Cloud computing paradigms for pleasingly parallel biomedical applications

Cloud computing offers exciting new approaches for scientific computing that leverages the hardware and software investments on large scale data centers by major commercial players. Loosely coupled problems are very important in many scientific fields and are on the rise with the ongoing move towards data intensive computing. There exist several approaches to leverage clouds & cloud oriented data processing frameworks to perform pleasingly parallel computations. In this paper we present two pleasingly parallel biomedical applications, 1) assembly of genome fragments 2) dimension reduction in the analysis of chemical structures, implemented utilizing cloud infrastructure service based utility computing models of Amazon AWS and Microsoft Windows Azure as well as utilizing MapReduce based data processing frameworks, Apache Hadoop and Microsoft DryadLINQ. We review and compare each of the frameworks and perform a comparative study among them based on performance, efficiency, cost and the usability. Cloud service based utility computing model and the managed parallelism (MapReduce) exhibited comparable performance and efficiencies for the applications we considered. We analyze the variations in cost between the different platform choices (eg: EC2 instance types), highlighting the need to select the appropriate platform based on the nature of the computation.


The psychometric properties and utility of the Short Sadistic Impulse Scale (SSIS).

Sadistic personality disorder (SPD) has been underresearched and often misunderstood in forensic settings. Furthermore, personality disorders in general are the subject of much controversy in terms of their classification (i.e., whether they should be categorical or dimensional). The Sadistic Attitudes and Behaviors Scale (SABS; Davies & Hand, 2003; O'Meara, Davies, & Barnes-Holmes, 2004) is a recently developed scale for measuring sadistic inclinations. Derived from this is the Short Sadistic Impulse Scale (SSIS), which has proved to be a strong unidimensional measure of sadistic inclination. Through cumulative scaling, it was investigated whether the SSIS could measure sadism on a continuum of interest, thus providing a dimensional view of the construct. Further, the SSIS was administered along with a number of other measures related to sadism in order to assess the validity of the scale. Results showed that the SSIS has strong construct and discriminant validity and may be useful as a screening measure for sadistic impulse.


Cyber military strategy for cyberspace superiority in cyber warfare

In this paper, we proposed robust and operational cyber military strategy for cyberspace superiority in cyber warfare. We considered cyber forces manpower, cyber intelligence capability, and an organization of cyber forces for improving cyber military strategy. In cyber forces power field, we should cultivated personals that can perform network computer operations and hold cyber security technology such as cyber intelligence collection, cyber-attack, cyber defence, and cyber forensics. Cyber intelligence capability includes cyber surveillance/reconnaissance, cyber order of battle, pre-CTO, and cyber damage assessment. An organization of cyber forces has to change tree structure to network structure and has to organize task or functional centric organizations. Our proposed cyber military strategy provides prior decision of action to operate desired effects in cyberspace.


The emulation theory of representation: motor control, imagery, and perception.

The emulation theory of representation is developed and explored as a framework that can revealingly synthesize a wide variety of representational functions of the brain. The framework is based on constructs from control theory (forward models) and signal processing (Kalman filters). The idea is that in addition to simply engaging with the body and environment, the brain constructs neural circuits that act as models of the body and environment. During overt sensorimotor engagement, these models are driven by efference copies in parallel with the body and environment, in order to provide expectations of the sensory feedback, and to enhance and process sensory information. These models can also be run off-line in order to produce imagery, estimate outcomes of different actions, and evaluate and develop motor plans. The framework is initially developed within the context of motor control, where it has been shown that inner models running in parallel with the body can reduce the effects of feedback delay problems. The same mechanisms can account for motor imagery as the off-line driving of the emulator via efference copies. The framework is extended to account for visual imagery as the off-line driving of an emulator of the motor-visual loop. I also show how such systems can provide for amodal spatial imagery. Perception, including visual perception, results from such models being used to form expectations of, and to interpret, sensory input. I close by briefly outlining other cognitive functions that might also be synthesized within this framework, including reasoning, theory of mind phenomena, and language.


DC Bus Voltage Control for a Distributed Power System

This paper addresses voltage control of distributed dc power systems. DC power systems have been discussed as a result of the introduction of renewable, small-scale power generation units. Also, telecommunication power systems featuring UPS properties might benefit from a broader introduction of dc power systems. Droop control is utilized to distribute the load between the source converters. In order to make the loading of the source converters equal, in per unit, the voltage control algorithm for each converter has to be designed to act similar. The dc side capacitor of each converter, needed for filtering, is also determined as a consequence. The root locus is investigated for varying dc bus impedance. It is found that the risk of entering converter over-modulation is a stronger limitation than stability, at least for reasonable dc bus cable parameters. The stationary and dynamic properties during load variations are also investigated.


REFRAMING SUPPLY CHAIN MANAGEMENT : A SERVICE-DOMINANT

Shifting the dominant thinking of supply chain management toward the concepts of service, value cocreation, value propositions, operant resources, networks, service ecosystems and learning opens up many research opportunities and strategies for improved organizational performance. The emerging thought world of service-dominant logic is presented as a means to reframe supply chain scholarship and practice for increased relevance and impact.


The Intersecting Roles of Consumer and Producer : A Critical Perspective on Co-production , Co-creation and Prosumption

The terms ‘co-creation’, ‘co-production’, and ‘prosumption’ refer to situations in which consumers collaborate with companies or with other consumers to produce things of value. These situations sometimes appear to blur the traditional roles of ‘producer’ and ‘consumer’. Building on Marx’s distinction between ‘use value’ and ‘exchange value’, we argue that, when consumers perform tasks normally handled by the company, this does not necessarily represent a fundamental change in exchange roles or economic organization. We then argue that, when individuals who are traditionally defined as ‘consumers’ produce exchange value for companies, this does represent a fundamental change. Thanks to recent advances in information technology, customers today are contributing to organizational processes in ways that would not have been possible 10 years ago. For example, at Threadless.com, customers can not only vote for the clothing designs that they would like to see produced but also submit their own designs for voting (Walker 2007). Similarly, at National Instruments, a company that makes measurement software and sensors, nearly half of the company’s research and development activity is done by an on-line community of customers who collaborate to discover how NI’s products can solve members’ problems (Seybold 2007). Another example is Proctor & Gamble’s ‘Vocalpoint’ program, an on-line community of product enthusiasts who are rewarded with coupons and samples in return for talking about products with their friends. The program has more than 500,000 members and is being used by other companies such as WD40 and the Discovery Channel to promote their products (Neff 2006). Some have argued that examples like these are symptomatic of a fundamental and dramatic shift that is occurring in relationships between organizations and their customers – a change that calls into question previously clear distinctions between consumers and producers and between customers and employees. For instance, some have suggested 2 Intersecting Roles of Consumer and Producer © 2008 The Authors Sociology Compass 2 (2008): 10.1111/j.1751-9020.2008.00112.x Journal Compilation © 2008 Blackwell Publishing Ltd that customers who help with product design or assist with product marketing are part of a ‘consumer-as-creator revolution’ (Nadeau 2006, 105) and represent a ‘new paradigm of message creation and delivery’ (McConnell and Huba 2006, 42). Others have written that ‘the gap between producers and consumers is blurring’ (Tapscott and Williams 2006, 125) and that ‘in the emerging ... paradigm, a person can seamlessly shift from consumer to contributor and creator’ (143). Still others have argued that in today’s customer-organization relationships ‘power and control are radically decentralized and heterarchical: producers and consumers coalesce’ (Pitt et al. 2006, 118). We have also recently seen a resurgence of the word ‘prosumer’, which was first coined by cultural critic Alvin Toffler in 1980, to emphasize the novelty of asking individuals to simultaneously play the role of consumer and producer (Kotler 1986; Tapscott and Williams 2006). In this article, we offer a critical analysis of the ways in which the role of the consumer may be changing and therefore also an investigation of this role in the broader capitalist system, a system in which consumers have traditionally served a central function. We argue that although consumers are increasingly performing tasks normally handled by the company, this role redefinition may be, at least in some cases, illusory. We also find that although the employee/customer distinction can be blurred on many dimensions, one dimension on which the distinction remains is between those who create use value and those who create exchange value (Marx 1867 [2001]). Increasingly, individuals who have traditionally been defined as ‘consumers’ are producing exchange value for companies, and this, we argue, is where the so-called ‘prosumer’ does indeed represent a fundamental change in economic organization. We then use the distinction between use value and exchange value to further explore the normative and ethical implications of consumer production. Consumer versus producer The consumer–producer relationship has traditionally been conceived of as an exchange relationship in which each party trades one kind of value for another (Bagozzi 1975). In this article, we focus on the exchange relationship between an end user (such as a person buying coffee beans for home use) and the organization from which this end user buys a product or service (such as a supermarket or coffee shop). Before an end user buys something, a series of transformations are usually applied to it to make the product or service usable to the end user and to therefore enhance its value. To make a cup of coffee, for example, someone must grow and harvest the coffee beans, roast and grind them, transport and package them, offer them for retail sale, and finally brew the cup of coffee. These are all steps in what Michael Porter (1985) calls the ‘value chain’, the series of transformations required to make a product for an end user.


Evolving to a New Dominant Logic for Marketing

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


The Emerging Role of Electronic Marketplaces on the Internet

Markets play a central role in the economy, facilitating the exchange of information, goods, services and payments. In the process, they create economic value for buyers, sellers, market intermediaries and for society at large. Recent years have seen a dramatic increase in the role of information technology in markets, both in traditional markets, and in the emergence of electronic marketplaces, such as the multitude of Internet-based online auctions.


