2008), doi:10.1016/j.indmarman.2007.07.004 ample, in the mid 20th Century, it caused Alderson (1957, p. 69) to declare: “What is needed is not an interpretation of the utility created bymarketing, but a marketing interpretation of the whole process of creating utility”. But the G-D-logic-based economic theory, with its co-supportive concepts of embedded value (production) and value destruction (consumption) was itself deeply embedded in marketing thought. It was not long after this period, we believe for related reasons, that academic marketing started becoming fragmented, with various marketing concerns taking on an increasingly separate, or sub-disciplinarian, identity. 3. Subdividing and breaking free from G-D logic Arguably, the establishment of many of the sub-disciplines of marketing, such as business-to-business marketing, services marketing, and international marketing, is a response to the limitations and lack of robustness of G-D logic as a foundation for understanding value creation and exchange. That is, while GD logic might have been reasonably adequate as a foundation when marketing was primarily concerned with the distribution of commodities, the foundation was severely restricted as marketing expanded its scope to the more general issues of value creation and exchange. 3.1. Business-to-business marketing Initial sub-disciplinary approaches have typically involved trying to fit the models of mainstream marketing to the particular phenomena of concern. For example, as marketers (both academic and applied) began to address issues of industrial marketing and found that manymainstreammarketingmodels did not seem to apply, the natural course of action was not to question the paradigmatic foundation but rather first to identify how B2B marketing was different from mainstream, consumer marketing and then to identify the ways that business marketers needed to adjust. Thus, early attempts led to the identification of prototypical characteristics of business marketing — derived demand, fluctuating demand, professional buyers, etc. (see Fern & Brown, 1984). But we suggest that the creation of business-tobusiness marketing as a sub-discipline was more because of the inability of the G-D-logic-grounded mainstream marketing to provide a suitable foundation for understanding inter-enterprise exchange phenomena than it was because of any real and essential difference compared to enterprise-to-individual exchange. Support for this contention can be found in the fact that busin

A research manifesto for services science <=SEP=> The services sector has grown over the last 50 years to dominate economic activity in most advanced industrial economies, yet scientific understanding of modern services is rudimentary. Here, we argue for a services science discipline to integrate across academic silos and advance service innovation more rapidly.

Cutting Corners and Working Overtime: Quality Erosion in the Service Industry <=SEP=> The erosion of service quality throughout the economy is a frequent concern in the popular press. The American Customer Satisfaction Index for services fell in 2000 to 69.4%, down 5 percentage points from 1994. We hypothesize that the characteristics of services— inseparability, intangibility, and labor intensity—interact with management practices to bias service providers toward reducing the level of service they deliver, often locking entire industries into a vicious cycle of eroding service standards. To explore this proposition we develop a formal model that integrates the structural elements of service delivery. We use econometric estimation, interviews, observations, and archival data to calibrate the model for a consumer-lending service center in a major bank in the United Kingdom. We find that temporary imbalances between service capacity and demand interact with decision rules for effort allocation, capacity management, overtime, and quality aspirations to yield permanent erosion of the service standards and loss of revenue. We explore policies to improve performance and implications for organizational design in the service sector. (Organizational Learning; Service Management Performance; Service Operations; Service Quality; Simulation; System Dynamics)

Evolving to a New Dominant Logic for Marketing <=SEP=> Marketing inherited a model of exchange from economics, which had a dominant logic based on the exchange of “goods,” which usually are manufactured output. The dominant logic focused on tangible resources, embedded value, and transactions. Over the past several decades, new perspectives have emerged that have a revised logic focused on intangible resources, the cocreation of value, and relationships. The authors believe that the new perspectives are converging to form a new dominant logic for marketing, one in which service provision rather than goods is fundamental to economic exchange. The authors explore this evolving logic and the corresponding shift in perspective for marketing scholars, marketing practitioners, and marketing educators.

