Gang involvement: psychological and behavioral characteristics of gang members, peripheral youth, and nongang youth.

Research has noted the existence of a loose and dynamic gang structure. However, the psychological processes that underpin gang membership have only begun to be addressed. This study examined gang members, peripheral youth, and nongang youth across measures of criminal activity, the importance they attach to status, their levels of moral disengagement, their perceptions of out-group threat, and their attitudes toward authority. Of the 798 high school students who participated in this study, 59 were identified as gang members, 75 as peripheral youth, and 664 as nongang youth. Gang members and peripheral youth were more delinquent than nongang youth overall; however, gang members committed more minor offenses than nongang youth and peripheral youth committed more violent offenses than nongang youth. Gang members were more anti-authority than nongang youth, and both gang and peripheral youth valued social status more than nongang youth. Gang members were also more likely to blame their victims for their actions and use euphemisms to sanitize their behavior than nongang youth, whereas peripheral youth were more likely than nongang youth to displace responsibility onto their superiors. These findings are discussed as they highlight the importance of examining individual differences in the cognitive processes that relate to gang involvement.


Analysis and detection of low quality information in social networks

With social networks like Facebook, Twitter and Google+ attracting audiences of millions of users, they have been an important communication platform in daily life. This in turn attracts malicious users to the social networks as well, causing an increase in the incidence of low quality information. Low quality information such as spam and rumors is a nuisance to people and hinders them from consuming information that is pertinent to them or that they are looking for. Although individual social networks are capable of filtering a significant amount of low quality information they receive, they usually require large amounts of resources (e.g, personnel) and incur a delay before detecting new types of low quality information. Also the evolution of various low quality information posts lots of challenges to defensive techniques. My PhD thesis work focuses on the analysis and detection of low quality information in social networks. We introduce social spam analytics and detection framework SPADE across multiple social networks showing the efficiency and flexibility of cross-domain classification and associative classification. For evolutionary study of low quality information, we present the results on large-scale study on Web spam and email spam over a long period of time. Furthermore, we provide activity-based detection approaches to filter out low quality information in social networks: click traffic analysis of short URL spam, behavior analysis of URL spam and information diffusion analysis of rumor. Our framework and detection techniques show promising results in analyzing and detecting low quality information in social networks.


African-American rhinoplasty.

Increased width, loss of definition, and lack of projection characterize the stereotypical African-American nose. Early rhinoplasty surgeons attempted strict adherence to neoclassical aesthetic ideals. However, in reality, the anatomy and aesthetic desires of these patients are much more complex. Building dorsal height, achieving nasal tip definition amidst thick skin, and producing a more aesthetically pleasing alar base are the major challenges. Surgical planning should be sensitive to both individual and cultural differences in aesthetic perception and expectations. Here we describe the techniques used by the senior author (R.W.H.K.).


Business Case and Technology Analysis for 5G Low Latency Applications

A large number of new consumer and industrial applications are likely to change the classic operator’s business models and provide a wide range of new markets to enter. This paper analyzes the most relevant 5G use cases that require ultra-low latency, from both technical and business perspectives. Low latency services pose challenging requirements to the network, and to fulfill them, operators need to invest in costly changes in their network. In this sense, it is not clear whether such investments are going to be amortized with these new business models. In light of this, specific applications and requirements are described and the potential market benefits for operators are analyzed. Conclusions show that the operators have clear opportunities to add value and position themselves strongly with the increasing number of services to be provided by 5G.


Parallel vision for perception and understanding of complex scenes: methods, framework, and perspectives

In the study of image and vision computing, the generalization capability of an algorithm often determines whether it is able to work well in complex scenes. The goal of this review article is to survey the use of photorealistic image synthesis methods in addressing the problems of visual perception and understanding. Currently, the ACP Methodology comprising artificial systems, computational experiments, and parallel execution is playing an essential role in modeling and control of complex systems. This paper extends the ACP Methodology into the computer vision field, by proposing the concept and basic framework of Parallel Vision. In this paper, we first review previous works related to Parallel Vision, in terms of synthetic data generation and utilization. We detail the utility of synthetic data for feature analysis, object analysis, scene analysis, and other analyses. Then we propose the basic framework of Parallel Vision, which is composed of an ACP trilogy (artificial scenes, computational experiments, and parallel execution). We also present some in-depth thoughts and perspectives on Parallel Vision. This paper emphasizes the significance of synthetic data to vision system design and suggests a novel research methodology for perception and understanding of complex scenes.


The Semantic Web - ISWC 2003

The Semantic Network, a component of the Unified Medical Language System® (UMLS), describes core biomedical knowledge consisting of semantic types and relationships. It is a well established, semi-formal ontology in widespread use for over a decade. We expected to “publish” this ontology on the Semantic Web, using OWL, with relatively little effort. However, we ran into a number of problems concerning alternative interpretations of the SN notation and the inability to express some of the interpretations in OWL. We detail these problems, as a cautionary tale to others planning to publish pre-existing ontologies on the Semantic Web, as a list of issues to consider when describing formally concepts in any ontology, and as a collection of criteria for evaluating alternative representations, which could form part of a methodology of ontology development.


The Basal Ganglia and Chunking of Action Repertoires

The basal ganglia have been shown to contribute to habit and stimulus-response (S-R) learning. These forms of learning have the property of slow acquisition and, in humans, can occur without conscious awareness. This paper proposes that one aspect of basal ganglia-based learning is the recoding of cortically derived information within the striatum. Modular corticostriatal projection patterns, demonstrated experimentally, are viewed as producing recoded templates suitable for the gradual selection of new input-output relations in cortico-basal ganglia loops. Recordings from striatal projection neurons and interneurons show that activity patterns in the striatum are modified gradually during the course of S-R learning. It is proposed that this recoding within the striatum can chunk the representations of motor and cognitive action sequences so that they can be implemented as performance units. This scheme generalizes Miller's notion of information chunking to action control. The formation and the efficient implementation of action chunks are viewed as being based on predictive signals. It is suggested that information chunking provides a mechanism for the acquisition and the expression of action repertoires that, without such information compression would be biologically unwieldy or difficult to implement. The learning and memory functions of the basal ganglia are thus seen as core features of the basal ganglia's influence on motor and cognitive pattern generators.


ADVANCED POLARIMETRIC CONCEPTS – PART 1 ( Polarimetric Target Description , Speckle filtering and Decomposition Theorems )

There is currently a great deal of interest in the use of polarimetry for radar remote sensing. In this context, different and important objectives are to classify Earth terrain components within a fully polarimetric SAR image and then extract physical information from the observed scattering of microwaves by surface and volume structures. The most important observable measured by such radar systems is the 3x3-coherency matrix [T]. This matrix accounts for local variations in the scattering matrix and is the lowest order operator suitable to extract polarimetric parameters for distributed scatterers in the presence of additive (system) and/or multiplicative (speckle) noise. In the first part of this paper, the most important Target Polarimetry descriptors: Sinclair Matrix, target vectors, coherency matrix and the covariance matrix as well are presented, their interconnections and equivalences will be shown together with the respective transformations.


Chapter 2 INTRUSION DETECTION : A SURVEY

This chapter provides the overview of the state of the art in intrusion detection research. Intrusion detection systems are software and/or hardware components that monitor computer systems and analyze events occurring in them for signs of intrusions. Due to widespread diversity and complexity of computer infrastructures, it is difficult to provide a completely secure computer system. Therefore, there are numerous security systems and intrusion detection systems that address different aspects of computer security. This chapter first provides taxonomy of computer intrusions, along with brief descriptions of major computer attack categories. Second, a common architecture of intrusion detection systems and their basic characteristics are presented. Third, taxonomy of intrusion detection systems based on five criteria (information source, analysis strategy, time aspects, architecture, response) is given. Finally, intrusion detection systems are classified according to each of these categories and the most representative research prototypes are briefly described.


Reforming education sector through Big Data

Big data, which is maturing over time, has a high impact in analyzing scenarios and coming up with decision making strategies pertaining to any sector. Consequently the number of applications are also increasing where in the past, they were limited to the confines of company sales departments. In lieu, this paper is primarily focused on applying Data Sciences within the education sector. We will be discussing the overall application to address higher-education issues. We will also present a framework for capturing, analyzing it and concluding on data in order to benefit decision-makers, thus ultimately manifesting in informing design and finalizing policy.


Agile Modeling with the UML

This paper discusses a model-based approach to software development. It argues that an approach using models as central development artifact needs to be added to the portfolio of software engineering techniques, to further increase efficiency and flexibility of the development as well as quality and reusability of the results. Two major and strongly related techniques are identified and discussed: Test case modeling and an evolutionary approach to model transformation.


Near-Saddle-Node Bifurcation Behavior as Dynamics in Working Memory for Goal-Directed Behavior

In consideration of working memory as a means for goal-directed behavior in nonstationary environments, we argue that the dynamics of working memory should satisfy two opposing demands: long-term maintenance and quick transition. These two characteristics are contradictory within the linear domain. We propose the near-saddle-node bifurcation behavior of a sigmoidal unit with a self-connection as a candidate of the dynamical mechanism that satisfies both of these demands. It is shown in evolutionary programming experiments that the near-saddle-node bifurcation behavior can be found in recurrent networks optimized for a task that requires efficient use of working memory. The result suggests that the near-saddle-node bifurcation behavior may be a functional necessity for survival in nonstationary environments.