Information systems in supply chain integration and management

Supply chain management (SCM) is the 21st century global operations strategy for achieving organizational competitiveness. Companies are attempting to find ways to improve their flexibility and responsiveness and in turn competitiveness by changing their operations strategy, methods and technologies that include the implementation of SCM paradigm and information technology (IT). However, a thorough and critical review of literature is yet to be carried out with the objective of bringing out pertinent factors and useful insights into the role and implications of IT in SCM. In this paper, the literature available on IT in SCM have been classified using suitable criteria and then critically reviewed to develop a framework for studying the applications of IT in SCM. Based on this review and analysis, recommendations have been made regarding the application of IT in SCM and some future research directions are indicated. 2003 Elsevier B.V. All rights reserved.


The American Economic Review VOLUME XLV MARCH , 1955 NUMBER ONE ECONOMIC GROWTH AND INCOME INEQUALITY


A Three-Phase Current-Fed DC/DC Converter With Active Clamp for Low-DC Renewable Energy Sources

This paper focuses on a new three-phase high power current-fed DC/DC converter with an active clamp. A three-phase DC/DC converter with high efficiency and voltage boosting capability is designed for use in the interface between a low-voltage fuel-cell source and a high-voltage DC bus for inverters. Zero-voltage switching in all active switches is achieved through using a common active clamp branch, and zero current switching in the rectifier diodes is achieved through discontinuous current conduction in the secondary side. Further, the converter is capable of increased power transfer due to its three-phase power configuration, and it reduces the RMS current per phase, thus reducing conduction losses. Moreover, a delta-delta connection on the three-phase transformer provides parallel current paths and reduces conduction losses in the transformer windings. An efficiency of above 93% is achieved through both improvements in the switching and through reducing conduction losses. A high voltage ratio is achieved by combining inherent voltage boost characteristics of the current-fed converter and the transformer turns ratio. The proposed converter and three-phase PWM strategy is analyzed, simulated, and implemented in hardware. Experimental results are obtained on a 500-W prototype unit, with all of the design verified and analyzed.


Par4: very high speed parallel robot for pick-and-place

This paper introduces a four-degree-of-freedom parallel manipulator dedicated to pick-and-place. It has been developed with the goal of reaching very high speed. This paper shows that its architecture is particularly well adapted to high dynamics. Indeed, it is an evolution of Delta, H4 and 14 robots architectures: it keeps the advantages of these existing robots, while overcoming their drawbacks. In addition, an optimization method based on velocity using adept motion has been developed and applied to this high speed parallel robot. All these considerations led to experimentations that proved we can reach high accelerations (13 G) and obtain a cycle time of 0.28 s.


Towards Controlled Transformation of Sentiment in Sentences

An obstacle to the development of many natural language processing products is the vast amount of training examples necessary to get satisfactory results. The generation of these examples is often a tedious and timeconsuming task. This paper this paper proposes a method to transform the sentiment of sentences in order to limit the work necessary to generate more training data. This means that one sentence can be transformed to an opposite sentiment sentence and should reduce by half the work required in the generation of text. The proposed pipeline consists of a sentiment classifier with an attention mechanism to highlight the short phrases that determine the sentiment of a sentence. Then, these phrases are changed to phrases of the opposite sentiment using a baseline model and an autoencoder approach. Experiments are run on both the separate parts of the pipeline as well as on the end-to-end model. The sentiment classifier is tested on its accuracy and is found to perform adequately. The autoencoder is tested on how well it is able to change the sentiment of an encoded phrase and it was found that such a task is possible. We use human evaluation to judge the performance of the full (end-to-end) pipeline and that reveals that a model using word vectors outperforms the encoder model. Numerical evaluation shows that a success rate of 54.7% is achieved on the sentiment change.


Design, Analysis, and Test of a Novel 2-DOF Nanopositioning System Driven by Dual Mode

Piezodriven flexure-based motion stages, with a large workspace and high positioning precision, are really attractive for the realization of high-performance atomic force microscope (AFM) scanning. In this paper, a modified lever displacement amplifier is proposed for the mechanism design of a novel compliant two-degree-of-freedom (2-DOF) nanopositioning stage, which can be selected to drive in dual modes. Besides, the modified double four-bar parallelogram, P (P denotes prismatic) joints are adopted in designing the flexure limbs. The established models for the mechanical performance evaluation of the stage, in terms of kinetostatics, dynamics, and workspace, are validated by the finite-element analysis. After a series of dimension optimizations carried out through the particle swarm optimization algorithm, a novel active disturbance rejection controller, including the nonlinearity tracking differentiator, the extended state observer, and the nonlinear state error feedback, is proposed to automatically estimate and suppress plant uncertainties arising from the hysteresis nonlinearity, creep effect, sensor noises, and unknown disturbances. The simulation and prototype test results indicate that the first natural frequency of the proposed stage is approximated to be 831 Hz, the amplification ratio in two axes is about 4.2, and the workspace is 119.7 μm × 121.4 μm, while the cross coupling between the two axes is kept within 2%. All the results prove that the developed stage possesses a good property for high-performance AFM scanning.


Large Receptive Field Networks for High-Scale Image Super-Resolution

Convolutional Neural Networks have been the backbone of recent rapid progress in Single-Image Super-Resolution. However, existing networks are very deep with many network parameters, thus having a large memory footprint and being challenging to train. We propose Large Receptive Field Networks which strive to directly expand the receptive field of Super-Resolution networks without increasing depth or parameter count. In particular, we use two different methods to expand the network receptive field: 1-D separable kernels and atrous convolutions. We conduct considerable experiments to study the performance of various arrangement schemes of the 1-D separable kernels and atrous convolution in terms of accuracy (PSNR / SSIM), parameter count, and speed, while focusing on the more challenging high upscaling factors. Extensive benchmark evaluations demonstrate the effectiveness of our approach.


3D face recognition: a survey

3D face recognition has become a trending research direction in both industry and academia. It inherits advantages from traditional 2D face recognition, such as the natural recognition process and a wide range of applications. Moreover, 3D face recognition systems could accurately recognize human faces even under dim lights and with variant facial positions and expressions, in such conditions 2D face recognition systems would have immense difficulty to operate. This paper summarizes the history and the most recent progresses in 3D face recognition research domain. The frontier research results are introduced in three categories: pose-invariant recognition, expression-invariant recognition, and occlusion-invariant recognition. To promote future research, this paper collects information about publicly available 3D face databases. This paper also lists important open problems.


Hybrid distribution transformer: Concept development and field demonstration

Today's distribution system is expected to supply power to loads for which it was not designed. Moreover, high penetration of distributed generation units is redefining the requirements for the design, control and operation of the electric distribution system. A Hybrid Distribution Transformer is a potential cost-effective alternative solution to various distribution grid control devices. The Hybrid Distribution Transformer is realized by augmenting a regular transformer with a fractionally rated power electronic converter, which provides the transformer with additional control capabilities. The Hybrid Distribution Transformer concept can provide dynamic ac voltage regulation, reactive power compensation and, in future designs, form an interface with energy storage devices. Other potential functionalities that can be realized from the Hybrid Distribution Transformer include voltage phase angle control, harmonic compensation and voltage sag compensation. This paper presents the concept of a Hybrid Distribution Transformer and the status of our efforts towards a 500 kVA, 12.47 kV/480 V field demonstrator.


Designing a Tag-Based Statistical Math Word Problem Solver with Reasoning and Explanation

Background Since Big Data mainly aims to explore the correlation between surface features but not their underlying causality relationship, the Big Mechanism 2 program has been proposed by DARPA to find out “why” behind the “Big Data”. However, the pre-requisite for it is that the machine can read each document and learn its associated knowledge, which is the task of Machine Reading (MR). Since a domain-independent MR system is complicated and difficult to build, the math word problem (MWP) [1] is frequently chosen as the first test case to study MR (as it usually uses less complicated syntax and requires less amount of domain knowledge). According to the framework for making the decision while there are several candidates, previous MWP algebra solvers can be classified into: (1) Rule-based approaches with logic inference [2-7], which apply rules to get the answer (via identifying entities, quantities, operations, etc.) with a logic inference engine. (2) Rule-based approaches without logic inference [8-13], which apply rules to get the answer without a logic inference engine. (3) Statistics-based approaches [14, 15], which use statistical models to identify entities, quantities, operations, and get the answer. To our knowledge, all the statistics-based approaches do not adopt logic inference. The main problem of the rule-based approaches mentioned above is that the coverage rate problem is serious, as rules with wide coverage are difficult and expensive to construct. Also, since they adopt Go/No-Go approach (unlike statistical approaches which can adopt a large Top-N to have high including rates), the error accumulation problem would be severe. On the other hand, the main problem of those approaches without adopting logic inference is that they usually need to implement a new handling procedure for each new type of problems (as the general logic inference mechanism is not adopted). Also, as there is no inference engine to generate the reasoning chain [16], additional effort would be required for


Attribute-based People Search: Lessons Learnt from a Practical Surveillance System

We address the problem of attribute-based people search in real surveillance environments. The system we developed is capable of answering user queries such as "show me all people with a beard and sunglasses, wearing a white hat and a patterned blue shirt, from all metro cameras in the downtown area, from 2pm to 4pm last Saturday". In this paper, we describe the lessons we learned from practical deployments of our system, and how we made our algorithms achieve the accuracy and efficiency required by many police departments around the world. In particular, we show that a novel set of multimodal integral filters and proper normalization of attribute scores are critical to obtain good performance. We conduct a comprehensive experimental analysis on video footage captured from a large set of surveillance cameras monitoring metro chokepoints, in both crowded and normal activity periods. Moreover, we show impressive results using images from the recent Boston marathon bombing event, where our system can rapidly retrieve the two suspects based on their attributes from a database containing more than one thousand people present at the event.


