Compact E-Cash

This paper presents efficient off-line anonymous e-cash schemes where a user can withdraw a wallet containing 2 coins each of which she can spend unlinkably. Our first result is a scheme, secure under the strong RSA and the y-DDHI assumptions, where the complexity of the withdrawal and spend operations is O( + k) and the user’s wallet can be stored using O( +k) bits, where k is a security parameter. The best previously known schemes require at least one of these complexities to be O(2 · k). In fact, compared to previous e-cash schemes, our whole wallet of 2 coins has about the same size as one coin in these schemes. Our scheme also offers exculpability of users, that is, the bank can prove to third parties that a user has double-spent. We then extend our scheme to our second result, the first e-cash scheme that provides traceable coins without a trusted third party. That is, once a user has double spent one of the 2 coins in her wallet, all her spendings of these coins can be traced. However, the price for this is that the complexity of the spending and of the withdrawal protocols becomes O( · k) and O( · k + k) bits, respectively, and wallets take O( · k) bits of storage. All our schemes are secure in the random oracle model.


Chain : Operator Scheduling for Memory Minimization in Data Stream Systems

In many applications involving continuous data streams, data arrival is bursty and data rate fluctuates over time. Systems that seek to give rapid or real-time query responses in such an environment must be prepared to deal gracefully with bursts in data arrival without compromising system performance. We discuss one strategy for processing bursty streams --- adaptive, load-aware scheduling of query operators to minimize resource consumption during times of peak load. We show that the choice of an operator scheduling strategy can have significant impact on the run-time system memory usage. We then present Chain scheduling, an operator scheduling strategy for data stream systems that is near-optimal in minimizing run-time memory usage for any collection of single-stream queries involving selections, projections, and foreign-key joins with stored relations. Chain scheduling also performs well for queries with sliding-window joins over multiple streams, and multiple queries of the above types. A thorough experimental evaluation is provided where we demonstrate the potential benefits of Chain scheduling, compare it with competing scheduling strategies, and validate our analytical conclusions.


LaBB-CAT: an Annotation Store

“ONZE Miner”, an open-source tool for storing and automatically annotating Transcriber transcripts, has been redeveloped to use “annotation graphs” as its data model. The annotation graph framework provides the new software, “LaBB-CAT”, greater flexibility for automatic and manual annotation of corpus data at various independent levels of granularity, and allows more sophisticated annotation structures, opening up new possibilities for corpus mining and conversion between tool formats.


Agile in Public Administration: Oxymoron or Reality? An Experience Report

In the last 10 years, Agile methods and practices have emerged as an alternative for software development. Different "flavors" of Agile have appeared ranging from project management to tests organization. These approaches have being gaining popularity and involve now a solid option for organizations developing software, but what about Public Administrations? Is Agile a suitable option for developing software in Public Administrations? Even if Public Administrations have been traditionally regarded as changeresistant, Agile approach can also provide them with the benefits of quick adaptation and frequent value delivery. This paper presents the results of two different projects, which use an Agile framework based on Scrum, developed by a Spanish Public Administration. Additionally, after considering the obtained results, it takes out some relevant learned lessons on the suitability of applying Agile approaches to Public Administration environments.


Machine Health Monitoring Using Local Feature-Based Gated Recurrent Unit Networks

In modern industries, machine health monitoring systems (MHMS) have been applied wildly with the goal of realizing predictive maintenance including failures tracking, downtime reduction, and assets preservation. In the era of big machinery data, data-driven MHMS have achieved remarkable results in the detection of faults after the occurrence of certain failures (diagnosis) and prediction of the future working conditions and the remaining useful life (prognosis). The numerical representation for raw sensory data is the key stone for various successful MHMS. Conventional methods are the labor-extensive as they usually depend on handcrafted features, which require expert knowledge. Inspired by the success of deep learning methods that redefine representation learning from raw data, we propose local feature-based gated recurrent unit (LFGRU) networks. It is a hybrid approach that combines handcrafted feature design with automatic feature learning for machine health monitoring. First, features from windows of input time series are extracted. Then, an enhanced bidirectional GRU network is designed and applied on the generated sequence of local features to learn the representation. A supervised learning layer is finally trained to predict machine condition. Experiments on three machine health monitoring tasks: tool wear prediction, gearbox fault diagnosis, and incipient bearing fault detection verify the effectiveness and generalization of the proposed LFGRU.


Sparse Probabilistic Principal Component Analysis

Principal component analysis (PCA) is a popular dimensionality reduction algorithm. However, it is not easy to interpret which of the original features are important based on the principal components. Recent methods improve interpretability by sparsifying PCA through adding an L1 regularizer. In this paper, we introduce a probabilistic formulation for sparse PCA. By presenting sparse PCA as a probabilistic Bayesian formulation, we gain the benefit of automatic model selection. We examine three different priors for achieving sparsification: (1) a two-level hierarchical prior equivalent to a Laplacian distribution and consequently to an L1 regularization, (2) an inverse-Gaussian prior, and (3) a Jeffrey’s prior. We learn these models by applying variational inference. Our experiments verify that indeed our sparse probabilistic model results in a sparse PCA solution.


Template-Free Construction of Poems with Thematic Cohesion and Enjambment

Existing poetry generation systems usually focus on particular features of poetry (such as rhythm, rhyme, metaphor) and specific techniques to achieve them. They often resort to template-base solutions, in which it is not always clear how many of the alleged features of the outputs were already present in the template employed. The present paper considers two specific features – thematic consistency, and enjambment – and presents an ngram based construction method that achieves these features without the use of templates. The construction procedure is not intended to produce poetry of high quality, only to achieve the features considered specifically in its design. A set of metrics is defined to capture these features in quantitative terms, and the metrics are applied to system outputs and to samples of both human and computer generated poetry. The results of these tests are discussed in terms of the danger of to ignoring certain features when designing construction procedures but valuing them during evaluation even if they arise from hard-wiring in the resources or serendipitous emergence, and the fundamental need for poets to develop a personal voice – fundamental for human poets and inconsistent with the application of Turing tests.


MovieGEN : A Movie Recommendation System

In this paper we introduce MovieGEN, an expert system for movie recommendation. We implement the system using machine learning and cluster analysis based on a hybrid recommendation approach. The system takes in the users’ personal information and predicts their movie preferences using well-trained support vector machine (SVM) models. Based on the SVM prediction it selects movies from the dataset, clusters the movies and generates questions to the users. Based on the users’ answers, it refines its movie set and it finally recommends movies for the users. In the process we traverse the parameter space, thus enabling the customizability of the system.


A harmonic suppression circularly polarized patch antenna for an RF ambient energy harvesting system

This paper presented a new design of harmonic suppression microstrip patch antenna with the circular polarization properties at frequency 900MHz for an RF ambient energy harvesting system. The purpose of harmonic suppression antenna is to eliminate the using of a harmonic filter circuit in filtering the unwanted signal at the harmonic frequencies in a rectenna system. By introducing a truncating to corner of the rectangular microstrip patch antenna to achieved the required axial ratio, an antenna with circular polarization property is presented. The dimension of patch is reduced by applying partially ground plane. Then an implementation of a defected ground structure (DGS) with a circular shape slot, the unwanted radiation at harmonic frequencies are successfully suppressed. The patch antenna is designed on an FR4 substrate with εr=4.7 and a thickness of 1.6 mm has -20dB return loss at 900 MHz and suppress the second and third harmonic up to -2dB. The proposed circularly polarized harmonic suppression antenna is then fabricated and measured for the verification of the design.


Conceptualizing and testing formative constructs: tutorial and annotated example

Although abundant advice is available for how to develop and validate multi-item scales based on reflective constructs, scant attention has been directed to how to construct and validate formative constructs. Such advice is important because (1) theory suggests many constructs are formative and (2) recent advances in software render testing models with formative constructs more tractable. In this tutorial, our goal is to enhance understanding of formative constructs at the conceptual, statistical and methodological levels. Specifically, we (1) provide general principles for specifying whether a construct should be conceptually modeled as reflective or formative, (2) discuss the statistical logic behind formative constructs, and (3) illustrate how to model and evaluate formative constructs. In particular, we provide a tutorial in which we test and validate professional reward structure, a formative construct, in two popular structural equation modeling programs: EQS and PLS. We conclude with a summary of guidelines for how to conduct and evaluate research using formative constructs.


Exemplar-Based Portrait Style Transfer

Transferring the style of an example image to a content image opens the door of artistic creation for end users. However, it is especially challenging for portrait photos since human vision system is sensitive to the slight artifacts on portraits. Previous methods use facial landmarks to densely align the content face with the style face to reduce the artifacts. However, they can only handle the facial region. As for the whole image, building the dense correspondence is difficult and may easily introduce errors. In this paper, we propose a robust approach for portrait style transfer that gets rid of dense correspondence. Our approach is based on the guided image synthesis framework. We propose three novel guidance maps for the synthesis process. Contrary to former methods, these maps do not require the dense correspondence between content image and style image, which allows our method to handle the whole portrait photo instead of facial region only. In comparison with recent neural style transfer methods, our method achieves more pleasing results and preserves more texture details. Extensive experiments demonstrate our advantage over former methods on portrait style transfer.