Data Base Sublanguage Founded on the Relational Calculus

Three principal types of language for data base manipulation are identified: the low-level, procedure-oriented (typified by the CODASYL-proposed DML), the intermediate level, algebraic (typified by the Project MAC MacAIMS language), and the high level, relational calculus-based data sublanguage, an example of which is described in this paper. The language description is informal and stresses concepts and principles. Following this, arguments are presented for the superiority of the calculus-based type of data base sub-language over the algebraic, and for the algebraic over the low-level procedural. These arguments are particularly relevant to the questions of inter-system compatibility and standardization.


The art of appeal in electronic commerce: Understanding the impact of product and website quality on online purchases

Purpose – This study advances product appeal and website appeal as focal psychological mechanisms that can be invoked by business-to-consumer (B2C) e-commerce sites to mitigate problems of information asymmetry via signaling to bolster consumers’ purchase intention under the influence of trust. Design/methodology/approach – Survey approach was employed to validate our research model. Findings – Website appeal partially mediates the positive effect of product appeal on consumers’ purchase intention. Trust in e-commerce sites not only increases purchase intention directly, but it also reinforces the positive relationship between website appeal and purchase intention while attenuating the positive relationship between product appeal and purchase intention. Service content quality, search delivery quality, and enjoyment are confirmed as positive antecedents of website appeal whereas diagnosticity and justifiability are established as positive antecedents of product appeal. Research implications – This study not only delineates product and website appeal as complementary drivers of consumer purchase on e-commerce sites, but it also derives five signals that aid in bolstering both product and website appeal. Trust is revealed to exert a moderating influence on the impact of product and website appeal on purchase intention. Practical implications – Practitioners should prioritize their resource allocation to enhance qualities most pertinent to product and website appeal. E-commerce sites should offer product-oriented functionalities to facilitate product diagnosticity and reassure consumers of their purchase decisions. Originality/value – This study distinguishes between product and website appeal as well as between their respective antecedents. It also uncovers how trust can alter the effects of both website and product appeal on consumers’ purchase intention.


From Classical To Hip-Hop : Can Machines Learn Genres ?

The ability to classify the genre of a song is an easy task for the human ear. After listening to a song for just several seconds, it is often not difficult to take note of that song’s characteristics and subsequently identify this genre. However, this task is one that computers have historically not been able to solve well, at least not until the advent of more sophisticated machine learning techniques. Our team explored several of these techniques over the course of this project and successfully built a system that achieves 61.873% accuracy in classifying music genres. This paper discusses the methods we used for exploratory data analysis, feature selection, hyperparameter optimization, and eventual implementation of several algorithms for classification.


Approximate Inference in Additive Factorial HMMs with Application to Energy Disaggregation

This paper considers additive factorial hidden Markov models, an extension to HMMs where the state factors into multiple independent chains, and the output is an additive function of all the hidden states. Although such models are very powerful, accurate inference is unfortunately difficult: exact inference is not computationally tractable, and existing approximate inference techniques are highly susceptible to local optima. In this paper we propose an alternative inference method for such models, which exploits their additive structure by 1) looking at the observed difference signal of the observation, 2) incorporating a “robust” mixture component that can account for unmodeled observations, and 3) constraining the posterior to allow at most one hidden state to change at a time. Combining these elements we develop a convex formulation of approximate inference that is computationally efficient, has no issues of local optima, and which performs much better than existing approaches in practice. The method is motivated by the problem of energy disaggregation, the task of taking a whole home electricity signal and decomposing it into its component appliances; applied to this task, our algorithm achieves state-of-the-art performance, and is able to separate many appliances almost perfectly using just the total aggregate signal.


Recent approaches to non-intrusive load monitoring techniques in residential settings

The concept of Smart Grids is closely related to energy conservation and load shedding concepts. However, it is difficult to quantify the effectiveness of energy conservation efforts in residential settings without any sort of end-use energy information as feedback. In order to achieve that, load monitoring methods are normally used. In recent years, non-intrusive load monitoring (NILM) approaches are gaining popularity due to their minimal installation requirements and cost effectiveness. For a NILM system to work, only one sensor at the entry point to a home is required. Fluctuations in the aggregate power consumption signals are used to mathematically estimate the composition of operation of appliances. This approach eliminates the requirement of installing plug-meters for every appliance in the house. In this paper, we provide a review of recent research efforts on state-of-the-art NILM algorithms before concluding with a baseline and overall vision for our future research direction.


Toward Non-Intrusive Load Monitoring via Multi-Label Classification

Demand-side management technology is a key element of the proposed smart grid, which will help utilities make more efficient use of their generation assets by reducing consumers' energy demand during peak load periods. However, although some modern appliances can respond to price signals from the utility companies, there is a vast stock of older appliances that cannot. For such appliances, utilities must infer what appliances are operating in a home, given only the power signals on the main feeder to the home (i.e., the home's power consumption must be disaggregated into individual appliances). We report on an in-depth investigation of multi-label classification algorithms for disaggregating appliances in a power signal. A systematic review of this research topic shows that this class of algorithms has received little attention in the literature, even though it is arguably a more natural fit to the disaggregation problem than the traditional single-label classifiers used to date. We examine a multi-label meta-classification framework (RAkEL), and a bespoke multi-label classification algorithm (MLkNN), employing both time-domain and wavelet-domain feature sets. We test these classifiers on two real houses from the Reference Energy Disaggregation Dataset. We found that the multilabel algorithms are effective and competitive with published results on the datasets.


ElectriSense: single-point sensing using EMI for electrical event detection and classification in the home

This paper presents ElectriSense, a new solution for automatically detecting and classifying the use of electronic devices in a home from a single point of sensing. ElectriSense relies on the fact that most modern consumer electronics and fluorescent lighting employ switch mode power supplies (SMPS) to achieve high efficiency. These power supplies continuously generate high frequency electromagnetic interference (EMI) during operation that propagates throughout a home's power wiring. We show both analytically and by in-home experimentation that EMI signals are stable and predictable based on the device's switching frequency characteristics. Unlike past transient noise-based solutions, this new approach provides the ability for EMI signatures to be applicable across homes while still being able to differentiate between similar devices in a home. We have evaluated our solution in seven homes, including one six-month deployment. Our results show that ElectriSense can identify and classify the usage of individual devices with a mean accuracy of 93.82%.


Updatable Block-Level Message-Locked Encryption

Deduplication is a widely used technique for reducing storage space of cloud service providers. Yet, it is unclear how to support deduplication of encrypted data securely until the study of Bellareetal on message-locked encryption (Eurocrypt 2013). Since then, there are many improvements such as strengthening its security, reducing client storage, etc. While updating a (shared) file is common, there is little attention on how to efficiently update large encrypted files in a remote storage with deduplication. To modify even a single bit, existing solutions require the trivial and expensive way of downloading and decrypting the large ciphertext.
 We initiate the study of updatable block-level message-locked encryption. We propose a provably secure construction that is efficiently updatable with O(log|F|) computational cost, where |F| is the file size. It also supports proof-of-ownership, a nice feature which protects storage providers from being abused as a free content distribution network.


LIGHTNETs: Smart LIGHTing and Mobile Optical Wireless NETworks — A Survey

Recently, rapid increase of mobile devices pushed the radio frequency (RF)-based wireless technologies to their limits. Free-space-optical (FSO), a.k.a. optical wireless, communication has been considered as one of the viable solutions to respond to the ever-increasing wireless capacity demand. Particularly, Visible Light Communication (VLC) which uses light emitting diode (LED) based smart lighting technology provides an opportunity and infrastructure for the high-speed low-cost wireless communication. Though stemming from the same core technology, the smart lighting and FSO communication have inherent tradeoffs amongst each other. In this paper, we present a tutorial and survey of advances in these two technologies and explore the potential for integration of the two as a single field of study: LIGHTNETs. We focus our survey to the context of mobile communications given the recent pressing needs in mobile wireless networking. We deliberate on key challenges involved in designing technologies jointly performing the two functions simultaneously: LIGHTing and NETworking.


Automatic Caption Generation for News Images

This paper is concerned with the task of automatically generating captions for images, which is important for many image-related applications. Examples include video and image retrieval as well as the development of tools that aid visually impaired individuals to access pictorial information. Our approach leverages the vast resource of pictures available on the web and the fact that many of them are captioned and colocated with thematically related documents. Our model learns to create captions from a database of news articles, the pictures embedded in them, and their captions, and consists of two stages. Content selection identifies what the image and accompanying article are about, whereas surface realization determines how to verbalize the chosen content. We approximate content selection with a probabilistic image annotation model that suggests keywords for an image. The model postulates that images and their textual descriptions are generated by a shared set of latent variables (topics) and is trained on a weakly labeled dataset (which treats the captions and associated news articles as image labels). Inspired by recent work in summarization, we propose extractive and abstractive surface realization models. Experimental results show that it is viable to generate captions that are pertinent to the specific content of an image and its associated article, while permitting creativity in the description. Indeed, the output of our abstractive model compares favorably to handwritten captions and is often superior to extractive methods.


