Applications of data mining in robotics

From a high-level viewpoint, robotics is all about data. In other words, each robot agent for performing its designated tasks should deal with data in some form - from collecting data from the environment to generating internal data. For this reason and due to broad range of possible applications, managing and dealing with such data is an important issue in the field. On the other side, data - in its different types - is known as an invaluable treasure of knowledge and insight within any information system. In this regard, extracting such hidden knowledge through data mining has been in the center of focus for more than two decades. In fact, data mining could be considered as a super-influential phenomenon on all scientific fields and robotics is not an exception. Over the recent years, many studies have been conducted on different aspects of leveraging data mining techniques in the robotic domain to improve performance of robots in various ways. However, despite the current disparate and unorganized studies in different directions, there are some potential areas that have not received enough attention. In this paper, we provide a high-level overview of the field and try to organize the current studies as a reference point and road map for future works. Moreover, challenges and future possible directions will be studied.


A carpet cloak for visible light.

We report an invisibility carpet cloak device, which is capable of making an object undetectable by visible light. The cloak is designed using quasi conformal mapping and is fabricated in a silicon nitride waveguide on a specially developed nanoporous silicon oxide substrate with a very low refractive index (n<1.25). The spatial index variation is realized by etching holes of various sizes in the nitride layer at deep subwavelength scale creating a local effective medium index. The fabricated device demonstrates wideband invisibility throughout the visible spectrum with low loss. This silicon nitride on low index substrate can also be a general scheme for implementation of transformation optical devices at visible frequencies.


Tag suggestion and localization in user-generated videos based on social knowledge

Nowadays, almost any web site that provides means for sharing user-generated multimedia content, like Flickr, Facebook, YouTube and Vimeo, has tagging functionalities to let users annotate the material that they want to share. The tags are then used to retrieve the uploaded content, and to ease browsing and exploration of these collections, e.g. using tag clouds. However, while tagging a single image is straightforward, and sites like Flickr and Facebook allow also to tag easily portions of the uploaded photos, tagging a video sequence is more cumbersome, so that users just tend to tag the overall content of a video. Moreover, the tagging process is completely manual, and often users tend to spend as few time as possible to annotate the material, resulting in a sparse annotation of the visual content. A semi-automatic process, that helps the users to tag a video sequence would improve the quality of annotations and thus the overall user experience. While research on image tagging has received a considerable attention in the latest years, there are still very few works that address the problem of automatically assigning tags to videos, locating them temporally within the video sequence. In this paper we present a system for video tag suggestion and temporal localization based on collective knowledge and visual similarity of frames. The algorithm suggests new tags that can be associated to a given keyframe exploiting the tags associated to videos and images uploaded to social sites like YouTube and Flickr and visual features.


ESD protection circuit schemes for DDR3 DQ drivers

The high-speed interface of DQ pins in DDR3 DRAM requires special ESD considerations. During ESD characterization testing, high voltages from the power rail could pass through the pre-driver PMOSFET to damage the pull-down NMOSFET gate oxide. Special circuit design of the pre-driver circuit is required to eliminate this ESD failure mechanism.


2 A Physically Unclonable Function with BER < 10-8 for Robust Chip Authentication Using Oscillator Collapse in 40 nm CMOS

Security is a key concern in today’s mobile devices and a number of hardware implementations of security primitives have been proposed, including true random number generators, differential power attack avoidance, and chip-ID generators [1-4]. Recently, physically unclonable functions (PUFs) were proposed as a secure method for chip authentication in unsecure environments [5-7]. A PUF is a function that maps an input code (“challenge”) to an output code (“response”) in a manner that is unique for every chip. PUFs are increasingly used for IC authentication to offer protection against identity theft, cloning, and counterfeit components [2-4].


Bootstrap Confidence Intervals

This article surveys bootstrap methods for producing good approximate confidence intervals. The goal is to improve by an order of magnitude upon the accuracy of the standard intervals θ̂ ± zασ̂ , in a way that allows routine application even to very complicated problems. Both theory and examples are used to show how this is done. The first seven sections provide a heuristic overview of four bootstrap confidence interval procedures: BCa, bootstrap-t, ABC and calibration. Sections 8 and 9 describe the theory behind these methods, and their close connection with the likelihood-based confidence interval theory developed by Barndorff-Nielsen, Cox and Reid and others.


Wireless Body Area Network Security and Privacy Issue in E-Healthcare

Wireless Body Area Network (WBAN) is a collection of wireless sensor nodes which can be placed within the body or outside the body of a human or a living person which in result observes or monitors the functionality and adjoining situations of the body. Utilizing a Wireless Body Area Network, the patient encounters a greater physical versatility and is never again constrained to remain in the hospital. As the Wireless Body Area Network sensor devices is being utilized for gathering the sensitive data and possibly will run into antagonistic situations, they require complicated and very secure security medium or structure to avoid the vitriolic communications within the system. These devices represent various security and privacy protection of touchy and private patient medical data. The medical records of patients are a significant and an unsolved situation due to which a changing or exploitation to the system is possible. In this research paper, we first present an overview of WBAN, how they utilized for healthcare monitoring, its architecture then highlight major security and privacy requirements and assaults at different network layer in a WBAN and we finally talk about various cryptographic algorithms and laws for providing solution of security and privacy in WBAN. Keywords—E-Health; privacy; security; wireless body area networks


Self-Compassion and Body Dissatisfaction in Women : A Randomized Controlled Trial of a Brief Meditation Intervention

