Towards Implicit Content-Introducing for Generative Short-Text Conversation Systems

The study on human-computer conversation systems is a hot research topic nowadays. One of the prevailing methods to build the system is using the generative Sequence-to-Sequence (Seq2Seq) model through neural networks. However, the standard Seq2Seq model is prone to generate trivial responses. In this paper, we aim to generate a more meaningful and informative reply when answering a given question. We propose an implicit content-introducing method which incorporates additional information into the Seq2Seq model in a flexible way. Specifically, we fuse the general decoding and the auxiliary cue word information through our proposed hierarchical gated fusion unit. Experiments on real-life data demonstrate that our model consistently outperforms a set of competitive baselines in terms of BLEU scores and human evaluation.


A 100 watts L-band power amplifier

In this paper, a 1100–1350 MHz power amplifier is introduced. For the realization of the RF power amplifier an LDMOS transistor is used. The amplifier delivers 100W power to a 50 ohm load with 50% efficiency. The transducer power gain of the amplifier is has 16dB at 100W output power. A mixed lumped element and transmission line matching circuit is used to implement matching network. The simulations are performed by the nonlinear model of the transistor, which is provided by the manufacturer. Finally the results of simulations are compared with the measurements.


RF power amplifiers for wireless communications

A wide variety of semiconductor devices are used in wireless power amplifiers. The RF performance and other attributes of cellphone RF power amplifiers using Si and GaAs based technologies are reviewed and compared.


A design methodology for the realization of multi-decade baluns at microwave frequencies

A new methodology is presented for designing baluns exhibiting multi-decade bandwidths at microwave frequencies. Simulations show that resistors terminating the outer transmission line suppress the half-wavelength resonance and greatly extend the bandwidth. Using linear measurements at microwave frequencies, ferrite beads have been shown to behave as resistors with a small reactance, suitable for terminating the outer transmission line. This paper shows that ferrite beads can perform the dual role of improving magnetic coupling at low frequency and suppressing resonance at higher frequencies. The design methodology was applied to produce a balun that operates between 30MHz and 6GHz, displays less than 1dB of power loss up to 4.4GHz, and delivers an impedance transformation of 2∶1.


100 W GaN HEMT power amplifier module with > 60% efficiency over 100–1000 MHz bandwidth

We have demonstrated a decade bandwidth 100 W GaN HEMT power amplifier module with 15.5–18.6 dB gain, 104–121 W CW output power and 61.4–76.6 % drain efficiency over the 100–1000 MHz band. The 2 × 2 inch compact power amplifier module combines four 30 W lossy matched broadband GaN HEMT PAs packaged in a ceramic SO8 package. Each of the 4 devices is fully matched to 50 Ω and obtains 30.8–35.7 W with 68.6–79.6 % drain efficiency over the band. The packaged amplifiers contain a GaN on SiC device operating at 48V drain voltage, alongside GaAs integrated passive matching circuitry. The four devices are combined using a broadband low loss coaxial balun. We believe this combination of output power, bandwidth and efficiency is the best reported to date. These amplifiers are targeted for use in multi-band public mobile radios and for instrumentation applications.


10W ultra-broadband power amplifier

We report the design and performance of an ultra-broadband power amplifier. It achieves 10 Watts output power with 21dB ± 1.5dB gain from 20 MHz to 3000 MHz. At lower frequencies from 20 to 1000 MHz the output power is 15 Watts with 22% efficiency. To achieve this performance, we employ a new design concept to control the device impedance and the power combiner impedance to be naturally 50 Ohms, such that no impedance matching is needed. Also, we developed a broadband microwave balun as a push-pull power combiner, which doubles as an impedance transformer. We believe the combination of output power, bandwidth and efficiency is the best reported to date.


Full-Body Compliant Human–Humanoid Interaction: Balancing in the Presence of Unknown External Forces

This paper proposes an effective framework of human-humanoid robot physical interaction. Its key component is a new control technique for full-body balancing in the presence of external forces, which is presented and then validated empirically. We have adopted an integrated system approach to develop humanoid robots. Herein, we describe the importance of replicating human-like capabilities and responses during human-robot interaction in this context. Our balancing controller provides gravity compensation, making the robot passive and thereby facilitating safe physical interactions. The method operates by setting an appropriate ground reaction force and transforming these forces into full-body joint torques. It handles an arbitrary number of force interaction points on the robot. It does not require force measurement at interested contact points. It requires neither inverse kinematics nor inverse dynamics. It can adapt to uneven ground surfaces. It operates as a force control process, and can therefore, accommodate simultaneous control processes using force-, velocity-, or position-based control. Forces are distributed over supporting contact points in an optimal manner. Joint redundancy is resolved by damping injection in the context of passivity. We present various force interaction experiments using our full-sized bipedal humanoid platform, including compliant balance, even when affected by unknown external forces, which demonstrates the effectiveness of the method.