Informed Recommender: Basing Recommendations on Consumer Product Reviews <=SEP=> Recommender systems attempt to predict items in which a user might be interested, given some information about the user's and items' profiles. Most existing recommender systems use content-based or collaborative filtering methods or hybrid methods that combine both techniques (see the sidebar for more details). We created Informed Recommender to address the problem of using consumer opinion about products, expressed online in free-form text, to generate product recommendations. Informed recommender uses prioritized consumer product reviews to make recommendations. Using text-mining techniques, it maps each piece of each review comment automatically into an ontology.

Performance comparison of adder architectures on 28nm FPGA <=SEP=> Adders are one of the most desirable entities in processor data path architecture. Since long, VLSI engineers are working towards optimization and miniaturization of adder architectures to ultimately improve the performance of processors. As the technology is scaling down, challenges towards optimization are also increasing. In this paper we present the implementation of various 16 bit adder architectures including parallel prefix adders and their comparative analysis based on ultimate performance parameters-area and power at 28nm technology. All designs have been synthesized and implemented on Xilinx Zynq-7000 FPGA board using Xilinx Vivado 14.4 design tool. Verilog HDL is used as programming HDL. Weinberger adder provides the best area solution.

Smart Homes and Quality of Life for the Elderly: Perspective of Competing Models <=SEP=> The percentage of the elderly population has been on rise steadily over the past few years across the globe, which is a cause of serious concern among the research fraternity. A lot of research is going on that tries to harness the benefits of the various information and communication technologies to enable these elderly people to live independently and promote a sense of overall well-being. Although in the evolutionary phase, yet smart homes can help these elderly people in their daily life. However, for the success of such smart systems, the intention of the users towards using those systems must be understood. But, there is a serious lack of relevant exploratory research that tries to measure and explain the adoption of such smart homes by the elderly population from a multiple theory perspective: namely, the technology acceptance model, the theory of reasoned action and the theory of planned behavior. The main aim of this paper is to fill up this void of a lack of theoretical approach by testing the three models in the context of the adoption of smart homes by the elderly population. In order to do so, we conducted a survey with  $N = 239$  (after screening) and analyzed the results using the structural equation modeling and confirmatory factor analysis techniques. Results suggest that all the three models are valid although they do not take into account certain factors that are unique to this context. This paper paper provides the initial groundwork to explore the process of adopting smart home services by the elderly with potential future research areas.

Paraphrase Detection Using Recursive Autoencoder <=SEP=> In this paper, we tackle the paraphrase detection task. We present a novel recursive autoencoder architecture that learns representations of phrases in an unsupervised way. Using these representations, we are able to extract features for classification algorithms that allow us to outperform many results from previous works.

Scaling Scrum in a Large Distributed Project <=SEP=> This paper presents a currently ongoing single case study on adopting and scaling Scrum in a large software development project distributed across four sites. The data was gathered by 19 semi-structured interviews of project personnel, including managers, architects, developers and testers. At the time of the interviews the project had grown in size during the past 2,5 years from two collocated Scrum teams to 20 teams located in four countries and employing over 170 persons. In this paper we first describe our research approach and goals. Then we briefly summarize the preliminary results of this ongoing study: we explain how Scrum practices were scaled, as well as discuss the successes and challenges experienced when adopting the agile practices and scaling them, while at the same time growing the project size at a fast pace. Even though this project has been very successful from the business point of view, it has experienced a lot of problems in applying Scrum, especially related to scaling the agile practices. Thus, it seems that adapting Scrum practices to work well in a large distributed setting is challenging.

Skin force sensor using piezoresistive PEDOT:PSS with arabitol on flexible PDMS <=SEP=> In this paper, a flexible conducting polymer based force sensor array has been developed. A conducting polymer, namely poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS) with additive arabitol, was utilized as the piezoresistive layer on a flexible polydimethylsiloxane (PDMS) substrate owing to its high conductivity and stretchability. A simplified strain sensor has been developed to demonstrate the properties of the arabitol-PEDOT:PSS. Suitable curing conditions and arabitol concentration have been established with design of experiment. The arabitol-PEDOT:PSS can be stretched up to 20% and it shows a significant gauge factor of 11.5. A flexible interdigital force sensor array has been developed to demonstrate the effectiveness of arabitol-PEDOT:PSS on PDMS as a force sensor.

