Clustering cancer gene expression data: a comparative study

The use of clustering methods for the discovery of cancer subtypes has drawn a great deal of attention in the scientific community. While bioinformaticians have proposed new clustering methods that take advantage of characteristics of the gene expression data, the medical community has a preference for using "classic" clustering methods. There have been no studies thus far performing a large-scale evaluation of different clustering methods in this context. We present the first large-scale analysis of seven different clustering methods and four proximity measures for the analysis of 35 cancer gene expression data sets. Our results reveal that the finite mixture of Gaussians, followed closely by k-means, exhibited the best performance in terms of recovering the true structure of the data sets. These methods also exhibited, on average, the smallest difference between the actual number of classes in the data sets and the best number of clusters as indicated by our validation criteria. Furthermore, hierarchical methods, which have been widely used by the medical community, exhibited a poorer recovery performance than that of the other methods evaluated. Moreover, as a stable basis for the assessment and comparison of different clustering methods for cancer gene expression data, this study provides a common group of data sets (benchmark data sets) to be shared among researchers and used for comparisons with new methods. The data sets analyzed in this study are available at http://algorithmics.molgen.mpg.de/Supplements/CompCancer/ .


Frequency Response Analysis and Bandwidth Extension of the Doherty Amplifier

A complete frequency response analysis of the Doherty amplifier is presented with the conventional output combining network consisting of two quarter-wavelength (λ/4) transmission lines at a center frequency f0 . Expressions for output power and efficiency were derived over the whole dynamic range and at any frequency f. The analysis shows that the amount of efficiency enhancement, as well as the maximum output power, reduce as the deviation from f0 increases. For instance, the derived expressions show that a conventional Doherty amplifier has a drain efficiency of η ≥ 52.7%, which represents at least 13.4% efficiency enhancement over a class B amplifier, and up to 33.3% fractional bandwidth. A modified output combining network, using λ/4 lines with reduced impedance transformation ratio, is also analyzed, which results in a bandwidth extension of the Doherty amplifier when compared to the conventional design. To verify the derived analyses, three unsymmetrical GaN Doherty power amplifiers (DPAs) were designed and characterized; the first DPA was based on the conventional output combining network, while the second DPA was based on the proposed network. Measurements showed that the first DPA had, at 5-6-dB back-off, a drain efficiency of η ≥ 44% and over 28% fractional bandwidth (1.7-2.25 GHz), while the DPA with the proposed output combining network had a better wideband performance than the third reference conventional DPA, with a back-off drain efficiency of η ≥ 41%, and over 42% fractional bandwidth (1.7-2.6 GHz). To the best of authors' knowledge, the designed DPAs have the highest bandwidths reported thus far.


The Colletotrichum boninense species complex

UNLABELLED
Although only recently described, Colletotrichum boninense is well established in literature as an anthracnose pathogen or endophyte of a diverse range of host plants worldwide. It is especially prominent on members of Amaryllidaceae, Orchidaceae, Proteaceae and Solanaceae. Reports from literature and preliminary studies using ITS sequence data indicated that C. boninense represents a species complex. A multilocus molecular phylogenetic analysis (ITS, ACT, TUB2, CHS-1, GAPDH, HIS3, CAL) of 86 strains previously identified as C. boninense and other related strains revealed 18 clades. These clades are recognised here as separate species, including C. boninense s. str., C. hippeastri, C. karstii and 12 previously undescribed species, C. annellatum, C. beeveri, C. brassicicola, C. brasiliense, C. colombiense, C. constrictum, C. cymbidiicola, C. dacrycarpi, C. novae-zelandiae, C. oncidii, C. parsonsiae and C. torulosum. Seven of the new species are only known from New Zealand, perhaps reflecting a sampling bias. The new combination C. phyllanthi was made, and C. dracaenae Petch was epitypified and the name replaced with C. petchii. Typical for species of the C. boninense species complex are the conidiogenous cells with rather prominent periclinal thickening that also sometimes extend to form a new conidiogenous locus or annellations as well as conidia that have a prominent basal scar. Many species in the C. boninense complex form teleomorphs in culture.

TAXONOMIC NOVELTIES
New combination - Colletotrichum phyllanthi (H. Surendranath Pai) Damm, P.F. Cannon & Crous. Name replacement - C. petchii Damm, P.F. Cannon & Crous. New species - C. annellatum Damm, P.F. Cannon & Crous, C. beeveri Damm, P.F. Cannon, Crous, P.R. Johnst. & B. Weir, C. brassicicola Damm, P.F. Cannon & Crous, C. brasiliense Damm, P.F. Cannon, Crous & Massola, C. colombiense Damm, P.F. Cannon, Crous, C. constrictum Damm, P.F. Cannon, Crous, P.R. Johnst. & B. Weir, C. cymbidiicola Damm, P.F. Cannon, Crous, P.R. Johnst. & B. Weir, C. dacrycarpi Damm, P.F. Cannon, Crous, P.R. Johnst. & B. Weir, C. novae-zelandiae Damm, P.F. Cannon, Crous, P.R. Johnst. & B. Weir, C. oncidii Damm, P.F. Cannon & Crous, C. parsonsiae Damm, P.F. Cannon, Crous, P.R. Johnst. & B. Weir, C. torulosum Damm, P.F. Cannon, Crous, P.R. Johnst. & B. Weir. Typifications: Epitypifications - C. dracaenae Petch.


A centralized detection and prevention technique against ARP poisoning

The Man-in-the-Middle (MITM) attack on ARP is presently a common attack and nuisance to the typical LAN environment. This type of MITM is brought to effect by ARP cache poisoning which is achieved using forged ARP packets. ARP poisoning is a mechanism in which a node poisons ARP cache table of another node forcing it to send packets to a destination other than the intended one. This paper presents a feasible solution to the ARP cache poisoning, removing inconsistencies from all ARP tables of all hosts in the network. This paper uses a centralized system and ARP Central Server (ACS) to manage ARP table entries in all hosts. All hosts in the network uses the ACS to validate their ARP table entries. The ACS validates and corrects the poisoned ARP entries of the attacked hosts and hence prevents ARP poisoning in the network.


Microwave Doherty Power Amplifier for High Efficiency and Linearity

This paper explains the basic circuit configuration of microwave Doherty amplifier and additional design methods for wide bandwidth and high power applications. This paper also presents the recent experimental results of the group using the uneven power drive. Most of the previous works about microwave Doherty amplifier have employed identical input drives. In this works, two 1:1(2-way) and a 1:2(3-way) Doherty amplifiers are implemented at 2.14GHz center frequency using LDMOS with a peak envelope power of 180 watt and 285 watt, respectively. The 2-way Doherty amplifiers are optimized at 25 watt average output power for high efficiency and linearity and the 3-way Doherty amplifier at 16 watt average output power for high linearity. The amplifiers are tested using WCDMA 4FA signal. The performances of the 2-way Doherty amplifier are compared with that of the comparable class AB amplifier and the Doherty amplifier with even power drive. The performances of the 3-way Doherty amplifier are compared with that of the comparable class AB amplifier. The experimental results show the superior performances of our Doherty amplifiers


Three-Dimensional Face Recognition

An expression-invariant 3D face recognition approach is presented. Our basic assumption is that facial expressions can be modelled as isometries of the facial surface. This allows to construct expression-invariant representations of faces using the bending-invariant canonical forms approach. The result is an efficient and accurate face recognition algorithm, robust to facial expressions, that can distinguish between identical twins (the first two authors). We demonstrate a prototype system based on the proposed algorithm and compare its performance to classical face recognition methods. The numerical methods employed by our approach do not require the facial surface explicitly. The surface gradients field, or the surface metric, are sufficient for constructing the expression-invariant representation of any given face. It allows us to perform the 3D face recognition task while avoiding the surface reconstruction stage.


Pareto improvement and joint cash management optimisation for banks and cash-in-transit firms

Improving the ATM cash management techniques of banks has already received significant attention in the literature as a separate optimisation problem for banks and the independent firms that supply cash to automated teller machines. This article concentrates instead on a further possibility of cost reduction: optimising the cash management problem as one single problem. Doing so, contractual prices between banks and the cash in transit firms can be in general modified allowing for further cost reduction relative to individual optimisations. In order to show the pertinence of this procedure, we have determined possible Pareto-improvement re-contracting schemes based on a Baumol-type cash demand forecast for a Hungarian commercial bank resulting in substantial cost reduction. © 2016 Elsevier B.V. All rights reserved.


Why Teaching Ethics to AI Practitioners Is Important

We argue that it is crucial to the future of AI that our students be trained in multiple complementary modes of ethical reasoning, so that they may make ethical design and implementation choices, ethical career decisions, and that their software will be programmed to take into account the complexities of acting ethically in the world.


Maximizing rumor containment in social networks with constrained time

The spread of rumor or misinformation in social networks may cause bad effects among the public. Thus, it is necessary to find effective strategies to control the spread of rumor. Specifically, in our paper, we consider such a setting: initially, a subset of nodes is chosen as the set of protectors, and the influence of protector diffuses competitively with the diffusion of rumor. However, in real world, we generally have limited budget (limited number of protectors) and time to fight with rumor. Therefore, we study the problem of maximizing rumor containment within a fixed number of initial protectors and a given time deadline. Generalizing two seminal models in the field—the Independent Cascade (IC) model and the Linear Threshold (LT) model—we propose two new models of competitive influence diffusion in social networks with the following three factors: a time deadline for information diffusion, random time delay of information exchange and personal interests regarding the acceptance of information. Under these two models, we show that the optimization problems are NP-hard. Furthermore, we prove that the objective functions are submodular. As a result, the greedy algorithm is used as constant-factor approximation algorithms with performance guarantee $$1-\frac{1}{e}$$ 1 - 1 e for the two optimization problems.