Pileus: protecting user resources from vulnerable cloud services

Cloud computing platforms are now constructed as distributed, modular systems of cloud services, which enable cloud users to manage their cloud resources. However, in current cloud platforms, cloud services fully trust each other, so a malicious user may exploit a vulnerability in a cloud service to obtain unauthorized access to another user's data. To date, over 150 vulnerabilities have been reported in cloud services in the OpenStack cloud. Research efforts in cloud security have focused primarily on attacks originating from user VMs or compromised operating systems rather than threats caused by the compromise of distributed cloud services, leaving cloud users open to attacks from these vulnerable cloud services. In this paper, we propose the Pileus cloud service architecture, which isolates each user's cloud operations to prevent vulnerabilities in cloud services from enabling malicious users to gain unauthorized access. Pileus deploys stateless cloud services "on demand" to service each user's cloud operations, limiting cloud services to the permissions of individual users. Pileus leverages the decentralized information flow control (DIFC) model for permission management, but the Pileus design addresses special challenges in the cloud environment to: (1) restrict how cloud services may be allowed to make security decisions; (2) select trustworthy nodes for access enforcement in a dynamic, distributed environment; and (3) limit the set of nodes a user must trust to service each operation. We have ported the OpenStack cloud platform to Pileus, finding that we can systematically prevent compromised cloud services from attacking other users' cloud operations with less than 3% additional latency for the operation. Application of the Pileus architecture to Open-Stack shows that confined cloud services can service users' cloud operations effectively for a modest overhead.


Novelty and Reinforcement Learning in the Value System of Developmental Robots

The value system of a developmental robot signals the occurrence of salient sensory inputs, modulates the mapping from sensory inputs to action outputs, and evaluates candidate actions. In the work reported here, a low level value system is modeled and implemented. It simulates the non-associative animal learning mechanism known as habituation e ect. Reinforcement learning is also integrated with novelty. Experimental results show that the proposed value system works as designed in a study of robot viewing angle selection.


A Linear Permanent-Magnet Motor for Active Vehicle Suspension

Traditionally, automotive suspension designs with passive components have been a compromise between the three conflicting demands of road holding, load carrying, and passenger comfort. Linear electromagnetic motor-based active suspension has superior controllability and bandwidth, provides shock load isolation between the vehicle chassis and wheel, and, therefore, has great potential. It also has the ability to recover energy that is dissipated in the shock absorber in the passive systems and results in a much more energy-efficient suspension system. This paper describes the issues pertinent to the design of a high force density tubular permanent-magnet (PM) motor for active suspension in terms of performance optimization, the use of a solid stator core for low-cost production and its impact on thrust force, and the assessment of demagnetization risk.


Hide-and-Seek: Forcing a Network to be Meticulous for Weakly-Supervised Object and Action Localization

We propose ‘Hide-and-Seek’, a weakly-supervised framework that aims to improve object localization in images and action localization in videos. Most existing weakly-supervised methods localize only the most discriminative parts of an object rather than all relevant parts, which leads to suboptimal performance. Our key idea is to hide patches in a training image randomly, forcing the network to seek other relevant parts when the most discriminative part is hidden. Our approach only needs to modify the input image and can work with any network designed for object localization. During testing, we do not need to hide any patches. Our Hide-and-Seek approach obtains superior performance compared to previous methods for weakly-supervised object localization on the ILSVRC dataset. We also demonstrate that our framework can be easily extended to weakly-supervised action localization.


Externalising the autobiographical self: sharing personal memories online facilitated memory retention.

Internet technology provides a new means of recalling and sharing personal memories in the digital age. What is the mnemonic consequence of posting personal memories online? Theories of transactive memory and autobiographical memory would make contrasting predictions. In the present study, college students completed a daily diary for a week, listing at the end of each day all the events that happened to them on that day. They also reported whether they posted any of the events online. Participants received a surprise memory test after the completion of the diary recording and then another test a week later. At both tests, events posted online were significantly more likely than those not posted online to be recalled. It appears that sharing memories online may provide unique opportunities for rehearsal and meaning-making that facilitate memory retention.


Iterated extended Kalman filter based visual-inertial odometry using direct photometric feedback

This paper presents a visual-inertial odometry framework which tightly fuses inertial measurements with visual data from one or more cameras, by means of an iterated extended Kalman filter (IEKF). By employing image patches as landmark descriptors, a photometric error is derived, which is directly integrated as an innovation term in the filter update step. Consequently, the data association is an inherent part of the estimation process and no additional feature extraction or matching processes are required. Furthermore, it enables the tracking of non-corner shaped features, such as lines, and thereby increases the set of possible landmarks. The filter state is formulated in a fully robocentric fashion, which reduces errors related to nonlinearities. This also includes partitioning of a landmark’s location estimate into a bearing vector and distance and thereby allows an undelayed initialization of landmarks. Overall, this results in a compact approach which exhibits a high level of robustness with respect to low scene texture and motion blur. Furthermore, there is no time-consuming initialization procedure and pose estimates are available starting at the second image frame. We test the filter on different real datasets and compare it to other state-of-the-art visual-inertial frameworks. The experimental results show that robust localization with high accuracy can be achieved with this filterbased framework.


CareLog: a selective archiving tool for behavior management in schools

Identifying the function of problem behavior can lead to the development of more effective interventions. One way to identify the function is through functional behavior assessment (FBA). Teachers conduct FBA in schools. However, the task load of recording the data manually is high, and the challenge of accurately identifying antecedents and consequences is significant while interacting with students. These issues often result in imperfect information capture. CareLog allows teachers more easily to conduct FBAs and enhances the capture of relevant information. In this paper, we describe the design process that led to five design principles that governed the development of CareLog. We present results from a five-month, quasi-controlled study aimed at validating those design principles. We reflect on how various constraints imposed by special education settings impact the design and evaluation process for HCI practitioners and researchers.


A Note on the Convergence of SOR for the PageRank Problem

A curious phenomenon when it comes to solving the linear system formulation of the PageRank problem is that while the convergence rate of Gauss–Seidel shows an improvement over Jacobi by a factor of approximately two, successive overrelaxation (SOR) does not seem to offer a meaningful improvement over Gauss–Seidel. This has been observed experimentally and noted in the literature, but to the best of our knowledge there has been no analytical explanation for this thus far. This convergence behavior is surprising because there are classes of matrices for which Gauss–Seidel is faster than Jacobi by a similar factor of two, and SOR accelerates convergence by an order of magnitude compared to Gauss–Seidel. In this short paper we prove analytically that the PageRank model has the unique feature that there exist PageRank linear systems for which SOR does not converge outside a very narrow interval depending on the damping factor, and that in such situations Gauss–Seidel may be the best choice among the relaxation parameters. Conversely, we show that within that narrow interval, there exists no PageRank problem for which SOR does not converge. Our result may give an analytical justification for the popularity of Gauss–Seidel as a solver for the linear system formulation of PageRank.


A Card Game Description Language

We present initial research regarding a system capable of generating novel card games. We furthermore propose a method for computationally analysing existing games of the same genre. Ultimately, we present a formalisation of card game rules, and a context-free grammar Gcardgame capable of expressing the rules of a large variety of card games. Example derivations are given for the poker variant Texas hold ’em, Blackjack and UNO. Stochastic simulations are used both to verify the implementation of these well-known games, and to evaluate the results of new game rules derived from the grammar. In future work, this grammar will be used to evolve completely novel card games using a grammarguided genetic program.


A Hexanucleotide Repeat Expansion in C9ORF72 Is the Cause of Chromosome 9p21-Linked ALS-FTD

The chromosome 9p21 amyotrophic lateral sclerosis-frontotemporal dementia (ALS-FTD) locus contains one of the last major unidentified autosomal-dominant genes underlying these common neurodegenerative diseases. We have previously shown that a founder haplotype, covering the MOBKL2b, IFNK, and C9ORF72 genes, is present in the majority of cases linked to this region. Here we show that there is a large hexanucleotide (GGGGCC) repeat expansion in the first intron of C9ORF72 on the affected haplotype. This repeat expansion segregates perfectly with disease in the Finnish population, underlying 46.0% of familial ALS and 21.1% of sporadic ALS in that population. Taken together with the D90A SOD1 mutation, 87% of familial ALS in Finland is now explained by a simple monogenic cause. The repeat expansion is also present in one-third of familial ALS cases of outbred European descent, making it the most common genetic cause of these fatal neurodegenerative diseases identified to date.


Precision and Recall for Regression

Cost sensitive prediction is a key task in many real world applications. Most existing research in this area deals with classification problems. This paper addresses a related regression problem: the prediction of rare extreme values of a continuous variable. These values are often regarded as outliers and removed from posterior analysis. However, for many applications (e.g. in finance, meteorology, biology, etc.) these are the key values that we want to accurately predict. Any learning method obtains models by optimizing some preference criteria. In this paper we propose new evaluation criteria that are more adequate for these applications. We describe a generalization for regression of the concepts of precision and recall often used in classification. Using these new evaluation metrics we are able to focus the evaluation of predictive models on the cases that really matter for these applications. Our experiments indicate the advantages of the use of these new measures when comparing predictive models in the context of our target applications.