The Statistical Recurrent Unit <=SEP=> Sophisticated gated recurrent neural network architectures like LSTMs and GRUs have been shown to be highly effective in a myriad of applications. We develop an un-gated unit, the statistical recurrent unit (SRU), that is able to learn long term dependencies in data by only keeping moving averages of statistics. The SRU’s architecture is simple, un-gated, and contains a comparable number of parameters to LSTMs; yet, SRUs perform favorably to more sophisticated LSTM and GRU alternatives, often outperforming one or both in various tasks. We show the efficacy of SRUs as compared to LSTMs and GRUs in an unbiased manner by optimizing respective architectures’ hyperparameters in a Bayesian optimization scheme for both synthetic and realworld tasks.

Normalizing Flows on Riemannian Manifolds <=SEP=> We consider the problem of density estimation on Riemannian manifolds. Density estimation on manifolds has many applications in fluid-mechanics, optics and plasma physics and it appears often when dealing with angular variables (such as used in protein folding, robot limbs, gene-expression) and in general directional statistics. In spite of the multitude of algorithms available for density estimation in the Euclidean spaces R that scale to large n (e.g. normalizing flows, kernel methods and variational approximations), most of these methods are not immediately suitable for density estimation in more general Riemannian manifolds. We revisit techniques related to homeomorphisms from differential geometry for projecting densities to sub-manifolds and use it to generalize the idea of normalizing flows to more general Riemannian manifolds. The resulting algorithm is scalable, simple to implement and suitable for use with automatic differentiation. We demonstrate concrete examples of this method on the n-sphere S. In recent years, there has been much interest in applying variational inference techniques to learning large scale probabilistic models in various domains, such as images and text [1, 2, 3, 4, 5, 6]. One of the main issues in variational inference is finding the best approximation to an intractable posterior distribution of interest by searching through a class of known probability distributions. The class of approximations used is often limited, e.g., mean-field approximations, implying that no solution is ever able to resemble the true posterior distribution. This is a widely raised objection to variational methods, in that unlike MCMC, the true posterior distribution may not be recovered even in the asymptotic regime. To address this problem, recent work on Normalizing Flows [7], Inverse Autoregressive Flows [8], and others [9, 10] (referred collectively as normalizing flows), focused on developing scalable methods of constructing arbitrarily complex and flexible approximate posteriors from simple distributions using transformations parameterized by neural networks, which gives these models universal approximation capability in the asymptotic regime. In all of these works, the distributions of interest are restricted to be defined over high dimensional Euclidean spaces. There are many other distributions defined over special homeomorphisms of Euclidean spaces that are of interest in statistics, such as Beta and Dirichlet (n-Simplex); Norm-Truncated Gaussian (n-Ball); Wrapped Cauchy and Von-Misses Fisher (n-Sphere), which find little applicability in variational inference with large scale probabilistic models due to the limitations related to density complexity and gradient computation [11, 12, 13, 14]. Many such distributions are unimodal and generating complicated distributions from them would require creating mixture densities or using auxiliary random variables. Mixture methods require further knowledge or tuning, e.g. number of mixture components necessary, and a heavy computational burden on the gradient computation in general, e.g. with quantile functions [15]. Further, mode complexity increases only linearly with mixtures as opposed to exponential increase with normalizing flows. Conditioning on auxiliary variables [16] on the other hand constrains the use of the created distribution, due to the need for integrating out the auxiliary factors in certain scenarios. In all of these methods, computation of low-variance gradients is difficult due to the fact that simulation of random variables cannot be in general reparameterized (e.g. rejection sampling [17]). In this work, we present methods that generalizes previous work on improving variational inference in R using normalizing flows to Riemannian manifolds of interest such as spheres S, tori T and their product topologies with R, like infinite cylinders. ar X iv :1 61 1. 02 30 4v 1 [ st at .M L ] 7 N ov 2 01 6 Figure 1: Left: Construction of a complex density on S by first projecting the manifold to R, transforming the density and projecting it back to S. Right: Illustration of transformed (S → R) densities corresponding to an uniform density on the sphere. Blue: empirical density (obtained by Monte Carlo); Red: Analytical density from equation (4); Green: Density computed ignoring the intrinsic dimensionality of S. These special manifolds M ⊂ R are homeomorphic to the Euclidean space R where n corresponds to the dimensionality of the tangent space of M at each point. A homeomorphism is a continuous function between topological spaces with a continuous inverse (bijective and bicontinuous). It maps point in one space to the other in a unique and continuous manner. An example manifold is the unit 2-sphere, the surface of a unit ball, which is embedded in R and homeomorphic to R (see Figure 1). In normalizing flows, the main result of differential geometry that is used for computing the density updates is given by, d~x = |det Jφ| d~u and represents the relationship between differentials (infinitesimal volumes) between two equidimensional Euclidean spaces using the Jacobian of the function φ : R → R that transforms one space to the other. This result only applies to transforms that preserve the dimensionality. However, transforms that map an embedded manifold to its intrinsic Euclidean space, do not preserve the dimensionality of the points and the result above become obsolete. Jacobian of such transforms φ : R → R with m > n are rectangular and an infinitesimal cube on R maps to an infinitesimal parallelepiped on the manifold. The relation between these volumes is given by d~x = √ det G d~u, where G = J φ Jφ is the metric induced by the embedding φ on the tangent space TxM, [18, 19, 20]. The correct formula for computing the density over M now becomes : ∫