Body dissatisfaction is a major source of suffering among women of all ages. One potential factor that could mitigate body dissatisfaction is self-compassion, a construct that is garnering increasing research attention due to its strong association with psychological health. This study investigated whether a brief 3-week period of self-compassion meditation training would improve body satisfaction in a multigenerational group of women. Participants were randomized either to the meditation intervention group (N=98; M age=38.42) or to a waitlist control group (N=130; M age=36.42). Results suggested that compared to the control group, intervention participants experienced significantly greater reductions in body dissatisfaction, body shame, and contingent self-worth based on appearance, as well as greater gains in self-compassion and body appreciation. All improvements were maintained when assessed 3 months later. Self-compassion meditation may be a useful and cost-effective means of improving body image in


How Affective Is a "Like"?: The Effect of Paralinguistic Digital Affordances on Perceived Social Support

A national survey asked 323 U.S. adults about paralinguistic digital affordances (PDAs) and how these forms of lightweight feedback within social media were associated with their perceived social support. People perceived PDAs (e.g., Likes, Favorites, and Upvotes) as socially supportive both quantitatively and qualitatively, even without implicit meaning associated with them. People who are highly sensitive about what others think of them and have high self-esteem are more likely to perceive higher social support from PDAs.


Tweet Sarcasm Detection Using Deep Neural Network

Sarcasm detection has been modeled as a binary document classification task, with rich features being defined manually over input documents. Traditional models employ discrete manual features to address the task, with much research effect being devoted to the design of effective feature templates. We investigate the use of neural network for tweet sarcasm detection, and compare the effects of the continuous automatic features with discrete manual features. In particular, we use a bi-directional gated recurrent neural network to capture syntactic and semantic information over tweets locally, and a pooling neural network to extract contextual features automatically from history tweets. Results show that neural features give improved accuracies for sarcasm detection, with different error distributions compared with discrete manual features.


Stable Proportional-Derivative Controllers

In computer animation, the proportional-derivative (PD) controller is a common technique for tracking characters' motion. A new formulation of the PD controller-stable PD (SPD)-allows arbitrarily high gains, even at large time steps. The key is to determine joint forces and torques while taking into account the character's positions and velocities in the next time step. SPD is stable even when combined with a physics simulator that uses simple Euler integration. SPD controllers have a variety of uses, including motion tracking in a physics simulator, keyframe interpolation with secondary motion, and constraint satisfaction for simulation.


Flocks , Herds , and Schools : A Distributed Behavioral Model

The aggregate motion of a flock of birds, a herd of land animals, or a school of fish is a beautiful and familiar part of the n a t u r a l world. But this type of complex motion is rarely seen in computer animation. This paper explores an approach based on simulation as an alternative to scripting the paths of each bird individually. The simulated flock is an elaboration of a particle system, with the simulated birds being the particles. The aggregate motion of the simulated flock is created by a distributed behavioral model much like that at work in a natural flock; the birds choose their own course. Each simulated bird is implemented as an independent actor that navigates according to its local perception of the dynamic environment, the laws of simulated physics that rule its motion, and a set of behaviors programmed into it by the "animator." The aggregate motion of the simulated flock is the result of the dense interaction of the relatively simple behaviors of the individual simulated birds.


A new optimizer using particle swarm theory

The optimization of nonlinear functions using particle swarm methodology is described. Implementations of two paradigms are discussed and compared, including a recently developed locally oriented paradigm. Benchmark testing of both paradigms is described, and applications, including neural network training and robot task learning, are proposed. Relationships between particle swarm optimization and both artificial life and evolutionary computation are reviewed.


Smalltalk-80: The Language and Its Implementation

Covers Smalltalk language and concepts from Xerox PARC. This need to a valid in with hexadecimal number before. Dwight thanks adele goldberg david robson copyright addison wesley isbn edition used was at compile. Smalltalk 80 syntax uses punctuation in, the language they can be significantly more popular. There is not this may be written as artifacts such messages. By storing them are parsed according to evaluate? The boolean receiver as lisp often even the virtual machine. One left smalltalk 80 is, the modern open. The increasing usage visualworks has recently been used was an image. Alan kay one thing developers to a transcript window would. Even primitives such interleaving of the, language and dec objectstudio each restart. Enfin was an object holds is, extremely important. Also be recreated explicitly define the, book presents a copy of the agile methods messages.


Handbook of genetic algorithms

In this paper, the effect of Genetic algorithm (GA) on the enhancement of the texture and quality of plants visual L. Davis, The handbook of Genetic Algorithms. Genetic Algorithm is one of many methods that can be used to create a schedule. This method Davis, L.: Handbook on Genetic Algorithms. Van Nostrand. In this research, genetic algorithm approach has been applied for solving exam (18) Davis L., Handbook of Genetic Algorithms, New York, Van Nostrand.


Solid state transformer application to grid connected photovoltaic inverters

In the paper, an architecture, including a solid state transformer (SST) which is different from the conventional style is proposed The photovoltaic system with SST consists of two power stages. A dual half bridge (DHB) converter constitutes the first power stage, which aims to achieve the maximum power point tracking (MPPT) functions. A grid connected inverter is used to stabilize the DC bus voltage and make the output current sinusoidal. The control technique is presented and validated by simulation implemented on a photovoltaic system with SST. The simulation results prove the effectiveness of the proposed photovoltaic system and the proposed control technique.