Tracking football player movement from a single moving camera using particle filters

This paper deals with the problem of tracking football players in a football match using data from a single moving camera. Tracking footballers from a single video source is di cult: not only do the football players occlude each other, but they frequently enter and leave the camera's eld of view, making initialisation and destruction of a player's tracking a di cult task. The system presented here uses particle lters to track players. The multiple state estimates used by a particle lter provide an elegant method for maintaining tracking of players following an occlusion. Automated tracking can be achieved by creating and stopping particle lters depending on the input player data.


Compact hashing with joint optimization of search accuracy and time

Similarity search, namely, finding approximate nearest neighborhoods, is the core of many large scale machine learning or vision applications. Recently, many research results demonstrate that hashing with compact codes can achieve promising performance for large scale similarity search. However, most of the previous hashing methods with compact codes only model and optimize the search accuracy. Search time, which is an important factor for hashing in practice, is usually not addressed explicitly. In this paper, we develop a new scalable hashing algorithm with joint optimization of search accuracy and search time simultaneously. Our method generates compact hash codes for data of general formats with any similarity function. We evaluate our method using diverse data sets up to 1 million samples (e.g., web images). Our comprehensive results show the proposed method significantly outperforms several state-of-the-art hashing approaches.


Blockchain as a Platform for Secure Inter-Organizational Business Processes

Today, most of the services one may think of are based on a collaborative paradigm (e.g., social media services, IoT-based services, etc.). One of the most relevant representative of such class of services are inter-organizational processes, where an organized group of joined activities is carried out by two or more organizations to achieve a common business goal. Inter-organizational processes are therefore vital to achieve business partnerships among different organizations. However, they may also pose serious security and privacy threats to the data each organization exposes. This is mainly due to the weak trust relationships that may hold among the collaborating parties, which result in a potential lack of trust on how data/operations are managed. In this paper, we discuss, how blockchain, one of today hottest technology, can be used in support of secure inter-organizational processes. We further point out which additional security issues the use of blockchain can bring, illustrate the ongoing research projects in the area and discuss future research directions.


Exploiting 2D Floorplan for Building-Scale Panorama RGBD Alignment

This paper presents a novel algorithm that utilizes a 2D floorplan to align panorama RGBD scans. While effective panorama RGBD alignment techniques exist, such a system requires extremely dense RGBD image sampling. Our approach can significantly reduce the number of necessary scans with the aid of a floorplan image. We formulate a novel Markov Random Field inference problem as a scan placement over the floorplan, as opposed to the conventional scan-to-scan alignment. The technical contributions lie in multi-modal image correspondence cues (between scans and schematic floorplan) as well as a novel coverage potential avoiding an inherent stacking bias. The proposed approach has been evaluated on five challenging large indoor spaces. To the best of our knowledge, we present the first effective system that utilizes a 2D floorplan image for building-scale 3D pointcloud alignment. The source code and the data are shared with the community to further enhance indoor mapping research.


Impact of singular excessive computer game and television exposure on sleep patterns and memory performance of school-aged children.

OBJECTIVE
Television and computer game consumption are a powerful influence in the lives of most children. Previous evidence has supported the notion that media exposure could impair a variety of behavioral characteristics. Excessive television viewing and computer game playing have been associated with many psychiatric symptoms, especially emotional and behavioral symptoms, somatic complaints, attention problems such as hyperactivity, and family interaction problems. Nevertheless, there is insufficient knowledge about the relationship between singular excessive media consumption on sleep patterns and linked implications on children. The aim of this study was to investigate the effects of singular excessive television and computer game consumption on sleep patterns and memory performance of children.

METHODS
Eleven school-aged children were recruited for this polysomnographic study. Children were exposed to voluntary excessive television and computer game consumption. In the subsequent night, polysomnographic measurements were conducted to measure sleep-architecture and sleep-continuity parameters. In addition, a visual and verbal memory test was conducted before media stimulation and after the subsequent sleeping period to determine visuospatial and verbal memory performance.

RESULTS
Only computer game playing resulted in significant reduced amounts of slow-wave sleep as well as significant declines in verbal memory performance. Prolonged sleep-onset latency and more stage 2 sleep were also detected after previous computer game consumption. No effects on rapid eye movement sleep were observed. Television viewing reduced sleep efficiency significantly but did not affect sleep patterns.

CONCLUSIONS
The results suggest that television and computer game exposure affect children's sleep and deteriorate verbal cognitive performance, which supports the hypothesis of the negative influence of media consumption on children's sleep, learning, and memory.


A Nonisolated Three-Port DC–DC Converter and Three-Domain Control Method for PV-Battery Power Systems

In order to interface one photovoltaic (PV) port, one bidirectional battery port, and one load port of a PV-battery dc power system, a novel nonisolated three-port dc/dc converter named boost bidirectional buck converter (B3C) and its control method based on three-domain control are proposed in this paper. The power flow and operating principles of the proposed B3C are analyzed in detail, and then, the dc voltage relation between three ports is deduced. The proposed converter features high integration and single-stage power conversion from both PV and battery ports to the load port, thus leading to high efficiency. The current of all three ports is continuous; hence, the electromagnetic noise can be reduced. Furthermore, the control and modulation method for B3C has been proposed for realizing maximum power point tracking (MPPT), battery management, and bus voltage regulation simultaneously. The operation can be transited between conductance mode and MPPT mode automatically according to the load power. Finally, experimental verifications are given to illustrate the feasibility and effectiveness of the proposed topology and control method.


A CRISPR/Cas-Mediated Selection-free Knockin Strategy in Human Embryonic Stem Cells

The development of new gene-editing tools, in particular the CRISPR/Cas system, has greatly facilitated site-specific mutagenesis in human embryonic stem cells (hESCs), including the introduction or correction of patient-specific mutations for disease modeling. However, integration of a reporter gene into an endogenous locus in hESCs still requires a lengthy and laborious two-step strategy that involves first drug selection to identify correctly targeted clones and then excision of the drug-resistance cassette. Through the use of iCRISPR, an efficient gene-editing platform we recently developed, this study demonstrates a knockin strategy without drug selection for both active and silent genes in hESCs. Lineage-specific hESC reporter lines are useful for real-time monitoring of cell-fate decisions and lineage tracing, as well as enrichment of specific cell populations during hESC differentiation. Thus, this selection-free knockin strategy is expected to greatly facilitate the use of hESCs for developmental studies, disease modeling, and cell-replacement therapy.


Computer vision technology for food quality assurance Sundaram Gunasekaran

Quality assurance is one of the most important goals of any industry. The ability to manufacture high-quality products consistently is the basis for success in the highly competitive food industry. It encourages loyalty in customers and results in an expanding market share. The quality assurance methods used in the food industry have traditionally involved human visual inspection. Such methods are tedious, laborious, time-consuming and inconsistent. As plant throughput increased and quality tolerance tightened, it became necessary to employ automatic methods for quality assurance and quality control. During the past decade, computers have been introduced widely for automation of the quality assurance task. The food industry has been traditionally slow in adopting this new technology and moving towards programmable automation’. Renard? cited the following barriers to automating food processing plants: diverging opinions of what to measure because manufacturing processes themselves are not well understood; lack of appropriate standards against which properties can be compared; and the age of plants and equipment. However, the speed, reliability and cost savings inherent to process automation have provided sufficient impetus to the food industry to pursue aggressively the automatic, on-line control of all plant operations. Innovation of new technology often undergoes four phases3. The first is a research or discovery phase, in which new knowledge results in advances in the technology. Phase two is the early commercialization phase, in which a small market for the technology develops. Phase three represents the emergence of niche-specific applications of the technology. The last phase is that of widespread proliferation. A key characteristic of phase four is that the technology is transparent to users, that is,


Partial entity structure: a compact non-manifold boundary representation based on partial topological entities

Non-manifold boundary representations have gained a great deal of popularity in recent years and various representation schemes have been proposed because they allow an even wider range of objects for various applications than conventional manifold representations. However, since these schemes are mainly interested in describing sufficient adjacency relationships of topological entities, the models represented in these schemes occupy too much storage space redundantly although they are very efficient in answering queries on topological adjacency relationships. Storage requirement can arise as a crucial problem in models in which topological data is more dominant than geometric data, such as tessellated or mesh models.
To solve this problem, in this paper, we propose a compact non-manifold boundary representation, called the partial entity structure, which allows the reduction of the storage size to half that of the radial edge structure, which is known as a time efficient non-manifold data structure, while allowing full topological adjacency relationships to be derived without loss of efficiency. This representation contains not only the conventional primitive entities like the region, face, edge, and vertex, but also the partial topological entities such as the partial-face, partial-edge, and partial-vertex for describing non-manifold conditions at vertices, edges, and faces. In order to verify the time and storage efficiency of the partial entity structure, the time complexity of basic query procedures and the storage requirement for typical geometric models are derived and compared with those of existing schemes. Furthermore, a set of the generalized Euler operators and typical high-level modeling capabilities such as Boolean operations are also implemented to confirm that our data structure is sound and easy to be manipulated.