Review on trajectory similarity measures

The availability of devices that can be used to track moving objects has increased dramatically leading to a great growth in movement data from almost every application domain. Therefore, there has been an increasing interest in proposing new methodologies for indexing, classifying, clustering, querying and measuring similarity between moving objects' data. One of the main functions for a wide range of application domains is to measure the similarity between two moving objects' trajectories. In this paper, we present a comparative study between widely used trajectory similarity measures observing the advantages and disadvantages of these measures.


Use of Network Latency Profiling and Redundancy for Cloud Server Selection

As servers are placed in diverse locations in networked services today, it becomes vital to direct a client's request to the best server(s) to achieve both high performance and reliability. In this distributed setting, non-negligible latency and server availability become two major concerns, especially for highly-interactive applications. Profiling latencies and sending redundant data have been investigated as solutions to these issues. The notion of a cloudlet in mobile-cloud computing is also relevant in this context, as the cloudlet can supply these solution approaches on behalf of the mobile. In this paper, we investigate the effects of profiling and redundancy on latency when a client has a choice of multiple servers to connect to, using measurements from real experiments and simulations. We devise and test different server selection and data partitioning strategies in terms of profiling and redundancy. Our key findings are summarized as follows. First, intelligent server selection algorithms help find the optimal group of servers that minimize latency with profiling. Second, we can achieve good performance with relatively simple approaches using redundancy. Our analysis of profiling and redundancy provides insight to help designers determine how many servers and which servers to select to reduce latency.


Sustainable, effective implementation of a surgical preprocedural checklist: an "attestation" format for all operating team members.

BACKGROUND
Adoption ofa preprocedural pause (PPP) associated with a checklist and a team briefing has been shown to improve teamwork function in operating rooms (ORs) and has resulted in improved outcomes. The format of the World Health Organization Safe Surgery Saves Lives checklist has been used as a template for a PPP. Performing a PPP, described as a "time-out," is one of the three principal components, along with a preprocedure verification process and marking the procedure site, of the Joint Commission's Universal Protocol for Preventing Wrong Site, Wrong Procedure, Wrong Person Surgery. However, if the surgeon alone leads the pause, its effectiveness may be decreased by lack of input from other operating team members.

METHODS
In this study, the PPP was assessed to measure participation and input from operating team members. On the basis of low participation levels, the pause was modified to include an attestation from each member of the team.

RESULTS
Preliminary analysis of our surgeon-led pause revealed only 54% completion of all items, which increased to 97% after the intervention. With the new format, operating team members stopped for the pause in 96% of cases, compared with 78% before the change. Operating team members introduced themselves in 94% of cases, compared with 44% before the change. Follow-up analysis showed sustained performance at 18 months after implementation.

CONCLUSIONS
A preprocedural checklist format in which each member of the operating team provides a personal attestation can improve pause compliance and may contribute to improvements in the culture of teamwork within an OR. Successful online implementation of a PPP, which includes participation by all operating team members, requires little or no additional expense and only minimal formal coaching outside working situations.


Local multidimensional scaling

In a visualization task, every nonlinear projection method needs to make a compromise between trustworthiness and continuity. In a trustworthy projection the visualized proximities hold in the original data as well, whereas a continuous projection visualizes all proximities of the original data. We show experimentally that one of the multidimensional scaling methods, curvilinear components analysis, is good at maximizing trustworthiness. We then extend it to focus on local proximities both in the input and output space, and to explicitly make a user-tunable parameterized compromise between trustworthiness and continuity. The new method compares favorably to alternative nonlinear projection methods.


A Mixed Fragmentation Methodology For Initial Distributed Database Design

We deene mixed fragmentation as a process of simultaneously applying the horizontal and vertical fragmentation on a relation. It can be achieved in one of two ways: by performing horizontal fragmentation followed by vertical fragmentation or by performing vertical fragmentation followed by horizontal fragmentation. The need for mixed fragmentation arises in distributed databases because database users usually access subsets of data which are vertical and horizontal fragments of global relations and there is a need to process queries or transactions that would access these fragments optimally. We present algorithms for generating candidate vertical and horizontal fragmentation schemes and propose a methodology for distributed database design using these fragmentation schemes. When applied together these schemes form a grid. This grid consisting of cells is then merged to form mixed fragments so as to minimize the number of disk accesses required to process the distributed transactions. We have implemented the vertical and horizontal fragmentation algorithms and are developing a Distributed Database Design Tool to support the proposed methodology.


Getting plugged in: an overview of internet addiction.

Internet addiction is not formally recognised as a clinical disorder by the WHO despite increasing evidence that excessive internet use can interfere with daily life and work. There is increasing pressure from Psychologists for Internet addiction to be recognised. This article explores the prevalence, symptoms and management of Internet addiction and the consequences of ignoring the ever growing concerns from public figures and institutions.


A 96-GHz Ortho-Mode Transducer for the Polatron

We describe the design, simulation, fabrication, and performance of a 96-GHz ortho-mode transducer (OMT) to be used for the Polatron—a bolometric receiver with polarization capability. The OMT has low loss, good isolation, and moderately broad bandwidth, and its performance closely resembles simulation results.


A11y Attacks: Exploiting Accessibility in Operating Systems

Driven in part by federal law, accessibility (a11y) support for disabled users is becoming ubiquitous in commodity OSs. Some assistive technologies such as natural language user interfaces in mobile devices are welcomed by the general user population. Unfortunately, adding new features in modern, complex OSs usually introduces new security vulnerabilities. Accessibility support is no exception. Assistive technologies can be defined as computing subsystems that either transform user input into interaction requests for other applications and the underlying OS, or transform application and OS output for display on alternative devices. Inadequate security checks on these new I/O paths make it possible to launch attacks from accessibility interfaces. In this paper, we present the first security evaluation of accessibility support for four of the most popular computing platforms: Microsoft Windows, Ubuntu Linux, iOS, and Android. We identify twelve attacks that can bypass state-of-the-art defense mechanisms deployed on these OSs, including UAC, the Yama security module, the iOS sandbox, and the Android sandbox. Further analysis of the identified vulnerabilities shows that their root cause is that the design and implementation of accessibility support involves inevitable trade-offs among compatibility, usability, security, and (economic) cost. These trade-offs make it difficult to secure a system against misuse of accessibility support. Based on our findings, we propose a number of recommendations to either make the implementation of all necessary security checks easier and more intuitive, or to alleviate the impact of missing/incorrect checks. We also point out open problems and challenges in automatically analyzing accessibility support and identifying security vulnerabilities.


Low-cost traffic analysis of Tor

Tor is the second generation onion router supporting the anonymous transport of TCP streams over the Internet. Its low latency makes it very suitable for common tasks, such as Web browsing, but insecure against traffic-analysis attacks by a global passive adversary. We present new traffic-analysis techniques that allow adversaries with only a partial view of the network to infer which nodes are being used to relay the anonymous streams and therefore greatly reduce the anonymity provided by Tor. Furthermore, we show that otherwise unrelated streams can be linked back to the same initiator Our attack is feasible for the adversary anticipated by the Tor designers. Our theoretical attacks are backed up by experiments performed on the deployed, albeit experimental, Tor network. Our techniques should also be applicable to any low latency anonymous network. These attacks highlight the relationship between the field of traffic-analysis and more traditional computer security issues, such as covert channel analysis. Our research also highlights that the inability to directly observe network links does not prevent an attacker from performing traffic-analysis: the adversary can use the anonymising network as an oracle to infer the traffic load on remote nodes in order to perform traffic-analysis.


Connecting ” and “ Disconnecting ” With Civic Life : Patterns of Internet Use and the Production of Social Capital

This article explores the relationship between Internet use and the individual-level production of social capital. To do so, the authors adopt a motivational perspective to distinguish among types of Internet use when examining the factors predicting civic engagement, interpersonal trust, and life contentment. The predictive power of new media use is then analyzed relative to key demographic, contextual, and traditional media use variables using the 1999 DDB Life Style Study. Although the size of associations is generally small, the data suggest that informational uses of the Internet are positively related to individual differences in the production of social capital, whereas social-recreational uses are negatively related to these civic indicators. Analyses within subsamples defined by generational age breaks further suggest that social capital production is related to Internet use among Generation X, while it is tied to television use among Baby Boomers and newspaper use among members of the Civic Generation. The possibility of life cycle and cohort effects is discussed.


The spatio-temporal generalized additive model for criminal incidents

Law enforcement agencies need to model spatio-temporal patterns of criminal incidents. With well developed models, they can study the causality of crimes and predict future criminal incidents, and they can use the results to help prevent crimes. In this paper, we described our newly developed spatio-temporal generalized additive model (S-T GAM) to discover underlying factors related to crimes and predict future incidents. The model can fully utilize many different types of data, such as spatial, temporal, geographic, and demographic data, to make predictions. We efficiently estimated the parameters for S-T GAM using iteratively re-weighted least squares and maximum likelihood and the resulting estimates provided for model interpretability. In this paper we showed the evaluation of S-T GAM with the actual criminal incident data from Charlottesville, Virginia. The evaluation results showed that S-T GAM outperformed the previous spatial prediction models in predicting future criminal incidents.