Statistical Machine Translation: A Guide for Linguists and Translators

This paper presents an overview of Statistical Machine Translation (SMT), which is currently the dominant approach in Machine Translation (MT) research. In Way and Hearne (2010), we describe how central linguists and translators are to the MT process, so that SMT developers and researchers may better understand how to include these groups in continuing to advance the stateof-the-art. If these constituencies are to make an impact in the field of MT, they need to know how their input is used by SMT systems. Accordingly, our objective in this paper is to present the basic principles underpinning SMT in a way that linguists and translators will find accessible


Organic Computing – Technical Systems for Survival in the Real World

ion is the selection and (possibly) coarsening (i.e. quantisation) of certain system characteristics (attributes, performance indicators, parameters) from the total set of system characteristics. The abstraction process comprises: • A simplification (Example: The colour Red is an abstraction, which neglects the different possible shades of Red, the wavelengths, the intensity etc.), • an aggregation (Example: ‘Temperature’ condenses the myriad of individual molecule movements in a gas volume into a single number.), • and consequently: loss of information. The opposite of abstraction is concretisation. It comprises: • gain of information, • detailing, • refinement, • disaggregation, • in engineering: the design process. 3.2.2 System Boundary Systems are defined by their system boundary, which makes a distinction between inside and outside possible (i.e. between self and non-self, see Sect. 4.1). When 3.2 What is a System? 87 Fig. 3.3 Trade-off between initial development cost and later adaptation cost systems are designed, we must choose where to place the boundary. This involves a consideration of cost (Fig. 3.3): A narrow boundary will reduce the cost for planning and development in the first place but bears the risk of a high effort in case a subsequent adaptation and extension of the system should be necessary. A wide system boundary reverses the cost curves. Apparently, the total cost minimum lies somewhere in the middle. 3.2.3 Some System Types and Properties Most systems are transient, i.e. they change over time. They are developed, assembled, modified, destroyed. Reactive systems react on inputs (events, signals, sensor data) by applying outputs (events, control signals, commands) to the environment. All open systems are reactive! In a more focused definition: Only systems reacting in real time (within a predefined time) are reactive systems. Such real-time systems are characterised by (1) time restrictions (deadlines), and (2) time determinism (i.e. guarantees). Planned vs. Unplanned systems: During the development process of technical systems, only predictable events are taken into account in the designed core. But there will always be an unpredictable rest. Therefore, the ‘spontaneous closure’ (Fig. 3.4) has to cover these events. In technical systems, the exception handler or the diagnosis system play the role of a simple spontaneous closure. Living systems are characterised by a powerful spontaneous closure. Recurring reactions of the spontaneous closure become part of the designed core. This amounts to learning.


Micro-punch technique for treatment of Fordyce spots: a surgical approach for an unpleasant condition of the male genital.

Fordyce spots are ectopic sebaceous glands, ranging between 2 and 3 mm in diameter. These benign lesions are most frequently located in the oral mucosa and the genital skin. Especially in the male genital region they can cause itching, discomfort during sexual activities and are aesthetically unpleasant. So far, a variety of therapeutic procedures have been reported with varying success and recurrence rates. In the present retrospective study (n = 23 patients between 2003 and 2011), we present our surgical approach by means of the micro-punch technique. Using this effective method, we achieved very satisfactory functional and cosmetic results. There were no signs of recurrence during postoperative observations from 12 up to 84 months (median = 51.3 months).


ERMIA: Fast Memory-Optimized Database System for Heterogeneous Workloads

Large main memories and massively parallel processors have triggered not only a resurgence of high-performance transaction processing systems optimized for large main-memory and massively parallel processors, but also an increasing demand for processing heterogeneous workloads that include read-mostly transactions. Many modern transaction processing systems adopt a lightweight optimistic concurrency control (OCC) scheme to leverage its low overhead in low contention workloads. However, we observe that the lightweight OCC is not suitable for heterogeneous workloads, causing significant starvation of read-mostly transactions and overall performance degradation.
 In this paper, we present ERMIA, a memory-optimized database system built from scratch to cater the need of handling heterogeneous workloads. ERMIA adopts snapshot isolation concurrency control to coordinate heterogeneous transactions and provides serializability when desired. Its physical layer supports the concurrency control schemes in a scalable way. Experimental results show that ERMIA delivers comparable or superior performance and near-linear scalability in a variety of workloads, compared to a recent lightweight OCC-based system. At the same time, ERMIA maintains high throughput on read-mostly transactions when the performance of the OCC-based system drops by orders of magnitude.


Abstraction-Based Livelock/Deadlock Checking for Hardware Verification

ion-Based Livelock/Deadlock Checking for Hardware Verification In-Ho Moon and Kevin Harer Synopsys Inc. {mooni, kevinh}@synopsys.com


Automatic Forest-Fire Measuring Using Ground Stations and Unmanned Aerial Systems

This paper presents a novel system for automatic forest-fire measurement using cameras distributed at ground stations and mounted on Unmanned Aerial Systems (UAS). It can obtain geometrical measurements of forest fires in real-time such as the location and shape of the fire front, flame height and rate of spread, among others. Measurement of forest fires is a challenging problem that is affected by numerous potential sources of error. The proposed system addresses them by exploiting the complementarities between infrared and visual cameras located at different ground locations together with others onboard Unmanned Aerial Systems (UAS). The system applies image processing and geo-location techniques to obtain forest-fire measurements individually from each camera and then integrates the results from all the cameras using statistical data fusion techniques. The proposed system has been extensively tested and validated in close-to-operational conditions in field fire experiments with controlled safety conditions carried out in Portugal and Spain from 2001 to 2006.


Travi-Navi: self-deployable indoor navigation system

We present Travi-Navi - a vision-guided navigation system that enables a self-motivated user to easily bootstrap and deploy indoor navigation services, without comprehensive indoor localization systems or even the availability of floor maps. Travi-Navi records high quality images during the course of a guider's walk on the navigation paths, collects a rich set of sensor readings, and packs them into a navigation trace. The followers track the navigation trace, get prompt visual instructions and image tips, and receive alerts when they deviate from the correct paths. Travi-Navi also finds the most efficient shortcuts whenever possible. We encounter and solve several challenges, including robust tracking, shortcut identification, and high quality image capture while walking. We implement Travi-Navi and conduct extensive experiments. The evaluation results show that Travi-Navi can track and navigate users with timely instructions, typically within a 4-step offset, and detect deviation events within 9 steps.