Flight Dynamics Modeling of Dual-Spin Guided Projectiles <=SEP=> This paper presents a complete nonlinear parameter-dependent mathematical model, as well as a procedure for computing the quasi-linear parameter-varying (q-LPV) model of a class of spin-stabilized canard-controlled guided projectiles. The proposed projectile concept possesses a so-called dual-spin configuration: the forward section contains the necessary control and guidance software and hardware, whereas the aft roll-decoupled and rapidly spinning section contains the payload. Wind-axes instead of body-axes variables, as is the case in the existing literature, are preferred for the modeling of the highly coupled pitch/yaw airframe nonlinear dynamics, since they are better suited to control synthesis. A q-LPV model, approximating these nonlinear dynamics around the system equilibrium manifold and capturing their dependence on diverse varying flight parameters, is analytically obtained. In addition, a detailed stability analysis specific to this kind of weapons is performed throughout their large flight envelope using the aforementioned q-LPV model. Furthermore, a study is conducted in order to quantify the influence of reducing the dimension of the flight parameter vector on the exactness of the q-LPV model. Finally, the critical influence on the pitch/yaw-dynamics of the nose-embedded sensor position, and of uncertainty on the various static and dynamic aerodynamic coefficients as well as the aerodynamic angles, is shown.

Information Retrieval <=SEP=> Chinese input recommendation plays an important role in alleviating human cost in typing Chinese words, especially in the scenario of mobile applications. The fundamental problem is to predict the conditional probability of the next word given the sequence of previous words. Therefore, statistical language models, i.e. n-grams based models, have been extensively used on this task in real application. However, the characteristics of extremely different typing behaviors usually lead to serious sparsity problem, even n-gram with smoothing will fail. A reasonable approach to tackle this problem is to use the recently proposed neural models, such as probabilistic neural language model, recurrent neural network and word2vec. They can leverage more semantically similar words for estimating the probability. However, there is no conclusion on which approach of the two will work better in real application. In this paper, we conduct an extensive empirical study to show the differences between statistical and neural language models. The experimental results show that the two different approach have individual advantages, and a hybrid approach will bring a significant improvement.

On Trade-offs of Applying Block Chains for Electronic Voting Bulletin Boards <=SEP=> This paper takes a critical look at the recent trend of building electronic voting systems on top of block chain technology. Even though being very appealing from the election integrity perspective, block chains have numerous technical, economical and even political drawbacks that need to be taken into account. Selecting a good trade-off between desirable properties and restrictions imposed by different block chain implementations is a highly non-trivial task. This paper aims at bringing some clarity into performing this task. We will mostly be concentrating on public permissionless block chains and their applications as bulletin board implementations as these are the favourite choices in majority of the recent block chain based voting protocol proposals.