An Efficient Method for Traffic Sign Recognition Based on Extreme Learning Machine

This paper proposes a computationally efficient method for traffic sign recognition (TSR). This proposed method consists of two modules: 1) extraction of histogram of oriented gradient variant (HOGv) feature and 2) a single classifier trained by extreme learning machine (ELM) algorithm. The presented HOGv feature keeps a good balance between redundancy and local details such that it can represent distinctive shapes better. The classifier is a single-hidden-layer feedforward network. Based on ELM algorithm, the connection between input and hidden layers realizes the random feature mapping while only the weights between hidden and output layers are trained. As a result, layer-by-layer tuning is not required. Meanwhile, the norm of output weights is included in the cost function. Therefore, the ELM-based classifier can achieve an optimal and generalized solution for multiclass TSR. Furthermore, it can balance the recognition accuracy and computational cost. Three datasets, including the German TSR benchmark dataset, the Belgium traffic sign classification dataset and the revised mapping and assessing the state of traffic infrastructure (revised MASTIF) dataset, are used to evaluate this proposed method. Experimental results have shown that this proposed method obtains not only high recognition accuracy but also extremely high computational efficiency in both training and recognition processes in these three datasets.


Goal-oriented requirements engineering: an extended systematic mapping study

Over the last two decades, much attention has been paid to the area of goal-oriented requirements engineering (GORE), where goals are used as a useful conceptualization to elicit, model, and analyze requirements, capturing alternatives and conflicts. Goal modeling has been adapted and applied to many sub-topics within requirements engineering (RE) and beyond, such as agent orientation, aspect orientation, business intelligence, model-driven development, and security. Despite extensive efforts in this field, the RE community lacks a recent, general systematic literature review of the area. In this work, we present a systematic mapping study, covering the 246 top-cited GORE-related conference and journal papers, according to Scopus. Our literature map addresses several research questions: we classify the types of papers (e.g., proposals, formalizations, meta-studies), look at the presence of evaluation, the topics covered (e.g., security, agents, scenarios), frameworks used, venues, citations, author networks, and overall publication numbers. For most questions, we evaluate trends over time. Our findings show a proliferation of papers with new ideas and few citations, with a small number of authors and papers dominating citations; however, there is a slight rise in papers which build upon past work (implementations, integrations, and extensions). We see a rise in papers concerning adaptation/variability/evolution and a slight rise in case studies. Overall, interest in GORE has increased. We use our analysis results to make recommendations concerning future GORE research and make our data publicly available.


Energy-credit scheduler: An energy-aware virtual machine scheduler for cloud systems

Virtualization facilitates the provision of flexible resources and improves energy efficiency through the consolidation of virtualized servers into a smaller number of physical servers. As an increasingly essential component of the emerging cloud computing model, virtualized environments bill their users based on processor time or the number of virtual machine instances. However, accounting based only on the depreciation of server hardware is not sufficient because the cooling and energy costs for data centers will exceed the purchase costs for hardware. This paper suggests a model for estimating the energy consumption of each virtual machine without dedicated measurement hardware. Our model estimates the energy consumption of a virtual machine based on in-processor events generated by the virtual machine. Based on this estimation model, we also propose a virtual machine scheduling algorithm that can provide computing resources according to the energy budget of each virtual machine. The suggested schemes are implemented in the Xen virtualization system, and an evaluation shows that the suggested schemes estimate and provide energy consumption with errors of less than 5% of the total energy


Open Sesame!: a Look at Personal Assistants

Software agents, or softbots, are supposedly intelligent programs that assist the user in performing repetitive , boring, and time-consuming tasks. An overview of software agents, with special attention focused on the so-called personal assistants, is presented as an introduction. Following that, an in-depth look at one of the rst commercially available intelligent agents Open Sesame!, a personal assistant for managing the Macintosh desktop environment, is presented. The existing intelligent agent paradigms, we argue, break down when it comes to actively helping the end user because of aws inherent in the current approaches to intelligent agent design. To that end, we discuss the reasons why this is, particularly with respect to Open Sesame!, and promote the notion of situatedness to be taken into account more seriously for developing software agents that actively support the user for the duration of the task at hand. As an example for a more situated approach to personal assistant design, a quick overview of a project to develop a personal assistant for news ltering is discussed.


On the importance of initialization and momentum in deep learning

Deep and recurrent neural networks (DNNs and RNNs respectively) are powerful models that were considered to be almost impossible to train using stochastic gradient descent with momentum. In this paper, we show that when stochastic gradient descent with momentum uses a well-designed random initialization and a particular type of slowly increasing schedule for the momentum parameter, it can train both DNNs and RNNs (on datasets with long-term dependencies) to levels of performance that were previously achievable only with Hessian-Free optimization. We find that both the initialization and the momentum are crucial since poorly initialized networks cannot be trained with momentum and well-initialized networks perform markedly worse when the momentum is absent or poorly tuned. Our success training these models suggests that previous attempts to train deep and recurrent neural networks from random initializations have likely failed due to poor initialization schemes. Furthermore, carefully tuned momentum methods su ce for dealing with the curvature issues in deep and recurrent network training objectives without the need for sophisticated second-order methods.


A Fully Convolutional Neural Network for Speech Enhancement

In hearing aids, the presence of babble noise degrades hearing intelligibility of human speech greatly. However, removing the babble without creating artifacts in human speech is a challenging task in a low SNR environment. Here, we sought to solve the problem by finding a ‘mapping’ between noisy speech spectra and clean speech spectra via supervised learning. Specifically, we propose using fully Convolutional Neural Networks, which consist of lesser number of parameters than fully connected networks. The proposed network, Redundant Convolutional Encoder Decoder (R-CED), demonstrates that a convolutional network can be 12 times smaller than a recurrent network and yet achieves better performance, which shows its applicability for an embedded system: the hearing aids.


Suppression of acoustic noise in speech using spectral subtraction

A new technique for the modelling of perceptual systems called formal modelling is developed. This technique begins with qualitative observations about the perceptual system, the so-called perceptual symmetries, to obtain through mathematical analysis certain model structures which may then be calibrated by experiment. The analysis proceeds in two different ways depending upon the choice of linear or nonlinear models. For the linear case, the analysis proceeds through the methods of unitary representation theory. It begins with a unitary group representation on the image space and produces what we have called the fundamental structure theorem. For the nonlinear case, the analysis makes essential use of infinite-dimensional manifold theory. It ber ins with a Lie group action on an image manifold and produces the fundamental structure formula. These techniques will be used to study the brightness perception mechanism of the human visual system. Several visual groups are defined and their corresponding structures for visual system models are obtained. A new transform called the Mandala transform will be deduced from a certain visual group and its implications for image processing will be discussed. Several new phenomena of brightness perception will be presented. New facts about the Mach band illusion along with new adaptation phenomena will be presented. Also a new visual illusion will be presented. A visual model based on the above techniques will be presented. It will also be shown how use of statistical estimation theory can be made in the study of contrast adaptation.


Dictionary Learning Algorithms for Sparse Representation

Algorithms for data-driven learning of domain-specific overcomplete dictionaries are developed to obtain maximum likelihood and maximum a posteriori dictionary estimates based on the use of Bayesian models with concave/Schur-concave (CSC) negative log priors. Such priors are appropriate for obtaining sparse representations of environmental signals within an appropriately chosen (environmentally matched) dictionary. The elements of the dictionary can be interpreted as concepts, features, or words capable of succinct expression of events encountered in the environment (the source of the measured signals). This is a generalization of vector quantization in that one is interested in a description involving a few dictionary entries (the proverbial 25 words or less), but not necessarily as succinct as one entry. To learn an environmentally adapted dictionary capable of concise expression of signals generated by the environment, we develop algorithms that iterate between a representative set of sparse representations found by variants of FOCUSS and an update of the dictionary using these sparse representations. Experiments were performed using synthetic data and natural images. For complete dictionaries, we demonstrate that our algorithms have improved performance over other independent component analysis (ICA) methods, measured in terms of signal-to-noise ratios of separated sources. In the overcomplete case, we show that the true underlying dictionary and sparse sources can be accurately recovered. In tests with natural images, learned overcomplete dictionaries are shown to have higher coding efficiency than complete dictionaries; that is, images encoded with an overcomplete dictionary have both higher compression (fewer bits per pixel) and higher accuracy (lower mean square error).


Tracing traitors

We give cryptographic schemes that help trace the source of leaks when sensitive or proprietary data is made available to a large set of parties. A very relevant application is in the context of pay television, where only paying customers should be able to view certain programs. In this application, the programs are normally encrypted, and then the sensitive data is the decryption keys that are given to paying customers. If a pirate decoder is found, it is desirable to reveal the source of its decryption keys. We describe fully resilient schemes which can be used against any decoder which decrypts with nonnegligible probability. Since there is typically little demand for decoders which decrypt only a small fraction of the transmissions (even if it is nonnegligible), we further introduce threshold tracing schemes which can only be used against decoders which succeed in decryption with probability greater than some threshold. Threshold schemes are considerably more efficient than fully resilient schemes.