Blood pressure estimation from pulse wave velocity measured on the chest

Recently, monitoring of blood pressure fluctuation in the daily life is focused on in the hypertension care area to predict the risk of cardiovascular and cerebrovascular disease events. In this paper, in order to propose an alternative system to the existed ambulatory blood pressure monitoring (ABPM) sphygmomanometer, we have developed a prototype of small wearable device consisting of electrocardiogram (ECG) and photopelthysmograph (PPG) sensors. In addition, it was examined whether blood pressure can be estimated based on pulse wave transit time (PWTT) only by attaching that device on the surface of the chest. We indicated that our system could also sense tendency of time-dependent change of blood pressure by measuring pulse of vessel over the sternum while its propagation distance is short.


School burnout and engagement in the context of demands-resources model.

BACKGROUND
A four-wave longitudinal study tested the demands-resources model in the school context.

AIM
To examine the applicability of the demands-resources to the school context.

METHOD
Data of 1,709 adolescents were gathered, once during the transition from comprehensive to post-comprehensive education, twice during post-comprehensive education, and once 2 years later.

RESULTS
The hypotheses were supported, path analysis showing that study demands were related to school burnout a year later, while study resources were related to schoolwork engagement. Self-efficacy was positively related to engagement and negatively to burnout. School burnout predicted schoolwork engagement negatively 1 year later. Engagement was positively related to life satisfaction 2 years later, while burnout was related to depressive symptoms. Finally, burnout mediated the relationship between study demands and mental health outcomes.

CONCLUSIONS
The demands-resources model can usefully be applied to the school context, including the associations between school-related burnout and engagement among adolescents. The model comprises two processes, the energy-depleting process and the motivational process.


Search , Obfuscation , and Price Elasticities on the Internet 1

We examine the competition between a group of Internet retailers who operate in an environment where a price search engine plays a dominant role. We show that for some products in this environment, the easy price search makes demand tremendously pricesensitive. Retailers, though, engage in obfuscation—practices that frustrate consumer search or make it less damaging to firms—resulting in much less price sensitivity on some other products. We discuss several models of obfuscation and examine its effects on demand and markups empirically.


Cost-aware depth map estimation for Lytro camera

Since commercial light field cameras became available, the light field camera has aroused much interest from computer vision and image processing communities due to its versatile functions. Most of its special features are based on an estimated depth map, so reliable depth estimation is a crucial step. However, estimating depth on real light field cameras is a challenging problem due to noise and short baselines among sub-aperture images. We propose a depth map estimation method for light field cameras by exploiting correspondence and focus cues. We aggregate costs among all the sub-aperture images on cost volume to alleviate noise effects. With efficiency of the cost volume, cost-aware depth estimation is quickly achieved by discrete-continuous optimization. In addition, we analyze each property of correspondence and focus cues and utilize them to select reliable anchor points. A well reconstructed initial depth map from the anchors is shown to enhance convergence. We show our method outperforms the state-of-the-art methods by validating it on real datasets acquired with a Lytro camera.


OpenFlow: enabling innovation in campus networks

This whitepaper proposes OpenFlow: a way for researchers to run experimental protocols in the networks they use every day. OpenFlow is based on an Ethernet switch, with an internal flow-table, and a standardized interface to add and remove flow entries. Our goal is to encourage networking vendors to add OpenFlow to their switch products for deployment in college campus backbones and wiring closets. We believe that OpenFlow is a pragmatic compromise: on one hand, it allows researchers to run experiments on heterogeneous switches in a uniform way at line-rate and with high port-density; while on the other hand, vendors do not need to expose the internal workings of their switches. In addition to allowing researchers to evaluate their ideas in real-world traffic settings, OpenFlow could serve as a useful campus component in proposed large-scale testbeds like GENI. Two buildings at Stanford University will soon run OpenFlow networks, using commercial Ethernet switches and routers. We will work to encourage deployment at other schools; and We encourage you to consider deploying OpenFlow in your university network too


Software-Defined Wireless Networking Opportunities and Challenges for Internet-of-Things: A Review

With the emergence of Internet-of-Things (IoT), there is now growing interest to simplify wireless network controls. This is a very challenging task, comprising information acquisition, information analysis, decision-making, and action implementation on large scale IoT networks. Resulting in research to explore the integration of software-defined networking (SDN) and IoT for a simpler, easier, and strain less network control. SDN is a promising novel paradigm shift which has the capability to enable a simplified and robust programmable wireless network serving an array of physical objects and applications. This paper starts with the emergence of SDN and then highlights recent significant developments in the wireless and optical domains with the aim of integrating SDN and IoT. Challenges in SDN and IoT integration are also discussed from both security and scalability perspectives.


Publish/subscribe-enabled software defined networking for efficient and scalable IoT communications

The Internet of Things (IoT) is the result of many different enabling technologies such as embedded systems, wireless sensor networks, cloud computing, big-data, etc., which are used to gather, process, infer, and transmit data. Combining all these technologies requires a research effort to address all the challenges of these technologies, especially for sensing and delivering information from the physical world to cloud-hosted services. In this article we outline the most important issues related to standardization efforts, mobility of objects, networking and gateway access, and QoS support. In particular, we describe a novel IoT network architecture that integrates software defined networking (SDN) and data distribution service (DDS) middleware. The proposed architecture will improve service delivery of IoT systems and will bring flexibility to the network.


Maestro: A System for Scalable OpenFlow Control

The fundamental feature of an OpenFlow network is that the controller is responsible for the initial establishment of every flow by contacting related switches. Thus the performance of the controller could be a bottleneck. This paper shows how this fundamental problem is addressed by parallelism. The state of the art OpenFlow controller, called NOX, achieves a simple programming model for control function development by having a single-threaded event-loop. Yet NOX has not considered exploiting parallelism. We propose Maestro which keeps the simple programming model for programmers, and exploits parallelism in every corner together with additional throughput optimization techniques. We experimentally show that the throughput of Maestro can achieve near linear scalability on an eight core server machine. Keywords-OpenFlow, network management, multithreading, performance optimization


A Survey of Fog Computing: Concepts, Applications and Issues

Despite the increasing usage of cloud computing, there are still issues unsolved due to inherent problems of cloud computing such as unreliable latency, lack of mobility support and location-awareness. Fog computing can address those problems by providing elastic resources and services to end users at the edge of network, while cloud computing are more about providing resources distributed in the core network. This survey discusses the definition of fog computing and similar concepts, introduces representative application scenarios, and identifies various aspects of issues we may encounter when designing and implementing fog computing systems. It also highlights some opportunities and challenges, as direction of potential future work, in related techniques that need to be considered in the context of fog computing.


Grounding New Words on the Physical World in Multi-Domain Human-Robot Dialogues

This paper summarizes our ongoing project on developing an architecture for a robot that can acquire new words and their meanings while engaging in multidomain dialogues. These two functions are crucial in making conversational service robots work in real tasks in the real world. Household robots and office robots need to be able to work in multiple task domains and they also need to engage in dialogues in multiple domains corresponding to those task domains. Lexical acquisition is necessary because speech understanding cannot be done without enough knowledge on words that are possibly spoken in the task domain. Our architecture is based on a multi-expert model in which multiple domain experts are employed and one of them is selected based on the user utterance and the situation to engage in the control of the dialogue and physical behaviors. We incorporate experts that have an ability to acquire new lexical entries and their meanings grounded on the physical world through spoken interactions. By appropriately selecting those experts, lexical acquisition in multi-domain dialogues becomes possible. An example robotic system based on this architecture that can acquire object names and location names demonstrates the viability of the architecture.


A Brief Tour Through Provenance in Scientific Workflows and Databases

Within computer science, the term provenance has multiple meanings, due to different motivations, perspectives, and assumptions prevalent in the respective communities. This chapter provides a high-level “sightseeing tour” of some of those different notions and uses of provenance in scientific workflows and databases.


The raven roosting optimisation algorithm

A significant stream of literature which draws inspiration from the foraging activities of various organisms to design optimisation algorithms has emerged over the past decade. The success of these algorithms across a wide variety of application domains has spurred interest in the examination of the foraging behaviours of other organisms to develop novel and powerful, optimisation algorithms. A variety of animals, including some species of birds and bats, engage in social roosting whereby large numbers of conspecifics gather together to roost, either overnight or for longer periods. It has been claimed that these roosts can serve as information centres to spread knowledge concerning the location of food resources in the environment. In this paper we look at the social roosting and foraging behaviour of one species of bird, the common raven, and take inspiration from this to design a novel optimisation algorithm which we call the raven roosting optimisation algorithm. The utility of the algorithm is assessed on a series of benchmark problems and the results are found to be competitive. We also provide a novel taxonomy which classifies foraging-inspired optimisation algorithms based on the underlying social communication mechanism embedded in the algorithms.


Quantifying driver stress: developing a system for collecting and processing bio-metric signals in natural situations.

A system for quantifying the physiological features of emotional stress is being developed for use during a driving task. Two prototypes, using sensors that measure the driver's skin conductance, respiration, muscle activity, and heart activity are presented. The first system allows sampling rates of 200 Hz on two fast channels and 20 Hz on six additional channels. It uses a wearable computer to do real-time processing on the signals and has an attached digital camera which was used to capture images of the driver's facial expression once every minute. The second system uses a car-based computer that allows a sampling rate of 1984 samples per second on eight channels. This system uses multiple video cameras to continuously capture the driver's facial expression and road conditions. The data is then synchronized with the physiological signals using a video quad-splitter. The methods for extracting physiological features in the driving environment are discussed, including measurement of the skin conductance orienting response, muscle activity, pulse, and respiration patterns. Preliminary studies show how using multiple modalities of sensors can help discriminate reactions to driving events and how individual's response to similar driving conditions can vary from day to day.