Neuropsychology and clinical neuroscience of persistent post-concussive syndrome.

On the mild end of the acquired brain injury spectrum, the terms concussion and mild traumatic brain injury (mTBI) have been used interchangeably, where persistent post-concussive syndrome (PPCS) has been a label given when symptoms persist for more than three months post-concussion. Whereas a brief history of concussion research is overviewed, the focus of this review is on the current status of PPCS as a clinical entity from the perspective of recent advances in the biomechanical modeling of concussion in human and animal studies, particularly directed at a better understanding of the neuropathology associated with concussion. These studies implicate common regions of injury, including the upper brainstem, base of the frontal lobe, hypothalamic-pituitary axis, medial temporal lobe, fornix, and corpus callosum. Limitations of current neuropsychological techniques for the clinical assessment of memory and executive function are explored and recommendations for improved research designs offered, that may enhance the study of long-term neuropsychological sequelae of concussion.


Computer-aided generation of multiple-choice tests

Summary form only given. The paper describes a novel automatic procedure for the generation of multiple-choice tests from electronic documents. In addition to employing various NLP techniques including term extraction and shallow parsing, the system makes use of language resources such as corpora and ontologies. The system operates in a fully automatic mode and also a semiautomatic environment where the user is offered the option to post-edit the generated test items. The results from the conducted evaluation suggest that the new procedure is very effective saving time and labour considerably and that the test items produced with the help of the program are not of inferior quality to those produced manually.


Model-Driven Reverse Engineering Approaches: A Systematic Literature Review

This paper explores and describes the state of the art for what concerns the model-driven approaches proposed in the literature to support reverse engineering. We conducted a systematic literature review on this topic with the aim to answer three research questions. We focus on various solutions developed for model-driven reverse engineering, outlining in particular the models they use and the transformations applied to the models. We also consider the tools used for model definition, extraction, and transformation and the level of automation reached by the available tools. The model-driven reverse engineering approaches are also analyzed based on various features such as genericity, extensibility, automation of the reverse engineering process, and coverage of the full or partial source artifacts. We describe in detail and compare fifteen approaches applying model-driven reverse engineering. Based on this analysis, we identify and indicate some hints on choosing a model-driven reverse engineering approach from the available ones, and we outline open issues concerning the model-driven reverse engineering approaches.


Evaluation of Three Methods for MRI Brain Tumor Segmentation

Imaging plays a central role in the diagnosis and treatment planning of brain tumor. An accurate segmentation is critical, especially when the tumor morphological changes remain subtle, irregular and difficult to assess by clinical examination. Traditionally, segmentation is performed manually in clinical environment that is operator dependent and very tedious and time consuming labor intensive work. However, automated tumor segmentation in MRI brain tumor poses many challenges with regard to characteristics of an image. A comparison of three different semi-automated methods, viz., modified gradient magnitude region growing technique (MGRRGT), level set and a marker controlled watershed method is undertaken here for evaluating their relative performance in the segmentation of tumor. A study on 9 samples using MGRRGT reveals that all the errors are within 6 to 23% in comparison to other two methods.


Ways to React: Comparing Reactive Languages and Complex Event Processing

Reactive applications demand for detecting the changes that occur in a domain of interest and for timely reactions. Examples range from simple interactive applications to complex monitoring tasks involving distributed and heterogeneous systems. Over the last years, different programming paradigms and solutions have been proposed to support such applications. In this paper, we focus on two prominent approaches: event-based programming, specifically Complex Event Processing (CEP), and Reactive Languages (RLs). CEP systems enable the definition of high level situations of interest from low level primitive events detected in the external environment. On the other hand, RLs support time-changing values and their composition as dedicated language abstractions. These research fields have been investigated by different communities, belonging respectively to the database and the distributed systems areas and to the programming language area. It is our belief that a deeper understanding of these research fields, including their benefits and limitations, their similarities and differences, could drive further developments in supporting reactive applications. For this reason, we propose a first comparison of the two fields. Despite huge differences, we believe that such a comparison can trigger an interesting discussion across the communities, favor knowledge sharing, and let new ideas emerge.


Frontal fibrosing alopecia – A Case report * Alopecia frontal fibrosante-Relato de caso

Frontal fibrosing alopecia is a kind of progressive and frequently irreversible cicatricial alopecia marked by a lichenoid infiltrate in histology. Since its first description, in 1994, in Australia, some cases have been documented all over the world. The article reports, for the second time in the medical literature, a Brazilian case and reviews the main aspects of this dermatosis.