Efficacy and safety of a hyaluronic acid filler in subjects treated for correction of midface volume deficiency: a 24 month study

BACKGROUND
Hyaluronic acid (HA) fillers are an established intervention for correcting facial volume deficiency. Few studies have evaluated treatment outcomes for longer than 6 months. The purpose of this study was to determine the durability of an HA filler in the correction of midface volume deficiency over 24 months, as independently evaluated by physician investigators and subjects.

METHODS
Subjects received treatment with Juvéderm(™) Voluma(™) to the malar area, based on the investigators' determination of baseline severity and aesthetic goals. The treatment was administered in one or two sessions over an initial 4-week period. Supplementary treatment was permissible at week 78, based on protocol-defined criteria. A clinically meaningful response was predefined as at least a one-point improvement on the MidFace Volume Deficit Scale (MFVDS) and on the Global Aesthetic Improvement Scale (GAIS).

RESULTS
Of the 103 subjects enrolled, 84% had moderate or significant volume deficiency at baseline. At the first post-treatment evaluation (week 8), 96% were documented to be MFVDS responders, with 98% and 100% graded as GAIS responders when assessed by the subjects and investigators, respectively. At week 78, 81.7% of subjects were still MFVDS responders, with 73.2% and 78.1% being GAIS responders, respectively. Seventy-two subjects completed the 24-month study, of whom 45 did not receive supplementary Voluma(™) at week 78. Forty-three of the 45 (95.6%) subjects were MFVDS responders, with 82.2% and 91.1% being GAIS responders, respectively. At end of the study, 66/72 subjects were either satisfied or very satisfied with Voluma(™), with 70/72 indicating that they would recommend the product to others. Adverse events were transient and infrequent, with injection site bruising and swelling being the most commonly reported.

CONCLUSION
Voluma(™) is safe and effective in the correction of mild to severe facial volume deficiency, achieving long-term clinically meaningful results. There was a high degree of satisfaction with the treatment outcome over the 24 months of the study.


Validity in Quantitative Content Analysis

Over the past 15 years, educational technologists have been dabbling with a research technique known as quantitative content analysis (QCA). Although it is characterized as a systematic and objective procedure for describing communication, readers find insufficient evidence of either quality in published reports. In this paper, it is argued that QCA should be conceived of as a form of testing and measurement. If this argument is successful, it becomes possible to frame many of the problems associated with QCA studies under the well-articulated rubric of test validity. Two sets of procedures for developing the validity of a QCA coding protocol are provided, (a) one for developing a protocol that is theoretically valid and (b) one for establishing its validity empirically. The paper is concerned specifically with the use of QCA to study educational applications of computer-mediated communication. The primary role of networked computers in higher education has shifted from presenting structured, preprogrammed learning materials to facilitating communication. In turn, the role of educational technology researchers has expanded to include the role of communication researcher. In the late 1980s, studies began to appear that incorporated new perspectives, new methods, and new techniques. One of the most promising was quantitative content analysis (QCA). Berelson (1952) defined QCA as “a research technique for the systematic, objective, and quantitative description of the manifest content of communication” (p. 18). In this context, description is a process that includes segmenting communication content into units, assigning each unit to a category, and providing tallies for each category. Bullen (1998), for instance, studied participation and critical thinking in an online discussion by counting the number of times each student contributed to the discussion and by assigning each contribution to one of three categories of critical thinking. By 1999, enough of these types of studies had been conducted in the field of educational technology that we were able to prepare a literature review (Rourke, Anderson, Garrison, & Archer, 2001). We used published reports to illustrate many of the basic issues of QCA, including objectivity, reliability, units of analysis, types of content, research designs, and ethics. Our intent was to summarize the experiences of researchers like us who were struggling to apply this unfamiliar technique in a disciplined and efficient manner. We found the technique promising but chided researchers on the rigor of their reports, particularly on the lack of reliability data. Those who persisted with the technique found their own way to these conclusions, and today


FlexFlow: A Flexible Dataflow Accelerator Architecture for Convolutional Neural Networks

Convolutional Neural Networks (CNN) are verycomputation-intensive. Recently, a lot of CNN accelerators based on the CNN intrinsic parallelism are proposed. However, we observed that there is a big mismatch between the parallel types supported by computing engine and the dominant parallel types of CNN workloads. This mismatch seriously degrades resource utilization of existing accelerators. In this paper, we propose aflexible dataflow architecture (FlexFlow) that can leverage the complementary effects among feature map, neuron, and synapse parallelism to mitigate the mismatch. We evaluated our design with six typical practical workloads, it acquires 2-10x performance speedup and 2.5-10x power efficiency improvement compared with three state-of-the-art accelerator architectures. Meanwhile, FlexFlow is highly scalable with growing computing engine scale.


Robust Mapping and Localization in Indoor Environments Using Sonar Data

In this paper we describe a new technique for the creation of featurebased stochastic maps using standard Polaroid sonar sensors. The fundamental contributions of our proposal are: (1) a perceptual grouping process that permits the robust identification and localization of environmental features, such as straight segments and corners, from the sparse and noisy sonar data; (2) a map joining technique that allows the system to build a sequence of independent limited-size stochastic maps and join them in a globally consistent way; (3) a robust mechanism to determine which features in a stochastic map correspond to the same environment feature, allowing the system to update the stochastic map accordingly, and perform tasks such as revisiting and loop closing. We demonstrate the practicality of this approach by building a geometric map of a medium size, real indoor environment, with several people moving around the robot. Maps built from laser data for the same experiment are provided for comparison. KEY WORDS—map building, local maps, data association, sonar sensors, Hough transform


A design process for the development of innovative smart clothing that addresses end-user needs from technical, functional, aesthetic and cultural view points

The research and development of attractive smart clothing, with truly embedded technologies, demands the merging of science and technology with art and design. This paper looks at a comparatively new and unique design discipline that has been given little prior consideration. The concept of 'wearables' crosses the boundaries between many disciplines. A gap exists for a common 'language' that facilitates creativity and a systematic design process. Designers of smart clothing require guidance in their enquiry, as gaining an understanding of issues such as; usability, manufacture, fashion, consumer culture, recycling and end user needs can seem forbidding and difficult to prioritise. This paper presents a design tool in a format that can be understood by practitioners who come from a range of backgrounds. The representation of the 'critical path' is intended as a tool to guide the design research and development process in the application of smart technologies.


The Runtime System Problem Identification Method Based on Log Analysis

Currently system logs are an important source of information for system administrators to monitor system behaviors and to identify system problems. The manual examining is infeasible for the complex system and the existing automated methods for identifying system problems have different disadvantages such as the extreme dependency on the source code of the system, the low accuracy of predicting or identifying the system problems, or the requirement of the balanced and labeled training data set. This paper proposes a one- class Support Vector Machine (OCSVM) based method to identify the runtime system problems. Firstly, log sequences are generated for describing the running trajectories of the monitored system by parsing log messages; Secondly, variable length n-gram features are extracted, and moreover, the log sequences are represented as feature vectors based on these variable length n-gram features and Vector Space Model (VSM). Finally, all the feature vectors of the training log sequence set, which only includes the labeled normal log sequences, are input into OCSVM. Experimental results show that it performs better to use linear kernel to train OCSVM on our feature vectors than Gaussian kernel and the size of the sliding window hardly affects the performance of our method. Moreover, the proposed method achieves better performance on unbalanced training dataset than the method based on Random Indexing (RI) and weighted SVM.


Medical Sentiment Analysis using Social Media: Towards building a Patient Assisted System

With the enormous growth of Internet, more users have engaged in health communities such as medical forums to gather health-related information, to share experiences about drugs, treatments, diagnosis or to interact with other users with similar condition in communities. Monitoring social media platforms has recently fascinated medical natural language processing researchers to detect various medical abnormalities such as adverse drug reaction. In this paper, we present a benchmark setup for analyzing the sentiment with respect to users’ medical condition considering the information, available in social media in particular. To this end, we have crawled the medical forum website ‘patient.info’ with opinions about medical condition self narrated by the users. We constrained ourselves to some of the popular domains such as depression, anxiety, asthma, and allergy. The focus is given on the identification of multiple forms of medical sentiments which can be inferred from users’ medical condition, treatment, and medication. Thereafter, a deep Convolutional Neural Network (CNN) based medical sentiment analysis system is developed for the purpose of evaluation. The resources are made available to the community through LRE map for further research.


Kilobot: A low cost robot with scalable operations designed for collective behaviors

In current robotics research there is a vast body of work on algorithms and control methods for groups of decentralized cooperating robots, called a swarm or collective. These algorithms are generally meant to control collectives of hundreds or even thousands of robots; however, for reasons of cost, time, or complexity, they are generally validated in simulation only, or on a group of a few tens of robots. To address this issue, this paper presents Kilobot, an open-source, low cost robot designed to make testing collective algorithms on hundreds or thousands of robots accessible to robotics researchers. To enable the possibility of large Kilobot collectives where the number of robots is an order of magnitude larger than the largest that exist today, each robot is made with only $14 worth of parts and takes 5 min to assemble. Furthermore, the robot design allows a single user to easily operate a large Kilobot collective, such as programming, powering on, and charging all robots, which would be difficult or impossible to do with many existing robotic systems. We demonstrate the capabilities of the Kilobot as a collective robot, by using a small robot test collective to implement four popular swarm behaviors: foraging, formation control, phototaxis, and synchronization. © 2013 Elsevier B.V. All rights reserved.