Characterizing and detecting performance bugs for smartphone applications

Smartphone applications’ performance has a vital impact on user experience. However, many smartphone applications suffer from bugs that cause significant performance degradation, thereby losing their competitive edge. Unfortunately, people have little understanding of these performance bugs. They also lack effective techniques to fight with such bugs. To bridge this gap, we conducted a study of 70 real-world performance bugs collected from eight large-scale and popular Android applications. We studied the characteristics (e.g., bug types and how they manifested) of these bugs and identified their common patterns. These findings can support follow-up research on performance bug avoidance, testing, debugging and analysis for smartphone applications. To demonstrate the usefulness of our findings, we implemented a static code analyzer, PerfChecker, to detect our identified performance bug patterns. We experimentally evaluated PerfChecker by applying it to 29 popular Android applications, which comprise 1.1 million lines of Java code. PerfChecker successfully detected 126 matching instances of our performance bug patterns. Among them, 68 were quickly confirmed by developers as previously-unknown issues that affect application performance, and 20 were fixed soon afterwards by following our optimization suggestions.


Person re-identification in multi-camera system by signature based on interest point descriptors collected on short video sequences

We present and evaluate a person re-identification scheme for multi-camera surveillance system. Our approach uses matching of signatures based on interest-points descriptors collected on short video sequences. One of the originalities of our method is to accumulate interest points on several sufficiently time-spaced images during person tracking within each camera, in order to capture appearance variability. A first experimental evaluation conducted on a publicly available set of low-resolution videos in a commercial mall shows very promising inter-camera person re-identification performances (a precision of 82% for a recall of 78%). It should also be noted that our matching method is very fast: ~ 1/8s for re-identification of one target person among 10 previously seen persons, and a logarithmic dependence with the number of stored person models, making re- identification among hundreds of persons computationally feasible in less than ~ 1/5 second.


ThingTalk : A Distributed Language for a Social Internet of Things

The Internet of Things has increased the number of connected devices by an order of magnitude. This increase comes with a corresponding increase in complexity, which has led to centralized architectures that do not respect the privacy of the users and incur high development and deployment costs. We propose the Open Thing Platform as a new distributed architecture which is built around ThingTalk, a new high level declarative programming language that can express the interaction between multiple physical devices and online services. The architecture abstracts away the details of the communication without sacrificing privacy. We experiment with ThingTalk in multiple domains and we show that several applications can be written in a few lines of code and with no need for any server infrastructure.


Using Mobile LiDAR Data for Rapidly Updating Road Markings

Updating road markings is one of the routine tasks of transportation agencies. Compared with traditional road inventory mapping techniques, vehicle-borne mobile light detection and ranging (LiDAR) systems can undertake the job safely and efficiently. However, current hurdles include software and computing challenges when handling huge volumes of highly dense and irregularly distributed 3-D mobile LiDAR point clouds. This paper presents the development and implementation aspects of an automated object extraction strategy for rapid and accurate road marking inventory. The proposed road marking extraction method is based on 2-D georeferenced feature (GRF) images, which are interpolated from 3-D road surface points through a modified inverse distance weighted (IDW) interpolation. Weighted neighboring difference histogram (WNDH)-based dynamic thresholding and multiscale tensor voting (MSTV) are proposed to segment and extract road markings from the noisy corrupted GRF images. The results obtained using 3-D point clouds acquired by a RIEGL VMX-450 mobile LiDAR system in a subtropical urban environment are encouraging.


A review on the platform design, dynamic modeling and control of hybrid UAVs

This article presents a review on the platform design, dynamic modeling and control of hybrid Unmanned Aerial Vehicles (UAVs). For now, miniature UAVs which have experienced a tremendous development are dominated by two main types, i.e., fixed-wing UAV and Vertical Take-Off and Landing (VTOL) UAV, each of which, however, has its own inherent limitations on such as flexibility, payload, axnd endurance. Enhanced popularity and interest are recently gained by a newer type of UAVs, named hybrid UAV that integrates the beneficial features of both conventional ones. In this paper, a technical overview of the recent advances of the hybrid UAV is presented. More specifically, the hybrid UAV's platform design together with the associated technical details and features are introduced first. Next, the work on hybrid UAV's flight dynamics modeling is then categorized and explained. As for the flight control system design for the hybrid UAV, several flight control strategies implemented are discussed and compared in terms of theory, linearity and implementation.


Power divider in ridge gap waveguide technology

A power divider in ridge gap waveguide technology has been designed to work at 15GHz. Measurements and simulations are provided. Besides, a broad numerical study of the characteristic impedance of a ridge gap waveguide is presented.


Mapping the antigenic and genetic evolution of influenza virus.

The antigenic evolution of influenza A (H3N2) virus was quantified and visualized from its introduction into humans in 1968 to 2003. Although there was remarkable correspondence between antigenic and genetic evolution, significant differences were observed: Antigenic evolution was more punctuated than genetic evolution, and genetic change sometimes had a disproportionately large antigenic effect. The method readily allows monitoring of antigenic differences among vaccine and circulating strains and thus estimation of the effects of vaccination. Further, this approach offers a route to predicting the relative success of emerging strains, which could be achieved by quantifying the combined effects of population level immune escape and viral fitness on strain evolution.


Color image processing [basics and special issue overview]

umans have always seen the world in color but only recently have we been able to generate vast quantities of color images with such ease. In the last three decades, we have seen a rapid and enormous transition from grayscale images to color ones. Today, we are exposed to color images on a daily basis in print, photographs, television, computer displays, and cinema movies, where color now plays a vital role in the advertising and dissemination of information throughout the world. Color monitors, printers, and copiers now dominate the office and home environments, with color becoming increasingly cheaper and easier to generate and reproduce. Color demands have soared in the marketplace and are projected to do so for years to come. With this rapid progression, color and multispectral properties of images are becoming increasingly crucial to the field of image processing, often extending and/or replacing previously known grayscale techniques. We have seen the birth of color algorithms that range from direct extensions of grayscale ones, where images are treated as three monochrome separations, to more sophisticated approaches that exploit the correlations among the color bands, yielding more accurate results. Hence, it is becoming increasingly necessary for the signal processing community to understand the fundamental differences between color and grayscale imaging. There are more than a few extensions of concepts


Relational active learning for link-based classification

Many information tasks involve objects that are explicitly or implicitly connected in a network (or graph), such as webpages connected by hyperlinks or people linked by “friendships” in a social network. Research on link-based classification (LBC) has shown how to leverage these connections to improve classification accuracy. Unfortunately, acquiring a sufficient number of labeled examples to enable accurate learning for LBC can often be expensive or impractical. In response, some recent work has proposed the use of active learning, where the LBC method can intelligently select a limited set of additional labels to acquire, so as to reduce the overall cost of learning a model with sufficient accuracy. This work, however, has produced conflicting results and has not considered recent progress for LBC inference and semi-supervised learning. In this paper, we evaluate multiple prior methods for active learning and demonstrate that none consistently improve upon random guessing. We then introduce two new methods that both seek to improve active learning by leveraging the link structure to identify nodes to acquire that are more representative of the underlying data. We show that both approaches have some merit, but that one method, by proactively acquiring nodes so as to produce a more representative distribution of known labels, often leads to significant accuracy increases with minimal computational cost.


MRJS : A JavaScript MapReduce Framework for Web Browsers

This paper describes MRJS, a JavaScript based MapReduce framework. MRJS runs on the open internet over HTTP rather than in a data center. Instead of using clusters of machines for processing power, it relies on volunteer clients in the form of JavaScript engines in web browsers. Users can specify, execute, and fetch data for arbitrary MapReduce jobs with HTTP requests. Our centralized job server acts as a coordinator. We have implemented MRJS and results show that it performs well for certain types of MapReduce computations.


Privacy-preserving personal health record using multi-authority attribute-based encryption with revocation

Personal health record (PHR) service is an emerging model for health information exchange. In PHR systems, patient’s health records and information are maintained by the patient himself through the Web. In reality, PHRs are often outsourced to be stored at the third parties like cloud service providers. However, there have been serious privacy concerns about cloud service as it may expose user’s sensitive data like PHRs to those cloud service providers or unauthorized users. Using attribute-based encryption (ABE) to encrypt patient’s PHRs in cloud environment, secure and flexible access control can be achieved. Yet, problems like scalability in key management, fine-grained access control, and efficient user revocation remain to be addressed. In this paper, we propose a privacy-preserving PHR, which supports fine-grained access control and efficient revocation. To be specific, our scheme achieves the goals (1) scalable and fine-grained access control for PHRs by using multi-authority ABE scheme, and (2) efficient on-demand user/attribute revocation and dynamic policy update. In our scheme, we consider the situation that multiple data owners exist, and patient’s PHRs are encrypted and stored in semi-trust servers. The access structure in our scheme is expressive access tree structure, and the security of our scheme can be reduced to the standard decisional bilinear Diffie–Hellman assumption.