Gender and language-variety Identification with MicroTC

In this notebook, we describe our approach to cope with the Author Profiling task on PAN17 which consists of both gender and language identification for Twitter’s users. We used our MicroTC (μTC) framework as the primary tool to create our classifiers. μTC follows a simple approach to text classification; it converts the problem of text classification to a model selection problem using several simple text transformations, a combination of tokenizers, a term-weighting scheme, and finally, it classifies using a Support Vector Machine. Our approach reaches accuracies of 0.7838, 0.8054, 0.7957, and 0.8538, for gender identification; and for language variety, it achieves 0.8275, 0.9004, 0.9554, and 0.9850. All these, for Arabic, English, Spanish, and Portuguese languages, respectively.


Multilingual Neural Network Acoustic Modelling for ASR of Under-Resourced English-isiZulu Code-Switched Speech

Although isiZulu speakers code-switch with English as a matter of course, extremely little appropriate data is available for acoustic modelling. Recently, a small five-language corpus of code-switched South African soap opera speech was compiled. We used this corpus to evaluate the application of multilingual neural network acoustic modelling to English-isiZulu code-switched speech recognition. Our aim was to determine whether English-isiZulu speech recognition accuracy can be improved by incorporating three other language pairs in the corpus: English-isiXhosa, English-Setswana and English-Sesotho. Since isiXhosa, like isiZulu, belongs to the Nguni language family, while Setswana and Sesotho belong to the more distant Sotho family, we could also investigate the merits of additional data from within and across language groups. Our experiments using both fully connected DNN and TDNN-LSTM architectures show that English-isiZulu speech recognition accuracy as well as language identification after code-switching is improved more by the incorporation of English-isiXhosa data than by the incorporation of the other language pairs. However additional data from the more distant language group remained beneficial, and the best overall performance was always achieved with a multilingual neural network trained on all four language pairs.


Accelerating MapReduce on a coupled CPU-GPU architecture

The work presented here is driven by two observations. First, heterogeneous architectures that integrate a CPU and a GPU on the same chip are emerging, and hold much promise for supporting power-efficient and scalable high performance computing. Second, MapReduce has emerged as a suitable framework for simplified parallel application development for many classes of applications, including data mining and machine learning applications that benefit from accelerators.
 This paper focuses on the challenge of scaling a MapReduce application using the CPU and GPU together in an integrated architecture. We develop different methods for dividing the work, which are the map-dividing scheme, where map tasks are divided between both devices, and the pipelining scheme, which pipelines the map and the reduce stages on different devices. We develop dynamic work distribution schemes for both the approaches. To achieve high load balance while keeping scheduling costs low, we use a runtime tuning method to adjust task block sizes for the map-dividing scheme. Our implementation of MapReduce is based on a continuous reduction method, which avoids the memory overheads of storing key-value pairs.
 We have evaluated the different design decisions using 5 popular MapReduce applications. For 4 of the applications, our system achieves 1.21 to 2.1 speedup over the better of the CPU-only and GPU-only versions. The speedups over a single CPU core execution range from 3.25 to 28.68. The runtime tuning method we have developed achieves very low load imbalance, while keeping scheduling overheads low. Though our current work is specific to MapReduce, many underlying ideas are also applicable towards intra-node acceleration of other applications on integrated CPU-GPU nodes.


Security Challenges in the IP-based Internet of Things

A direct interpretation of the term Internet of Things refers to the use of standard Internet protocols for the human-to-thing or thingto-thing communication in embedded networks. Although the security needs are well-recognized in this domain, it is still not fully understood how existing IP security protocols and architectures can be deployed. In this paper, we discuss the applicability and limitations of existing Internet protocols and security architectures in the context of the Internet of Things. First, we give an overview of the deployment model and general security needs. We then present challenges and requirements for IP-based security solutions and highlight specific technical limitations of standard IP security protocols.


Privacy is dead, long live privacy

Protecting social norms as confidentiality wanes.


PMSG fault diagnosis in marine application

This paper presents a fault diagnosis (FD) method for on line monitoring of stator winding partial inter-turn fault and severe faults, in permanent magnet synchronous generator (PMSG) used in shaft generator system in marine applications. The faulty machine is represented in state space model based on the dq-frame transformation. The Extended Kalman Filter (EKF) parameters estimation technique is used to estimate the value of stator winding short-circuit parameters of the machine. The proposed technique has been simulated for different fault scenarios using Matlab®/Simulink®. Simulation results show that the proposed technique is robust and effective. Moreover it can be applied at different operating conditions to prevent development of complete failure.


Why are some people happier than others? The role of cognitive and motivational processes in well-being.

Addressing the question of why some people are happier than others is important for both theoretical and practical reasons and should be a central goal of a comprehensive positive psychology. Following a construal theory of happiness, the author proposes that multiple cognitive and motivational processes moderate the impact of the objective environment on well-being. Thus, to understand why some people are happier than others, one must understand the cognitive and motivational processes that serve to maintain, and even enhance, enduring happiness and transient mood. The author's approach has been to explore hedonically relevant psychological processes, such as social comparison, dissonance reduction, self-reflection, self-evaluation, and person perception, in chronically happy and unhappy individuals. In support of a construal framework, self-rated happy and unhappy people have been shown to differ systematically in the particular cognitive and motivational strategies they use. Promising research directions for positive psychology in pursuit of the sources of happiness, as well as the implications of the construal approach for prescriptions for enhancing well-being, are discussed.