Design and Analysis of Energy-Efficient Reconfigurable Pre-Emphasis Voltage-Mode Transmitters

This paper analyzes the signaling and digital power overhead of pre-emphasis voltage-mode transmitters. Utilizing a shunt branch in parallel with the differential channel to implement pre-emphasis is shown to have the best overall energy-efficiency. Leveraging this technique, an efficient pre-emphasis voltage mode transmitter architecture with output amplitude control, pre-emphasis coefficient control, and online impedance calibration is proposed and demonstrated. A 65 nm LP CMOS implementation of this architecture dissipates ~ 10 mW from a 1.2 V supply when transmitting 10 Gb/s 200 mV differential amplitude data with 2-tap pre-emphasis, achieving 1 pJ/bit energy efficiency.


Affective style, psychopathology, and resilience: brain mechanisms and plasticity.

The brain circuitry underlying emotion includes several territories of the prefrontal cortex (PFC), the amygdala, hippocampus, anterior cingulate, and related structures. In general, the PFC represents emotion in the absence of immediately present incentives and thus plays a crucial role in the anticipation of the future affective consequences of action, as well as in the persistence of emotion following the offset of an elicitor. The functions of the other structures in this circuit are also considered. Individual differences in this circuitry are reviewed, with an emphasis on asymmetries within the PFC and activation of the amygdala as 2 key components of affective style. These individual differences are related to both behavioral and biological variables associated with affective style and emotion regulation. Plasticity in this circuitry and its implications for transforming emotion and cultivating positive affect and resilience are considered.


MobileCDP: A mobile framework for the consumer decision process

............................................................................................................. iii ÖZ ............................................................................................................................ iv ACKNOWLEDGEMENTS ........................................................................................ vi TABLE OF CONTENTS .......................................................................................... vii LIST OF TABLES ................................................................................................... xii LIST OF FIGURES ................................................................................................ xiii ABBREVIATIONS ................................................................................................... xv


Recognizing Textual Entailment Using a Subsequence Kernel Method

We present a novel approach to recognizing Textual Entailment. Structural features are constructed from abstract tree descriptions, which are automatically extracted from syntactic dependency trees. These features are then applied in a subsequence-kernel-based classifier to learn whether an entailment relation holds between two texts. Our method makes use of machine learning techniques using a limited data set, no external knowledge bases (e.g. WordNet), and no handcrafted inference rules. We achieve an accuracy of 74.5% for text pairs in the Information Extraction and Question Answering task, 63.6% for the RTE-2 test data, and 66.9% for the RET-3 test data.


Understanding what concerns consumers: a semantic approach to product feature extraction from consumer reviews

The Web has become an excellent source for gathering consumer opinions (more specifically, consumer reviews) about products. Consumer reviews are essential for retailers and product manufacturers to understand the general responses of customers to their products and improve their marketing campaigns or products accordingly. In addition, consumer reviews enable retailers to recognize the specific preferences of each customer, which facilitates effective marketing decisions. As the number of consumer reviews expands, it is essential and desirable to develop an efficient and effective sentiment analysis technique that is capable of extracting product features stated in consumer reviews (i.e., product feature extraction) and determining the sentiments (positive or negative semantic orientations) of consumers for these product features (i.e., opinion orientation identification). Product feature extraction is critical to sentiment analysis, because its effectiveness significantly affects the performance of opinion orientation identification, as well as the ultimate effectiveness of sentiment analysis. Therefore, this study concentrates on product feature extraction from consumer reviews. C.-P. Wei (&) Institute of Service Science, College of Technology Management, National Tsing Hua University, Hsinchu, Taiwan, ROC e-mail: cpwei@mx.nthu.edu.tw Y.-M. Chen Department of Information Management, College of Management, National Sun Yat-Sen University, Kaohsiung, Taiwan, ROC C.-S. Yang Department of Information Management, College of Informatics, Yuan-Ze University, Chung-Li, Taiwan, ROC e-mail: csyang@saturn.yzu.edu.tw C. C. Yang College of Information Science and Technology, Drexel University, Philadelphia, PA, USA e-mail: chris.yang@ischool.drexel.edu 123 Inf Syst E-Bus Manage (2010) 8:149–167 DOI 10.1007/s10257-009-0113-9


A random graph model of kidney exchanges: efficiency, individual-rationality and incentives

In kidney exchanges, hospitals share patient lists and receive transplantations. A kidney-paired donation (KPD) mechanism needs to promote full sharing of information about donor-patient pairs, and identify a Pareto efficient outcome that also satisfies participation constraints of hospitals. We introduce a random graph model of the KPD exchange and then fully characterize the structure of the efficient outcome and the expected number of transplantations that can be performed. Random graph theory allows early experimental results to be explained analytically, and enables the study of participation incentives in a methodological way. We derive a square-root law between the welfare gains from sharing patient-donor pairs in a central pool and the individual sizes of hospitals, illustrating the urgent need for the nationwide expansion of such programs. Finally, we establish through theoretical and computational analysis that enforcing simple individual rationality constraints on the outcome can mitigate the negative impact of strategic behavior by hospitals.


A continuous-time sigma-delta modulator with 88-dB dynamic range and 1.1-MHz signal bandwidth

This paper presents the design and experimental results of a continuous-time /spl Sigma//spl Delta/ modulator for ADSL applications. Multibit nonreturn-to-zero (NRZ) DAC pulse shaping is used to reduce clock jitter sensitivity. The nonzero excess loop delay problem in conventional continuous-time /spl Sigma//spl Delta/ modulators is solved by our proposed architecture. A prototype third-order continuous-time /spl Sigma//spl Delta/ modulator with 5-bit internal quantization was realized in a 0.5-/spl mu/m double-poly triple-metal CMOS technology, with a chip area of 2.4 /spl times/ 2.4 mm/sup 2/. Experimental results show that the modulator achieves 88-dB dynamic range, 84-dB SNR, and 83-dB SNDR over a 1.1-MHz signal bandwidth with an oversampling ratio of 16, while dissipating 62 mW from a 3.3-V supply.


Code coverage for suite evaluation by developers

One of the key challenges of developers testing code is determining a test suite's quality -- its ability to find faults. The most common approach is to use code coverage as a measure for test suite quality, and diminishing returns in coverage or high absolute coverage as a stopping rule. In testing research, suite quality is often evaluated by a suite's ability to kill mutants (artificially seeded potential faults). Determining which criteria best predict mutation kills is critical to practical estimation of test suite quality. Previous work has only used small sets of programs, and usually compares multiple suites for a single program. Practitioners, however, seldom compare suites --- they evaluate one suite. Using suites (both manual and automatically generated) from a large set of real-world open-source projects shows that evaluation results differ from those for suite-comparison: statement (not block, branch, or path) coverage predicts mutation kills best.


MiMaze, a 3D Multi-Player Game on the Internet

This paper describes the design, implementation, and evaluation of MiMaze, a distributed 3D multiplayer game on the Internet. The major contribution of this work is to provide the first experimental results for this new type of application. An original fully distributed communication architecture has been designed for MiMaze. It uses multicast communication system based on RTP/UDP/IP, and a distributed synchronization mechanisms to guarantee the consistency of the game, regardless of heterogeneous network delay. This paper provides evaluation results on the Mbone. The experimental evaluation shows that the major problem will not be with real-time interaction, but with scalability.


Scalable Distributed Monte-Carlo Tree Search

Monte-Carlo Tree Search (MCTS) is remarkably successful in two-player games, but parallelizing MCTS has been notoriously difficult to scale well, especially in distributed environments. For a distributed parallel search, transposition-table driven scheduling (TDS) is known to be efficient in several domains. We present a massively parallel MCTS algorithm, that applies the TDS parallelism to the Upper Confidence bound Applied to Trees (UCT) algorithm, which is the most representative MCTS algorithm. To drastically decrease communication overhead, we introduce a reformulation of UCT called Depth-First UCT. The parallel performance of the algorithm is evaluated on clusters using up to 1,200 cores in artificial game-trees. We show that this approach scales well, achieving 740-fold speedups in the best case.


Diverse and Controllable Image Captioning with Part-of-Speech Guidance

Automatically describing an image is an important capability for virtual assistants. Significant progress has been achieved in recent years on this task of image captioning. However, classical prediction techniques based on maximum likelihood trained LSTM nets don’t embrace the inherent ambiguity of image captioning. To address this concern, recent variational auto-encoder and generative adversarial network based methods produce a set of captions by sampling from an abstract latent space. But, this latent space has limited interpretability and therefore, a control mechanism for captioning remains an open problem. This paper proposes a captioning technique conditioned on part-of-speech. Our method provides human interpretable control in form of part-of-speech. Importantly, part-of-speech is a language prior, and conditioning on it provides: (i) more diversity as evaluated by counting n-grams and the novel sentences generated, (ii) achieves high accuracy for the diverse captions on standard captioning metrics.