Identity-based encryption with efficient revocation

Identity-based encryption (IBE) is an exciting alternative to public-key encryption, as IBE eliminates the need for a Public Key Infrastructure (PKI). The senders using an IBE do not need to look up the public keys and the corresponding certificates of the receivers, the identities (e.g. emails or IP addresses) of the latter are sufficient to encrypt. Any setting, PKI- or identity-based, must provide a means to revoke users from the system. Efficient revocation is a well-studied problem in the traditional PKI setting. However in the setting of IBE, there has been little work on studying the revocation mechanisms. The most practical solution requires the senders to also use time periods when encrypting, and all the receivers (regardless of whether their keys have been compromised or not) to update their private keys regularly by contacting the trusted authority. We note that this solution does not scale well -- as the number of users increases, the work on key updates becomes a bottleneck. We propose an IBE scheme that significantly improves key-update efficiency on the side of the trusted party (from linear to logarithmic in the number of users), while staying efficient for the users. Our scheme builds on the ideas of the Fuzzy IBE primitive and binary tree data structure, and is provably secure.


Using Markov decision process in cognitive radio networks towards the optimal reward

The Learning is an indispensable phase in the cognition cycle of cognitive radio network. It corresponds between the executed actions and the estimated rewards. Based on this phase, the agent learns from past experiences to improve his actions in the next interventions. In the literature, there are several methods that treat the artificial learning. Among them, we cite the reinforcement learning that look for the optimal policy, for ensuring the maximum reward.
 The present work exposes an approach, based on a model of reinforcement learning, namely Markov decision process, to maximize the sum of transfer rates of all secondary users. Such conception defines all notions relative to an environment with finite set of states, including: the agent, all states, the allowed actions with a given state, the obtained reward after the execution of an action and the optimal policy. After the implementation, we remark a correlation between the started policy and the optimal policy, and we improve the performances by referring to a previous work.


High Performance Transactions via Early Write Visibility

In order to guarantee recoverable transaction execution, database systems permit a transaction’s writes to be observable only at the end of its execution. As a consequence, there is generally a delay between the time a transaction performs a write and the time later transactions are permitted to read it. This delayed write visibility can significantly impact the performance of serializable database systems by reducing concurrency among conflicting transactions. This paper makes the observation that delayed write visibility stems from the fact that database systems can arbitrarily abort transactions at any point during their execution. Accordingly, we make the case for database systems which only abort transactions under a restricted set of conditions, thereby enabling a new recoverability mechanism, early write visibility, which safely makes transactions’ writes visible prior to the end of their execution. We design a new serializable concurrency control protocol, piece-wise visibility (PWV), with the explicit goal of enabling early write visibility. We evaluate PWV against state-of-the-art serializable protocols and a highly optimized implementation of read committed, and find that PWV can outperform serializable protocols by an order of magnitude and read committed by 3X on high contention workloads.


A Genetic NewGreedy Algorithm for Influence Maximization in Social Network

A user may be influenced by the other users of a social network by sharing information. Influence maximization is one of the critical research topics aimed at knowing the current circumstances of a social network, such as the general mood of the society. The goal of this problem is to find a seed set which has a maximum influence with respect to a propagation model. For the influence maximization problem is NP-Hard, it is obvious that an exhausted search algorithm is not able to find the solution in a reasonable time. It is also obvious that a greedy algorithm may not find a solution that satisfies all the requirements. Hence, a high-performance algorithm for solving the influence maximization problem, which leverages the strength of the greedy method and the genetic algorithm (GA) is presented in this paper. Experimental results show that the proposed algorithm can provide a better result than simple GA by about 10% in terms of the quality.


Hyperdimensional biosignal processing: A case study for EMG-based hand gesture recognition

The mathematical properties of high-dimensional spaces seem remarkably suited for describing behaviors produces by brains. Brain-inspired hyperdimensional computing (HDC) explores the emulation of cognition by computing with hypervectors as an alternative to computing with numbers. Hypervectors are high-dimensional, holographic, and (pseudo)random with independent and identically distributed (i.i.d.) components. These features provide an opportunity for energy-efficient computing applied to cyberbiological and cybernetic systems. We describe the use of HDC in a smart prosthetic application, namely hand gesture recognition from a stream of Electromyography (EMG) signals. Our algorithm encodes a stream of analog EMG signals that are simultaneously generated from four channels to a single hypervector. The proposed encoding effectively captures spatial and temporal relations across and within the channels to represent a gesture. This HDC encoder achieves a high level of classification accuracy (97.8%) with only 1/3 the training data required by state-of-the-art SVM on the same task. HDC exhibits fast and accurate learning explicitly allowing online and continuous learning. We further enhance the encoder to adaptively mitigate the effect of gesture-timing uncertainties across different subjects endogenously; further, the encoder inherently maintains the same accuracy when there is up to 30% overlapping between two consecutive gestures in a classification window.


Ambulance redeployment: An approximate dynamic programming approach

Emergency medical service (EMS) providers are charged with the task of managing ambulances so that the time required to respond to emergency calls is minimized. One approach that may assist in reducing response times is ambulance redeployment, i.e., repositioning idle ambulances in real time. We formulate a simulation model of EMS operations to evaluate the performance of a given allocation policy and use this model in an approximate dynamic programming (ADP) context to compute high-quality redeployment policies. We find that the resulting ADP policies perform much better than sub-optimal static policies and marginally better than near-optimal static policies. Representative computational results for Edmonton, Alberta are included.


Design of household appliances for a Dc-based nanogrid system: An induction heating cooktop study case

Efficient energy management is becoming an important issue when designing any electrical system. Recently, a significant effort has been devoted to the design of optimized micro and nanogrids comprising residential area subsystems. One of these approaches consists on the design of a dc-based nanogrid optimized for the interoperation of electric loads, sources, and storage elements. Home appliances are one of the main loads in such dc-based nanogrids. In this paper, the design and optimization of an appliance for operation in a dc-based nanogrid is detailed. An induction heating cooktop appliance will be considered as a reference example, being some of the design considerations generalizable to other appliances. The main design aspects, including the inductor system, power converter, EMC filter, and control are considered. Finally, some simulation results of the expected converter performance are shown.


PCS/W-CDMA dual-band MMIC power amplifier with a newly proposed linearizing bias circuit

A personal communications service/wide-band code division multiple access (PCS/W-CDMA) dual-band monolithic microwave integrated circuit (MMIC) power amplifier with a single-chip MMIC and a single-path output matching network is demonstrated by adopting a newly proposed on-chip linearizer. The linearizer is composed of the base–emitter diode of an active bias transistor and a capacitor to provide an RF short at the base node of the active bias transistor. The linearizer enhances the linearity of the power amplifier effectively for both PCS and W-CDMA bands with no additional dc power consumption, and has negligible insertion power loss with almost no increase in die area. It improves the input 1-dB gain compression point by 18.5 (20) dB and phase distortion by 6.1 (12.42 ) at an output power of 28 (28) dBm for the PCS (W-CDMA) band while keeping the base bias voltage of the power amplifier as designed. A PCS and W-CDMA dual-band InGaP heterojunction bipolar transistor MMIC power amplifier with single input and output and no switch for band selection is embodied by implementing the linearizer and by designing the amplifier to have broad-band characteristics. The dual-band power amplifier exhibits an output power of 30 (28.5) dBm, power-added efficiency of 39.5% (36%), and adjacent channel power ratio of 46 ( 50) dBc at the output power of 28 (28) dBm under 3.4-V operation voltage for PCS (W-CDMA) applications.


Punica granatum (pomegranate) and its potential for prevention and treatment of inflammation and cancer.

The last 7 years have seen over seven times as many publications indexed by Medline dealing with pomegranate and Punica granatum than in all the years preceding them. Because of this, and the virtual explosion of interest in pomegranate as a medicinal and nutritional product that has followed, this review is accordingly launched. The pomegranate tree, Punica granatum, especially its fruit, possesses a vast ethnomedical history and represents a phytochemical reservoir of heuristic medicinal value. The tree/fruit can be divided into several anatomical compartments: (1) seed, (2) juice, (3) peel, (4) leaf, (5) flower, (6) bark, and (7) roots, each of which has interesting pharmacologic activity. Juice and peels, for example, possess potent antioxidant properties, while juice, peel and oil are all weakly estrogenic and heuristically of interest for the treatment of menopausal symptoms and sequellae. The use of juice, peel and oil have also been shown to possess anticancer activities, including interference with tumor cell proliferation, cell cycle, invasion and angiogenesis. These may be associated with plant based anti-inflammatory effects, The phytochemistry and pharmacological actions of all Punica granatum components suggest a wide range of clinical applications for the treatment and prevention of cancer, as well as other diseases where chronic inflammation is believed to play an essential etiologic role.


Transfer learning using computational intelligence: A survey

26 27 28 29 30 31 32 33 34 35 36 37 38 Article history: Received 3 December 2014 Received in revised form 7 January 2015 Accepted 17 January 2015 Available online xxxx


The development of a new test of agility for rugby league.