SOCIAL SPACE AND SYMBOLIC POWER *

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A customizable pipeline for social media text normalization

Social networks are persistently generating text-based data that encapsulate vast amounts of knowledge. However, the presence of non-standard terms and misspellings in texts originating from social networks poses a crucial challenge for natural language processing and machine learning systems that attempt to mine this knowledge. To address this problem, we propose a sequential, modular, and hybrid pipeline for social media text normalization. In the first phase, text preprocessing techniques and social media-specific vocabularies gathered from publicly available sources are used to transform, with high precision, out-of-vocabulary terms into in-vocabulary terms. A sequential language model, generated using the partially normalized texts from the first phase, is then utilized to normalize short, high-frequency, ambiguous terms. A supervised learning module is employed to normalize terms based on a manually annotated training corpus. Finally, a tunable, distributed language model-based backoff module at the end of the pipeline enables further customization of the system to specific domains of text. We performed intrinsic evaluations of the system on a publicly available domain-independent dataset from Twitter, and our system obtained an F-score of 0.836, outperforming other benchmark systems for the task. We further performed brief, task-oriented evaluations of the system to illustrate the customizability of the system to domain-specific tasks and the effects of normalization on downstream applications. The modular design enables the easy customization of the system to distinct types domain-specific social media text, in addition to its off-the-shelf application to generic social media text.


Friend or Foe: Cyberbullying in Social Network Sites

As the use of social media technologies proliferates in organizations, it is important to understand the nefarious behaviors, such as cyberbullying, that may accompany such technology use and how to discourage these behaviors. We draw from neutralization theory and the criminological theory of general deterrence to develop and empirically test a research model to explain why cyberbullying may occur and how the behavior may be discouraged. We created a research model of three second-order formative constructs to examine their predictive influence on intentions to cyberbully. We used PLS- SEM to analyze the responses of 174 Facebook users in two different cyberbullying scenarios. Our model suggests that neutralization techniques enable cyberbullying behavior and while sanction certainty is an important deterrent, sanction severity appears ineffective. We discuss the theoretical and practical implications of our model and results.


Face Recognition: Features Versus Templates

Over the last 20 years, several different techniques have been proposed for computer recognition of human faces. The purpose of this paper is to compare two simple but general strategies on a common database (frontal images of faces of 47 people: 26 males and 21 females, four images per person). We have developed and implemented two new algorithms; the first one is based on the computation of a set of geometrical features, such as nose width and length, mouth position, and chin shape, and the second one is based on almost-grey-level template matching. The results obtained on the testing sets (about 90% correct recognition using geometrical features and perfect recognition using template matching) favor our implementation of the template-matching approach.


Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond

ive Text Summarization using Sequence-to-sequence RNNs and Beyond Ramesh Nallapati IBM Watson nallapati@us.ibm.com Bowen Zhou IBM Watson zhou@us.ibm.com Cicero dos Santos IBM Watson


QR Code Security: A Survey of Attacks and Challenges for Usable Security

QR (Quick Response) codes are two-dimensional barcodes with the ability to encode different types of information. Because of their high information density and robustness, QR codes have gained popularity in various fields of application. Even though they offer a broad range of advantages, QR codes pose significant security risks. Attackers can encode malicious links that lead e.g. to phishing sites. Such malicious QR codes can be printed on small stickers and replace benign ones on billboard advertisements. Although many real world examples of QR code based attacks have been reported in the media, only little research has been conducted in this field and almost no attention has been paid on the interplay of security and human-computer interaction. In this work, we describe the manifold use cases of QR codes. Furthermore, we analyze the most significant attack scenarios with respect to the specific use cases. Additionally, we systemize the research that has already been conducted and identified usable security and security awareness as the main research challenges. Finally we propose design requirements with respect to the QR code itself, the reader application and usability aspects in order to support further research into to making QR code processing both secure and usable.


Assessment of exposure to sexually explicit materials and factors associated with exposure among preparatory school youths in Hawassa City, Southern Ethiopia: a cross-sectional institution based survey

BACKGROUND
According to the 2007 Ethiopian census, youths aged 15-24 years were more than 15.2 million which contributes to 20.6% of the whole population. These very large and productive groups of the population are exposed to various sexual and reproductive health risks. The aim of this study was to assess exposure to Sexually Explicit Materials (SEM) and factors associated with exposure among preparatory school students in Hawassa city, Southern Ethiopia.

METHODOLOGY
A cross-sectional institution based study involving 770 randomly selected youth students of preparatory schools at Hawassa city. Multi stage sampling technique was used to select study subjects. Data was collected using pre-tested and self-administered questionnaire. Data was entered by EPI INFO version 3.5.1 and analyzed using SPSS version 20.0 statistical software packages. The result was displayed using descriptive, bivariate and multivariate analysis. Statistical association was done for independent predictors (at p < 0.05).

RESULT AND DISCUSSION
About 750 students were participated in this study with a response rate of 97.4%. Among this, about 77.3% of students knew about the presence of SEM and most of the respondents 566(75.5%) were watched SEM films/movies and 554(73.9%) were exposed to SE texts. The overall exposure to SEM in school youths was 579(77.2%). Among the total respondents, about 522(70.4%) claimed as having no open discussion on sexual issues with in their family. Furthermore, About 450 (60.0%) respondents complained for having no sexual and reproductive health education at their school. Male students had faced almost two times higher exposure to SEM than female students (95 % CI: AOR 1.84(C.I = 1.22, 2.78). Students who attended private school were more than two times more likely exposed to SEM than public schools (95 % CI: AOR 2.07(C.I = 1.29, 3.30). Students who drink alcohol and labelled as 'sometimes' were two times more likely exposed to SEM than those who never drink alcohol (95 % CI = AOR 2.33(C.I = 1.26, 4.30). Khat chewers who labelled "rarely", "sometimes" and "often" had shown higher exposure (95 % CI: AOR 3.02(CI = 1.65, 5.52), (95 % CI: AOR 3.40(CI = 1.93, 6.00) and (95 % CI: AOR 2.67(CI = 1.46, 4.86) than those who never chew khat, respectively. Regarding SEM access, school youths with label 'easy access were exposed in odds of six folds than youths of no access (95 % CI: AOR 5.64(C.I = 3.56, 8.9).

CONCLUSION
High number of students was exposed to sexually explicit materials. Sex, school type, substance use and access to SEM were observed independent predictors of exposure to SEM.

MOTIVATION
The current generation of young people is the healthiest, most educated, and most urbanized in history. However, there still remain some serious concerns. Most people become sexually active during adolescence. Premarital sexual activity is common and is on the rise worldwide. Rates are highest in sub Saharan Africa, where more than half of girls aged 15-19 are sexually experienced. Millions of adolescents are bearing children, in sub-Saharan Africa. More than half of women give birth before age 20. The need for improved health and social services aimed at adolescents, including reproductive health services, is being increasingly recognized throughout the world. Approximately 85 % of world adolescents live in developing countries. Each year, up to 100 million becomes infected with a curable sexually transmitted disease (STI). About 40 % of all new global human immunodeficiency virus (HIV) infections occur among 15-24 year olds; with recent estimates of 7000 infected each day. These health risks are influenced by many interrelated factors, such as expectations concerning early marriage and sexual relationships, access to education and employment, gender inequities, sexual violence, and the influence of mass media and popular culture. Furthermore, many adolescents lack strong stable relationships with parents or other adults whom they can talk to about their reproductive health concerns. Despite these challenges, programs that meet the information and service needs of adolescents can make a real difference. Successful programs help young people develop life-planning skill, respect the needs and concerns of young people, involve communities in their efforts, and provide respectful and confidential clinical services. Accordingly, the government of Ethiopia now works on improving adolescent's health as one part of MDG (Goal VI-halting transmission of HIV/AIDS, STI, and other communicable diseases) with a focus on adolescents, since they are most affected population. This finding, therefore, will benefit the government to partly evaluate the goal achieving through adolescents exposure status to sexually explicit materials and improvement of sexual issues free talk with in school with class mates and their family at home. For that matter, we authors decided to publish this finding in BMC Reproductive Health Journal so that on line access will be easy to all governing bodies that they use to re-plan their strategies for better product of plan. Moreover, Researchers, Practitioners, policy makers, Students, school leaders and professionals will also benefit from this finding for their future researches references, knowledge gain and practice.


Additive Manufacturing Defect Detection using Neural Networks

Currently defect detection in additive manufacturing is predominately done by traditional image processing, approximation, and statistical methods. Two important aspects of defect detection are edge detection and porosity detection. Both problems appear to be good candidates to apply machine learning. This project describes the implementation of neural networks as an alternative method for defect detection. Results from current methods and the neural networks are compared for both speed and accuracy.