Credit Card Fraud Detection: A Novel Approach Using Aggregation Strategy and Feedback Mechanism

With the rapid development of electronic commerce, the number of transactions by credit cards are increasing rapidly. As online shopping becomes the most popular transaction mode, cases of transaction fraud are also increasing. In this paper, we propose a novel fraud detection method that composes of four stages. To enrich a cardholder’s behavioral patterns, we first utilize the cardholders’ historical transaction data to divide all cardholders into different groups such that the transaction behaviors of the members in the same group are similar. We thus propose a window-sliding strategy to aggregate the transactions in each group. Next, we extract a collection of specific behavioral patterns for each cardholder based on the aggregated transactions and the cardholder’s historical transactions. Then we train a set of classifiers for each group on the base of all behavioral patterns. Finally, we use the classifier set to detect fraud online and if a new transaction is fraudulent, a feedback mechanism is taken in the detection process in order to solve the problem of concept drift. The results of our experiments show that our approach is better than others.


Dynamic Memory Networks for Visual and Textual Question Answering

Neural network architectures with memory and attention mechanisms exhibit certain reasoning capabilities required for question answering. One such architecture, the dynamic memory network (DMN), obtained high accuracy on a variety of language tasks. However, it was not shown whether the architecture achieves strong results for question answering when supporting facts are not marked during training or whether it could be applied to other modalities such as images. Based on an analysis of the DMN, we propose several improvements to its memory and input modules. Together with these changes we introduce a novel input module for images in order to be able to answer visual questions. Our new DMN+ model improves the state of the art on both the Visual Question Answering dataset and the bAbI-10k text question-answering dataset without supporting fact supervision.


3D human face description: landmarks measures and geometrical features

Morphometric measures and geometrical features are widely used to descript faces. Generally, they are extracted punctually from landmarks, namely anthropometric reference points. The aims are various, such as face recognition, facial expression recognition, face detection, study of changes in facial morphology due to growth, or dysmorphologies. Most of the time, landmarks were extracted with the help of an algorithm or manually located on the faces. Then, measures are computed or geometrical features are extracted to perform the scope of the study. This paper is intended as a survey collecting and explaining all these features, in order to provide a structured user database of the potential parameters and their charateristics. Firstly, facial soft-tissue landmarks are defined and contextualized; then the various morphometric measures are introduced and some results are given; lastly, the most important measures are compared to identify the best one for face recognition applications.


Supporting public health priorities: recommendations for physical education and physical activity promotion in schools.

Physical activity (PA) provides numerous physiological and psychosocial benefits. However, lifestyle changes, including reduced PA opportunities in multiple settings, have resulted in an escalation of overweight and obesity and related health problems. Poor physical and mental health, including metabolic and cardiovascular problems is seen in progressively younger ages, and the systematic decline in school PA has contributed to this trend. Of note, the crowded school curriculum with an intense focus on academic achievement, lack of school leadership support, funding and resources, plus poor quality teaching are barriers to PA promotion in schools. The school setting and physical educators in particular, must embrace their role in public health by adopting a comprehensive school PA program. We provide an overview of key issues and challenges in the area plus best bets and recommendations for physical education and PA promotion in the school system moving forward.


Integrating movement in academic classrooms: understanding, applying and advancing the knowledge base.

In the context of comprehensive and coordinated approaches to school health, academic classrooms have gained attention as a promising setting for increasing physical activity and reducing sedentary time among children. The aims of this paper are to review the rationale and knowledge base related to movement integration in academic classrooms, consider the practical applications of current knowledge to interventions and teacher education, and suggest directions for future research. Specifically, this paper (i) situates movement integration amid policy and research related to children's health and the school as a health-promoting environment; (ii) highlights the benefits of movement integration; (iii) summarizes movement integration programs and interventions; (iv) examines factors associated with classroom teachers' movement integration; (v) offers strategies for translating research to practice and (vi) forwards recommendations for future inquiry related to the effectiveness and sustainability of efforts to integrate movement into classroom routines. This paper provides a comprehensive resource for developing state-of-the-art initiatives to maximize children's movement in academic classrooms as a key strategy for important goals in both education and public health.


The “ What ” and “ Why ” of Goal Pursuits : Human Needs and the Self-Determination of Behavior

Self-determination theory (SDT) maintains that an understanding of human motivation requires a consideration of innate psychological needs for competence, autonomy, and relatedness. We discuss the SDT concept of needs as it relates to previous need theories, emphasizing that needs specify the necessary conditions for psychological growth, integrity, and well-being. This concept of needs leads to the hypotheses that different regulatory processes underlying goal pursuits are differentially associated with effective functioning and well-being and also that different goal contents have different relations to the quality of behavior and mental health, specifically because different regulatory processes and different goal contents are associated with differing degrees of need satisfaction. Social contexts and individual differences that support satisfaction of the basic needs facilitate natural growth processes including intrinsically motivated behavior and integration of extrinsic motivations, whereas those that forestall autonomy, competence, or relatedness are associated with poorer motivation, performance, and well-being. We also discuss the relation of the psychological needs to cultural values, evolutionary processes, and other contemporary motivation theories.


Barriers to Providing Physical Education and Physical Activity in Victorian State Secondary Schools

An on-line questionnaire was completed by 115 physical education teachers to establish the barriers to their implementation of physical education in Victorian state secondary schools. In addition, the barriers perceived by teachers to impact on students’ participation in school-based physical education and physical activity were examined. The barriers to the provision of physical education were found to be largely institutional, although two-thirds of respondents recognised their own difficulties in engaging students when teaching as potential obstacles to student participation. Students were also perceived to be influenced by their own (45 per cent) and their peers (62 per cent) low levels of interest when choosing to participate. An awareness of these barriers has implications for physical education teaching, curriculum design, teacher training and adolescent participation in the school environment.


Self-efficacy: toward a unifying theory of behavioral change.

The present article presents an integrative theoretical framework to explain and to predict psychological changes achieved by different modes of treatment. This theory states that psychological procedures, whatever their form, alter the level and strength of self-efficacy. It is hypothesized that expectations of personal efficacy determine whether coping behavior will be initiated, how much effort will be expended, and how long it will be sustained in the face of obstacles and aversive experiences. Persistence in activities that are subjectively threatening but in fact relatively safe produces, through experiences of mastery, further enhancement of self-efficacy and corresponding reductions in defensive behavior. In the proposed model, expectations of personal efficacy are derived from four principal sources of information: performance accomplishments, vicarious experience, verbal persuasion, and physiological states. The more dependable the experiential sources, the greater are the changes in perceived selfefficacy. A number of factors are identified as influencing the cognitive processing of efficacy information arising from enactive, vicarious, exhortative, and emotive sources. The differential power of diverse therapeutic procedures is analyzed in terms of the postulated cognitive mechanism of operation. Findings are reported from microanalyses of enactive, vicarious, and emotive modes of treatment that support the hypothesized relationship between perceived self-efficacy and behavioral changes. Possible directions for further research are discussed.


Web-Based Ontology Languages and its Based Description Logics

The main object of semantic web languages is to add semantics to the existing information on the Web. These web languages have been developed to represent or express ontologies. Therefore, these description languages provide richer constructors for forming complex class expressions and axioms. An ontology is expressed in a knowledge representation language, which provides a formal frame of semantics. Since description logic is the basis of most ontology language, it is appropriate to explain briefly the base of the description to understand the ontology language clearly. The aim of this paper is to provide a brief survey of state-of-the-art ontology languages which are used to express ontology over the Web is provided. Also the gaol of this paper is to provide a basic understanding of ontologies and description logics, which are the basis of ontology languages.


Smurf-based Distributed Denial of Service (DDoS) Attack Amplification in Internet

The Smurf-based distributed denial of service (DDoS) attack is an amplification attack where the attacker uses unprotected intermediate networks to amplify the attack traffic load and direct it to the victim computer. In this paper, we investigate the factors that contribute to the amplification of the smurf attack traffic and understand the relation among the original attack traffic, intermediate unprotected network and the final amplified attack traffic. We also define a new term called attack amplification factor which represents the degree of amplification that original attack traffic undergoes during its transmission towards the victim computer. It is also shown in this paper that utilizing this attack, it is possible for an attacker to just use a dialup modem and an unprotected intermediary network to exhaust even an ultra high speed optical line such as OC-192 of the victim network.


An Attention-based Regression Model for Grounding Textual Phrases in Images

Grounding, or localizing, a textual phrase in an image is a challenging problem that is integral to visual language understanding. Previous approaches to this task typically make use of candidate region proposals, where end performance depends on that of the region proposal method and additional computational costs are incurred. In this paper, we treat grounding as a regression problem and propose a method to directly identify the region referred to by a textual phrase, eliminating the need for external candidate region prediction. Our approach uses deep neural networks to combine image and text representations and refines the target region with attention models over both image subregions and words in the textual phrase. Despite the challenging nature of this task and sparsity of available data, in evaluation on the ReferIt dataset, our proposed method achieves a new state-of-the-art in performance of 37.26% accuracy, surpassing the previously reported best by over 5 percentage points. We find that combining image and text attention models and an image attention area-sensitive loss function contribute to substantial improvements.