Agility requires change of direction speed (CODS) and also perceptual and decision-making skills and reaction speed. The purpose of this study was to develop a reliable and valid agility test for rugby league, which stressed all those dimensions. Players from a subelite rugby league team were tested twice on a sport-specific reactive agility test (RAT) and CODS test. Data were analyzed for reliability. For validity results from the subelite groups, first test was compared with data from an elite group. The RAT required participants to run toward an unpredictable life-size video of an attacking opponent and react to that video by changing direction. The CODS test required the same movement patterns however direction changes were preplanned. The subelite group's mean time to complete the CODS test and RAT on their first test was 1.67 ± 0.15 and 1.98 ± 0.16 seconds, respectively, and 1.62 ± 0.14 and 1.91 ± 0.17 seconds, respectively, on their second test (results are ± σ). Statistical analyses revealed no significant difference in means (p < 0.05) and good correlation (intraclass correlation coefficient = 0.87 and 0.82, respectively). The elite group's mean time to complete the tests was 1.65 ± 0.09 and 1.79 ±0.12 seconds, respectively. Statistical analyses revealed a significant difference in mean RAT time between the elite group and the subelite group (p < 0.05). The RAT was reliable and valid. Performance differences on the RAT were attributed to differences in perceptual skills and/or reaction ability. Testing and training agility should therefore stress those dimensions of agility and not just CODS.


Diabetes Prediction by using Bacterial Foraging Optimization Algorithm And Artificial Neural Network

Diagnosis of any diseases earlier is always preferable .diabetes is one such diseases.it reaches to the epidemic proportional in many developing countries and also reaching to industrialized nations. In this study, we investigate a programmable method to analyze diabetes diseases based on BFO and ANN.ANN is a powerful tool and solves the problems such as classification and prediction. Its performance is highly dependent on its architecture and weights. To gain high efficiency in ANN, an appropriate Architecture and learning Algorithm is preferable. The experiments on dataset occupied from undertaking repository to verify the potential of BFO algorithm and showed that BFO-ANN is more accurate than traditional ANN in terms of classification. General Terms-Artificial Neural Network, BacterialForaging Optimization Algorithm.


Cross-Domain Ranking via Latent Space Learning

We study the problem of cross-domain ranking, which addresses learning to rank objects from multiple interrelated domains. In many applications, we may have multiple interrelated domains, some of them with a large amount of training data and others with very little. We often wish to utilize the training data from all these related domains to help improve ranking performance. In this paper, we present a unified model: BayCDR for cross-domain ranking. BayCDR uses a latent space to measure the correlation between different domains, and learns the ranking functions from the interrelated domains via the latent space by a Bayesian model, where each ranking function is based on a weighted average model. An efficient learning algorithm based on variational inference and a generalization bound has been developed. To scale up to handle real large data, we also present a learning algorithm under the Map-Reduce programming model. Finally, we demonstrate the effectiveness and efficiency of BayCDR on large datasets.


Mitigation of Continuous and Pulsed Radio Interference with GNSS Antenna Arrays

Dr. Andriy Konovaltsev received his engineer diploma and the Ph.D. degree in electrical engineering from Kharkov State Technical University of Radio Electronics, Ukraine in 1993 and 1996, correspondingly. He joined the Institute of Communications and Navigation of DLR in 2001. His research interests are in array processing for satellite navigation systems, signal processing algorithms for navigation receivers including synchronisation, multipath and radio interference mitigation.


Facial Expression Recognition in Older Adults using Deep Machine Learning

Facial Expression Recognition is still one of the challenging fields in pattern recognition and machine learning science. Despite efforts made in developing various methods for this topic, existing approaches lack generalizability and almost all studies focus on more traditional hand-crafted features extraction to characterize facial expressions. Moreover, effective classifiers to model the spatial and temporary patterns embedded in facial expressions ignore the effects of facial attributes, such as age, on expression recognition even though research indicates that facial expression manifestation varies with ages. Although there are large amount of benchmark datasets available for the recognition of facial expressions, only few datasets contains faces of older adults. Consequently the current scientific literature has not exhausted this topic. Recently, deep learning methods have been attracting more and more researchers due to their great success in various computer vision tasks, mainly because they avoid a process of feature definition and extraction which is often very difficult due to the wide variability of the facial expressions. Based on the deep learning theory, a neural network for facial expression recognition in older adults is constructed by combining a Stacked Denoising Auto-Encoder method to pre-train the network and a supervised training that provides a fine-tuning adjustment of the network. For the supervised classification layer, the -class softmax classifier was implemented, where is the number of expressions to be recognized. The performance are evaluated on two benchmark datasets (FACES and Lifespan), that are the only ones that contain facial expressions of the elderly. The achieved results show the superiority of the proposed deep learning approach compared to the conventional non-deep learning based facial expression recognition methods used in this context.


Wasserstein Propagation for Semi-Supervised Learning

Probability distributions and histograms are natural representations for product ratings, traffic measurements, and other data considered in many machine learning applications. Thus, this paper introduces a technique for graph-based semisupervised learning of histograms, derived from the theory of optimal transportation. Our method has several properties making it suitable for this application; in particular, its behavior can be characterized by the moments and shapes of the histograms at the labeled nodes. In addition, it can be used for histograms on non-standard domains like circles, revealing a strategy for manifold-valued semi-supervised learning. We also extend this technique to related problems such as smoothing distributions on graph nodes.


Cuckoo search algorithm and wind driven optimization based study of satellite image segmentation for multilevel thresholding using Kapur's entropy

The objective of image segmentation is to extract meaningful objects. A meaningful segmentation selects the proper threshold values to optimize a criterion using entropy. The conventional multilevel thresholding methods are efficient for bi-level thresholding. However, they are computationally expensive when extended to multilevel thresholding since they exhaustively search the optimal thresholds to optimize the objective functions. To overcome this problem, two successful swarm-intelligence-based global optimization algorithms, cuckoo search (CS) algorithm and wind driven optimization (WDO) for multilevel thresholding using Kapur’s entropy has been employed. For this purpose, best solution as fitness function is achieved through CS and WDO algorithm using Kapur’s entropy for optimal multilevel thresholding. A new approach of CS andWDO algorithm is used for selection of optimal threshold value. This algorithm is used to obtain the best solution or best fitness value from the initial random threshold values, and to evaluate the quality of a solution, correlation function is used. Experimental results have been examined on standard set of satellite images using various numbers of thresholds. The results based on Kapur’s entropy reveal that CS, ELR-CS and WDO method can be accurately and efficiently used in multilevel thresholding problem. 2013 Elsevier Ltd. All rights reserved.


Improved Relation Extraction with Feature-Rich Compositional Embedding Models

Compositional embedding models build a representation (or embedding) for a linguistic structure based on its component word embeddings. We propose a Feature-rich Compositional Embedding Model (FCM) for relation extraction that is expressive, generalizes to new domains, and is easy-to-implement. The key idea is to combine both (unlexicalized) handcrafted features with learned word embeddings. The model is able to directly tackle the difficulties met by traditional compositional embeddings models, such as handling arbitrary types of sentence annotations and utilizing global information for composition. We test the proposed model on two relation extraction tasks, and demonstrate that our model outperforms both previous compositional models and traditional feature rich models on the ACE 2005 relation extraction task, and the SemEval 2010 relation classification task. The combination of our model and a loglinear classifier with hand-crafted features gives state-of-the-art results. We made our implementation available for general use1.


Exploring Semantic Properties of Sentence Embeddings

Neural vector representations are ubiquitous throughout all subfields of NLP. While word vectors have been studied in much detail, thus far only little light has been shed on the properties of sentence embeddings. In this paper, we assess to what extent prominent sentence embedding methods exhibit select semantic properties. We propose a framework that generate triplets of sentences to explore how changes in the syntactic structure or semantics of a given sentence affect the similarities obtained between their sentence embeddings.


High Gain Wide-Band U-Shaped Patch Antennas With Modified Ground Planes

A high gain wideband U-shaped patch antenna with two equal arms on poly tetra fluoro ethylene (PTFE) substrate is presented. An inverted U-shaped slot is introduced on the circular or square shaped ground plane just under the U-shaped patch. In this communication the effect of size and shape of the ground plane on impedance bandwidth is studied. Maximum impedance bandwidth of 86.79% (4.5–11.4 GHz) is obtained with circular shaped ground plane with diameter 36 mm. The highest gain achieved is 4.1 dBi. The simulated results are confirmed experimentally. The proposed antenna is simple in structure compared to the regular stacked or coplanar parasitic patch antennas. It is highly suitable for wireless communications.


Influence and correlation in social networks

In many online social systems, social ties between users play an important role in dictating their behavior. One of the ways this can happen is through social influence, the phenomenon that the actions of a user can induce his/her friends to behave in a similar way. In systems where social influence exists, ideas, modes of behavior, or new technologies can diffuse through the network like an epidemic. Therefore, identifying and understanding social influence is of tremendous interest from both analysis and design points of view.
 This is a difficult task in general, since there are factors such as homophily or unobserved confounding variables that can induce statistical correlation between the actions of friends in a social network. Distinguishing influence from these is essentially the problem of distinguishing correlation from causality, a notoriously hard statistical problem.
 In this paper we study this problem systematically. We define fairly general models that replicate the aforementioned sources of social correlation. We then propose two simple tests that can identify influence as a source of social correlation when the time series of user actions is available.
 We give a theoretical justification of one of the tests by proving that with high probability it succeeds in ruling out influence in a rather general model of social correlation. We also simulate our tests on a number of examples designed by randomly generating actions of nodes on a real social network (from Flickr) according to one of several models. Simulation results confirm that our test performs well on these data. Finally, we apply them to real tagging data on Flickr, exhibiting that while there is significant social correlation in tagging behavior on this system, this correlation cannot be attributed to social influence.