Why does the microbiome affect behaviour?

Growing evidence indicates that the mammalian microbiome can affect behaviour, and several symbionts even produce neurotransmitters. One common explanation for these observations is that symbionts have evolved to manipulate host behaviour for their benefit. Here, we evaluate the manipulation hypothesis by applying evolutionary theory to recent work on the gut–brain axis. Although the theory predicts manipulation by symbionts under certain conditions, these appear rarely satisfied by the genetically diverse communities of the mammalian microbiome. Specifically, any symbiont investing its resources to manipulate host behaviour is expected to be outcompeted within the microbiome by strains that do not manipulate and redirect their resources into growth and survival. Moreover, current data provide no clear evidence for manipulation. Instead, we show how behavioural effects can readily arise as a by-product of natural selection on microorganisms to grow within the host and natural selection on hosts to depend upon their symbionts. We argue that understanding why the microbiome influences behaviour requires a focus on microbial ecology and local effects within the host. The microbiota can influence host behaviour through the gut–brain axis. In this Opinion, Johnson and Foster explore the evolution of this relationship and propose that adaptations of competing gut microorganisms may affect behaviour as a by‑product, leading to host dependence.


Monolingual and Cross-Lingual Information Retrieval Models Based on (Bilingual) Word Embeddings

We propose a new unified framework for monolingual (MoIR) and cross-lingual information retrieval (CLIR) which relies on the induction of dense real-valued word vectors known as word embeddings (WE) from comparable data. To this end, we make several important contributions: (1) We present a novel word representation learning model called Bilingual Word Embeddings Skip-Gram (BWESG) which is the first model able to learn bilingual word embeddings solely on the basis of document-aligned comparable data; (2) We demonstrate a simple yet effective approach to building document embeddings from single word embeddings by utilizing models from compositional distributional semantics. BWESG induces a shared cross-lingual embedding vector space in which both words, queries, and documents may be presented as dense real-valued vectors; (3) We build novel ad-hoc MoIR and CLIR models which rely on the induced word and document embeddings and the shared cross-lingual embedding space; (4) Experiments for English and Dutch MoIR, as well as for English-to-Dutch and Dutch-to-English CLIR using benchmarking CLEF 2001-2003 collections and queries demonstrate the utility of our WE-based MoIR and CLIR models. The best results on the CLEF collections are obtained by the combination of the WE-based approach and a unigram language model. We also report on significant improvements in ad-hoc IR tasks of our WE-based framework over the state-of-the-art framework for learning text representations from comparable data based on latent Dirichlet allocation (LDA).


Easily Add Significance Testing to your Market Basket Analysis in SAS ® Enterprise Miner TM

Market Basket Analysis is a popular data mining tool that can be used to search through data to find patterns of co-occurrence among objects. It is an algorithmic process that generates business rules and several metrics for each business rule such as support, confidence and lift that help researchers identify “interesting” patterns. Although useful, these popular metrics do not incorporate traditional significance testing. This paper describes how to easily add a well-known statistical significance test, the Pearson’s Chi Squared statistic, to the existing output generated by SAS® Enterprise Miner’s Association Node. The addition of this significance test enhances the ability of data analysts to make better decisions about which business rules are likely to be more useful.


Leveraging social media networks for classification

Social media has reshaped the way in which people interact with each other. The rapid development of participatory web and social networking sites like YouTube, Twitter, and Facebook, also brings about many data mining opportunities and novel challenges. In particular, we focus on classification tasks with user interaction information in a social network. Networks in social media are heterogeneous, consisting of various relations. Since the relation-type information may not be available in social media, most existing approaches treat these inhomogeneous connections homogeneously, leading to an unsatisfactory classification performance. In order to handle the network heterogeneity, we propose the concept of social dimension to represent actors’ latent affiliations, and develop a classification framework based on that. The proposed framework, SocioDim, first extracts social dimensions based on the network structure to accurately capture prominent interaction patterns between actors, then learns a discriminative classifier to select relevant social dimensions. SocioDim, by differentiating different types of network connections, outperforms existing representative methods of classification in social media, and offers a simple yet effective approach to integrating two types of seemingly orthogonal information: the network of actors and their attributes.


From Word To Sense Embeddings: A Survey on Vector Representations of Meaning

Over the past years, distributed semantic representations have proved to be effective and flexible keepers of prior knowledge to be integrated into downstream applications. This survey focuses on the representation of meaning. We start from the theoretical background behind word vector space models and highlight one of their major limitations: the meaning conflation deficiency, which arises from representing a word with all its possible meanings as a single vector. Then, we explain how this deficiency can be addressed through a transition from the word level to the more fine-grained level of word senses (in its broader acceptation) as a method for modelling unambiguous lexical meaning. We present a comprehensive overview of the wide range of techniques in the two main branches of sense representation, i.e., unsupervised and knowledge-based. Finally, this survey covers the main evaluation procedures and applications for this type of representation, and provides an analysis of four of its important aspects: interpretability, sense granularity, adaptability to different domains and compositionality.


Demographic Attributes Prediction on the Real-World Mobile Data

The deluge of the data generated by mobile phone devices imposes new challenges on the data mining community. User activities recorded by mobile phones could be useful for uncovering behavioral patterns. An interesting question is whether patterns in mobile phone usage can reveal demographic characteristics of the user? Demographic information about gender, age, marital status, job type, etc. is a key for applications with customer centric strategies. In this paper, we describe our approach to feature extraction from raw data and building predictive models for the task of demographic attributes predictions. We experimented with graph based representation of users inferred from similarity of their feature vectors, feature selections and classifications algorithms. Our work contributes to the Nokia Mobile Data Challenge (MDC) in the endeavor of exploring the real-world mobile data.


The Effects of Profile Pictures and Friends' Comments on Social Network Site Users' Body Image and Adherence to the Norm

This study sought to explore the effects of exposure to Facebook body ideal profile pictures and norm conforming comments on users' body image. In addition, the social identity and self-categorization theoretical frameworks were used to explore users' endorsement of a body ideal norm. A mock Facebook page was used to conduct a pretest posttest 2 × 2 between-group web-based experiment that featured body ideal profile pictures (body ideal vs. no body) and body ideal comments (conforming vs. nonconforming). Five hundred and one participants completed the experiment and passed all manipulation checks. Participants viewed pictures and comments on the status page and were able to leave their own comment before exiting. Results demonstrated no significant main effects. However, predispositional body satisfaction significantly moderated the relationship between body ideal pictures and body satisfaction. Most comments supported the body ideal norm. However, in support of self-categorization theory, participants exposed to nonconforming comments made nonconforming comments themselves significantly more than those exposed to conforming comments. The findings demonstrated the importance of continued body image research in social network sites, as well as the potential for self-categorization theory to guide such research.


(Invited) Nanoimprinted perovskite for optoelectronics

Organic-inorganic hybrid perovskites have recently emerged as promising materials for optoelectronics. Here we show successful patterning of hybrid perovskite into nanostructures with cost-effective nanoimprint technology. Photodetectors are fabricated on nanoimprinted perovskite with improved responsivity. Nanoimprinted perovskite metasurface forms with significantly enhanced photoluminescence. Lasing is expected on nanoimprinted perovskite with optimized cavity design and process.


A 0.7V Fully-on-Chip Pseudo-Digital LDO Regulator with 6.3μA Quiescent Current and 100mV Dropout Voltage in 0.18-μm CMOS

This paper presents an NMOS pseudo-digital low-dropout (PD-LDO) regulator that supports low-voltage operation by eliminating the amplifier of an analog LDO. The proposed pseudo-digital control loop consists of a latched comparator, a 2X charge pump and a RC network. It detects the output voltage and provides a continuous gate control signal for the power transistor by charging and discharging the RC network. Fast transient response is achieved due to the source follower structure of the power NMOS, with a small output capacitor and small occupied chip area and without consuming large quiescent current. The proof-of-concept design of the proposed PD-LDO is implemented in a 0.18-J.1m CMOS process. The minimum supply voltage is 0.7 V, with a dropout voltage of 100 mV and a maximum load current of 100 mA. Using only 20 pF of on-chip output capacitor and 10 MHz comparator clock frequency, the undershoot is 106 mV with 90 mA load current step and 150 ns edge time. The quiescent current is only 6.3 μA and the active chip area is 0.08 mm2.


Heart rate variability and psychometric responses to overload and tapering in collegiate sprint-swimmers.

OBJECTIVES
The purpose of this study was to evaluate cardiac-parasympathetic and psychometric responses to competition preparation in collegiate sprint-swimmers. Additionally, we aimed to determine the relationship between average vagal activity and its daily fluctuation during each training phase.

DESIGN
Observational.