A Discriminative Representation of Convolutional Features for Indoor Scene Recognition

Indoor scene recognition is a multi-faceted and challenging problem due to the diverse intra-class variations and the confusing inter-class similarities that characterize such scenes. This paper presents a novel approach that exploits rich mid-level convolutional features to categorize indoor scenes. Traditional convolutional features retain the global spatial structure, which is a desirable property for general object recognition. We, however, argue that the structure-preserving property of the convolutional neural network activations is not of substantial help in the presence of large variations in scene layouts, e.g., in indoor scenes. We propose to transform the structured convolutional activations to another highly discriminative feature space. The representation in the transformed space not only incorporates the discriminative aspects of the target data set but also encodes the features in terms of the general object categories that are present in indoor scenes. To this end, we introduce a new large-scale data set of 1300 object categories that are commonly present in indoor scenes. Our proposed approach achieves a significant performance boost over the previous state-of-the-art approaches on five major scene classification data sets.


An Artificial Light Source Influences Mating and Oviposition of Black Soldier Flies, Hermetia illucens

Current methods for mass-rearing black soldier flies, Hermetia illucens (L.) (Diptera: Stratiomyidae), in the laboratory are dependent on sunlight. Quartz-iodine lamps and rare earth lamps were examined as artificial light sources for stimulating H. illucens to mate and lay eggs. Sunlight was used as the control. Adults in the quartz-iodine lamp treatment had a mating rate of 61% of those in the sunlight control. No mating occurred when the rare earth lamp was used as a substitute. Egg hatch for the quartz-iodine lamp and sunlight treatments occurred in approximately 4 days, and the hatch rate was similar between these two treatments. Larval and pupal development under these treatments required approximately 18 and 15 days at 28°C, respectively. Development of methods for mass rearing of H. illucens using artificial light will enable production of this fly throughout the year without investing in greenhouse space or requiring sunlight.


Heart rate variability reflects self-regulatory strength, effort, and fatigue.

Experimental research reliably demonstrates that self-regulatory deficits are a consequence of prior self-regulatory effort. However, in naturalistic settings, although people know that they are sometimes vulnerable to saying, eating, or doing the wrong thing, they cannot accurately gauge their capacity to self-regulate at any given time. Because self-regulation and autonomic regulation colocalize in the brain, an autonomic measure, heart rate variability (HRV), could provide an index of self-regulatory strength and activity. During an experimental manipulation of self-regulation (eating carrots or cookies), HRV was elevated during high self-regulatory effort (eat carrots, resist cookies) compared with low self-regulatory effort (eat cookies, resist carrots). The experimental manipulation and higher HRV at baseline independently predicted persistence at a subsequent anagram task. HRV appears to index self-regulatory strength and effort, making it possible to study these phenomena in the field as well as the lab.


Predicting Neural Activity Patterns Associated with Sentences Using a Neurobiologically Motivated Model of Semantic Representation.

We introduce an approach that predicts neural representations of word meanings contained in sentences then superposes these to predict neural representations of new sentences. A neurobiological semantic model based on sensory, motor, social, emotional, and cognitive attributes was used as a foundation to define semantic content. Previous studies have predominantly predicted neural patterns for isolated words, using models that lack neurobiological interpretation. Fourteen participants read 240 sentences describing everyday situations while undergoing fMRI. To connect sentence-level fMRI activation patterns to the word-level semantic model, we devised methods to decompose the fMRI data into individual words. Activation patterns associated with each attribute in the model were then estimated using multiple-regression. This enabled synthesis of activation patterns for trained and new words, which were subsequently averaged to predict new sentences. Region-of-interest analyses revealed that prediction accuracy was highest using voxels in the left temporal and inferior parietal cortex, although a broad range of regions returned statistically significant results, showing that semantic information is widely distributed across the brain. The results show how a neurobiologically motivated semantic model can decompose sentence-level fMRI data into activation features for component words, which can be recombined to predict activation patterns for new sentences.


Dynamic Action Repetition for Deep Reinforcement Learning

One of the long standing goals of Artificial Intelligence (AI) is to build cognitive agents which can perform complex tasks from raw sensory inputs without explicit supervision (Lake et al. 2016). Recent progress in combining Reinforcement Learning objective functions and Deep Learning architectures has achieved promising results for such tasks. An important aspect of such sequential decision making problems, which has largely been neglected, is for the agent to decide on the duration of time for which to commit to actions. Such action repetition is important for computational efficiency, which is necessary for the agent to respond in real-time to events (in applications such as self-driving cars). Action Repetition arises naturally in real life as well as simulated environments. The time scale of executing an action enables an agent (both humans and AI) to decide the granularity of control during task execution. Current state of the art Deep Reinforcement Learning models, whether they are off-policy (Mnih et al. 2015; Wang et al. 2015) or on-policy (Mnih et al. 2016), consist of a framework with a static action repetition paradigm, wherein the action decided by the agent is repeated for a fixed number of time steps regardless of the contextual state while executing the task. In this paper, we propose a new framework Dynamic Action Repetition which changes Action Repetition Rate (the time scale of repeating an action) from a hyper-parameter of an algorithm to a dynamically learnable quantity. At every decision-making step, our models allow the agent to commit to an action and the time scale of executing the action. We show empirically that such a dynamic time scale mechanism improves the performance on relatively harder games in the Atari 2600 domain, independent of the underlying Deep Reinforcement Learning algorithm used.