Q-Learning-Based Power Control for LTE Enterprise Femtocell Networks <=SEP=> Small cells are beneficial for both users and operators to improve communication quality. The femtocell is one type of small cell used for indoor coverage. Enterprise femtocell installation inevitably produces intralayer and interlayer interference management issues. Enterprise femtocell optimization and indoor propagation modeling are built in this work based on the communications environment characteristics. Q-learning-based distributed and hybrid power control strategies are then proposed. The throughput, energy efficiency, and user experience satisfaction with conventional scheduling methods are compared. The simulation results show that distributed Q-learning performs better local optimization while hybrid Q-learning enhances global performance.

Discovering the invisible city: Location-based games for learning in smart cities <=SEP=> In this paper we discuss how location-based mobile games can be designed for learning in modern technology enhanced public spaces. We start with the description of the design process and we identify the main challenges faced. We elaborate the case of the game Invisible City: Rebels vs. Spies, a game to be played in a city centre using mobile devices. Through this case we highlight the adaptation of an original party game into a mobile form, the issues we faced and the key aspects conductive to learning in a smart city. It is claimed that creating mobile city games for learning is a new challenge, as our city landscapes are augmented with an increasing number of layers of digital information in which a new generation of city games are played.

Security and Privacy in Cloud Computing: Vision, Trends, and Challenges <=SEP=> Cloud computing offers cost-effective solutions via a variety of flexible services. However, security concerns related to managing data, applications, and interactions hamper the rapid deployment of cloud-based services on a large scale. Although many solutions exist, efficiency, scalability, and provable security still have issues that need to be properly addressed. This article explores the various challenges, existing solutions, and limitations of cloud security, with a focus on data utilization management aspects, including data storage, data analytics, and access control. The article concludes with a discussion on future research directions that might lead to more trustworthy cloud security and privacy.

The sequential maximum angle convex cone ( SMACC ) endmember model <=SEP=> A new endmember extraction method has been developed that is based on a convex cone model for representing vector data. The endmembers are selected directly from the data set. The algorithm for finding the endmembers is sequential: the convex cone model starts with a single endmember and increases incrementally in dimension. Abundance maps are simultaneously generated and updated at each step. A new endmember is identified based on the angle it makes with the existing cone. The data vector making the maximum angle with the existing cone is chosen as the next endmember to add to enlarge the endmember set. The algorithm updates the abundances of previous endmembers and ensures that the abundances of previous and current endmembers remain positive or zero. The algorithm terminates when all of the data vectors are within the convex cone, to some tolerance. The method offers advantages for hyperspectral data sets where high correlation among channels and pixels can impair un-mixing by standard techniques. The method can also be applied as a band-selection tool, finding end-images that are unique and forming a convex cone for modeling the remaining hyperspectral channels. The method is described and applied to hyperspectral data sets.

A survey: Attacks on RPL and 6LoWPAN in IoT <=SEP=> 6LoWPAN (IPv6 over Low-Power Wireless Personal Area Networks) standard allows heavily constrained devices to connect to IPv6 networks. 6LoWPAN is novel IPv6 header compression protocol, it may go easily under attack. Internet of Things consist of devices which are limited in resource like battery powered, memory and processing capability etc. for this a new network layer routing protocol is designed called RPL (Routing Protocol for low power Lossy network). RPL is light weight protocol and doesn't have the functionality like of traditional routing protocols. This rank based routing protocol may goes under attack. Providing security in Internet of Things is challenging as the devices are connected to the unsecured Internet, limited resources, the communication links are lossy and set of novel technologies used such as RPL, 6LoWPAN etc. This paper focus on possible attacks on RPL and 6LoWPAN network, counter measure against them and consequences on network parameters. Along with comparative analysis of methods to mitigate these attacks are done and finally the research opportunities in network layer security are discussed.

Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning <=SEP=> imbalanced-learn is an open-source python toolbox aiming at providing a wide range of methods to cope with the problem of imbalanced dataset frequently encountered in machine learning and pattern recognition. The implemented state-of-the-art methods can be categorized into 4 groups: (i) under-sampling, (ii) over-sampling, (iii) combination of overand under-sampling, and (iv) ensemble learning methods. The proposed toolbox depends only on numpy, scipy, and scikit-learn and is distributed under MIT license. Furthermore, it is fully compatible with scikit-learn and is part of the scikit-learn-contrib supported project. Documentation, unit tests as well as integration tests are provided to ease usage and contribution. Source code, binaries, and documentation can be downloaded from https://github.com/scikit-learn-contrib/imbalanced-learn.

Function-Private Functional Encryption in the Private-Key Setting <=SEP=> Functional encryption supports restricted decryption keys that allow users to learn specific functions of the encrypted messages. Although the vast majority of research on functional encryption has so far focused on the privacy of the encrypted messages, in many realistic scenarios it is crucial to offer privacy also for the functions for which decryption keys are provided. Whereas function privacy is inherently limited in the public-key setting, in the private-key setting it has a tremendous potential. Specifically, one can hope to construct schemes where encryptions of messages $$\mathsf{m}_1, \ldots , \mathsf{m}_T$$ m 1 , … , m T together with decryption keys corresponding to functions $$f_1, \ldots , f_T$$ f 1 , … , f T , reveal essentially no information other than the values $$\{ f_i(\mathsf{m}_j)\}_{i,j\in [T]}$$ { f i ( m j ) } i , j ∈ [ T ] . Despite its great potential, the known function-private private-key schemes either support rather limited families of functions (such as inner products) or offer somewhat weak notions of function privacy. We present a generic transformation that yields a function-private functional encryption scheme, starting with any non-function-private scheme for a sufficiently rich function class. Our transformation preserves the message privacy of the underlying scheme and can be instantiated using a variety of existing schemes. Plugging in known constructions of functional encryption schemes, we obtain function-private schemes based either on the learning with errors assumption, on obfuscation assumptions, on simple multilinear-maps assumptions, and even on the existence of any one-way function (offering various trade-offs between security and efficiency).

Shape matching and object recognition using shape contexts <=SEP=> This paper presents my work on computing shape models that are computationally fast and invariant basic transformations like translation, scaling and rotation. In this paper, I propose shape detection using a feature called shape context. Shape context describes all boundary points of a shape with respect to any single boundary point. Thus it is descriptive of the shape of the object. Object recognition can be achieved by matching this feature with a priori knowledge of the shape context of the boundary points of the object. Experimental results are promising on handwritten digits, trademark images.

Dermoscopy: basic concepts. <=SEP=> Dermoscopy is a very useful technique for the analysis of pigmented skin lesions. It represents a link between clinical and histological views, permitting an earlier diagnosis of skin melanoma. It also helps in the diagnosis of many other pigmented skin lesions, such as seborrheic keratosis, pigmented basal cell carcinoma, hemangioma, blue nevus, atypical nevus, and mole, which can often clinically simulate melanoma. In this article, dermoscopy is reviewed from its history to the basic concepts of the interpretation of dermoscopic images. The goal is to introduce this subject to those not yet familiar with it, in order to instigate and encourage the training and practice of this technique of growing importance for everyday usage.

Deep learning to classify difference image for image change detection <=SEP=> Image change detection is a process to analyze multi-temproal images of the same scene for identifying the changes that have occurred. In this paper, we propose a novel difference image analysis approach based on deep neural networks for image change detection problems. The deep neural network learning algorithm for classification includes unsupervised feature learning and supervised fine-tuning. Some samples with the labels of high accuracy obtained by a pre-classification are used for fine-tuning. Since a deep neural network can learn complicated functions that can represent high-level abstractions, it can obtain satisfactory results. Theoretical analysis and experiment results on real datasets show that the proposed method outperforms some other methods.