METHODS
Ten Division-1 collegiate sprint-swimmers performed heart rate variability recordings (i.e., log transformed root mean square of successive RR intervals, lnRMSSD) and completed a brief wellness questionnaire with a smartphone application daily after waking. Mean values for psychometrics and lnRMSSD (lnRMSSDmean) as well as the coefficient of variation (lnRMSSDcv) were calculated from 1 week of baseline (BL) followed by 2 weeks of overload (OL) and 2 weeks of tapering (TP) leading up to a championship competition.

RESULTS
Competition preparation resulted in improved race times (p<0.01). Moderate decreases in lnRMSSDmean, and Large to Very Large increases in lnRMSSDcv, perceived fatigue and soreness were observed during the OL and returned to BL levels or peaked during TP (p<0.05). Inverse correlations between lnRMSSDmean and lnRMSSDcv were Very Large at BL and OL (p<0.05) but only Moderate at TP (p>0.05).

CONCLUSIONS
OL training is associated with a reduction and greater daily fluctuation in vagal activity compared with BL, concurrent with decrements in perceived fatigue and muscle soreness. These effects are reversed during TP where these values returned to baseline or peaked leading into successful competition. The strong inverse relationship between average vagal activity and its daily fluctuation weakened during TP.


基於對照表以及語言模型之簡繁字體轉換 (Chinese Characters Conversion System based on Lookup Table and Language Model) [In Chinese]

The character sets used in China and Taiwan are both Chinese, but they are divided into simplified and traditional Chinese characters. There are large amount of information exchange between China and Taiwan through books and Internet. To provide readers a convenient reading environment, the character conversion between simplified and traditional Chinese is necessary. The conversion between simplified and traditional Chinese characters has two problems: one-to-many ambiguity and term usage problems. Since there are many traditional Chinese characters that have only one corresponding simplified character, when converting simplified Chinese into traditional Chinese, the system will face the one-to-many ambiguity. Also, there are many terms that have different usages between the two Chinese societies. This paper focus on designing an extensible conversion system, that can take the advantage of community knowledge by accumulating lookup tables through Wikipedia to tackle the term usage problem and can integrate language model to disambiguate the one-to-many ambiguity. The system can reduce the cost of proofreading of character conversion for books, e-books, or online publications. The extensible architecture makes it easy to improve the system with new training data.


Checklists for improving rigour in qualitative research: a case of the tail wagging the dog?

Qualitative research methods are enjoying unprecedented popularity. Although checklists have undoubtedly contributed to the wider acceptance of such methods, these can be counterproductive if used prescriptively. The uncritical adoption of a range of “technical fixes” (such as purposive sampling, grounded theory, multiple coding, triangulation, and respondent validation) does not, in itself, confer rigour. In this article I discuss the limitations of these procedures and argue that there is no substitute for systematic and thorough application of the principles of qualitative research. Technical fixes will achieve little unless they are embedded in a broader understanding of the rationale and assumptions behind qualitative research.


Yet Another Visit to Paxos

This paper presents a modular introduction to crash-tolerant and Byzantine-tolerant protocols for reaching consensus that use the method introduced by the Paxos algorithm of Lamport and by the viewstamped replication algorithm of Oki and Liskov. The consensus protocol runs a sequence of epoch abstractions as governed by an epoch-change abstraction. Implementations of epoch and epoch-change that tolerate crash faults yield the consensus algorithm in Paxos and in viewstamped replication. Implementations of epoch and epoch-change that tolerate Byzantine faults yield the consensus algorithm in the PBFT protocol of Castro and Liskov.


Internet inter-domain traffic

In this paper, we examine changes in Internet inter-domain traffic demands and interconnection policies. We analyze more than 200 Exabytes of commercial Internet traffic over a two year period through the instrumentation of 110 large and geographically diverse cable operators, international transit backbones, regional networks and content providers. Our analysis shows significant changes in inter-AS traffic patterns and an evolution of provider peering strategies. Specifically, we find the majority of inter-domain traffic by volume now flows directly between large content providers, data center / CDNs and consumer networks. We also show significant changes in Internet application usage, including a global decline of P2P and a significant rise in video traffic. We conclude with estimates of the current size of the Internet by inter-domain traffic volume and rate of annualized inter-domain traffic growth.


Strategies and struggles with privacy in an online social networking community

Online social networking communities such as Facebook and MySpace are extremely popular. These sites have changed how many people develop and maintain relationships through posting and sharing personal information. The amount and depth of these personal disclosures have raised concerns regarding online privacy. We expand upon previous research on users’ under-utilization of available privacy options by examining users’ current strategies for maintaining their privacy, and where those strategies fail, on the online social network site Facebook. Our results demonstrate the need for mechanisms that provide awareness of the privacy impact of users’ daily interactions.


Moving beyond untagging: photo privacy in a tagged world

Photo tagging is a popular feature of many social network sites that allows users to annotate uploaded images with those who are in them, explicitly linking the photo to each person's profile. In this paper, we examine privacy concerns and mechanisms surrounding these tagged images. Using a focus group, we explored the needs and concerns of users, resulting in a set of design considerations for tagged photo privacy. We then designed a privacy enhancing mechanism based on our findings, and validated it using a mixed methods approach. Our results identify the social tensions that tagging generates, and the needs of privacy tools to address the social implications of photo privacy management.


Information revelation and privacy in online social networks

Participation in social networking sites has dramatically increased in recent years. Services such as Friendster, Tribe, or the Facebook allow millions of individuals to create online profiles and share personal information with vast networks of friends - and, often, unknown numbers of strangers. In this paper we study patterns of information revelation in online social networks and their privacy implications. We analyze the online behavior of more than 4,000 Carnegie Mellon University students who have joined a popular social networking site catered to colleges. We evaluate the amount of information they disclose and study their usage of the site's privacy settings. We highlight potential attacks on various aspects of their privacy, and we show that only a minimal percentage of users changes the highly permeable privacy preferences.


Persona: an online social network with user-defined privacy

Online social networks (OSNs) are immensely popular, with some claiming over 200 million users. Users share private content, such as personal information or photographs, using OSN applications. Users must trust the OSN service to protect personal information even as the OSN provider benefits from examining and sharing that information. We present Persona, an OSN where users dictate who may access their information. Persona hides user data with attribute-based encryption (ABE), allowing users to apply fine-grained policies over who may view their data. Persona provides an effective means of creating applications in which users, not the OSN, define policy over access to private data. We demonstrate new cryptographic mechanisms that enhance the general applicability of ABE. We show how Persona provides the functionality of existing online social networks with additional privacy benefits. We describe an implementation of Persona that replicates Facebook applications and show that Persona provides acceptable performance when browsing privacy-enhanced web pages, even on mobile devices.


Estimating the Customer-Level Demand for Electricity Under Real-Time Market Prices

This paper presents estimates of the customer-level demand for electricity by industrial and commercial customers purchasing electricity according to the half-hourly energy prices from the England and Wales (E&W) electricity market. These customers also face the possibility of a demand charge on their electricity consumption during the three half-hour periods that are coincident with E&W system peaks. Although energy charges are largely known by 4:00 P.M. the day prior to consumption, a fraction of the energy charge and the identity of the half-hour periods when demand charges occur are only known with certainty ex post of consumption. Four years of data from a Regional Electricity Company (REC) in the United Kingdom is used to quantify the half-hourly customer-level demands under this real-time pricing program. The econometric model developed and estimated here quantifies the extent of intertemporal substitution in electricity consumption across pricing periods within the day due to changes in all of the components of the day-ahead E&W electricity prices, the level of the demand charge and the probability that a demand charge will be imposed. The results of this modeling framework can be used by retail companies supplying consumers purchasing electricity according to real-time market prices to construct demand-side bids into an electricity market open to competition. The paper closes with several examples of how this might be done.


Application of Spreading Activation Techniques in Information Retrieval

This paper surveys the use of Spreading Activation techniques onSemantic Networks in Associative Information Retrieval. The majorSpreading Activation models are presented and their applications toIR is surveyed. A number of works in this area are criticallyanalyzed in order to study the relevance of Spreading Activation forassociative IR.


Combined digital signature and digital watermark scheme for image authentication

Conventional digital signature schemes for image authentication encode the signature in a file separate from the original image, thus require extra bandwidth to transmit it. Meantime, the watermark technique embeds some information in the host image. In this paper, a combined digital watermark and digital signature scheme for image authentication is proposed. The scheme extracts signature from the original image and embeds them back into the image as watermark, avoiding additional signature file. Since images are always compressed before transmission in the Internet, the proposed scheme is tested for compression tolerance and shows good robustness against JPEG coding. Furthermore, the scheme not only can verify the authenticity and the integrity of images, but also can locate the illegal modifications.


Listening to Chaotic Whispers: A Deep Learning Framework for News-oriented Stock Trend Prediction

Stock trend prediction plays a critical role in seeking maximized profit from the stock investment. However, precise trend prediction is very difficult since the highly volatile and non-stationary nature of the stock market. Exploding information on the Internet together with the advancing development of natural language processing and text mining techniques have enabled investors to unveil market trends and volatility from online content. Unfortunately, the quality, trustworthiness, and comprehensiveness of online content related to stock market vary drastically, and a large portion consists of the low-quality news, comments, or even rumors. To address this challenge, we imitate the learning process of human beings facing such chaotic online news, driven by three principles: sequential content dependency, diverse influence, and effective and efficient learning. In this paper, to capture the first two principles, we designed a Hybrid Attention Networks(HAN) to predict the stock trend based on the sequence of recent related news. Moreover, we apply the self-paced learning mechanism to imitate the third principle. Extensive experiments on real-world stock market data demonstrate the effectiveness of our framework. A further simulation illustrates that a straightforward trading strategy based on our proposed framework can significantly increase the annualized return.