Towards Dependable Deep Convolutional Neural Networks (CNNs) with Out-distribution Learning

Detection and rejection of adversarial examples in security sensitive and safety-critical systems using deep CNNs is essential. In this paper, we propose an approach to augment CNNs with out-distribution learning in order to reduce misclassification rate by rejecting adversarial examples. We empirically show that our augmented CNNs can either reject or classify correctly most adversarial examples generated using well-known methods (> 95% for MNIST and > 75% for CIFAR-10 on average). Furthermore, we achieve this without requiring to train using any specific type of adversarial examples and without sacrificing the accuracy of models on clean samples significantly (< 4%).


Integrated microstrip and rectangular waveguide in planar form

Usually transitions from microstrip line to rectangular waveguide are made with three-dimensional complex mounting structures. In this paper, a new planar platform is developed in which the microstrip line and rectangular waveguide are fully integrated on the same substrate, and they are interconnected via a simple taper. Our experiments at 28 GHz show that an effective bandwidth of 12% at 20 dB return loss is obtained with an in-band insertion loss better than 0.3 dB. The new transition allows a complete integration of waveguide components on substrate with MICs and MMICs.


Teaching Computer Organization and Architecture Using Simulation and FPGA Applications

This paper presents the design concepts and realization of incorporating micro-operation simulation and FPGA implementation into a teaching tool for computer organization and architecture. This teaching tool helps computer engineering and computer science students to be familiarized practically with computer organization and architecture through the development of their own instruction set, computer programming and interfacing experiments. A two-pass assembler has been designed and implemented to write assembly programs in this teaching tool. In addition to the microoperation simulation, the complete configuration can be run on Xilinx Spartan-3 FPGA board. Such implementation offers good code density, easy customization, easily developed software, small area, and high performance at low cost.


A versatile wavelet domain noise filtration technique for medical imaging

We propose a robust wavelet domain method for noise filtering in medical images. The proposed method adapts itself to various types of image noise as well as to the preference of the medical expert; a single parameter can be used to balance the preservation of (expert-dependent) relevant details against the degree of noise reduction. The algorithm exploits generally valid knowledge about the correlation of significant image features across the resolution scales to perform a preliminary coefficient classification. This preliminary coefficient classification is used to empirically estimate the statistical distributions of the coefficients that represent useful image features on the one hand and mainly noise on the other. The adaptation to the spatial context in the image is achieved by using a wavelet domain indicator of the local spatial activity. The proposed method is of low complexity, both in its implementation and execution time. The results demonstrate its usefulness for noise suppression in medical ultrasound and magnetic resonance imaging. In these applications, the proposed method clearly outperforms single-resolution spatially adaptive algorithms, in terms of quantitative performance measures as well as in terms of visual quality of the images.


Estimating the class prior and posterior from noisy positives and unlabeled data

We develop a classification algorithm for estimating posterior distributions from positive-unlabeled data, that is robust to noise in the positive labels and effective for high-dimensional data. In recent years, several algorithms have been proposed to learn from positive-unlabeled data; however, many of these contributions remain theoretical, performing poorly on real high-dimensional data that is typically contaminated with noise. We build on this previous work to develop two practical classification algorithms that explicitly model the noise in the positive labels and utilize univariate transforms built on discriminative classifiers. We prove that these univariate transforms preserve the class prior, enabling estimation in the univariate space and avoiding kernel density estimation for high-dimensional data. The theoretical development and parametric and nonparametric algorithms proposed here constitute an important step towards wide-spread use of robust classification algorithms for positive-unlabeled data.


Charging algorithms of lithium-ion batteries: An overview

This paper presents the overview of charging algorithms for lithium-ion batteries, which include constant current-constant voltage (CC/CV), variants of the CC/CV, multistage constant current, pulse current and pulse voltage. The CC/CV charging algorithm is well developed and widely adopted in charging lithium-ion batteries. It is used as a benchmark to compare with other charging algorithms in terms of the charging time, the charging efficiency, the influences on battery life and other aspects, which can serve as a convenient reference for future work in developing a charger for lithium-ion battery.


Weighted PageRank algorithm

With the rapid growth of the Web, users easily get lost in the rich hyper structure. Providing the relevant information to users to cater to their needs is the primary goal of Website owners. Therefore, finding the content of the Web and retrieving the users' interests and needs from their behavior have become increasingly important. Web mining is used to categorize users and pages by analyzing user behavior, the content of the pages, and the order of the URLs that tend to be accessed. Web structure mining plays an important role in this approach. Two page ranking algorithms, HITS and PageRank, are commonly used in Web structure mining. Both algorithms treat all links equally when distributing rank scores. Several algorithms have been developed to improve the performance of these methods. The weighted PageRank algorithm (WPR), an extension to the standard PageRank algorithm, is introduced. WPR takes into account the importance of both the inlinks and the outlinks of the pages and distributes rank scores based on the popularity of the pages. The results of our simulation studies show that WPR performs better than the conventional PageRank algorithm in terms of returning a larger number of relevant pages to a given query.