Secure and light IoT protocol (SLIP) for anti-hacking

In the elemental technologies, it is necessary to realize the Internet service of things (IoT), sensors and devices, network, platform (hardware platforms, open software platform, such as specific OS platforms). Web services, data analysis and prediction, big data processing, such as security and privacy protection technology, there are a variety of techniques. These elements technology provide a specific function. The element technology is integrated with each other. However, by several techniques are integrated, it can be problems with integration of security technologies that existed for each element technology. Even if individual technologies basic security features are constituting Internet Services (CIA: Confidentiality, integrity, authentication or authorization). It also offers security technology not connected to each other. Therefore, I will look at the security technology and proposes a lightweight routing protocol indispensable for realizing a secure Internet services things.


A search engine for 3D models

As the number of 3D models available on the Web grows, there is an increasing need for a search engine to help people find them. Unfortunately, traditional text-based search techniques are not always effective for 3D data. In this article, we investigate new shape-based search methods. The key challenges are to develop query methods simple enough for novice users and matching algorithms robust enough to work for arbitrary polygonal models. We present a Web-based search engine system that supports queries based on 3D sketches, 2D sketches, 3D models, and/or text keywords. For the shape-based queries, we have developed a new matching algorithm that uses spherical harmonics to compute discriminating similarity measures without requiring repair of model degeneracies or alignment of orientations. It provides 46 to 245% better performance than related shape-matching methods during precision--recall experiments, and it is fast enough to return query results from a repository of 20,000 models in under a second. The net result is a growing interactive index of 3D models available on the Web (i.e., a Google for 3D models).


Robotic excavation in construction automation

C onstruction is of prime economic significance to many industry sectors. Intense competition, shortages of skilled labor, and technological advances are forcing rapid change in the construction industry, thus motivating construction automation [1]. Earthmoving machines, such as bulldozers, wheel loaders, excavators, scrapers, and graders, are common in construction. Excavationbased operations are used in general earthmoving, digging, and sheet-piling for displacing large amounts of material. On a smaller scale, operations such as trenching and footing formation require precisely controlled excavation. Although the fully automated construction site is still a dream of some civil engineers, research developments have shown the promise of robotics and automation in construction [2]. Despite the apparent economic importance of excavation in construction, there have been few implementations of autonomous or teleoperated excavators. A number of researchers have investigated the feasibility of automating excavation. Many of these studies have addressed the possible use of autonomous excavators during unmanned phases of establishing manned Lunar or Martian research stations [3], [4]. Much of the work on terrestrial excavation has focused on teleoperation, rather than on the system requirements for autonomous operation. Although there have been a number of valuable theoretical and experimental contributions to the field of autonomous, robotic or teleoperated excavation [5]-[8], autonomous operation of a full-scale excavator has not been commercially demonstrated. Many of the experimental studies reported in the literature involve using conventional industrial robots fitted with buckets to excavate in a bed of loose sand. While there are parallels between “classical” robotics and robotic excavation, there are also some pronounced differences. In particular, an excavator is not fixed relative to the work piece; it plastically deforms the work piece by applying large forces and is caused to move relative to the soil by the same large forces. Furthermore, strategic and bucket trajectory planning must necessarily occur in a dynamic environment; if the excavator is not changing the profile of the soil being worked, it is not doing useful work. This article presents some results of the autonomous excavation project conducted at the Australian Centre for Field Robotics (ACFR) with a focus on construction automation. The application of robotic technology and computer control is one key to construction industry automation. Excavation automation is a multidisciplinary task, encompassing a broad area of research and development planning monitoring environment sensing and modeling navigation machine modeling and control. The ultimate goal of the ACFR excavation project is to demonstrate fully autonomous execution of excavation tasks in


Convolutional CRFs for Semantic Segmentation

For the challenging semantic image segmentation task the most efficient models have traditionally combined the structured modelling capabilities of Conditional Random Fields (CRFs) with the feature extraction power of CNNs. In more recent works however, CRF post-processing has fallen out of favour. We argue that this is mainly due to the slow training and inference speeds of CRFs, as well as the difficulty of learning the internal CRF parameters. To overcome both issues we propose to add the assumption of conditional independence to the framework of fully-connected CRFs. This allows us to reformulate the inference in terms of convolutions, which can be implemented highly efficiently on GPUs. Doing so speeds up inference and training by two orders of magnitude. All parameters of the convolutional CRFs can easily be optimized using backpropagation. To facilitating further CRF research we make our implementation publicly available. Please visit: https://github.com/MarvinTeichmann/ConvCRF


CBR: Strengths and Weaknesses

There is considerable enthusiasm about Case-Based Reasoning as a means of developing knowledge-based systems. There are two broad reasons for this enthusiasm. First, it is evident that much of human expert competence is experience based and it makes sense to adopt a reuse-based methodology for developing knowledge based systems. The other reason is the expectation that using Case-Based Reasoning to develop knowledge based systems will i nvolve less knowledge engineering than alternative ‘ first-principles’ based approaches. In this paper I explore the veracity of this assertion and outline the types of situation in which it will be true. CBR is perceived to have this knowledge engineering advantage because it allows the development of knowledge based systems in weak theory domains. If CBR can work without formalising a domain theory then there is a question about the quality of solutions produced by case-based systems. This is the other issue discussed in this paper and situations where CBR will and will not produce good quality solutions are outlined.


Modeling of braided pneumatic actuators for robotic control

Braided Pneumatic Actuators exhibit non-linear forcelength properties grossly similar to muscle, and have a high strength-to-weight ratio. These properties make them desirable for legged robots. This work emphasizes understanding the actuator properties for later use in simulation and control of legged robots. Static and dynamic mathematical models for the actuators are reported and verified through testing and simulation. In addition, a dynamic air flow model modulated by a solenoid valve was developed to provide modular actuation subroutines for simulation.


Review of performance metrics for green data centers: a taxonomy study

Data centers now play an important role in modern IT infrastructures. Although much research effort has been made in the field of green data center computing, performance metrics for green data centers have been left ignored. This paper is devoted to categorization of green computing performance metrics in data centers, such as basic metrics like power metrics, thermal metrics and extended performance metrics i.e. multiple data center indicators. Based on a taxonomy of performance metrics, this paper summarizes features of currently available metrics and presents insights for the study on green data center computing.


Camera Augmented Mobile C-Arm (CAMC): Calibration, Accuracy Study, and Clinical Applications

Mobile C-arm is an essential tool in everyday trauma and orthopedics surgery. Minimally invasive solutions, based on X-ray imaging and coregistered external navigation created a lot of interest within the surgical community and started to replace the traditional open surgery for many procedures. These solutions usually increase the accuracy and reduce the trauma. In general, they introduce new hardware into the OR and add the line of sight constraints imposed by optical tracking systems. They thus impose radical changes to the surgical setup and overall procedure. We augment a commonly used mobile C-arm with a standard video camera and a double mirror system allowing real-time fusion of optical and X-ray images. The video camera is mounted such that its optical center virtually coincides with the C-arm's X-ray source. After a one-time calibration routine, the acquired X-ray and optical images are coregistered. This paper describes the design of such a system, quantifies its technical accuracy, and provides a qualitative proof of its efficiency through cadaver studies conducted by trauma surgeons. In particular, it studies the relevance of this system for surgical navigation within pedicle screw placement, vertebroplasty, and intramedullary nail locking procedures. The image overlay provides an intuitive interface for surgical guidance with an accuracy of <;1 mm, ideally with the use of only one single X-ray image. The new system is smoothly integrated into the clinical application with no additional hardware especially for down-the-beam instrument guidance based on the anteroposterior oblique view, where the instrument axis is aligned with the X-ray source. Throughout all experiments, the camera augmented mobile C-arm system proved to be an intuitive and robust guidance solution for selected clinical routines.


Semi-Supervised Classification with Graph Convolutional Networks

We present a scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of convolutional neural networks which operate directly on graphs. We motivate the choice of our convolutional architecture via a localized first-order approximation of spectral graph convolutions. Our model scales linearly in the number of graph edges and learns hidden layer representations that encode both local graph structure and features of nodes. In a number of experiments on citation networks and on a knowledge graph dataset we demonstrate that our approach outperforms related methods by a significant margin.


ArnetMiner: extraction and mining of academic social networks

This paper addresses several key issues in the ArnetMiner system, which aims at extracting and mining academic social networks. Specifically, the system focuses on: 1) Extracting researcher profiles automatically from the Web; 2) Integrating the publication data into the network from existing digital libraries; 3) Modeling the entire academic network; and 4) Providing search services for the academic network. So far, 448,470 researcher profiles have been extracted using a unified tagging approach. We integrate publications from online Web databases and propose a probabilistic framework to deal with the name ambiguity problem. Furthermore, we propose a unified modeling approach to simultaneously model topical aspects of papers, authors, and publication venues. Search services such as expertise search and people association search have been provided based on the modeling results. In this paper, we describe the architecture and main features of the system. We also present the empirical evaluation of the proposed methods.