FinDroidHR: Smartwatch Gesture Input with Optical Heartrate Monitor

We present FinDroidHR, a novel gesture input technique for off-the-shelf smartwatches. Our technique is designed to detect 10 hand gestures on the hand wearing a smartwatch. The technique is enabled by analysing features of the Photoplethysmography (PPG) signal that optical heart-rate sensors capture. In a study with 20 participants, we show that FinDroidHR achieves 90.55% accuracy and 90.73% recall. Our work is the first study to explore the feasibility of using optical sensors on the off-the-shelf wearable devices to recognise gestures. Without requiring bespoke hardware, FinDroidHR can be readily used on existing smartwatches.


Humanoid robotics platforms developed in HRP

This paper presents humanoid robotics platform that consists of a humanoid robot and an open architecture software platform developed in METI’s Humanoid Robotics Project (HRP). The final version of the robot, called HRP-2, has 1540 mm height, 58 kg weight and 30 degrees of the freedom. The software platform includes a dynamics simulator and motion controllers of the r ns and is e ©


Using web security scanners to detect vulnerabilities in web services

Although web services are becoming business-critical components, they are often deployed with critical software bugs that can be maliciously explored. Web vulnerability scanners allow detecting security vulnerabilities in web services by stressing the service from the point of view of an attacker. However, research and practice show that different scanners have different performance on vulnerabilities detection. In this paper we present an experimental evaluation of security vulnerabilities in 300 publicly available web services. Four well known vulnerability scanners have been used to identify security flaws in web services implementations. A large number of vulnerabilities has been observed, which confirms that many services are deployed without proper security testing. Additionally, the differences in the vulnerabilities detected and the high number of false-positives (35% and 40% in two cases) and low coverage (less than 20% for two of the scanners) observed highlight the limitations of web vulnerability scanners on detecting security vulnerabilities in web services.


Development of Generalized Photovoltaic Model Using MATLAB / SIMULINK

temperature into consideration, the output current and power characteristics of PV model are simulated and optimized using the proposed model. This enables the dynamics of PV power system to be easily simulated, analyzed, and optimized.


Social-Aware Video Recommendation for Online Social Groups

Group recommendation plays a significant role in today's social media systems, where users form social groups to receive multimedia content together and interact with each other, instead of consuming the online content individually. Limitations of traditional group recommendation approaches are as follows. First, they usually infer group members’ preferences by their historical behaviors, failing to capture inactive users’ preferences from the sparse historical data. Second, relationships between group members are not studied by these approaches, which fail to capture the inherent personality of members in a group. To address these issues, we propose a social-aware group recommendation framework that jointly utilizes both social relationships and social behaviors to not only infer a group's preference, but also model the tolerance and  altruism characteristics of group members. Based on the observation that the following relationship in the online social network reflects common interests of users, we propose a group preference model based on external experts of group members. Furthermore, we model users’ tolerance (willingness to receive content not preferred) and altruism (willingness to receive content preferred by friends). Finally, based on the group preference model, we design recommendation algorithms for users under different social contexts. Experimental results demonstrate the effectiveness of our approach, which significantly improves the recommendation accuracy against traditional approaches, especially in the cases of inactive group members.


ScreenerNet: Learning Curriculum for Neural Networks

We propose to learn a curriculum or a syllabus for supervised learning with deep neural networks. Specifically, we learn weights for each sample in training by an attached neural network, called ScreenerNet, to the original network and jointly train them in an end-to-end fashion. We show the networks augmented with our ScreenerNet achieve early convergence with better accuracy than the state-of-the-art rule-based curricular learning methods in extensive experiments using three popular vision datasets including MNIST, CIFAR10 and Pascal VOC2012, and a Cartpole task using Deep Q-learning.


ICTs for Improving Patients Rehabilitation Research Techniques

The world population is rapidly aging and becoming a burden to health systems around the world. In this work we present a conceptual framework to encourage the research community to develop more comprehensive and adaptive ICT solutions for prevention and rehabilitation of chronic conditions in the daily life of the aging population and beyond health facilities. We first present an overview of current international standards in human functioning and disability, and how chronic conditions are interconnected in older age. We then describe innovative mobile and sensor technologies, predictive data analysis in healthcare, and game-based prevention and rehabilitation techniques. We then set forth a multidisciplinary approach for the personalized prevention and rehabilitation of chronic conditions using unobtrusive and pervasive sensors, interactive activities, and predictive analytics, which also eases the tasks of health-related researchers, caregivers and providers. Our proposal represents a conceptual basis for future research, in which much remains to be done in terms of standardization of technologies and health terminology, as well as data protection and privacy legislation.


An Evaluation of Parser Robustness for Ungrammatical Sentences

For many NLP applications that require a parser, the sentences of interest may not be well-formed. If the parser can overlook problems such as grammar mistakes and produce a parse tree that closely resembles the correct analysis for the intended sentence, we say that the parser is robust. This paper compares the performances of eight state-of-the-art dependency parsers on two domains of ungrammatical sentences: learner English and machine translation outputs. We have developed an evaluation metric and conducted a suite of experiments. Our analyses may help practitioners to choose an appropriate parser for their tasks, and help developers to improve parser robustness against ungrammatical sentences.


Microglia-mediated recovery from ALS-relevant motor neuron degeneration in a mouse model of TDP-43 proteinopathy

Though motor neurons selectively degenerate in amyotrophic lateral sclerosis, other cell types are likely involved in this disease. We recently generated rNLS8 mice in which human TDP-43 (hTDP-43) pathology could be reversibly induced in neurons and expected that microglia would contribute to neurodegeneration. However, only subtle microglial changes were detected during disease in the spinal cord, despite progressive motor neuron loss; microglia still reacted to inflammatory triggers in these mice. Notably, after hTDP-43 expression was suppressed, microglia dramatically proliferated and changed their morphology and gene expression profiles. These abundant, reactive microglia selectively cleared neuronal hTDP-43. Finally, when microgliosis was blocked during the early recovery phase using PLX3397, a CSF1R and c-kit inhibitor, rNLS8 mice failed to regain full motor function, revealing an important neuroprotective role for microglia. Therefore, reactive microglia exert neuroprotective functions in this amyotrophic lateral sclerosis model, and definition of the underlying mechanism could point toward novel therapeutic strategies. Using an inducible mouse model of sporadic ALS, Spiller et al. show that spinal microgliosis is not a major feature of TDP-43-triggered disease. Instead, microglia mediate TDP-43 clearance and motor recovery, suggesting a neuroprotective role in ALS.


MazeBase: A Sandbox for Learning from Games

This paper introduces an environment for simple 2D maze games, designed as a sandbox for machine learning approaches to reasoning and planning. Within it, we create 10 simple games based on algorithmic tasks (e.g. embodying simple if-then statements). We deploy a range of neural models (fully connected, convolutional network, memory network) on these games, with and without a procedurally generated curriculum. We show that these architectures can be trained with reinforcement to respectable performance on these tasks, but are still far from optimal, despite their simplicity. We also apply these models to games involving combat, including StarCraft, demonstrating their ability to learn non-trivial tactics which enable them to consistently beat the in-game AI.


Multilabel Text Classification for Automated Tag Suggestion

The increased popularity of tagging during the last few years can be mainly attributed to its embracing by most of the recently thriving user-centric content publishing and management Web 2.0 applications. However, tagging systems have some limitations that have led researchers to develop methods that assist users in the tagging process, by automatically suggesting an appropriate set of tags. We have tried to model the automated tag suggestion problem as a multilabel text classification task in order to participate in the ECML/PKDD 2008 Discovery Challenge.


A taxonomy for user-healthcare robot interaction

This paper evaluates existing taxonomies aimed at characterizing the interaction between robots and their users and modifies them for health care applications. The modifications are based on existing robot technologies and user acceptance of robotics. Characterization of the user, or in this case the patient, is a primary focus of the paper, as they present a unique new role as robot users. While therapeutic and monitoring-related applications for robots are still relatively uncommon, we believe they will begin to grow and thus it is important that the spurring relationship between robot and patient is well understood.


Multiple Physical Layer Pipes performance for DVB-T2

The DVB-T2 terrestrial television standard is becoming increasingly important, and have been extensively studied and developed to provide many types of services with higher spectral efficiency and better performance. The Physical Layer Pipes in DVB-S2 are logical channels carrying one or more services with modulation scheme and robustness. The main changes are found in the physical layer where DVB-T2 incorporates a new physical layer pipe (PLP). Each physical layer pipe contains an individual configuration of modulation, coding and interleaving. This new concept allows a transmission with multiple physical layer pipes where each service can be transmitted with different physical layer configuration. The Advanced Television Systems Committee (ATSC3.0) standard will add value to broadcasting services, allowing a extending reach by adding new business models, providing higher quality, improved accessibility, personalization and interactivity and more flexible and efficient use of the spectrum.