Signal-Quality Indices for the Electrocardiogram and Photoplethysmogram: Derivation and Applications to Wireless Monitoring

The identification of invalid data in recordings obtained using wearable sensors is of particular importance since data obtained from mobile patients is, in general, noisier than data obtained from nonmobile patients. In this paper, we present a signal quality index (SQI), which is intended to assess whether reliable heart rates (HRs) can be obtained from electrocardiogram (ECG) and photoplethysmogram (PPG) signals collected using wearable sensors. The algorithms were validated on manually labeled data. Sensitivities and specificities of 94% and 97% were achieved for the ECG and 91% and 95% for the PPG. Additionally, we propose two applications of the SQI. First, we demonstrate that, by using the SQI as a trigger for a power-saving strategy, it is possible to reduce the recording time by up to 94% for the ECG and 93% for the PPG with only minimal loss of valid vital-sign data. Second, we demonstrate how an SQI can be used to reduce the error in the estimation of respiratory rate (RR) from the PPG. The performance of the two applications was assessed on data collected from a clinical study on hospital patients who were able to walk unassisted.


Learning Discriminative Latent Attributes for Zero-Shot Classification

Zero-shot learning (ZSL) aims to transfer knowledge from observed classes to the unseen classes, based on the assumption that both the seen and unseen classes share a common semantic space, among which attributes enjoy a great popularity. However, few works study whether the human-designed semantic attributes are discriminative enough to recognize different classes. Moreover, attributes are often correlated with each other, which makes it less desirable to learn each attribute independently. In this paper, we propose to learn a latent attribute space, which is not only discriminative but also semantic-preserving, to perform the ZSL task. Specifically, a dictionary learning framework is exploited to connect the latent attribute space with attribute space and similarity space. Extensive experiments on four benchmark datasets show the effectiveness of the proposed approach.


Epistemological perspectives on IS research: a framework for analysing and systematizing epistemological assumptions

Over the last three decades, a methodological pluralism has developed within information systems (IS) research. Various disciplines and many research communities as well, contribute to this discussion. However, working on the same research topic or studying the same phenomenon does not necessarily ensure mutual understanding. Especially within this multidisciplinary and international context, the epistemological assumptions made by different researchers may vary fundamentally. These assumptions exert a substantial impact on how concepts like validity, reliability, quality and rigour of research are understood. Thus, the extensive publication of epistemological assumptions is, in effect, almost mandatory. Hence, the aim of this paper is to develop an epistemological framework which can be used for systematically analysing the epistemological assumptions in IS research. Rather than attempting to identify and classify IS research paradigms, this research aims at a comprehensive discussion of epistemology within the context of IS. It seeks to contribute to building the basis for identifying similarities as well as differences between distinct IS approaches and methods. In order to demonstrate the epistemological framework, the consensus-oriented interpretivist approach to conceptual modelling is used as an example.


Learning physical parameters from dynamic scenes

Humans acquire their most basic physical concepts early in development, and continue to enrich and expand their intuitive physics throughout life as they are exposed to more and varied dynamical environments. We introduce a hierarchical Bayesian framework to explain how people can learn physical parameters at multiple levels. In contrast to previous Bayesian models of theory acquisition (Tenenbaum, Kemp, Griffiths, & Goodman, 2011), we work with more expressive probabilistic program representations suitable for learning the forces and properties that govern how objects interact in dynamic scenes unfolding over time. We compare our model to human learners on a challenging task of estimating multiple physical parameters in novel microworlds given short movies. This task requires people to reason simultaneously about multiple interacting physical laws and properties. People are generally able to learn in this setting and are consistent in their judgments. Yet they also make systematic errors indicative of the approximations people might make in solving this computationally demanding problem with limited computational resources. We propose two approximations that complement the top-down Bayesian approach. One approximation model relies on a more bottom-up feature-based inference scheme. The second approximation combines the strengths of the bottom-up and top-down approaches, by taking the feature-based inference as its point of departure for a search in physical-parameter space.


Architectures for Natural Language Generation: Problems and Perspectives

Current research in natural language generation is situated in a computational linguistics tradition that was founded several decades ago. We critically analyse some of the architectural assumptions underlying existing systems and point out some problems in the domains of text planning and lexicalization. Guided by the identification of major generation challenges viewed from the angles of knowledge-based systems and cognitive psychology, we sketch some new directions for future research.