Intelligent book positioning for library using RFID and book spine matching

In library, the management of books is very complication and timing costing. The location of books could be altered by librarian, students, teachers and any one around the library. Therefore, allocating a book is not an easy task in big library. Indoor positioning is an important technology to help storage management and customer services providing. RFID provides a good wireless platform to facilitate indoor positioning. However, duo to the small width of each book spine, adopting positioning based RFID alone is not enough to locate books in a library. In this work, we combine image matching with L-GEM based RBFNN to enhance the accuracy and robustness of the book locating system. We apply this new method in a library environment to position the certain books. Experimental results show that the proposed method is highly accurate and robust to white noise of RFID signals.


An expanding reference library for Peer-to-Peer content

Many Peer-to-Peer (P2P) networks have emerged as scalable platforms for enabling file-sharing and content-distribution applications. Due to their reliance on decentralized models encompassing millions of users, P2P networks are subjected to being infested with contraband material, copyright infringing content, as well as malicious viruses and programs. Moreover, the dynamics involved as users join and leave these networks make investigating them quite complex and time-dependent. To that end, this paper presents PxP, a tool that creates an expanding reference library for content that appears on P2P networks. PxP continuously searches the P2P networks for new files, downloads and fingerprints them, deletes them, and add their meta data to a public reference library. We envision that this library will aid investigators in criminal investigations that involve contraband material, civil investigations with copyright infringement content. In addition, this library can be used to speed up forensics examination of media through skipping over some of the files shared on P2P networks and in validating carving tools. We assess the performance of our framework through analysis, simulations and real experiments for content collected from the Gnutella P2P network.


An efficient sparse metric learning in high-dimensional space via l1-penalized log-determinant regularization

This paper proposes an efficient sparse metric learning algorithm in high dimensional space via an l1-penalized log-determinant regularization. Compare to the most existing distance metric learning algorithms, the proposed algorithm exploits the sparsity nature underlying the intrinsic high dimensional feature space. This sparsity prior of learning distance metric serves to regularize the complexity of the distance model especially in the "less example number p and high dimension d" setting. Theoretically, by analogy to the covariance estimation problem, we find the proposed distance learning algorithm has a consistent result at rate O (√m2 log d)/n) to the target distance matrix with at most m nonzeros per row. Moreover, from the implementation perspective, this l1-penalized log-determinant formulation can be efficiently optimized in a block coordinate descent fashion which is much faster than the standard semi-definite programming which has been widely adopted in many other advanced distance learning algorithms. We compare this algorithm with other state-of-the-art ones on various datasets and competitive results are obtained.


An Evaluation of Space Time Cube Representation of Spatiotemporal Patterns

Space time cube representation is an information visualization technique where spatiotemporal data points are mapped into a cube. Information visualization researchers have previously argued that space time cube representation is beneficial in revealing complex spatiotemporal patterns in a data set to users. The argument is based on the fact that both time and spatial information are displayed simultaneously to users, an effect difficult to achieve in other representations. However, to our knowledge the actual usefulness of space time cube representation in conveying complex spatiotemporal patterns to users has not been empirically validated. To fill this gap, we report on a between-subjects experiment comparing novice users' error rates and response times when answering a set of questions using either space time cube or a baseline 2D representation. For some simple questions, the error rates were lower when using the baseline representation. For complex questions where the participants needed an overall understanding of the spatiotemporal structure of the data set, the space time cube representation resulted in on average twice as fast response times with no difference in error rates compared to the baseline. These results provide an empirical foundation for the hypothesis that space time cube representation benefits users analyzing complex spatiotemporal patterns.


Noise cancellation in cognitive radio systems: A performance comparison of evolutionary algorithms

Noise cancellation is one of the important signal processing functions of any communication system, as noise affects data integrity. In existing systems, traditional filters are used to cancel the noise from the received signals. These filters use fixed hardware which is capable of filtering specific frequency or a range of frequencies. However, next generation communication technologies, such as cognitive radio, will require the use of adaptive filters that can dynamically reconfigure their filtering parameters for any frequency. To this end, a few noise cancellation techniques have been proposed, including least mean squares (LMS) and its variants. However, these algorithms are susceptible to non-linear noise and fail to locate the global optimum solution for de-noising. In this paper, we investigate the efficiency of two global search optimization based algorithms, genetic algorithm and particle swarm optimization in performing noise cancellation in cognitive radio systems. These algorithms are implemented and their performances are compared to that of LMS using bit error rate and mean square error as performance evaluation metrics. Simulations are performed with additive white Gaussian noise and random nonlinear noise. Results indicate that GA and PSO perform better than LMS for the case of AWGN corrupted signal but for non-linear random noise PSO outperforms the other two algorithms.


k-Nearest Neighbor Augmented Neural Networks for Text Classification

In recent years, many deep-learning based models are proposed for text classification. This kind of models well fits the training set from the statistical point of view. However, it lacks the capacity of utilizing instance-level information from individual instances in the training set. In this work, we propose to enhance neural network models by allowing them to leverage information from k-nearest neighbor (kNN) of the input text. Our model employs a neural network that encodes texts into text embeddings. Moreover, we also utilize k-nearest neighbor of the input text as an external memory, and utilize it to capture instance-level information from the training set. The final prediction is made based on features from both the neural network encoder and the kNN memory. Experimental results on several standard benchmark datasets show that our model outperforms the baseline model on all the datasets, and it even beats a very deep neural network model (with 29 layers) in several datasets. Our model also shows superior performance when training instances are scarce, and when the training set is severely unbalanced. Our model also leverages techniques such as semi-supervised training and transfer learning quite well.


Active intelligent antenna system for car2car

In this paper an innovative antenna system for car2car applications is described. The antenna system is based on an active antenna module containing the antennas as well as the complete car2car chip hardware and software. This module is very small, so that it can be integrated e.g. into a shark fin antenna on the cars rooftop. Moreover additional modules can be mounted somewhere else in the car and connected with the main module via Ethernet. This allows high flexibility and re-configurability of the system. Two test cars have been equipped with the new car2car demonstrator systems and measurements have been performed in the 3D measurement chamber as well as in dynamic outdoor scenarios. The new system shows excellent performance in measurements and field trials.


GenomeGraphs: integrated genomic data visualization with R

Biological studies involve a growing number of distinct high-throughput experiments to characterize samples of interest. There is a lack of methods to visualize these different genomic datasets in a versatile manner. In addition, genomic data analysis requires integrated visualization of experimental data along with constantly changing genomic annotation and statistical analyses. We developed GenomeGraphs, as an add-on software package for the statistical programming environment R, to facilitate integrated visualization of genomic datasets. GenomeGraphs uses the biomaRt package to perform on-line annotation queries to Ensembl and translates these to gene/transcript structures in viewports of the grid graphics package. This allows genomic annotation to be plotted together with experimental data. GenomeGraphs can also be used to plot custom annotation tracks in combination with different experimental data types together in one plot using the same genomic coordinate system. GenomeGraphs is a flexible and extensible software package which can be used to visualize a multitude of genomic datasets within the statistical programming environment R.


The effect of construal level on subjective probability estimates.

In a series of studies, we examined novel predictions drawn from a conceptualization of probability as psychological distance. Manipulating construal level in a number of different ways and examining a variety of probability judgments, we found that participants led to adopt a high-level-construal mind-set made lower probability assessments than did those led to adopt a low-level-construal mind-set. Moreover, this occurred even when construal level was manipulated in a context separate from the judgment task and the manipulation was unrelated in content to the events being judged. These findings suggest that broad processing variables can exert a widespread influence on probability judgment.


Auto-directed video stabilization with robust L1 optimal camera paths

We present a novel algorithm for automatically applying constrainable, L1-optimal camera paths to generate stabilized videos by removing undesired motions. Our goal is to compute camera paths that are composed of constant, linear and parabolic segments mimicking the camera motions employed by professional cinematographers. To this end, our algorithm is based on a linear programming framework to minimize the first, second, and third derivatives of the resulting camera path. Our method allows for video stabilization beyond the conventional filtering of camera paths that only suppresses high frequency jitter. We incorporate additional constraints on the path of the camera directly in our algorithm, allowing for stabilized and retargeted videos. Our approach accomplishes this without the need of user interaction or costly 3D reconstruction of the scene, and works as a post-process for videos from any camera or from an online source.


Learning Graphical State Transitions

Graph-structured data is important in modeling relationships between multiple entities, and can be used to represent states of the world as well as many data structures. Li et al. (2016) describe a model known as a Gated Graph Sequence Neural Network (GGS-NN) that produces sequences from graph-structured input. In this work I introduce the Gated Graph Transformer Neural Network (GGTNN), an extension of GGS-NNs that uses graph-structured data as an intermediate representation. The model can learn to construct and modify graphs in sophisticated ways based on textual input, and also to use the graphs to produce a variety of outputs. For example, the model successfully learns to solve almost all of the bAbI tasks (Weston et al., 2016), and also discovers the rules governing graphical formulations of a simple cellular automaton and a family of Turing machines.