Cooperative map building using qualitative reasoning for several AIBO robots

The problem that a robot navigates autonomously through its environment, builds its own map and localizes itself in the map, is still an open problem. It is known as the SLAM (Simultaneous Localization and Map Building) problem. This problem is made even more difficult when we have several robots cooperating to build a common map of an unknown environment, due to the problem of map integration of several submaps built independently by each robot, and with a high degree of error, making the map matching specially difficult. Most of the approaches to solve map building problems are quantitative, resulting in a great computational cost and a low level of abstraction. In order to fulfil these drawbacks qualitative models have been recently used. However, qualitative models are non deterministic. Therefore, the solution recently adopted has been to mix both qualitative and quantitative models to represent the environment and build maps. However, no reasoning process has been used to deal with the information stored in maps up to now, therefore maps are only static storage of landmarks. In this paper we propose a novel method for cooperative map building based on hybrid (qualitative+quantitative) representation which includes also a reasoning process. Distinctive landmarks acquisition for map representation is provided by the cognitive vision and infrared modules which compute differences from the expected data according to the current map and the actual information perceived. We will store in the map the relative orientation information of the landmarks which appear in the environment, after a qualitative reasoning process, therefore the map will be independent of the point of view of the robot. Map integration will then be achieved by localizing each robot in the maps made by the other robots, through a process of pattern matching of the hybrid maps elaborated by each robot, resulting in an integrated map which all robots share, and which is the main objective of this work. This map building method is currently being tested on a team of Sony AIBO four


Sword: Scalable and Flexible Workload Generator for Distributed Data Processing Systems

Workload generation is commonly employed for performance characterization, testing and benchmarking of computer systems and networks. Workload generation typically aims at simulating or emulating traffic generated by different types of applications, protocols and activities, such as web browsing, email, chat, as well as stream multimedia traffic. We present a Scalable WORkloaD generator (SWORD) that we have developed for the testing and benchmarking of high-volume data processing systems. The tool is not only scalable but is also flexible and extensible allowing the generation of workload of a variety of types of applications and of contents.


Design and implementation of a fire detection and control system for automobiles using fuzzy logic

The immense benefits of fire in road transport cannot be overemphasized. However more than two thousand vehicles are damaged by unwanted fire on a daily basis. On a global scale, incendiary losses to the automobile and insurance industries have ran into billions of dollars over the last decade. A not-so-distant contributory factor is the lack of a sophisticated fire safety system on the automobile. This has been addressed by designing and implementing fuzzy logic control system with feedback over an Arduino micro-controller system. The automatic system consisting of flame sensors, temperature sensors, smoke sensors and a re-engineered mobile carbon dioxide air-conditioning unit was tested on a medium sized physical car. Results show that the automobile fire detection and control system devoid of false alarms, detects and extinguishes fire under 20 seconds. An innovative, very promising solution module for hardware implementation in fire detection and control for automobiles has been developed by using new algorithms and fuzzy logic.


Seven V's of Big Data understanding Big Data to extract value

Big Data has shown lot of potential in real world industry and research community. We support the power and potential of it in solving real world problems. However, it is imperative to understand Big Data through the lens of 7 V's. 7th V as `Value' is desired output for industry challenges and issues. We provide a brief survey study of 7 Vs of Big Data in order to understand Big Data and extract Value concept in general. Finally we conclude by showing our vision of improved healthcare, a product of Big Data Utilization, as a future work for researchers and students, while moving forward.


Using the Output Embedding to Improve Language Models

We study the topmost weight matrix of neural network language models. We show that this matrix constitutes a valid word embedding. When training language models, we recommend tying the input embedding and this output embedding. We analyze the resulting update rules and show that the tied embedding evolves in a more similar way to the output embedding than to the input embedding in the untied model. We also offer a new method of regularizing the output embedding. Our methods lead to a significant reduction in perplexity, as we are able to show on a variety of neural network language models. Finally, we show that weight tying can reduce the size of neural translation models to less than half of their original size without harming their performance.


A Single-Handed Partial Zooming Technique for Touch-Screen Mobile Devices

Despite its ubiquitous use, the pinch zooming technique is not effective for one-handed interaction. We propose ContextZoom, a novel technique for single-handed zooming on touch-screen mobile devices. It allows users to specify any place on a device screen as the zooming center to ensure that the intended zooming target is always visible on the screen after zooming. ContextZoom supports zooming in/out a portion of a viewport, and provides a quick switch between the partial and whole viewports. We conducted an empirical evaluation of ContextZoom through a controlled lab experiment to compare ContextZoom and the Google maps’ single-handed zooming technique. Results show that ContextZoom outperforms the latter in task completion time and the number of discrete actions taken. Participants also reported higher levels of perceived effectiveness and overall satisfaction with ContextZoom than with the Google maps’ single-handed zooming technique, as well as a similar level of perceived ease of use.


From goods to service ( s ) : Divergences and convergences of logics

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

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

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)


Informed Recommender: Basing Recommendations on Consumer Product Reviews

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

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

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

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

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.


The Statistical Recurrent Unit

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

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

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

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

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

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

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

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

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

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

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

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).


Dermoscopy: basic concepts.

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

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.


Category fluency test: normative data for English- and Spanish-speaking elderly.

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.


Towards subjective quality of experience assessment for omnidirectional video streaming

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.


Three Decades of Driver Assistance Systems: Review and Future Perspectives

This contribution provides a review of fundamental goals, development and future perspectives of driver assistance systems. Mobility is a fundamental desire of mankind. Virtually any society strives for safe and efficient mobility at low ecological and economic costs. Nevertheless, its technical implementation significantly differs among societies, depending on their culture and their degree of industrialization. A potential evolutionary roadmap for driver assistance systems is discussed. Emerging from systems based on proprioceptive sensors, such as ABS or ESC, we review the progress incented by the use of exteroceptive sensors such as radar, video, or lidar. While the ultimate goal of automated and cooperative traffic still remains a vision of the future, intermediate steps towards that aim can be realized through systems that mitigate or avoid collisions in selected driving situations. Research extends the state-of-the-art in automated driving in urban traffic and in cooperative driving, the latter addressing communication and collaboration between different vehicles, as well as cooperative vehicle operation by its driver and its machine intelligence. These steps are considered important for the interim period, until reliable unsupervised automated driving for all conceivable traffic situations becomes available. The prospective evolution of driver assistance systems will be stimulated by several technological, societal and market trends. The paper closes with a view on current research fields.