The Second Dialog State Tracking Challenge

A spoken dialog system, while communicating with a user, must keep track of what the user wants from the system at each step. This process, termed dialog state tracking, is essential for a successful dialog system as it directly informs the system’s actions. The first Dialog State Tracking Challenge allowed for evaluation of different dialog state tracking techniques, providing common testbeds and evaluation suites. This paper presents a second challenge, which continues this tradition and introduces some additional features – a new domain, changing user goals and a richer dialog state. The challenge received 31 entries from 9 research groups. The results suggest that while large improvements on a competitive baseline are possible, trackers are still prone to degradation in mismatched conditions. An investigation into ensemble learning demonstrates the most accurate tracking can be achieved by combining multiple trackers.


Attention Is All You Need

The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 Englishto-German translation task, improving over the existing best results, including ensembles, by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.2 after training for 4.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data.


One Model To Learn Them All

Deep learning yields great results across many fields, from speech recognition, image classification, to translation. But for each problem, getting a deep model to work well involves research into the architecture and a long period of tuning. We present a single model that yields good results on a number of problems spanning multiple domains. In particular, this single model is trained concurrently on ImageNet, multiple translation tasks, image captioning (COCO dataset), a speech recognition corpus, and an English parsing task. Our model architecture incorporates building blocks from multiple domains. It contains convolutional layers, an attention mechanism, and sparsely-gated layers. Each of these computational blocks is crucial for a subset of the tasks we train on. Interestingly, even if a block is not crucial for a task, we observe that adding it never hurts performance and in most cases improves it on all tasks. We also show that tasks with less data benefit largely from joint training with other tasks, while performance on large tasks degrades only slightly if at all.


Incremental LSTM-based dialog state tracker

A dialog state tracker is an important component in modern spoken dialog systems. We present an incremental dialog state tracker, based on LSTM networks. It directly uses automatic speech recognition hypotheses to track the state. We also present the key non-standard aspects of the model that bring its performance close to the state-of-the-art and experimentally analyze their contribution: including the ASR confidence scores, abstracting scarcely represented values, including transcriptions in the training data, and model averaging.


Application of neural networks to inverse lens distortion modelling

The accurate and quick modelling of inverse lens distortion to rectify images or predict the image coordinates of real world objects has long been a challenge. This work investigates the use of artificial neural networks to perform this modelling. Several architectures are investigated and multiple methods of training the networks are used to achieve the best results. The error is expressed as a physical displacement on the imaging chip so that a fair comparison can be made between other published results which are based on cameras with different resolutions. It is shown that the novel application of locally optimised neural networks to residual lens calibration data yields an inverse distortion modelling that is highly competitive with prior published results.


Quantitative function for community detection.

We propose a quantitative function for community partition -- i.e., modularity density or D value. We demonstrate that this quantitative function is superior to the widely used modularity Q and also prove its equivalence with the objective function of the kernel k means. Both theoretical and numerical results show that optimizing the new criterion not only can resolve detailed modules that existing approaches cannot achieve, but also can correctly identify the number of communities.


Learning Rapid-Temporal Adaptations

A hallmark of human intelligence and cognition is its flexibility. One of the longstanding goals in AI research is to replicate this flexibility in a learning machine. In this work we describe a mechanism by which artificial neural networks can learn rapid-temporal adaptation – the ability to adapt quickly to new environments or tasks – that we call adaptive neurons. Adaptive neurons modify their activations with task-specific values retrieved from a working memory. On standard metalearning and few-shot learning benchmarks in both vision and language domains, models augmented with adaptive neurons achieve state-of-the-art results.


Exemplary Automotive Attack Scenarios : Trojan Horses for Electronic Throttle Control System ( ETC ) and Replay Attacks on the Power Window System

The consideration of targeted security attacks is not yet common in the automotive domain where primarily safety requirements are considered. Hence this survey addresses the relatively new field of IT-security in automotive technology in order to motivate further research. With the emergence of automotive technologies like car-to-car (c2c) communication, the challenges increase. In order to show the necessity for such research we describe how in theory, a potential attack on the Electronic Throttle Control (ETC) could succeed, if no countermeasures are taken, with potentially drastic consequences on the safety of the vehicle, its occupants and its environment as a result. Also an attack on the power window system is shown using the implementation techniques already employed in the design of automotive components. Our goal is to show, that such exploited security vulnerabilities are likely to have serious consequences with respect to the vehicles safety. Therefore such attacks need to be considered and suitable countermeasures have to be identified. In order to achieve this, the connection of the two disciplines Safety and Security for automotive IT components needs to be discussed. We introduce two exemplary attacks, the Trojan Horse targeting at the Electronic Throttle Control System (ETC) and a replay attack on the electric window lift. The first attack is mainly discussed in theory, the second is elaborated in a practical simulation scenario. Finally we introduce a general investigation of potential ways of intrusion and present some first practical results from tests with current automotive hardware.