Simulation on Simulink AC4 model (200hp DTC induction motor drive) using Fuzzy Logic controller

Classical direct torque control (DTC) has advantage in absence of coordinate transform and voltage modulator block. However, it may have disadvantage in controlling electromagnetic torque. DTC produces high ripple in electromagnetic torque as it is not directly controlled. High torque ripple causes vibrations to the motor which may lead to component lose, bearing failure or resonance. Thus, Fuzzy Logic controller is applied to reduce electromagnetic torque ripple. This paper presents the simulation analysis of Fuzzy Logic Direct Torque Controller (FLDTC) of induction machine drives. However, only the design of the controller using built in DTC induction motor drive model of Simulink AC4 will be discussed in this paper. Using FLDTC, the resulting electromagnetic torque from produced less ripple than classical DTC.


Camera calibration with spheres: linear approaches

This paper addresses the problem of camera calibration from spheres. By studying the relationship between the dual images of spheres and that of the absolute conic, a linear solution has been derived from a recently proposed non-linear semi-definite approach. However, experiments show that this approach is quite sensitive to noise. In order to overcome this problem, a second approach has been proposed, where the orthogonal calibration relationship is obtained by regarding any two spheres as a surface of revolution. This allows a camera to be fully calibrated from an image of three spheres. Besides, a conic homography is derived from the imaged spheres, and from its eigenvectors the orthogonal invariants can be computed directly. Experiments on synthetic and real data show the practicality of such an approach.


Smart music player integrating facial emotion recognition and music mood recommendation

Songs, as a medium of expression, have always been a popular choice to depict and understand human emotions. Reliable emotion based classification systems can go a long way in helping us parse their meaning. However, research in the field of emotion-based music classification has not yielded optimal results. In this paper, we present an affective cross-platform music player, EMP, which recommends music based on the real-time mood of the user. EMP provides smart mood based music recommendation by incorporating the capabilities of emotion context reasoning within our adaptive music recommendation system. Our music player contains three modules: Emotion Module, Music Classification Module and Recommendation Module. The Emotion Module takes an image of the user's face as an input and makes use of deep learning algorithms to identify their mood with an accuracy of 90.23%. The Music Classification Module makes use of audio features to achieve a remarkable result of 97.69% while classifying songs into 4 different mood classes. The Recommendation Module suggests songs to the user by mapping their emotions to the mood type of the song, taking into consideration the preferences of the user.


Jubatus : An Open Source Platform for Distributed Online Machine Learning

Distributed computing is essential for handling very large datasets. Online learning is also promising for learning from rapid data streams. However, it is still an unresolved problem how to combine them for scalable learning and prediction on big data streams. We propose a general computational framework called loose model sharing for online and distributed machine learning. The key is to share only models rather than data between distributed servers. We also introduce Jubatus, an open source software platform based on the framework. Finally, we describe the details of implementing classifier and nearest neighbor algorithms, and discuss our experimental evaluations.


Reinforcement Learning Testbed for Power-Consumption Optimization

Common approaches to control a data-center cooling system rely on approximated system/environment models that are built upon the knowledge of mechanical cooling and electrical and thermal management. These models are difficult to design and often lead to suboptimal or unstable performance. In this paper, we show how deep reinforcement learning techniques can be used to control the cooling system of a simulated data center. In contrast to common control algorithms, those based on reinforcement learning techniques can optimize a system’s performance automatically without the need of explicit model knowledge. Instead, only a reward signal needs to be designed. We evaluated the proposed algorithm on the open source simulation platform EnergyPlus. The experimental results indicate that we can achieve 22% improvement compared to a model-based control algorithm built into the EnergyPlus. To encourage the reproduction of our work as well as future research, we have also publicly released an open-source EnergyPlus wrapper interface 1 directly compatible with existing reinforcement learning frameworks.


Drawing on social network theories and previous studies, this research examines the dynamics of social network structures in open source software (OSS) teams. Three projects were selected from SourceForge.net in terms of their similarities as well as their differences. Monthly data were extracted from the bug tracking systems in order to achieve a longitudinal view of the interaction pattern of each project. Social network analysis was used to generate the indices of social structure. The finding suggests that the interaction pattern of OSS projects evolves from a single hub at the beginning to a core/periphery model as the projects move forward. INtrODUctION The information system development arena has seen many revolutions and evolutions. We have witnessed the movement from structured development to object-oriented (OO) development. Modeling methods, such as data flow diagram and entity relationship diagram, are facing new OO modeling languages, such as the unified modeling language (UML) (see Siau & Cao, 2001; Siau, Erickson, & Lee, 2005; Siau & Loo, 2006) and OO methodologies, such as unified process (UP). The latest development includes agile modeling (see Erickson, Lyytinen, & Siau, 2005), extreme programming, and OSS development. While many of these changes are related to systems development paradigms, methodologies, methods, and techniques, the phenomenon of OSS development entails a different structure for software development teams. Chapter 2.17 Social Network Structures in Open Source Software Development Teams Yuan Long Colorado State University-Pueblo, USA Keng Siau University of Nebraska-Lincoln, USA Copyright © 2009, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited. 646 Social Network Structures in Open Source Software Development Teams Unlike conventional software projects, the participants of OSS projects are volunteers. They are self-selected based on their interests and capability to contribute to the projects (Raymond, 2000). In addition, the developers of OSS projects are distributed all around the world. They communicate and collaborate with each other through the Internet, using e-mails or discussion boards. Therefore, effective and efficient communication and collaboration are critical to OSS success. However, little empirical research has been conducted to study the underlying interaction pattern of OSS teams, especially the dynamics of the social network structures in OSS development teams. To fill this gap, this study examines the evolvement of social structure in OSS teams. The study contributes to the enhancement of the understanding of OSS development, and provides foundation for future studies to analyze the antecedents and consequences of social networks in the OSS context. The remainder of the paper is structured as follows. First, prior studies on social network structures in OSS teams are reviewed. Second, theories related to social structure and social network theory are discussed. Third, the research methodology is presented, and the research results are reported. Next, discussions of the results, the limitations, and the implications are provided. The paper concludes with suggestions for future research.


Mermaid: Integrating Vertex-Centric with Edge-Centric for Real-World Graph Processing

There has been increasing interests in processing large-scale real-world graphs, and recently many graph systems have been proposed. Vertex-centric GAS (Gather-Apply-Scatter) and Edge-centric GAS are two graph computation models being widely adopted, and existing graph analytics systems commonly follow only one computation model, which is not the best choice for real-world graph processing. In fact, vertex degrees in real-world graphs often obey skewed power-law distributions: most vertices have relatively few neighbors while a few have many neighbors. We observe that vertex-centric GAS for high-degree vertices and edge-centric GAS for low-degree vertices is a much better choice for real-world graph processing. In this paper, we present Mermaid, a system for processing large-scale real-world graphs on a single machine. Mermaid skillfully integrates vertex-centric GAS with edge-centric GAS through a novel vertex-mapping mechanism, and supports streamlined graph processing. On a total of 6 practical natural graph processing tasks, we demonstrate that, on average, Mermaid achieves 1.83x better performance than the state-of-the-art graph system on a single machine.


Bayesian Optimization Algorithm

In this paper an algorithm based on the concepts of genetic algorithms that uses an estimation of a probability distribution of promising solutions in order to generate new candidate solutions is proposed To esti mate the distribution techniques for model ing multivariate data by Bayesian networks are used The proposed algorithm identi es reproduces and mixes building blocks up to a speci ed order It is independent of the ordering of the variables in the strings rep resenting the solutions Moreover prior in formation about the problem can be incor porated into the algorithm However prior information is not essential Preliminary ex periments show that the BOA outperforms the simple genetic algorithm even on decom posable functions with tight building blocks as a problem size grows


Space-Efficient Online Computation of Quantile Summaries

An ∈-approximate quantile summary of a sequence of N elements is a data structure that can answer quantile queries about the sequence to within a precision of ∈N.
We present a new online algorithm for computing∈-approximate quantile summaries of very large data sequences. The algorithm has a worst-case space requirement of &Ogr;(1÷∈ log(∈N)). This improves upon the previous best result of &Ogr;(1÷∈ log2(∈N)). Moreover, in contrast to earlier deterministic algorithms, our algorithm does not require a priori knowledge of the length of the input sequence.
Finally, the actual space bounds obtained on experimental data are significantly better than the worst case guarantees of our algorithm as well as the observed space requirements of earlier algorithms.


Load Disaggregation Based on Aided Linear Integer Programming

Load disaggregation based on aided linear integer programming (ALIP) is proposed. We start with a conventional linear integer programming (IP)-based disaggregation and enhance it in several ways. The enhancements include additional constraints, correction based on a state diagram, median filtering, and linear-programming-based refinement. With the aid of these enhancements, the performance of IP-based disaggregation is significantly improved. The proposed ALIP system relies only on the instantaneous load samples instead of waveform signatures and, hence, works well on low-frequency data. Experimental results show that the proposed ALIP system performs better than conventional IP-based load disaggregation.