SimRank: a measure of structural-context similarity

The problem of measuring "similarity" of objects arises in many applications, and many domain-specific measures have been developed, e.g., matching text across documents or computing overlap among item-sets. We propose a complementary approach, applicable in any domain with object-to-object relationships, that measures similarity of the structural context in which objects occur, based on their relationships with other objects. Effectively, we compute a measure that says "two objects are similar if they are related to similar objects:" This general similarity measure, called SimRank, is based on a simple and intuitive graph-theoretic model. For a given domain, SimRank can be combined with other domain-specific similarity measures. We suggest techniques for efficient computation of SimRank scores, and provide experimental results on two application domains showing the computational feasibility and effectiveness of our approach.


Inductive Conformal Prediction: Theory and Application to Neural Networks

Traditional machine learning algorithms for pattern recognition just output simple predictions, without any associated confidence values. Confidence values are an indication of how likely each prediction is of being correct. In the ideal case, a confidence of 99% or higher for all examples in a set, means that the percentage of erroneous predictions in that set will not exceed 1%. Knowing the likelihood of each prediction enables us to assess the extent to which we can rely on it. For this reason, predictions that are associated with some kind of confidence values are highly desirable in many risk-sensitive applications, such as those used for medical diagnosis or financial analysis. In fact, such information can benefit any application that requires human-computer interaction, as confidence values can be used to determine the way in which each prediction will be treated. For instance, a filtering mechanism can be employed so that only predictions which satisfy a certain level of confidence will be taken into account, while the rest can be discarded or passed on to a human for judgement. There are two main areas in mainstream machine learning that can be used in order to obtain some kind of confidence values; the Bayesian framework and the theory of Probably Approximately Correct learning (PAC theory). Quite often the Bayesian framework is used for producing algorithms that complement individual predictions with probabilistic measures of their quality. On the other hand, PAC theory can be used for producing upper bounds on the probability of error for a given algorithm with respect to some confidence level 1 − δ. Both of these approaches however, have their drawbacks. In order to apply the Bayesian framework one is required to have some prior knowledge about the distribution that generates the data. When the correct prior is known, Bayesian methods provide optimal decisions. For real world data sets though, as the required knowledge is not available, one has to assume the existence of an arbitrarily chosen prior. In this case, if the assumed prior is incorrect, the resulting confidence levels may also be “incorrect”; for example the predictive regions output for the 95% confidence level may contain the true label in much less than 95% of the cases. This signifies a major failure, as we would expect confidence levels to bound the percentage of expected errors. An experimental demonstration of how misleading Bayesian methods can become when their assumptions are violated can be found in (Melluish et al., 2001).


An unsupervised long short-term memory neural network for event detection in cell videos

We propose an automatic unsupervised cell event detection and classification method, which expands convolutional Long Short-Term Memory (LSTM) neural networks, for cellular events in cell video sequences. Cells in images that are captured from various biomedical applications usually have different shapes and motility, which pose difficulties for the automated event detection in cell videos. Current methods to detect cellular events are based on supervised machine learning and rely on tedious manual annotation from investigators with specific expertise. So that our LSTM network could be trained in an unsupervised manner, we designed it with a branched structure where one branch learns the frequent, regular appearance and movements of objects and the second learns the stochastic events, which occur rarely and without warning in a cell video sequence. We tested our network on a publicly available dataset of densely packed stem cell phase-contrast microscopy images undergoing cell division. This dataset is considered to be more challenging that a dataset with sparse cells. We compared our method to several published supervised methods evaluated on the same dataset and to a supervised LSTM method with a similar design and configuration to our unsupervised method. We used an F1-score, which is a balanced measure for both precision and recall. Our results show that our unsupervised method has a higher or similar F1-score when compared to two fully supervised methods that are based on Hidden Conditional Random Fields (HCRF), and has comparable accuracy with the current best supervised HCRF-based method. Our method was generalizable as after being trained on one video it could be applied to videos where the cells were in different conditions. The accuracy of our unsupervised method approached that of its supervised counterpart.


Ensuring Data Integrity in Cloud Computing

Cloud computing provides convenient on-demand network access to a shared pool of configurable computing resources. The resources can be rapidly deployed with great efficiency and minimal management overhead. Cloud is an insecure computing platform from the view point of the cloud users, the system must design mechanisms that not only protect sensitive information by enabling computations with encrypted data, but also protect users from malicious behaviours by enabling the validation of the computation result. In this paper, we propose a new data encoding scheme called layered interleaving, designed for time-sensitive packet recovery in the presence of bursty loss. It is high-speed data recovery scheme with minimal loss probability and using a forward error correction scheme to handle bursty loss. The proposed approach is highly efficient in recovering the singleton losses almost immediately and from bursty data losses.


Self-organized structures in a superorganism : do ants “ behave ” like molecules ?

While the striking structures (e.g. nest architecture, trail networks) of insect societies may seem familiar to many of us, the understanding of pattern formation still constitutes a challenging problem. Over the last two decades, self-organization has dramatically changed our view on how collective decision-making and structures may emerge out of a population of ant workers having each their own individuality as well as a limited access to information. A variety of collective behaviour spontaneously outcome from multiple interactions between nestmates, even when there is no directing influence imposed by an external template, a pacemaker or a leader. By focussing this review on foraging structures, we show that ant societies display some properties which are usually considered in physico-chemical systems, as typical signatures of self-organization. We detail the key role played by feed-back loops, fluctuations, number of interacting units and sensitivity to environmental factors in the emergence of a structured collective behaviour. Nonetheless, going beyond simple analogies with non-living self-organized patterns, we stress on the specificities of social structures made of complex living units of which the biological features have been selected throughout the evolution depending on their adaptive value. In particular, we consider the ability of each ant individual to process information about environmental and social parameters, to accordingly tune its interactions with nestmates and ultimately to determine the final pattern emerging at the collective level. We emphasize on the parsimony and simplicity of behavioural rules at the individual level which allow an efficient processing of information, energy and matter within the whole colony. © 2006 Elsevier B.V. All rights reserved. PACS: 87.18.Bb; 87.23.Cc; 87.23.Ge; 89.75.-k; 89.75.Fb; 89.75.Kd


One Vector is Not Enough: Entity-Augmented Distributed Semantics for Discourse Relations

Discourse relations bind smaller linguistic units into coherent texts. Automatically identifying discourse relations is difficult, because it requires understanding the semantics of the linked arguments. A more subtle challenge is that it is not enough to represent the meaning of each argument of a discourse relation, because the relation may depend on links between lowerlevel components, such as entity mentions. Our solution computes distributed meaning representations for each discourse argument by composition up the syntactic parse tree. We also perform a downward compositional pass to capture the meaning of coreferent entity mentions. Implicit discourse relations are then predicted from these two representations, obtaining substantial improvements on the Penn Discourse Treebank.


Analysis of blood samples for counting leukemia cells using Support vector machine and nearest neighbour

analysis of blood samples for counting leukemia cells is discussed in this paper. support vector machine and nearest neighbour concept is presented in this paper. The identification of blood disorders, it can lead to classification of certain diseases related to blood. Several classification techniques are investigated till today. One of the best methods for classification techniques nearest neighbour and SVM (Support Vector Machine).By identifying and counting blood cell within the blood smear using classification techniques it’s quite possible to detect so many diseases. If one of the new classifier is used i.e. nearest neighbour and SVM it is quiet possible to detect the cancer cell from the blood cell counting. Keyword: Blood, Classification technique, Nearest Neighbour Network, SVM