Real-time object detection, localization and verification for fast robotic depalletizing

Depalletizing is a challenging task for manipulation robots. Key to successful application are not only robustness of the approach, but also achievable cycle times in order to keep up with the rest of the process. In this paper, we propose a system for depalletizing and a complete pipeline for detecting and localizing objects as well as verifying that the found object does not deviate from the known object model, e.g., if it is not the object to pick. In order to achieve high robustness (e.g., with respect to different lighting conditions) and generality with respect to the objects to pick, our approach is based on multi-resolution surfel models. All components (both software and hardware) allow operation at high frame rates and, thus, allow for low cycle times. In experiments, we demonstrate depalletizing of automotive and other prefabricated parts with both high reliability (w.r.t. success rates) and efficiency (w.r.t. low cycle times).


Tagging Patient Notes With ICD-9 Codes

There is substantial growth in the amount of medical data being generated in hospitals. With over 96% adoption rate [1], Electronic Medical/Health Records are used to store most of this medical data. If harnessed correctly, this medium provides a very convenient platform for secondary data analysis of these records to improve medical and patient care. One crucial feature of the information stored in these systems are ICD9-diagnosis codes, which are used for billing purposes and integration to other databases. These codes are assigned to medical text and require expert annotators with experience and training. In this paper we formulate this problem as a multi-label classification problem and propose a deep learning framework to classify the ICD-9 codes a patient is assigned at the end of a visit. We demonstrate that a simple LSTM model with a single layer of non-linearity can learn to classify patient notes with their corresponding ICD-9 labels moderately well.


Visualization of patent analysis for emerging technology

Many methods have been developed to recognize those progresses of technologies, and one of them is to analyze patent information. And visualization methods are considered to be proper for representing patent information and its analysis results. However, current visualization methods for patent analysis patent maps have some drawbacks. Therefore, we propose an alternative visualization method in this paper. With colleted keywords from patent documents of a target technology field, we cluster patent documents by the k-Means algorithm. With the clustering results, we form a semantic network of keywords without respect of filing dates. And then we build up a patent map by rearranging each keyword node of the semantic network according to its earliest filing date and frequency in patent documents. Our approach contributes to establishing a patent map which considers both structured and unstructured items of a patent document. Besides, differently from previous visualization methods for patent analysis, ours is based on forming a semantic network of keywords from patent documents. And thereby it visualizes a clear overview of patent information in a more comprehensible way. And as a result of those contributions, it enables us to understand advances of emerging technologies and forecast its trend in the future. 2007 Elsevier Ltd. All rights reserved.


Perceptions of Sex, Gender, and Puberty Suppression: A Qualitative Analysis of Transgender Youth

International guidelines recommend the use of Gonadotropin-Releasing Hormone (GnRH) agonists in adolescents with gender dysphoria (GD) to suppress puberty. Little is known about the way gender dysphoric adolescents themselves think about this early medical intervention. The purpose of the present study was (1) to explicate the considerations of gender dysphoric adolescents in the Netherlands concerning the use of puberty suppression; (2) to explore whether the considerations of gender dysphoric adolescents differ from those of professionals working in treatment teams, and if so in what sense. This was a qualitative study designed to identify considerations of gender dysphoric adolescents regarding early treatment. All 13 adolescents, except for one, were treated with puberty suppression; five adolescents were trans girls and eight were trans boys. Their ages ranged between 13 and 18 years, with an average age of 16 years and 11 months, and a median age of 17 years and 4 months. Subsequently, the considerations of the adolescents were compared with views of clinicians treating youth with GD. From the interviews with the gender dysphoric adolescents, three themes emerged: (1) the difficulty of determining what is an appropriate lower age limit for starting puberty suppression. Most adolescents found it difficult to define an appropriate age limit and saw it as a dilemma; (2) the lack of data on the long-term effects of puberty suppression. Most adolescents stated that the lack of long-term data did not and would not stop them from wanting puberty suppression; (3) the role of the social context, for which there were two subthemes: (a) increased media-attention, on television, and on the Internet; (b) an imposed stereotype. Some adolescents were positive about the role of the social context, but others raised doubts about it. Compared to clinicians, adolescents were often more cautious in their treatment views. It is important to give voice to gender dysphoric adolescents when discussing the use of puberty suppression in GD. Otherwise, professionals might act based on assumptions about adolescents' opinions instead of their actual considerations. We encourage gathering more qualitative research data from gender dysphoric adolescents in other countries.


Good genes, complementary genes and human mate preferences

The past decade has witnessed a rapidly growing interest in the biological basis of human mate choice. Here we review recent studies that demonstrate preferences for traits which might reveal genetic quality to prospective mates, with potential but still largely unknown influence on offspring fitness. These include studies assessing visual, olfactory and auditory preferences for potential good-gene indicator traits, such as dominance or bilateral symmetry. Individual differences in these robust preferences mainly arise through within and between individual variation in condition and reproductive status. Another set of studies have revealed preferences for traits indicating complementary genes, focussing on discrimination of dissimilarity at genes in the major histocompatibility complex (MHC). As in animal studies, we are only just beginning to understand how preferences for specific traits vary and inter-relate, how consideration of good and compatible genes can lead to substantial variability in individual mate choice decisions and how preferences expressed in one sensory modality may reflect those in another. Humans may be an ideal model species in which to explore these interesting complexities.