Precision CMOS current reference with process and temperature compensation

This paper presents a new first-order temperature compensated CMOS current reference. To achieve a compact architecture able to operate under low voltage with low power consumption, it is based on a self-biasing beta multiplier current generator. Compensation against temperature is achieved by using instead of an ordinary resistor two triode transistors in parallel, acting as a negative and a positive temperature coefficient resistor, that generate a proportional to absolute temperature and a complementary to absolute temperature current which can be directly added to attain a temperature compensated current. Programmability is included to adjust the temperature coefficient and the reference current magnitude over process variations. Results for a 0.18 μm CMOS implementation show that the proposed 500 nA reference operate with supplies down to 1.2 V accomplishing over a (-40 to +120°C) range temperature drifts below 120 ppm/°C.


Modified post-filter to recover modulation spectrum for HMM-based speech synthesis

This paper proposes a modified post-filter to recover a Modulation Spectrum (MS) in HMM-based speech synthesis. To alleviate the over-smoothing effect which is one of the major problems in HMM-based speech synthesis, the MS-based post-filter has been proposed. It recovers the utterance-level MS of the generated speech trajectory, and we have reported its benefit to the quality improvement. However, this post-filter is not applicable to various lengths of speech parameter trajectories, such as phrases or segments, which are shorter than an utterance. To address this problem, we propose two modified post-filters, (1) the time-invariant filter with a simplified conversion form and (2) the segment-level post-filter which applicable to a short-term parameter sequence. Furthermore, we also propose (3) the post-filter to recover the phoneme-level MS of HMM-state duration. Experimental results show that the modified post-filters also yield significant quality improvements in synthetic speech as yielded by the conventional post-filter.


Fully convolutional network and sparsity-based dictionary learning for liver lesion detection in CT examinations


TABLA: A unified template-based framework for accelerating statistical machine learning

A growing number of commercial and enterprise systems increasingly rely on compute-intensive Machine Learning (ML) algorithms. While the demand for these compute-intensive applications is growing, the performance benefits from general-purpose platforms are diminishing. Field Programmable Gate Arrays (FPGAs) provide a promising path forward to accommodate the needs of machine learning algorithms and represent an intermediate point between the efficiency of ASICs and the programmability of general-purpose processors. However, acceleration with FPGAs still requires long development cycles and extensive expertise in hardware design. To tackle this challenge, instead of designing an accelerator for a machine learning algorithm, we present TABLA, a framework that generates accelerators for a class of machine learning algorithms. The key is to identify the commonalities across a wide range of machine learning algorithms and utilize this commonality to provide a high-level abstraction for programmers. TABLA leverages the insight that many learning algorithms can be expressed as a stochastic optimization problem. Therefore, learning becomes solving an optimization problem using stochastic gradient descent that minimizes an objective function over the training data. The gradient descent solver is fixed while the objective function changes for different learning algorithms. TABLA provides a template-based framework to accelerate this class of learning algorithms. Therefore, a developer can specify the learning task by only expressing the gradient of the objective function using our high-level language. Tabla then automatically generates the synthesizable implementation of the accelerator for FPGA realization using a set of hand-optimized templates. We use Tabla to generate accelerators for ten different learning tasks targeted at a Xilinx Zynq FPGA platform. We rigorously compare the benefits of FPGA acceleration to multi-core CPUs (ARM Cortex A15 and Xeon E3) and many-core GPUs (Tegra K1, GTX 650 Ti, and Tesla K40) using real hardware measurements. TABLA-generated accelerators provide 19.4x and 2.9x average speedup over the ARM and Xeon processors, respectively. These accelerators provide 17.57x, 20.2x, and 33.4x higher Performance-per-Watt in comparison to Tegra, GTX 650 Ti and Tesla, respectively. These benefits are achieved while the programmers write less than 50 lines of code.


Testing the reliability and efficiency of the pilot Mixed Methods Appraisal Tool (MMAT) for systematic mixed studies review.

BACKGROUND
Systematic literature reviews identify, select, appraise, and synthesize relevant literature on a particular topic. Typically, these reviews examine primary studies based on similar methods, e.g., experimental trials. In contrast, interest in a new form of review, known as mixed studies review (MSR), which includes qualitative, quantitative, and mixed methods studies, is growing. In MSRs, reviewers appraise studies that use different methods allowing them to obtain in-depth answers to complex research questions. However, appraising the quality of studies with different methods remains challenging. To facilitate systematic MSRs, a pilot Mixed Methods Appraisal Tool (MMAT) has been developed at McGill University (a checklist and a tutorial), which can be used to concurrently appraise the methodological quality of qualitative, quantitative, and mixed methods studies.

OBJECTIVES
The purpose of the present study is to test the reliability and efficiency of a pilot version of the MMAT.

METHODS
The Center for Participatory Research at McGill conducted a systematic MSR on the benefits of Participatory Research (PR). Thirty-two PR evaluation studies were appraised by two independent reviewers using the pilot MMAT. Among these, 11 (34%) involved nurses as researchers or research partners. Appraisal time was measured to assess efficiency. Inter-rater reliability was assessed by calculating a kappa statistic based on dichotomized responses for each criterion. An appraisal score was determined for each study, which allowed the calculation of an overall intra-class correlation.

RESULTS
On average, it took 14 min to appraise a study (excluding the initial reading of articles). Agreement between reviewers was moderate to perfect with regards to MMAT criteria, and substantial with respect to the overall quality score of appraised studies.

CONCLUSION
The MMAT is unique, thus the reliability of the pilot MMAT is promising, and encourages further development.


YAGO2: exploring and querying world knowledge in time, space, context, and many languages

We present YAGO2, an extension of the YAGO knowledge base with focus on temporal and spatial knowledge. It is automatically built from Wikipedia, GeoNames, and WordNet, and contains nearly 10 million entities and events, as well as 80 million facts representing general world knowledge. An enhanced data representation introduces time and location as first-class citizens. The wealth of spatio-temporal information in YAGO can be explored either graphically or through a special time- and space-aware query language.


Stretchable Heater Using Ligand-Exchanged Silver Nanowire Nanocomposite for Wearable Articular Thermotherapy.

Thermal therapy is one of the most popular physiotherapies and it is particularly useful for treating joint injuries. Conventional devices adapted for thermal therapy including heat packs and wraps have often caused discomfort to their wearers because of their rigidity and heavy weight. In our study, we developed a soft, thin, and stretchable heater by using a nanocomposite of silver nanowires and a thermoplastic elastomer. A ligand exchange reaction enabled the formation of a highly conductive and homogeneous nanocomposite. By patterning the nanocomposite with serpentine-mesh structures, conformal lamination of devices on curvilinear joints and effective heat transfer even during motion were achieved. The combination of homogeneous conductive elastomer, stretchable design, and a custom-designed electronic band created a novel wearable system for long-term, continuous articular thermotherapy.


Manifold Gaussian Processes for regression

Off-the-shelf Gaussian Process (GP) covariance functions encode smoothness assumptions on the structure of the function to be modeled. To model complex and non-differentiable functions, these smoothness assumptions are often too restrictive. One way to alleviate this limitation is to find a different representation of the data by introducing a feature space. This feature space is often learned in an unsupervised way, which might lead to data representations that are not useful for the overall regression task. In this paper, we propose Manifold Gaussian Processes, a novel supervised method that jointly learns a transformation of the data into a feature space and a GP regression from the feature space to observed space. The Manifold GP is a full GP and allows to learn data representations, which are useful for the overall regression task. As a proof-of-concept, we evaluate our approach on complex non-smooth functions where standard GPs perform poorly, such as step functions and robotics tasks with contacts.


Deep Gaussian Processes

In this paper we introduce deep Gaussian process (GP) models. Deep GPs are a deep belief network based on Gaussian process mappings. The data is modeled as the output of a multivariate GP. The inputs to that Gaussian process are then governed by another GP. A single layer model is equivalent to a standard GP or the GP latent variable model (GP-LVM). We perform inference in the model by approximate variational marginalization. This results in a strict lower bound on the marginal likelihood of the model which we use for model selection (number of layers and nodes per layer). Deep belief networks are typically applied to relatively large data sets using stochastic gradient descent for optimization. Our fully Bayesian treatment allows for the application of deep models even when data is scarce. Model selection by our variational bound shows that a five layer hierarchy is justified even when modelling a digit data set containing only 150 examples.


Variational Auto-encoded Deep Gaussian Processes

We develop a scalable deep non-parametric generative model by augmenting deep Gaussian processes with a recognition model. Inference is performed in a novel scalable variational framework where the variational posterior distributions are reparametrized through a multilayer perceptron. The key aspect of this reformulation is that it prevents the proliferation of variational parameters which otherwise grow linearly in proportion to the sample size. We derive a new formulation of the variational lower bound that allows us to distribute most of the computation in a way that enables to handle datasets of the size of mainstream deep learning tasks. We show the efficacy of the method on a variety of challenges including deep unsupervised learning and deep Bayesian optimization.


A Unifying View of Sparse Approximate Gaussian Process Regression

We provide a new unifying view, including all existing prope r probabilistic sparse approximations for Gaussian process regression. Our approach relies on exp ressing theeffective priorwhich the methods are using. This allows new insights to be gained, and highlights the relationship between existing methods. It also allows for a clear theoretically j ustified ranking of the closeness of the known approximations to the corresponding full GPs. Finall y we point directly to designs of new better sparse approximations, combining the best of the exi sting strategies, within attractive computational constraints.