The Fast Fourier Transform and Its Applications

The advent of the fast Fourier transform method has greatly extended our ability to implement Fourier methods on digital computers. A description of the alogorithm and its programming is given here and followed by a theorem relating its operands, the finite sample sequences, to the continuous functions they often are intended to approximate. An analysis of the error due to discrete sampling over finite ranges is given in terms of aliasing. Procedures for computing Fourier integrals, convolutions and lagged products are outlined. HISTORICAL BACKGROUND T HE FAST Fourier transform algorithm has an interesting history which has been described in [3]. Time does not permit repeating this history here in detail. The essentials are, however, that until the recent publication of fast Fourier transform methods, computer programs were using up hundreds of hours of computer time with procedures requiring something proportional to N2 operations to compute Fourier transforms of N data points. It is not surprising then that the "new" methods requiring a number of operations proportional to N log N received considerable attention and led to revisions in computer programs and in problemn-solving techniques using Fourier methods. It was discovered later that the base 2 form of the fast Fourier transform algorithm had been published many years ago by Runge and Konig [10] and by Stumpff [12], [13]. These are authors whose works are widely read and their papers certainly were used by those computing Fourier series. How then could these important algorithms have gone unnoticed? The answer is that the papers of Runge, K6nig, and Stumpif described primarily how one could use symmetries of the sine-cosine functions to reduce the amount of computation by factors of 4, 8, or even more. Relatively small portions of these papers mentioned the successive doubling algorithm which permitted one to take two Fourier analyses of N-point samples of data and combine them in N operations to obtain an analysis of a 2N-point sampling of the same data. Successive application of this algorithm obviously yields an N-point Fourier analysis in 10g2 N doublings, and therefore, takes N log2 N operations. Thus, while the computational method using symmetries reduced the proportionality factor in the KN2 operations required to transform an N-point sequence, the method based on the doubling algorithm took a number of operations proportional to N log2 N. Manuscript received August 16, 1968. The authors are with the IBM Watson Research Center, Yorktown Heights, N.Y. It is most likely that with the relatively small values of N used in preelectronic computer days, the former methods were easier to use and took fewer operations. Consequently, the methods requiring N log N operations were neglected. With the arrival of electronic computers capable of doing calculations of Fourier transforms with large values of N, the N log N methods were overlooked and the well-known hand calculator methods were programmed for the computers. Perhaps there is something to be learned from this experience, namely, that there may exist numerical methods in the older literature which should be reappraised whenever computing devices undergo radical changes. FAST FOURIER ALGORITHMS In the interest of coherent presentation, the definitions and procedures utilized herein will be developed in blocks and shown as figures. Thus, the discrete Fourier series is defined in Fig. 1 with A (n) being a sequence which gives the complex Fourier amplitudes as a function of frequency n. The X(j), j=0, 1, * * *, N-1 are regarded here as a complex sequence, and in a problem may represent a sampling of a signal at N sampling points. Also, (2iri' 2r 2r WN= exp .= cos-+ isinN1 N N is the principle Nth root of unity and if we substitute the expression for WN in terms of sines and cosines, we obtain the perhaps more familiar sine-cosine Fourier series. Complex Fourier series is employed for ease of notation and derivation of formulas. One should note next the inversion formula in Fig. 1, giving the A (n)'s in terms of the X(j)'s. Since A (n) is also a Fourier series, an algorithm or a program for computing the A (n)'s from the X(j)'s can be used to compute the X(j)'s from the A(n)'s. Since WNN= 1, the exponent of WN is to be interpreted modulo N. This leads to an essential property of the sequences X(j) and A (n), i.e., that they are periodic functions of j and n, respectively, with period N. It is shown in Fig. 2 that when N is a product, N= rs, the Fourier series can be calculated in a two-stage process. This is done just as though the sequences A (n) and X(j) were defined on two-dimensional rXs arrays with the array indices (ji, jo) and (n1, no) being defined as shown. When we substitute for j and n in WNin, and jn is reduced modulo N, it is found that the series can be 27 Authorized licensed use limited to: Peking University. Downloaded on March 24, 2009 at 01:10 from IEEE Xplore. Restrictions apply. IEEE TRANSACTIONS ON EDUCATION, MARCH 1969 Discrete Fourier Series: N-1 X(j) E A(n)wjn n,O N where WN exp(27riIN) Inversion Formula: N-1 N j.0 N Let us write: X(tjt4-A(n) Periodicity: WNN * I, Wj'N+N.


MOSFET switched 20 kV, 500 A, 100 ns pulse generator with series connected pulse transformers

This paper describes a MOSFET switched pulser, which is composed of 30 series connections of pulse generating modules (PGMs) that can produce a 700 V/500 A/100 ns pulse with the rising time of 60 ns or less. All PGMs are connected in series through the specially designed pulse transformers to obtain 20 kV/500 A/100 ns pulses at repetition rates up to 20 kHz. To obtain the fast rising time pulse, it is essential to minimize the leakage inductance of the pulse transformer and the wiring inductance between pulse transformers. In this paper, two kinds of layout for the series connection of pulse transformers are introduced and compared to the conventional methods. Also, the authors minimized the losses by adopting an energy recovery circuit that returns to the source the stored energy in the clamp circuit of the primary windings of the pulse transformers. Experimental results are given for the proposed pulser under the resistor load and the wire-cylinder plasma reactor load, respectively.


Provisioning Software-Defined IoT Cloud Systems

Cloud computing is ever stronger converging with the Internet of Things (IoT) offering novel techniques for IoT infrastructure virtualization and its management on the cloud. However, system designers and operations managers face numerous challenges to realize IoT cloud systems in practice, mainly due to the complexity involved with provisioning large-scale IoT cloud systems and diversity of their requirements in terms of IoT resources consumption, customization of IoT capabilities and runtime governance. In this paper, we introduce the concept of software-defined IoT units - a novel approach to IoT cloud computing that encapsulates fine-grained IoT resources and IoT capabilities in well-defined APIs in order to provide a unified view on accessing, configuring and operating IoT cloud systems. Our software-defined IoT units are the fundamental building blocks of software-defined IoT cloud systems. We present our framework for dynamic, on-demand provisioning and deploying such software-defined IoT cloud systems. By automating provisioning processes and supporting managed configuration models, our framework simplifies provisioning and enables flexible runtime customizations of software-defined IoT cloud systems. We demonstrate its advantages on a real-world IoT cloud system for managing electric fleet vehicles.


Determining embedding dimension for phase-space reconstruction using a geometrical construction.

A, et al. Nonlinear analysis of electroencephalogram at rest and during cognitive tasks in patients with schizophrenia. One-dimensional electroencephalography (EEG) data were transformed into multidimensional phase space. The phase space concept is crucial for nonlinear dynamics analysis. In a hypothetical system governed by n variables, the phase space is n-dimensional. Each state of the system corresponds to a point in phase space, with n coordinates that are the values assumed by the governing variables for this specific state. If the system is observed for a period of time, the sequence of the point in phase space converges into a subspace, called system's attractor. The technique of delay coordinates is carried out to reconstruct the attractor's trajectory. To unfold the projection back to a multivariate state space, a representation of the original system, the delay coordinates y(t) = [xj(t), xj(t + T), … xj(t + [d-1] T)] from a single time series xj and the embedding procedure were performed. In this formula, y(t) is a point of the trajectory in the phase space at time t, x (t is iT) are the coordinates in the phase space corresponding to the time-delayed values of the time series, T is the time delay between the points of the time series considered, and d is the embedding dimension. 1,2 The choice of the optimal time delay T and embedding dimension d are important to the success of reconstruction with finite data. For the time delay, the first local minimum of the average mutual information between the set of measurements v(t) and v(t + T) was used in the present study. For the embedding dimension d the minimum (optimal) embedding was computed in the reconstruction procedure. 3 The attractor's reconstruction is necessary for the analysis of mathematical quantities characterizing the attractor itself. One important metric property of the attractor is the correlation dimension (D2), which estimates the degrees of freedom, such as the number of independent variables necessary to describe the dynamic of the original system. The larger the D2 values of the attractor, the more complicated the behavior of the nonlinear system. The D2 is thus a measure of the complexity of the process being investigated, and it characterizes the distribution of points in phase space. 4 The Gassberger–Procaccia algorithm 5 was computed to evaluate D2 values of the attractors from EEG data. With this algorithm, D2 is derived by determining the relative number …


Obstacle Avoidance System for a Quadrotor UAV

This is the summary of the research being done at Cal Poly Pomona at an undergraduate level on the development of obstacle avoidance capability for unmanned aerial vehicles (UAVs). A quadrotor UAV is used as the research platform. Push and Rapidly Exploring Random Tree (RRT) algorithms are used for the obstacle avoidance capability. The paper shows the use of cheaper commercial off-the-shelf sensors and processor board in the design and implementation of obstacle avoidance algorithm.


A new linearization method of unbalanced electrical distribution networks

With increasing penetration of distributed generation in the distribution networks (DN), the secure and optimal operation of DN has become an important concern. As DN control and operation strategies are mostly based on the linearized sensitivity coefficients between controlled variables (e.g., node voltages, line currents, power loss) and control variables (e.g., power injections, transformer tap positions), efficient and precise calculation of these sensitivity coefficients, i.e. linearization of DN, is of fundamental importance. In this paper, the derivation of the node voltages and power loss as functions of the nodal power injections and transformers' tap-changers positions is presented, and then solved by a Gauss-Seidel method. Compared to other approaches presented in the literature, the proposed method takes into account different load characteristics (e.g., constant PQ, constant impedance, constant current and any combination of above) of a generic multi-phase unbalanced DN and improves the accuracy of linearization. Numerical simulations on both IEEE 13 and 34 nodes test feeders show the efficiency and accuracy of the proposed method.


Dielectric properties estimation of normal and malignant skin tissues at millimeter-wave frequencies using effective medium theory

Millimeter-wave reflectometry can be used as a non-invasive method for early detection of skin cancers. Less penetration and higher resolution are only some reasons to use mm-waves for skin cancer diagnosis. The dielectric parameters data of human skin at mm-wave frequencies are not widely addressed in the literature due to the expensive instruments and the complicated structure of human skin. Available dielectric properties of cancerous tissues in the literature are for other tissues rather than skin and are restricted to the frequencies below 20 GHz. In this paper, dielectric properties of healthy and malignant skin tissues were estimated in the range of 20-100 GHz by applying the effective medium theory. A stratified skin model and some skin tumors were presented. The possibility of mm-wave reflectometry for detection of skin cancers has also been studied. The results of this paper can be used as a theoretical basis for taking further steps in skin cancer research.


Ontology-Based Data Access for Extracting Event Logs from Legacy Data: The onprom Tool and Methodology

Process mining aims at discovering, monitoring, and improving business processes by extracting knowledge from event logs. In this respect, process mining can be applied only if there are proper event logs that are compatible with accepted standards, such as extensible event stream (XES). Unfortunately, in many real world set-ups, such event logs are not explicitly given, but instead are implicitly represented in legacy information systems. In this work, we exploit a framework and associated methodology for the extraction of XES event logs from relational data sources that we have recently introduced. Our approach is based on describing logs by means of suitable annotations of a conceptual model of the available data, and builds on the ontology-based data access (OBDA) paradigm for the actual log extraction. Making use of a realworld case study in the services domain, we compare our novel approach with a more traditional extract-transform-load based one, and are able to illustrate its added value. We also present a set of tools that we have developed and that support the OBDA-based log extraction framework. The tools are integrated as plugins of the ProM process mining suite.