USARSim: a robot simulator for research and education

This paper presents USARSim, an open source high fidelity robot simulator that can be used both for research and education. USARSim offers many characteristics that differentiate it from most existing simulators. Most notably, it constitutes the simulation engine used to run the virtual robots competition within the Robocup initiative. We describe its general architecture, describe examples of utilization, and provide a comprehensive overview for those interested in robot simulations for education, research and competitions.


Noise and the Reality Gap: The Use of Simulation in Evolutionary Robotics

The pitfalls of naive robot simulations have been recognised for areas such as evolutionary robotics. It has been suggested that carefully validated ispell slides.tex simulations with a proper treatment of noise may overcome these problems. This paper reports the results of experiments intended to test some of these claims. A simulation was constructed of a two-wheeled Khepera robot with IR and ambient light sensors. This included detailed mathematical models of the robot-environment interaction dynamics with empirically determined parameters. Artiicial evolution was used to develop recurrent dynamical network controllers for the simulated robot, for obstacle-avoidance and light-seeking tasks, using diierent levels of noise in the simulation. The evolved controllers were down-loaded onto the real robot and the correspondence between behaviour in simulation and in reality was tested. The level of correspondence varied according to how much noise was used in the simulation, with very good results achieved when realistic quantities were applied. It has been demonstrated that it is possible to develop successful robot controllers in simulation that generate almost identical behaviours in reality, at least for a particular class of robot-environment interaction dynamics.


Making Bertha Drive—An Autonomous Journey on a Historic Route

125 years after Bertha Benz completed the first overland journey in automotive history, the Mercedes Benz S-Class S 500 INTELLIGENT DRIVE followed the same route from Mannheim to Pforzheim, Germany, in fully autonomous manner. The autonomous vehicle was equipped with close-to-production sensor hardware and relied solely on vision and radar sensors in combination with accurate digital maps to obtain a comprehensive understanding of complex traffic situations. The historic Bertha Benz Memorial Route is particularly challenging for autonomous driving. The course taken by the autonomous vehicle had a length of 103 km and covered rural roads, 23 small villages and major cities (e.g. downtown Mannheim and Heidelberg). The route posed a large variety of difficult traffic scenarios including intersections with and without traffic lights, roundabouts, and narrow passages with oncoming traffic. This paper gives an overview of the autonomous vehicle and presents details on vision and radar-based perception, digital road maps and video-based self-localization, as well as motion planning in complex urban scenarios.


Identifying technological competition trends for R&D planning using dynamic patent maps: SAO-based content analysis

Patent maps showing competition trends in technological development can provide valuable input for decision support on research and development (R&D) strategies. By introducing semantic patent analysis with advantages in representing technological objectives and structures, this paper constructs dynamic patent maps to show technological competition trends and describes the strategic functions of the dynamic maps. The proposed maps are based on subject-action-object (SAO) structures that are syntactically ordered sentences extracted using the natural language processing of the patent text; the structures of a patent encode the key findings of the invention and expertise of its inventors. Therefore, this paper introduces a method of constructing dynamic patent maps using SAO-based content analysis of patents and presents several types of dynamic patent maps by combining patent bibliographic information and patent mapping and clustering techniques. Building on the maps, this paper provides further analyses to identify technological areas in which patents have not been granted (“patent vacuums”), areas in which many patents have actively appeared (“technological hot spots”), R&D overlap of technological competitors, and characteristics of patent clusters. The proposed analyses of dynamic patent maps are illustrated using patents related to the synthesis of carbon nanotubes. We expect that the proposed method will aid experts in understanding technological competition trends in the process of formulating R&D strategies.


Classification of Arrhythmia Using Conjunction of Machine Learning Algorithms and ECG Diagnostic Criteria

This paper proposes a classification technique using conjunction of Machine Learning Algorithms and ECG Diagnostic Criteria which improves the accuracy of detecting Arrhythmia using Electrocardiogram (ECG) data. ECG is the most widely used first line clinical instrument to record the electrical activities of the heart. The data-set from UC Irvine (UCI) Machine Learning Repository was used to implement a multi-class classification for different types of heart abnormalities. After implementing rigorous data preprocessing and feature selection techniques,different machine learning algorithms such as Neural Networks, Decision trees, Random Forest, Gradient Boosting and Support Vector Machines were used. Maximum experimental accuracy of 84.82% was obtained via the conjunction of SVM and Gradient Boosting. A further improvement in accuracy was obtained by validating the factors which were important for doctors to decide between normal and abnormal heart conditions.The performance of classification is evaluated using measures such as confusion matrix, kappa-score, confidence interval, Area under curve (AUC) and overall-accuracy. Key–Words: Machine Learning, Arrhythmia Classification, ECG, Neural Networks, SVM, Gradient Boosting


Document clustering using nonnegative matrix factorization

A methodology for automatically identifying and clustering semantic features or topics in a heterogeneous text collection is presented. Textual data is encoded using a low rank nonnegative matrix factorization algorithm to retain natural data nonnegativity, thereby eliminating the need to use subtractive basis vector and encoding calculations present in other techniques such as principal component analysis for semantic feature abstraction. Existing techniques for nonnegative matrix factorization are reviewed and a new hybrid technique for nonnegative matrix factorization is proposed. Performance evaluations of the proposed method are conducted on a few benchmark text collections used in standard topic detection studies.


A 107 GHz 55 dB-Ohm InP Broadband Transimpedance Amplifier IC for High-Speed Optical Communication Links

We report a 107 GHz baseband differential transimpedance amplifier IC for high speed optical communication links. The amplifier, comprised of two Darlington resistive feedback stages, was implemented in a 500 nm InP HBT process and demonstrates 55 dBΩ differential transimpedance gain, 30 ps group delay, P1dB = 1 dBm, and is powered by a 5.2 V supply. Differential input and output impedances are 50Ω. The IC interfaces to -2V DC at the input for connections to high-speed photodiodes and -450 mV DC at the output for interfaces to Gilbert-cell mixers and to ECL logic.