Caffe: Convolutional Architecture for Fast Feature Embedding

Caffe provides multimedia scientists and practitioners with a clean and modifiable framework for state-of-the-art deep learning algorithms and a collection of reference models. The framework is a BSD-licensed C++ library with Python and MATLAB bindings for training and deploying general-purpose convolutional neural networks and other deep models efficiently on commodity architectures. Caffe fits industry and internet-scale media needs by CUDA GPU computation, processing over 40 million images a day on a single K40 or Titan GPU (approx 2 ms per image). By separating model representation from actual implementation, Caffe allows experimentation and seamless switching among platforms for ease of development and deployment from prototyping machines to cloud environments.
 Caffe is maintained and developed by the Berkeley Vision and Learning Center (BVLC) with the help of an active community of contributors on GitHub. It powers ongoing research projects, large-scale industrial applications, and startup prototypes in vision, speech, and multimedia.


Multilayer networks: an architecture framework

We present an architecture framework for the control and management of multilayer networks and associated advanced network services. This material is identified as an "architecture framework" to emphasize its role in providing guidance and structure to our subsequent detailed architecture, design, and implementation activities. Our work is motivated by requirements from the Department of Energy science application community for real-time on-demand science-domain-specific network services and resource provisioning. We also summarize the current state of deployments and use of network services based on this multilayer network architecture framework.


IoT for Healthcare

The Internet of Things (IoT) is the network of physical objects or things embedded with electronics, software, sensors, and network connectivity, which enables these objects to collect and exchange data. The Internet of Things allows objects to be sensed and controlled remotely across existing network infrastructure. The IoT is enabled by the latest developments in RFID, smart sensors, communication technologies, and Internet protocols. The basic premise is to have smart sensors collaborate directly without human involvement to deliver a new class of applications. The current revolution in Internet, mobile, and machine-to-machine (M2M) technologies can be seen as the first phase of the IoT. In the coming years, the IoT is expected to bridge diverse technologies to enable new applications by connecting physical objects together in support of intelligent decision making. Smart healthcare plays a significant role in healthcare applications through embedding sensors and actuators in patients and their medicine for monitoring and tracking purposes. The IoT is used by clinical care to monitor physiological statuses of patients through sensors by collecting and analyzing their information and then sending analyzed patient’s data remotely to processing centers to make suitable actions. Not only for patients, it also useful for normal people to check the health status by using wearable devices with sensors.


User Experience in Service Design: A Case Study from Algeria

The design of crisis management services is crucial for emerging countries such as Algeria. It must take into account the experiences of diverse stakeholders. The authors investigate user experience (UX) practices from a service design perspective and describe a case study from Algeria exploring UX-driven service design for crisis management.


Towards Accurate Predictions of Customer Purchasing Patterns

A range of algorithms was used to classify online retail customers of a UK company using historical transaction data. The predictive capabilities of the classifiers were assessed using linear regression, Lasso and regression trees. Unlike most related studies, classifications were based upon specific and marketing focused customer behaviours. Prediction accuracy on untrained customers was generally better than 80%. The models implemented (and compared) for classification were: Logistic Regression, Quadratic Discriminant Analysis, Linear SVM, RBF SVM, Gaussian Process, Decision Tree, Random Forest and Multi-layer Perceptron (Neural Network). Postcode data was then used to classify solely on demographics derived from the UK Land Registry and similar public data sources. Prediction accuracy remained better than 60%.


Discriminative learning of visual words for 3D human pose estimation

This paper addresses the problem of recovering 3D human pose from a single monocular image, using a discriminative bag-of-words approach. In previous work, the visual words are learned by unsupervised clustering algorithms. They capture the most common patterns and are good features for coarse-grain recognition tasks like object classification. But for those tasks which deal with subtle differences such as pose estimation, such representation may lack the needed discriminative power. In this paper, we propose to jointly learn the visual words and the pose regressors in a supervised manner. More specifically, we learn an individual distance metric for each visual word to optimize the pose estimation performance. The learned metrics rescale the visual words to suppress unimportant dimensions such as those corresponding to background. Another contribution is that we design an appearance and position context (APC) local descriptor that achieves both selectivity and invariance while requiring no background subtraction. We test our approach on both a quasi-synthetic dataset and a real dataset (HumanEva) to verify its effectiveness. Our approach also achieves fast computational speed thanks to the integral histograms used in APC descriptor extraction and fast inference of pose regressors.


A graph-based recommender system for digital library

Research shows that recommendations comprise a valuable service for users of a digital library [11]. While most existing recommender systems rely either on a content-based approach or a collaborative approach to make recommendations, there is potential to improve recommendation quality by using a combination of both approaches (a hybrid approach). In this paper, we report how we tested the idea of using a graph-based recommender system that naturally combines the content-based and collaborative approaches. Due to the similarity between our problem and a concept retrieval task, a Hopfield net algorithm was used to exploit high-degree book-book, user-user and book-user associations. Sample hold-out testing and preliminary subject testing were conducted to evaluate the system, by which it was found that the system gained improvement with respect to both precision and recall by combining content-based and collaborative approaches. However, no significant improvement was observed by exploiting high-degree associations.


Real-time people counting system using video camera

In this MTech thesis experiments will be tried out on a people counting system in an effort to enhance the accuracy when separating counting groups of people, and nonhuman objects. This system features automatic color equalization, adaptive background subtraction, shadow detection algorithm and Kalman tracking. The aim is to develop a reliable and accurate computer vision alternative to sensor or contact based mechanisms. The problem for many computer vision based systems are making good separation between the background and foreground, and teaching the computers what parts make up a scene. We also want to find features to classify the foreground moving objects, an easy task for a human, but a complex task for a computer. Video has been captured with a birds eye view close to one of the entrances at the school about ten meters above the floor. From this video troublesome parts have been selected to test the changes done to the algorithms and program code.


Bicycle dynamics and control: adapted bicycles for education and research

In this paper, the dynamics of bicycles is analyzed from the perspective of control. Models of different complexity are presented, starting with simple ones and ending with more realistic models generated from multibody software. Models that capture essential behavior such as self-stabilization as well as models that demonstrate difficulties with rear wheel steering are considered. Experiences using bicycles in control education along with suggestions for fun and thought-provoking experiments with proven student attraction are presented. Finally, bicycles and clinical programs designed for children with disabilities are described.


A neostriatal habit learning system in humans.

Amnesic patients and nondemented patients with Parkinson's disease were given a probabilistic classification task in which they learned which of two outcomes would occur on each trial, given the particular combination of cues that appeared. Amnesic patients exhibited normal learning of the task but had severely impaired declarative memory for the training episode. In contrast, patients with Parkinson's disease failed to learn the probabilistic classification task, despite having intact memory for the training episode. This double dissociation shows that the limbic-diencephalic regions damaged in amnesia and the neostriatum damaged in Parkinson's disease support separate and parallel learning systems. In humans, the neostriatum (caudate nucleus and putamen) is essential for the gradual, incremental learning of associations that is characteristic of habit learning. The neostriatum is important not just for motor behavior and motor learning but also for acquiring nonmotor dispositions and tendencies that depend on new associations.


On Removing Routing Protocol from Future Wireless Networks: A Real-time Deep Learning Approach for Intelligent Traffic Control

Recently, deep learning has appeared as a breakthrough machine learning technique for various areas in computer science as well as other disciplines. However, the application of deep learning for network traffic control in wireless/heterogeneous networks is a relatively new area. With the evolution of wireless networks, efficient network traffic control such as routing methodology in the wireless backbone network appears as a key challenge. This is because the conventional routing protocols do not learn from their previous experiences regarding network abnormalities such as congestion and so forth. Therefore, an intelligent network traffic control method is essential to avoid this problem. In this article, we address this issue and propose a new, real-time deep learning based intelligent network traffic control method, exploiting deep Convolutional Neural Networks (deep CNNs) with uniquely characterized inputs and outputs to represent the considered Wireless Mesh Network (WMN) backbone. Simulation results demonstrate that our proposal achieves significantly lower average delay and packet loss rate compared to those observed with the existing routing methods. We particularly focus on our proposed method's independence from existing routing protocols, which makes it a potential candidate to remove routing protocol(s) from future wired/ wireless networks.


Development of a 6-DOF manipulator actuated with a straight-fiber-type artificial muscle

Robots have become an integral part of human life, and the relationship between humans and robots has grown closer. Thus, it is desired that robots have characteristics similar to humans. In this context, we paid attention to an artificial muscle actuator. We used straight-fiber-type artificial muscles, derived from the McKibben type, which have excellent characteristics with respect to the contraction rate and force. We developed a 6-DOF manipulator actuated by a straight fiber artificial muscle. Furthermore, we tried to control the manipulator position by considering its characteristics.


From smart to smarter cities: Bridging the dimensions of technology and urban planning

This paper discusses the importance of urban form in smart cities. Development of urban form has been a major concern for urban planners, designers and policy makers. Sprawl is one way of urban development which is not considered good for a better living standard. Compact form arose as an opposed idea to urban sprawl. Form based codes (FBCs) is a tool for urban planning and design that attempts to mitigate the problem of urban sprawl, whereas conventional zoning strengthens this. This paper highlights the importance of physical place in smart city and how FBCs attempt to create better physical place than conventional zoning. Our study shows that FBCs can lead to smart growth which can be a solution to bridge technology and urban planning together in a single platform.