WHY PEOPLE STAY : USING JOB EMBEDDEDNESS TO PREDICT VOLUNTARY TURNOVER <=SEP=> A new construct, entitled job embeddedness, is introduced. Assessing factors from on and off the job, it includes an individual’s (a) links to other people, teams and groups, (b) perception of their fit with their job, organization and community and (c) what they say they would have to sacrifice if they left their job. A measure of job embeddedness is developed with two samples. The results show that job embeddedness predicts the key outcomes of both intent to leave and voluntary turnover, and explains significant incremental variance over and above job satisfaction, organizational commitment, job alternatives and job search. Implications for theory and practice are discussed.

Category fluency test: normative data for English- and Spanish-speaking elderly. <=SEP=> Category fluency tasks are an important component of neuropsychological assessment, especially when evaluating for dementia syndromes. The growth in the number of Spanish-speaking elderly in the United States has increased the need for appropriate neuropsychological measures and normative data for this population. This study provides norms for English and Spanish speakers, over the age of 50, on 3 frequently used measures of category fluency: animals, vegetables, and fruits. In addition, it examines the impact of age, education, gender, language, and depressed mood on total fluency scores and on scores on each of these fluency measures. A sample of 702 cognitively intact elderly, 424 English speakers, and 278 Spanish speakers, participated in the study. Normative data are provided stratified by language, age, education, and gender. Results evidence that regardless of the primary language of the examinee, age, education, and gender are the strongest predictors of total category fluency scores, with gender being the best predictor of performance after adjusting for age and education. English and Spanish speakers obtained similar scores on animal and fruit fluency, but English speakers generated more vegetable exemplars than Spanish speakers. Results also indicate that different fluency measures are affected by various factors to different degrees.

High Performance Computing for Hyperspectral Remote Sensing <=SEP=> Advances in sensor and computer technology are revolutionizing the way remotely sensed data is collected, managed and analyzed. In particular, many current and future applications of remote sensing in Earth science, space science, and soon in exploration science will require real- or near real-time processing capabilities. In recent years, several efforts have been directed towards the incorporation of high-performance computing (HPC) models to remote sensing missions. A relevant example of a remote sensing application in which the use of HPC technologies (such as parallel and distributed computing) is becoming essential is hyperspectral remote sensing, in which an imaging spectrometer collects hundreds or even thousands of measurements (at multiple wavelength channels) for the same area on the surface of the Earth. In this paper, we review recent developments in the application of HPC techniques to hyperspectral imaging problems, with particular emphasis on commodity architectures such as clusters, heterogeneous networks of computers, and specialized hardware devices such as field programmable gate arrays (FPGAs) and commodity graphic processing units (GPUs). A quantitative comparison across these architectures is given by analyzing performance results of different parallel implementations of the same hyperspectral unmixing chain, delivering a snapshot of the state-of-the-art in this area and a thoughtful perspective on the potential and emerging challenges of applying HPC paradigms to hyperspectral remote sensing problems.

Towards subjective quality of experience assessment for omnidirectional video streaming <=SEP=> Currently, we witness dramatically increasing interest in immersive media technologies like Virtual Reality (VR), particularly in omnidirectional video (OV) streaming. Omnidirectional (also called 360-degree) videos are panoramic spherical videos in which the user can look around during playback and which therefore can be understood as hybrids between traditional movie streaming and interactive VR worlds. Unfortunately, streaming this kind of content is extremely bandwidth intensive (compared to traditional 2D video) and therefore, Quality of Experience (QoE) tends to deteriorate significantly in absence of continuous optimal bandwidth conditions. In this paper, we present a first approach towards subjective QoE assessment for omnidirectional video (OV) streaming. We present the results of a lab study on the QoE impact of stalling in the context of OV streaming using head-mounted displays (HMDs). Our findings show that subjective testing for immersive media like OV is not trivial, with even simple cases like stalling leading to unexpected results. After a discussion of characteristic pitfalls and lessons learned, we provide a a set of recommendations for upcoming OV assessment studies.