Gravity-independent mobility and drilling on natural rock using microspines

To grip rocks on the surfaces of asteroids and comets, and to grip the cliff faces and lava tubes of Mars, a 250 mm diameter omni-directional anchor is presented that utilizes a hierarchical array of claws with suspension flexures, called microspines, to create fast, strong attachment. Prototypes have been demonstrated on vesicular basalt and a'a lava rock supporting forces in all directions away from the rock. Each anchor can support >;160 N tangent, >;150 N at 45°, and >;180 N normal to the surface of the rock. A two-actuator selectively-compliant ankle interfaces these anchors to the Lemur IIB robot for climbing trials. A rotary percussive drill was also integrated into the anchor, demonstrating self-contained rock coring regardless of gravitational orientation. As a harder-than-zero-g proof of concept, 20mm diameter boreholes were drilled 83 mm deep in vesicular basalt samples, retaining a 12 mm diameter rock core in 3-6 pieces while in an inverted configuration, literally drilling into the ceiling.


Multilayer Extreme Learning Machine With Subnetwork Nodes for Representation Learning

The extreme learning machine (ELM), which was originally proposed for “generalized” single-hidden layer feedforward neural networks, provides efficient unified learning solutions for the applications of clustering, regression, and classification. It presents competitive accuracy with superb efficiency in many applications. However, ELM with subnetwork nodes architecture has not attracted much research attentions. Recently, many methods have been proposed for supervised/unsupervised dimension reduction or representation learning, but these methods normally only work for one type of problem. This paper studies the general architecture of multilayer ELM (ML-ELM) with subnetwork nodes, showing that: 1) the proposed method provides a representation learning platform with unsupervised/supervised and compressed/sparse representation learning and 2) experimental results on ten image datasets and 16 classification datasets show that, compared to other conventional feature learning methods, the proposed ML-ELM with subnetwork nodes performs competitively or much better than other feature learning methods.


Effects of users' envy and shame on social comparison that occurs on social network services

In the context of the social network service environment, we explore how discrete emotions—envy and shame, in particular—may mediate the effects of social comparison on behavior intention and psychological responses. Based on the survey responses of 446 university students, the results suggest that social comparison to media figures correlates with a range of emotional responses as well as with behavioral intention and psychological responses. Envy maintained a significantly greater association with switch intention as a behavioral intention compared to shame. Conversely, shame was significantly related to burnout as a psychological response. Further, mediational analyses were consistent with the argument that envy and shame mediate the social comparison–outcome variables relationship. This research helps to illuminate the driving mechanism for the emotional effect that social comparison on social network service could elicit from a user. This predicts the nature of the behavioral and psychological outcome associated with the comparison and has implications for an enhanced understanding of the way in which the unique social network service communication environment may stimulate this process. 2015 Elsevier Ltd. All rights reserved.


Pre-clinical antigenicity studies of an innovative multivalent vaccine for human visceral leishmaniasis

The notion that previous infection by Leishmania spp. in endemic areas leads to robust anti-Leishmania immunity, supports vaccination as a potentially effective approach to prevent disease development. Nevertheless, to date there is no vaccine available for human leishmaniasis. We optimized and assessed in vivo the safety and immunogenicity of an innovative vaccine candidate against human visceral leishmaniasis (VL), consisting of Virus-Like Particles (VLP) loaded with three different recombinant proteins (LJL143 from Lutzomyia longipalpis saliva as the vector-derived (VD) component, and KMP11 and LeishF3+, as parasite-derived (PD) antigens) and adjuvanted with GLA-SE, a TLR4 agonist. No apparent adverse reactions were observed during the experimental time-frame, which together with the normal hematological parameters detected seems to point to the safety of the formulation. Furthermore, measurements of antigen-specific cellular and humoral responses, generally higher in immunized versus control groups, confirmed the immunogenicity of the vaccine formulation. Interestingly, the immune responses against the VD protein were reproducibly more robust than those elicited against leishmanial antigens, and were apparently not caused by immunodominance of the VD antigen. Remarkably, priming with the VD protein alone and boosting with the complete vaccine candidate contributed towards an increase of the immune responses to the PD antigens, assessed in the form of increased ex vivo CD4+ and CD8+ T cell proliferation against both the PD antigens and total Leishmania antigen (TLA). Overall, our immunogenicity data indicate that this innovative vaccine formulation represents a promising anti-Leishmania vaccine whose efficacy deserves to be tested in the context of the "natural infection".


Optimization of photovoltaic farm under partial shading effects using artificial intelligent based matrix switch controller

Partial shading on photovoltaic array due to weather conditions reduces the energy generated and worsening the entire photovoltaic farm efficiency. The purpose of this research was to electric physically reconfigurate PV string through switch matrix by control of smart controller. It is great to find current and voltage at TCT point approaching work of range from MPPT, despite being awarded shading effect. The research method was reflected on the algorithms design. The aim of algorithms was to clustering in two steps. The first step was clustering one or more photovoltaic string based on parallelly current. Result of first clustering continued of second step clustering, clustering the first step result in series. The algorithm work smartly by current input from current sensors. Input processing from sensor by smart controller was signal control which for switch on/off the switches function in switch matrix circuit. Switch matrix output will be forwarded to MPPT circuit. This mechanism being novelty of this research. Based on the testing, it's mechanism able to protect the current at 18A level for all cases with each case voltage consecutively 3060V, 1360V, 2040V and 1020 V. By this mechanism, can be optimality power system increased up to 9.S % .similarly be affected or not partial shading effect.