How to learn a graph from smooth signals

We propose a framework that learns the graph structure underlying a set of smooth signals. Given X ∈ Rm×n whose rows reside on the vertices of an unknown graph, we learn the edge weights w ∈ R + under the smoothness assumption that tr ( X>LX ) is small. We show that the problem is a weighted `-1 minimization that leads to naturally sparse solutions. We point out how known graph learning or construction techniques fall within our framework and propose a new model that performs better than the state of the art in many settings. We present efficient, scalable primal-dual based algorithms for both our model and the previous state of the art, and evaluate their performance on artificial and real data.


Thin-Slicing for Pose: Learning to Understand Pose without Explicit Pose Estimation

We address the problem of learning a pose-aware, compact embedding that projects images with similar human poses to be placed close-by in the embedding space. The embedding function is built on a deep convolutional network, and trained with triplet-based rank constraints on real image data. This architecture allows us to learn a robust representation that captures differences in human poses by effectively factoring out variations in clothing, background, and imaging conditions in the wild. For a variety of pose-related tasks, the proposed pose embedding provides a cost-efficient and natural alternative to explicit pose estimation, circumventing challenges of localizing body joints. We demonstrate the efficacy of the embedding on pose-based image retrieval and action recognition problems.


Regions, Periods, Activities: Uncovering Urban Dynamics via Cross-Modal Representation Learning

With the ever-increasing urbanization process, systematically modeling people’s activities in the urban space is being recognized as a crucial socioeconomic task. This task was nearly impossible years ago due to the lack of reliable data sources, yet the emergence of geo-tagged social media (GTSM) data sheds new light on it. Recently, there have been fruitful studies on discovering geographical topics from GTSM data. However, their high computational costs and strong distributional assumptions about the latent topics hinder them from fully unleashing the power of GTSM. To bridge the gap, we present CrossMap, a novel crossmodal representation learning method that uncovers urban dynamics with massive GTSM data. CrossMap first employs an accelerated mode seeking procedure to detect spatiotemporal hotspots underlying people’s activities. Those detected hotspots not only address spatiotemporal variations, but also largely alleviate the sparsity of the GTSM data. With the detected hotspots, CrossMap then jointly embeds all spatial, temporal, and textual units into the same space using two different strategies: one is reconstructionbased and the other is graph-based. Both strategies capture the correlations among the units by encoding their cooccurrence and neighborhood relationships, and learn lowdimensional representations to preserve such correlations. Our experiments demonstrate that CrossMap not only significantly outperforms state-of-the-art methods for activity recovery and classification, but also achieves much better efficiency.


A 3.4 W Digital-In Class-D Audio Amplifier in 0.14 $\mu $m CMOS

In this paper a class-D audio amplifier for mobile applications is presented realized in a 0.14 μm CMOS technology tailored for mobile applications. The amplifier has a simple PDM-based digital interface for audio and control that requires only two pins and enables assembly in 9-bump WL-CSP. The complete audio path is discussed that consists of a Parser, Digital PWM controller, 1-bit DA-converters, analog feedback loop and the Class-D power stage. A reconfigurable gate driver is used that reduces quiescent current consumption and radiated emission.


A new paradigm of human gait analysis with Kinect

Analysis of human gait helps to find an intrinsic gait signature through which ubiquitous human identification and medical disorder problems can be investigated in a broad spectrum. The gait biometric provides an unobtrusive feature by which video gait data can be captured at a larger distance without prior awareness of the subject. In this paper, a new technique has been addressed to study the human gait analysis with Kinect Xbox device. It ensures us to minimize the segmentation errors with automated background subtraction technique. The closely similar human skeleton model can be generated from background subtracted gait images, altered by covariate conditions, such as change in walking speed and variations in clothing type. The gait signatures are captured from joint angle trajectories of left hip, left knee, right hip and right knee of subject's skeleton model. The experimental verification on Kinect gait data has been compared with our in-house development of sensor based biometric suit, Intelligent Gait Oscillation Detector (IGOD). An endeavor has been taken to investigate whether this sensor based biometric suit can be altered with a Kinect device for the proliferation of robust gait identification system. The Fisher discriminant analysis has been applied on training gait signature to look into the discriminatory power of feature vector. The Naïve Bayesian classifier demonstrates an encouraging classification result with estimation of errors on limited dataset captured by Kinect sensor.


Employee Engagement From a Self-Determination Theory Perspective

Macey and Schneider (2008) draw on numerous theories to explain what engagement is and how it is similar to and different from related constructs in the organizational behavior literature. As a result, we now have a better understanding of some of the key ‘‘components’’ of engagement. What appears to be missing, however, is a strong unifying theory to guide research and practice. We believe that such a theory exists in the form of self-determination theory (SDT; Deci & Ryan, 1985; Ryan & Deci, 2000) and its various corollaries, self-concordance theory (SCT; Sheldon & Elliot, 1999), hierarchical theory (Vallerand, 1997), and passion theory (Vallerand et al., 2003). Although Macey and Schneider acknowledged the relevance of SDT and SCT, we believe that much greater use of these theories could be made to justify and extend their conceptual model.


Cyber and Physical Security Vulnerability Assessment for IoT-Based Smart Homes

The Internet of Things (IoT) is an emerging paradigm focusing on the connection of devices, objects, or "things" to each other, to the Internet, and to users. IoT technology is anticipated to become an essential requirement in the development of smart homes, as it offers convenience and efficiency to home residents so that they can achieve better quality of life. Application of the IoT model to smart homes, by connecting objects to the Internet, poses new security and privacy challenges in terms of the confidentiality, authenticity, and integrity of the data sensed, collected, and exchanged by the IoT objects. These challenges make smart homes extremely vulnerable to different types of security attacks, resulting in IoT-based smart homes being insecure. Therefore, it is necessary to identify the possible security risks to develop a complete picture of the security status of smart homes. This article applies the operationally critical threat, asset, and vulnerability evaluation (OCTAVE) methodology, known as OCTAVE Allegro, to assess the security risks of smart homes. The OCTAVE Allegro method focuses on information assets and considers different information containers such as databases, physical papers, and humans. The key goals of this study are to highlight the various security vulnerabilities of IoT-based smart homes, to present the risks on home inhabitants, and to propose approaches to mitigating the identified risks. The research findings can be used as a foundation for improving the security requirements of IoT-based smart homes.


Handshaking, gender, personality, and first impressions.

Although people's handshakes are thought to reflect their personality and influence our first impressions of them, these relations have seldom been formally investigated. One hundred twelve participants had their hand shaken twice by 4 trained coders (2 men and 2 women) and completed 4 personality measures. The participants' handshakes were stable and consistent across time and coders. There were also gender differences on most of the handshaking characteristics. A firm handshake was related positively to extraversion and emotional expressiveness and negatively to shyness and neuroticism; it was also positively related to openness to experience, but only for women. Finally, handshake characteristics were related to the impressions of the participants formed by the coders. These results demonstrate that personality traits, assessed through self-report, can predict specific behaviors assessed by trained observers. The pattern of relations among openness, gender, handshaking, and first impressions suggests that a firm handshake may be an effective form of self-promotion for women.


Large-Scale Network Dysfunction in Major Depressive Disorder: A Meta-analysis of Resting-State Functional Connectivity.

IMPORTANCE
Major depressive disorder (MDD) has been linked to imbalanced communication among large-scale brain networks, as reflected by abnormal resting-state functional connectivity (rsFC). However, given variable methods and results across studies, identifying consistent patterns of network dysfunction in MDD has been elusive.

OBJECTIVE
To investigate network dysfunction in MDD through a meta-analysis of rsFC studies.

DATA SOURCES
Seed-based voxelwise rsFC studies comparing individuals with MDD with healthy controls (published before June 30, 2014) were retrieved from electronic databases (PubMed, Web of Science, and EMBASE) and authors contacted for additional data.

STUDY SELECTION
Twenty-seven seed-based voxel-wise rsFC data sets from 25 publications (556 individuals with MDD and 518 healthy controls) were included in the meta-analysis.

DATA EXTRACTION AND SYNTHESIS
Coordinates of seed regions of interest and between-group effects were extracted. Seeds were categorized into seed-networks by their location within a priori functional networks. Multilevel kernel density analysis of between-group effects identified brain systems in which MDD was associated with hyperconnectivity (increased positive or reduced negative connectivity) or hypoconnectivity (increased negative or reduced positive connectivity) with each seed-network.

RESULTS
Major depressive disorder was characterized by hypoconnectivity within the frontoparietal network, a set of regions involved in cognitive control of attention and emotion regulation, and hypoconnectivity between frontoparietal systems and parietal regions of the dorsal attention network involved in attending to the external environment. Major depressive disorder was also associated with hyperconnectivity within the default network, a network believed to support internally oriented and self-referential thought, and hyperconnectivity between frontoparietal control systems and regions of the default network. Finally, the MDD groups exhibited hypoconnectivity between neural systems involved in processing emotion or salience and midline cortical regions that may mediate top-down regulation of such functions.

CONCLUSIONS AND RELEVANCE
Reduced connectivity within frontoparietal control systems and imbalanced connectivity between control systems and networks involved in internal or external attention may reflect depressive biases toward internal thoughts at the cost of engaging with the external world. Meanwhile, altered connectivity between neural systems involved in cognitive control and those that support salience or emotion processing may relate to deficits regulating mood. These findings provide an empirical foundation for a neurocognitive model in which network dysfunction underlies core cognitive and affective abnormalities in depression.