Identifying the Higgs Boson

This paper discusses the identification of the Higgs boson subatomic particle from jet pull energy colorflow images of the particles’ decay, as modeled by the ATLAS Experiment at the Large Hadron Collider in CERN [1]. The Higgs field is an hypothesized energy field thought to permeate the entire universe. Without it, the Standard Model of particle physics would break down, as atomic particles would not have the required mass to attract each other, leading them to simply float around in the universe at the speed of light [20]. Its proof would completely alter our understanding of mass as a physical property, making the discovery of the Higgs field the fundamental unanswered question in particle physics in the last halfcentury [13]. The Standard Model suggests that if the Higgs field were to exist, then its quantum excitation, a particle referred to as the Higgs boson, would also have to exist [20]. The Large Hadron Collider (LHC), tasked with finding this particle, consisting of multiple super-powered electromagnets, collides charged particles traveling at near lightspeed. These collisions deform space upon impact, breaking the charged particles into subatomic constituents. It has been hypothesized that with a proton collision at high enough energy, the Higgs boson would decay in observable ways. The ATLAS detector at the LHC records 40 million proton collisions a second, making human curation of these events unfeasible [14]. An accurate classification system that would label the most promising observations as Higgs boson particle decays is, therefore, required. In collaboration with SLAC and the ATLAS Experiment at CERN we were given access to energy images for both Higgs boson and gluon decay, referred to as signal and background respectively, seen in Figures 1 and 2. The purpose of this project was to build a binary classifier which, given the colorflow energy image of the decay of an unknown particle, would accurately distinguish whether or not that particle was a Higgs boson. While we tested a wide array of supervised learning models, we focused primarily on ensemble methods. In particular we developed and fine-tuned an Adaptive Boosting classifier (AdaBoost) with a Random Forest classifier as its base estimator [8, 10]. We also utilized a number of image feature extraction mechanisms includ-


A new cell search scheme in 3GPP long term evolution downlink, OFDMA systems

This paper presents a cell search procedure in 3rd Generation Partnership Project (3GPP) Long Term Evolution (LTE) downlink systems. The proposed cell search procedure contains three steps. In the beginning, conventional mode detection, symbol timing detection and fractional carrier frequency offset (CFO) estimation methods are adopted so that frequency-domain signal processing can be performed subsequently. Secondly, a new joint detection method for integer carrier frequency offset and sector cell index information is proposed, which is shown to be capable of resisting symbol timing error. Finally, we also proposed a detection method for frame timing and cell identity (ID) group information without explicit channel estimates. Simulation results demonstrate that the proposed scheme with lower detection error probability is effective and robust compared with some conventional approaches.


Approximate graph mining with label costs

Many real-world graphs have complex labels on the nodes and edges. Mining only exact patterns yields limited insights, since it may be hard to find exact matches. However, in many domains it is relatively easy to define a cost (or distance) between different labels. Using this information, it becomes possible to mine a much richer set of approximate subgraph patterns, which preserve the topology but allow bounded label mismatches. We present novel and scalable methods to efficiently solve the approximate isomorphism problem. We show that approximate mining yields interesting patterns in several real-world graphs ranging from IT and protein interaction networks to protein structures.


Evaluating features and classifiers for road weather condition analysis

Weather-dependent road conditions are a major factor in many automobile incidents; computer vision algorithms for automatic classification of road conditions can thus be of great benefit. This paper presents a system for classification of road conditions using still-frames taken from an uncalibrated dashboard camera. The problem is challenging due to variability in camera placement, road layout, weather and illumination conditions. The system uses a prior distribution of road pixel locations learned from training data then fuses normalized luminance and texture features probabilistically to categorize the segmented road surface. We attain an accuracy of 80% for binary classification (bare vs. snow/ice-covered) and 68% for 3 classes (dry vs. wet vs. snow/ice-covered) on a challenging dataset, suggesting that a useful system may be viable.


Multi-modal and multi-brain-computer interfaces: A review

In this short paper we survey recent research views in non-traditional brain-computer interfaces. That is, interfaces that can process brain activity input, but that are designed for non-clinical purposes, that are meant to be used by `healthy' users, that process other user input as well, and that even allow input, voluntarily or involuntarily, from multiple users. This is a rather new research and BCI application area. Control of applications can be made more robust by fusing brain activity information with other information, either explicitly provided by a user (such as commands) or extracted from the user by interpreting his or her behavior (movements, posture, gaze, facial expression, nonverbal speech) or sensing (neuro-)physiological characteristics (skin conductivity, heart rate, brain activity). We see also research where brain activity of multiple users is fused to get a (sometimes critical) control task done. We see also the emergence of applications where BCI measurements help to improve group performance, for example, during meetings, or where information obtained from these measurements helps to learn about audience preferences. Multi-brain BCI has also become a paradigm in artistic and game applications. Artistic interactive BCI applications may require audience participation. In game environments brain activity of various players can be used to control or adapt the game. Both competition and collaboration in serious games, entertainment games and artistic installations requires fusion of EEG measurements from different subjects.


FACTORS AFFECTING CORPORATE SOCIAL RESPONSIBILITY DISCLOSURE IN EGYPT

The study makes a significant contribution to the corporate social responsibility (CSR) disclosure literature by offering the first study of its type undertaken in Egypt as an example of a developing country that examines the determinants of individual and aggregated types of CSR information. Using a sample of 111 Egyptian listed companies for the period of 2005–2010, we find that 66% of the Egyptian listed companies disclose on average 10–50 CSR statements. In addition, we find that product/customer information is used extensively by Egyptian listed companies compared with other types of CSR information. Finally we find that profitability is the main determinant for the aggregated and most of individual CSR information in Egypt.


Automatic fault tree generation from SysML system models

In this paper, a methodology is proposed to integrate safety analysis within a systems engineering approach. This methodology is based on SysML models and aims at generating (semi-) automatically safety analysis artifacts, mainly FMEA and FTA, from system models. Preliminary functional and component FMEA are automatically generated from the functional and structural models respectively, then completed by safety experts. By representing SysML structural diagram as a directed multi-graph, through a graph traversal algorithm and some identified patterns, generic fault trees are automatically derived with corresponding logic gates and events. The proposed methodology provides the safety expert with assistance during safety analysis. It helps reducing time and error proneness of the safety analysis process. It also helps ensuring consistency since the safety analysis artifacts are automatically generated from the latest system model version. The methodology is applied to a real case study, the electromechanical actuator EMA.


Securing communication in 6LoWPAN with compressed IPsec

Real-world deployments of wireless sensor networks (WSNs) require secure communication. It is important that a receiver is able to verify that sensor data was generated by trusted nodes. It may also be necessary to encrypt sensor data in transit. Recently, WSNs and traditional IP networks are more tightly integrated using IPv6 and 6LoWPAN. Available IPv6 protocol stacks can use IPsec to secure data exchange. Thus, it is desirable to extend 6LoWPAN such that IPsec communication with IPv6 nodes is possible. It is beneficial to use IPsec because the existing end-points on the Internet do not need to be modified to communicate securely with the WSN. Moreover, using IPsec, true end-to-end security is implemented and the need for a trustworthy gateway is removed. In this paper we provide End-to-End (E2E) secure communication between IP enabled sensor networks and the traditional Internet. This is the first compressed lightweight design, implementation, and evaluation of 6LoWPAN extension for IPsec. Our extension supports both IPsec's Authentication Header (AH) and Encapsulation Security Payload (ESP). Thus, communication endpoints are able to authenticate, encrypt and check the integrity of messages using standardized and established IPv6 mechanisms.


Structural equations modeling : Fit Indices , sample size , and advanced topics

This article is the second of two parts intended to serve as a primer for structural equations models for the behavioral researcher. The first article introduced the basics: the measurement model, the structural model, and the combined, full structural equations model. In this second article, advanced issues are addressed, including fit indices and sample size, moderators, longitudinal data, mediation, and so forth. © 2009 Society for Consumer Psychology. Published by Elsevier Inc. All rights reserved.


A series elastic actuator as a new load-sensitive continuously variable transmission mechanism for control actuation systems

Electrical motors are often used to actuate mechanisms with varying speed and under varying load e.g., missile control actuation systems or robot extremities. Sizing of the motor is critical in these applications where space is limited. Varying speed and load cause poor utilization of the motor if the reduction ratio is constant. This requires employment of a larger motor than the smallest possible with ideal variable transmission. To realize better utilization of the motor, we propose a series elastic actuator that works as a load sensitive continuously variable transmission system. This is achieved by combining a pin in slot mechanism with an elastic output shaft. The proposed mechanism is modeled and simulated using example missile flight data and a typical robot finger loading scenario. Results are compared to the constant reduction ratio case, and advantages of the proposed mechanism are shown as decreased actuator size and power.