High-Efficiency Single-Input Multiple-Output DC–DC Converter

The aim of this study is to develop a high-efficiency single-input multiple-output (SIMO) dc-dc converter. The proposed converter can boost the voltage of a low-voltage input power source to a controllable high-voltage dc bus and middle-voltage output terminals. The high-voltage dc bus can take as the main power for a high-voltage dc load or the front terminal of a dc-ac inverter. Moreover, middle-voltage output terminals can supply powers for individual middle-voltage dc loads or for charging auxiliary power sources (e.g., battery modules). In this study, a coupled-inductor-based dc-dc converter scheme utilizes only one power switch with the properties of voltage clamping and soft switching, and the corresponding device specifications are adequately designed. As a result, the objectives of high-efficiency power conversion, high step-up ratio, and various output voltages with different levels can be obtained. Some experimental results via a kilowatt-level prototype are given to verify the effectiveness of the proposed SIMO dc-dc converter in practical applications.


Phishing Detection: A Literature Survey

This article surveys the literature on the detection of phishing attacks. Phishing attacks target vulnerabilities that exist in systems due to the human factor. Many cyber attacks are spread via mechanisms that exploit weaknesses found in end-users, which makes users the weakest element in the security chain. The phishing problem is broad and no single silver-bullet solution exists to mitigate all the vulnerabilities effectively, thus multiple techniques are often implemented to mitigate specific attacks. This paper aims at surveying many of the recently proposed phishing mitigation techniques. A high-level overview of various categories of phishing mitigation techniques is also presented, such as: detection, offensive defense, correction, and prevention, which we belief is critical to present where the phishing detection techniques fit in the overall mitigation process.


A packing algorithm for non-Manhattan hexagon/triangle placement design by using an adaptive O-tree representation

A non-Manhattan Hexagon/Triangle Placement (HTP for short) paradigm is proposed in the present paper. Main feature of this paradigm lies in adapting to the Y- architecture which is one of the promising non-Manhattan VLSI circuit layout architectures. Aim of the HTP is to place a set of equilateral triangles with given size onto a hexagonal chip with maximal chip area usage. Based on the O-tree representation, some adaptive packing rules are adopted to develop an effective placement algorithm for solving the HTP problem in BBL mode. Two examples with benchmark data transformed from the Manhattan BBL mode placement (ami33/49) are presented to justify the feasibility and effectiveness of our algorithms. Experiment results demonstrate that the chip area usage of 94% is achieved through simulated annealing optimization.


Computer animation of knowledge-based human grasping

The synthesis of human hand motion and grasping of arbitrary shaped objects is a very complex problem. Therefore high-level control is needed to perform these actions. In order to satisfy the kinematic and physical constraints associated with the human hand and to reduce the enormous search space associated with the problem of grasping objects, a knowledge based approach is used. A three-phased scheme is presented which incorporates the role of the hand, the object, the environment and the animator. The implementation of a hand simulation system HANDS is discussed.


MIDeA: a multi-parallel intrusion detection architecture

Network intrusion detection systems are faced with the challenge of identifying diverse attacks, in extremely high speed networks. For this reason, they must operate at multi-Gigabit speeds, while performing highly-complex per-packet and per-flow data processing. In this paper, we present a multi-parallel intrusion detection architecture tailored for high speed networks. To cope with the increased processing throughput requirements, our system parallelizes network traffic processing and analysis at three levels, using multi-queue NICs, multiple CPUs, and multiple GPUs. The proposed design avoids locking, optimizes data transfers between the different processing units, and speeds up data processing by mapping different operations to the processing units where they are best suited. Our experimental evaluation shows that our prototype implementation based on commodity off-the-shelf equipment can reach processing speeds of up to 5.2 Gbit/s with zero packet loss when analyzing traffic in a real network, whereas the pattern matching engine alone reaches speeds of up to 70 Gbit/s, which is an almost four times improvement over prior solutions that use specialized hardware.


A German Twitter Snapshot

We present a new corpus of German tweets. Due to the relatively small number of German messages on Twitter, it is possible to collect a virtually complete snapshot of German twitter messages over a period of time. In this paper, we present our collection method which produced a 24 million tweet corpus, representing a large majority of all German tweets sent in April, 2013. Further, we analyze this representative data set and characterize the German twitterverse. While German Twitter data is similar to other Twitter data in terms of its temporal distribution, German Twitter users are much more reluctant to share geolocation information with their tweets. Finally, the corpus collection method allows for a study of discourse phenomena in the Twitter data, structured into discussion threads.


Work strain, health, and absenteeism: a meta-analysis.

Work strain has been argued to be a significant cause of absenteeism in the popular and academic press. However, definitive evidence for associations between absenteeism and strain is currently lacking. A theory focused meta-analysis of 275 effects from 153 studies revealed positive but small associations between absenteeism and work strain, psychological illness, and physical illness. Structural equation modeling results suggested that the strain-absence connection may be mediated by psychological and physical symptoms. Little support was received for the purported volitional distinction between absence frequency and time lost absence measures on the basis of illness. Among the moderators examined, common measurement, midterm and stable sources of variance, and publication year received support.


Extreme Learning Machine for Multi-Label Classification

Xia Sun 1,*, Jingting Xu 1, Changmeng Jiang 1, Jun Feng 1, Su-Shing Chen 2 and Feijuan He 3 1 School of Information Science and Technology, Northwest University, Xi’an 710069, China; jingtingxu03@gmail.com (J.X.); yadxjcm@163.com (C.J.); fengjun@nwu.edu.cn (J.F.) 2 Computer Information Science and Engineering, University of Florida, Gainesville, FL 32608, USA; suchen@cise.ufl.edu 3 Department of Computer Science, Xi’an Jiaotong University City College, Xi’an 710069, China; hfj@mail.xjtu.edu.cn * Correspondence: raindy@nwu.edu.cn; Tel.: +86-29-8830-8119