Understanding Image and Text Simultaneously: a Dual Vision-Language Machine Comprehension Task

We introduce a new multi-modal task for computer systems, posed as a combined vision-language comprehension challenge: identifying the most suitable text describing a scene, given several similar options. Accomplishing the task entails demonstrating comprehension beyond just recognizing “keywords” (or key-phrases) and their corresponding visual concepts. Instead, it requires an alignment between the representations of the two modalities that achieves a visually-grounded “understanding” of various linguistic elements and their dependencies. This new task also admits an easy-to-compute and well-studied metric: the accuracy in detecting the true target among the decoys. The paper makes several contributions: an effective and extensible mechanism for generating decoys from (humancreated) image captions; an instance of applying this mechanism, yielding a large-scale machine comprehension dataset (based on the COCO images and captions) that we make publicly available; human evaluation results on this dataset, informing a performance upper-bound; and several baseline and competitive learning approaches that illustrate the utility of the proposed task and dataset in advancing both image and language comprehension. We also show that, in a multi-task learning setting, the performance on the proposed task is positively correlated with the endto-end task of image captioning.


Scheduling a Major College Basketball Conference - Revisited

Nemhauser and Trick presented the problem of finding a timetable for the 1997/98 Atlantic Coast Conference (ACC) in basketball. Their solution, found with a combination of integer programming and exhaustive enumeration, was accepted by the ACC. Finite-domain constraint programming is another programming technique that can be used for solving combinatorial search problems such as sports tournament scheduling. This paper presents a solution of round robin tournament planning based on finite-domain constraint programming. The approach yields a dramatic performance improvement, which makes an integrated interactive software solution feasible.


Collection, exploration and analysis of crowdfunding social networks

Crowdfunding is a recent financing phenomenon that is gaining wide popularity as a means for startups to raise seed funding for their companies. This paper presents our initial results at understanding this phenomenon using an exploratory data driven approach. We have developed a big data platform for collecting and managing data from multiple sources, including company profiles (CrunchBase and AngelList) and social networks (Facebook and Twitter). We describe our data collection process that allows us to gather data from diverse sources at high throughput. Using Spark as our analysis tool, we study the impact of social engagement on startup fund raising success. We further define novel metrics that allow us to quantify the behavior of investors to follow and make investment decisions as communities rather than individuals. Finally, we explore visualization techniques that allow us to visualize communities of investors that make decisions in a close-knit fashion vs looser communities where investors largely make independent decisions. We conclude with a discussion on our ongoing research on causality analysis and new community detection algorithms.


Exploiting Emoticons in Polarity Classification of Text

With people increasingly using emoticons in written text on the Web in order to express, stress, or disambiguate their sentiment, it is crucial for automated sentiment analysis tools to correctly account for such graphical cues for sentiment. We analyze how emoticons typically convey sentiment and we subsequently propose and evaluate a novel method for exploiting this with a manually created emoticon sentiment lexicon in a lexicon-based polarity classification method. We evaluate our approach on 2,080 Dutch tweets and forum messages, which all contain emoticons. We validate our findings on 10,069 English reviews of apps, some of which contain emoticons. We find that accounting for the sentiment conveyed by emoticons on a paragraph level – and, to a lesser extent, on a sentence level – significantly improves polarity classification performance. Whenever emoticons are used, their associated sentiment tends to dominate the sentiment conveyed by textual cues and forms a good proxy for the polarity of text.


Full-Bridge+SRT Hybrid DC/DC Converter for a 6.6-kW EV On-Board Charger

This paper suggests a hybrid pulsewidth-modulation (PWM) dc/dc converter for an electric vehicle (EV) on-board charger (OBC). It is composed of a full-bridge (FB) converter and a resonant converter with secondary resonance tank (SRT). They are coupled in a single transformer so that any power distribution problem that causes a size increase in the transformer can be solved. Dual power flow of the proposed charger provides an increase in charger gain and a reduction of turn-off voltage spikes of the rectifying diodes caused by transformer leakage inductance and diode junction capacitance. These characteristics help reduce diode loss and remove dissipative snubber circuitry. In addition, this structure has the additional advantage of output inductor size due to the reduction of voltage variation applied to the inductor. The feasibility of the proposed charger is verified with a 6.6-kW prototype implemented by a design procedure.


Pronoun Translation and Prediction with or without Coreference Links

The Idiap NLP Group has participated in both DiscoMT 2015 sub-tasks: pronounfocused translation and pronoun prediction. The system for the first sub-task combines two knowledge sources: grammatical constraints from the hypothesized coreference links, and candidate translations from an SMT decoder. The system for the second sub-task avoids hypothesizing a coreference link, and uses instead a large set of source-side and target-side features from the noun phrases surrounding the pronoun to train a pronoun predictor.


Learning Simpler Language Models with the Delta Recurrent Neural Network Framework

Learning useful information across long time lags is a critical and difficult problem for temporal neural models in tasks like language modeling. Existing architectures that address the issue are often complex and costly to train. The Delta Recurrent Neural Network (Delta-RNN) framework is a simple and highperforming design that unifies previously proposed gated neural models. The DeltaRNN models maintain longer-term memory by learning to interpolate between a fast-changing data-driven representation and a slowly changing, implicitly stable state. This requires hardly any more parameters than a classical simple recurrent network. In language modeling at the word and character levels, the models outperform popular complex architectures, such as the Long Short Term Memory (LSTM) and the Gated Recurrent Unit (GRU), and, when regularized, perform comparably to several state-of-the-art baselines. At the subword level, we find the Delta-RNN’s performance to be comparable to that of complex gated architectures.


Modified reduced switch symmetrical multilevel inverter

Multilevel inverters (MLI) are widely used at the range of high voltage and high power for industrial applications. It is because of low harmonic distortion, less electromagnetic interference and upper range of dc link voltages. But the following type of inverter has few disadvantages like difficulty in voltage balancing, higher number of components and complexity in pulse width modulation control method. Objective of this paper is to compare the existing topologies by designing a 7-level modified reduced switch symmetrical MLI (MRSMLI) with five switches and same voltage sources in each level. Simulation and comparison of seven-level seven-switch, seven-level six-switch and seven-level five-switch MLI will be done using MATLAB/SIMULINK environment. The results obtained from a five-switch MLI will be verified with FFT Analysis design of seven-switch MLI. Output obtained will be then utilized for control of an induction motor. Simulation results will be verified by designing hardware setup of the proposed MRSMLI.


Searchable Encryption over Feature-Rich Data

Storage services allow data owners to store their huge amount of potentially sensitive data, such as audios, images, and videos, on remote cloud servers in encrypted form. To enable retrieval of encrypted files of interest, searchable symmetric encryption (SSE) schemes have been proposed. However, many schemes construct indexes based on keyword-file pairs and focus on boolean expressions of exact keyword matches. Moreover, most dynamic SSE schemes cannot achieve forward privacy and reveal unnecessary information when updating the encrypted databases. We tackle the challenge of supporting large-scale similarity search over encrypted feature-rich multimedia data, by considering the search criteria as a high-dimensional feature vector instead of a keyword. Our solutions are built on carefully-designed fuzzy Bloom filters which utilize locality sensitive hashing (LSH) to encode an index associating the file identifiers and feature vectors. Our schemes are proven to be secure against adaptively chosen query attack and forward private in the standard model. We have evaluated the performance of our scheme on real-world high-dimensional datasets, and achieved a search quality of 99 percent recall with only a few number of hash tables for LSH. This shows that our index is compact and searching is not only efficient but also accurate.


Practical byzantine fault tolerance and proactive recovery

Our growing reliance on online services accessible on the Internet demands highly available systems that provide correct service without interruptions. Software bugs, operator mistakes, and malicious attacks are a major cause of service interruptions and they can cause arbitrary behavior, that is, Byzantine faults. This article describes a new replication algorithm, BFT, that can be used to build highly available systems that tolerate Byzantine faults. BFT can be used in practice to implement real services: it performs well, it is safe in asynchronous environments such as the Internet, it incorporates mechanisms to defend against Byzantine-faulty clients, and it recovers replicas proactively. The recovery mechanism allows the algorithm to tolerate any number of faults over the lifetime of the system provided fewer than 1/3 of the replicas become faulty within a small window of vulnerability. BFT has been implemented as a generic program library with a simple interface. We used the library to implement the first Byzantine-fault-tolerant NFS file system, BFS. The BFT library and BFS perform well because the library incorporates several important optimizations, the most important of which is the use of symmetric cryptography to authenticate messages. The performance results show that BFS performs 2% faster to 24% slower than production implementations of the NFS protocol that are not replicated. This supports our claim that the BFT library can be used to build practical systems that tolerate Byzantine faults.


Blockchain-based secure firmware update for embedded devices in an Internet of Things environment

Embedded devices are going to be used extremely in Internet of Things (IoT) environments. The small and tiny IoT devices will operate and communicate each other without involvement of users, while their operations must be correct and protected against various attacks. In this paper, we focus on a secure firmware update issue, which is a fundamental security challenge for the embedded devices in an IoT environment. A new firmware update scheme that utilizes a blockchain technology is proposed to securely check a firmware version, validate the correctness of firmware, and download the latest firmware for the embedded devices. In the proposed scheme, an embedded device requests its firmware update to nodes in a blockchain network and gets a response to determine whether its firmware is up-to-date or not. If not latest, the embedded device downloads the latest firmware from a peer-to-peer firmware sharing network of the nodes. Even in the case that the version of the firmware is up-to-date, its integrity, i.e., correctness of firmware, is checked. The proposed scheme guarantees that the embedded device’s firmware is up-to-date while not tampered. Attacks targeting known vulnerabilities on firmware of embedded devices are thus mitigated.


ETHEREUM: A SECURE DECENTRALISED GENERALISED TRANSACTION LEDGER

The blockchain paradigm when coupled with cryptographically-secured transactions has demonstrated its utility through a number of projects, with Bitcoin being one of the most notable ones. Each such project can be seen as a simple application on a decentralised, but singleton, compute resource. We can call this paradigm a transactional singleton machine with shared-state. Ethereum implements this paradigm in a generalised manner. Furthermore it provides a plurality of such resources, each with a distinct state and operating code but able to interact through a message-passing framework with others. We discuss its design, implementation issues, the opportunities it provides and the future hurdles we envisage.


How the Blockchain Revolution Will Reshape the Consumer Electronics Industry [Future Directions]

^ IEEE ConsumEr ElECtronICs magazInE 19 By early 2016, Pabellon achieved another major milestone: a first touchfree control panel using solely the body’s electrolytes to control a matrix of magnetic pixels. Unlike conventional passive near-field communication technology that relies on receiver loop antennas for detection and identification, the magnetic pixel needs only our body salts to work. Each pixel represents a discrete sensing element. When arrayed or matrixed, the collection of pixels can be used to position index fingers approaching but not touching the surface. Pabellon’s powertransfer emitter turns into a touch-free pad. Unlike motion and other opticalsensing methods, the position of objects like fingers can be resolved by small changes in the magnetic near field. The ability to finely control the vast amount of things we contact on a daily basis may not require contact alone. The magnetic pixel has a very exciting future in the look-but-don’t-touch world that Pabellon envisions.


Asynchronous Consensus and Broadcast Protocols

A consensus protocol enables a system of n asynchronous processes, some of which are faulty, to reach agreement. There are two kinds of faulty processes: fail-stop processes that can only die and malicious processes that can also send false messages. The class of asynchronous systems with fair schedulers is defined, and consensus protocols that terminate with probability 1 for these systems are investigated. With fail-stop processes, it is shown that ⌈(n + 1)/2⌉ correct processes are necessary and sufficient to reach agreement. In the malicious case, it is shown that ⌈(2n + 1)/3⌉ correct processes are necessary and sufficient to reach agreement. This is contrasted with an earlier result, stating that there is no consensus protocol for the fail-stop case that always terminates within a bounded number of steps, even if only one process can fail. The possibility of reliable broadcast (Byzantine Agreement) in asynchronous systems is also investigated. Asynchronous Byzantine Agreement is defined, and it is shown that ⌈(2n + 1)/3⌉ correct processes are necessary and sufficient to achieve it.


C4.5: Programs for Machine Learning


Electromagnetic Vortex Imaging Using Uniform Concentric Circular Arrays

Electromagnetic vortex imaging based on orbital angular momentum (OAM) modulation has the ability of azimuthal resolution without relative motion, which needs various OAM modes. However, the main-lobe directions of different OAM beams differ from each other so that the echo energy is limited. In this letter, several concentric circular arrays are employed to adjust the directivity, each array generating a beam carrying special OAM mode. With this scheme, the target can be illuminated by main lobes of all OAM beams simultaneously. Our proposal can benefit the image reconstruction in noisy environment. Moreover, this scheme can successfully reduce the artifacts caused by high sidelobes at the cost of reduction in azimuthal resolution. Simulation results demonstrate the effectiveness of this scheme.


Supply Chain Risk Management: Literature Review and Future Research

Supply Chain Risk Management has increasingly becoming a more popular research area recently. Various papers, with different focus and approaches, have been published. This paper aims to survey supply chain risk management (SCRM) literature. Paper published in relevant journals from 2000 to 2006 will be analyzed and classified into five categories: conceptual, descriptive, empirical, exploratory cross-sectional, and exploratory longitudinal. The literature review will provide the basis for outlining future research opportunities in this field.


HOW TO IMPROVE FIRM PERFORMANCE USING BIG DATA ANALYTICS CAPABILITY

The recent interest in big data has led many companies to develop big data analytics capability (BDAC) in order to enhance firm performance (FPER). However, BDAC pays off for some companies but not for others. It appears that very few have achieved a big impact through big data. To address this challenge, this study proposes a BDAC model drawing on the resource-based theory (RBT) and the entanglement view of sociomaterialism. The findings show BDAC as a hierarchical model, which consists of three primary dimensions (i.e., management, technology, and talent capability) and 11 subdimensions (i.e., planning, investment, coordination, control, connectivity, compatibility, modularity, technology management knowledge, technical knowledge, business knowledge and relational knowledge). The findings confirm the value of the entanglement conceptualization of the higher-order BDAC model and its impact on FPER. The results also illuminate the significant moderating impact of analytics capability–business strategy alignment on the BDAC FPER relationship.


Towards cloud based mobile ad hoc network simulation

Simulations are considered as the most convenient approach for validating any research proposal. A simulator attempts to model a real-world setting in order to analyze the behavior of any algorithm. A large number of simulation software for ad hoc network has been proposed in literature. These simulators are based on execution of simulation on standalone computers. This paper introduces the notion of `simulation as a service' for mobile ad hoc network's simulation based on cloud computing. Cloud computing is an emerging discipline that enables outsourcing computing task remotely based on pay-as-you-go model. The paper provides a generalized architecture to perform ad hoc network simulations on cloud platforms. Similar to different service models provided by cloud, some service models have been introduced for cloud based simulation. The proposed approach provides different advantages such as dynamic service provisioning to support large scale simulation, sharing of simulation models and reduced cost for simulation etc. The proposed tool has been implemented on Heroku platform that demonstrates the scalability of proposed approach.


Comparison of orthodontic treatment outcomes in adults with skeletal open bite between conventional edgewise treatment and implant-anchored orthodontics.

INTRODUCTION
Open bites are known to be difficult malocclusions to treat. Generally, with conventional edgewise treatment, incisor extrusion rather than molar intrusion is observed. Recently, the use of miniscrews as anchorage has markedly increased. In this study, orthodontic treatment outcomes after conventional edgewise treatment and implant-anchored treatment were investigated by cephalometric analysis and several occlusal indexes. In addition, the stability of these patients 2 years after the retention phase was also analyzed.

METHODS
Thirty adults (15 for nonimplant treatment [non-IA group] and 15 for implant-anchored treatment [IA group]) were our subjects. Cephalometric analysis, peer assessment rating, discrepancy index, and objective grading system were used.

RESULTS
From the cephalometric values in the non-IA group, open-bite patients were generally treated by extrusion of the maxillary and mandibular incisors that resulted in clockwise rotation of the mandibular plane angle. In the IA group, intrusion of the maxillary and mandibular molars that resulted in counterclockwise rotation was noted. Furthermore, in the IA group, the soft-tissue analysis showed decreases in the facial convexity and the inferior labial sulcus angle that resulted in the disappearance of incompetent lips. In the retention phase, extrusion of the mandibular molars was observed in the IA group. From the objective grading system evaluation, significant reductions of overbite in canines and premolars were observed in both groups. Furthermore, less stability was observed in the IA group compared with the non-IA group according to the total objective grading system score.

CONCLUSIONS
Ideal occlusion can be achieved in adults with severe open bite with both conventional edgewise and implant-anchored orthodontic treatment. However, absolute intrusion of the molars and improvement in esthetics might be achieved more effectively by using miniscrews as an anchorage device. In addition, since a significant amount of tooth movement occurs with miniscrews, careful attention is required during the retention phase.


Parametric Optimization for Practical Control Systems Design

In this contribution, calculation machinery and code to compute different performance indices, which are necessary for the parametric optimization, are proposed. Moreover, a short review about parametric optimization is carried out. It is highlighted that normally only the ISE (Integral Square Error) performance index is used for control system design. One reason for that is the existence of several numerical algorithms to compute it along the infinite trajectory at once. Finally, an optimal design of a PI controller is used to illustrate and compare the new algorithms.


Embedding Factorization Models for Jointly Recommending Items and User Generated Lists

Existing recommender algorithms mainly focused on recommending individual items by utilizing user-item interactions. However, little attention has been paid to recommend user generated lists (e.g., playlists and booklists). On one hand, user generated lists contain rich signal about item co-occurrence, as items within a list are usually gathered based on a specific theme. On the other hand, a user's preference over a list also indicate her preference over items within the list. We believe that 1) if the rich relevance signal within user generated lists can be properly leveraged, an enhanced recommendation for individual items can be provided, and 2) if user-item and user-list interactions are properly utilized, and the relationship between a list and its contained items is discovered, the performance of user-item and user-list recommendations can be mutually reinforced.
 Towards this end, we devise embedding factorization models, which extend traditional factorization method by incorporating item-item (item-item-list) co-occurrence with embedding-based algorithms. Specifically, we employ factorization model to capture users' preferences over items and lists, and utilize embedding-based models to discover the co-occurrence information among items and lists. The gap between the two types of models is bridged by sharing items' latent factors. Remarkably, our proposed framework is capable of solving the new-item cold-start problem, where items have never been consumed by users but exist in user generated lists. Overall performance comparisons and micro-level analyses demonstrate the promising performance of our proposed approaches.