Anatomy of Forehead, Glabellar, Nasal and Orbital Muscles, and Their Correlation with Distinctive Patterns of Skin Lines on the Upper Third of the Face: Reviewing Concepts

The purpose of this study is to establish a relationship between the skin lines on the upper third of the face in cadavers, which represent the muscle activity in life and the skin lines achieved by voluntary contraction of the forehead, glabellar, and orbital muscles in patients. Anatomical dissection of fresh cadavers was performed in 20 fresh cadavers, 11 females and 9 males, with ages ranging from 53 to 77 years. Subcutaneous dissection identified the muscle shape and the continuity of the fibers of the eyebrow elevator and depress muscles. Subgaleal dissection identified the cutaneous insertions of the muscles. They were correlated with skin lines on the upper third of the face of the cadavers that represent the muscle activity in life. Voluntary contraction was performed by 20 voluntary patients, 13 females and 7 males, with ages ranging from 35 to 62 years. Distinct patterns of skin lines on the forehead, glabellar and orbital areas, and eyebrow displacement were identified. The frontalis exhibited four anatomical shapes with four different patterns of horizontal parallel lines on the forehead skin. The corrugator supercilii showed three shapes of muscles creating six patterns of vertical glabellar lines, three symmetrical and three asymmetrical. The orbicularis oculi and procerus had single patterns. The skin lines exhibited in voluntary contraction of the upper third of the face in patients showed the same patterns of the skin lines achieved in cadavers. Skin lines in cadavers, which are the expression of the muscle activity in life, were similar to those achieved in the voluntary contraction of patients, allowing us to assert that the muscle patterns of patients were similar to those identified in cadavers. This journal requires that authors assign a level of evidence to each submission to which Evidence-Based Medicine rankings are applicable. This excludes Review Articles, Book Reviews, and manuscripts that concern Basic Science, Animal Studies, Cadaver Studies, and Experimental Studies. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors http://www.springer.com/00266 .


Internet addiction: a systematic review of epidemiological research for the last decade.

In the last decade, Internet usage has grown tremendously on a global scale. The increasing popularity and frequency of Internet use has led to an increasing number of reports highlighting the potential negative consequences of overuse. Over the last decade, research into Internet addiction has proliferated. This paper reviews the existing 68 epidemiological studies of Internet addiction that (i) contain quantitative empirical data, (ii) have been published after 2000, (iii) include an analysis relating to Internet addiction, (iv) include a minimum of 1000 participants, and (v) provide a full-text article published in English using the database Web of Science. Assessment tools and conceptualisations, prevalence, and associated factors in adolescents and adults are scrutinised. The results reveal the following. First, no gold standard of Internet addiction classification exists as 21 different assessment instruments have been identified. They adopt official criteria for substance use disorders or pathological gambling, no or few criteria relevant for an addiction diagnosis, time spent online, or resulting problems. Second, reported prevalence rates differ as a consequence of different assessment tools and cut-offs, ranging from 0.8% in Italy to 26.7% in Hong Kong. Third, Internet addiction is associated with a number of sociodemographic, Internet use, and psychosocial factors, as well as comorbid symptoms and disorder in adolescents and adults. The results indicate that a number of core symptoms (i.e., compulsive use, negative outcomes and salience) appear relevant for diagnosis, which assimilates Internet addiction and other addictive disorders and also differentiates them, implying a conceptualisation as syndrome with similar etiology and components, but different expressions of addictions. Limitations include the exclusion of studies with smaller sample sizes and studies focusing on specific online behaviours. Conclusively, there is a need for nosological precision so that ultimately those in need can be helped by translating the scientific evidence established in the context of Internet addiction into actual clinical practice.


Analysis and Characterisation of Spoof Surface Plasmon Polaritons based Wideband Bandpass Filter at Microwave Frequency

This paper presents the wideband bandpass filter (BPF) in the microwave frequency domain. The realisation approach is based on spoof surface plasmon polaritons (SSPPs) phenomenon using plasmonic metamaterial. A novel unit cell is designed for filter design using an LC resonator concept. Then SSPPs BPF is realised using an optimised mode converter and five unit cells. This paper includes a brief design detail of the proposed novel unit cell. The passband of BPF is achieved at approximately 1.20 5.80 GHz, 3dB bandwidth is tentatively 4.60 GHz and the insertion loss is less than 2 dB approximately over the passband. The overall dimension of fabricated filter is (90 x 45) mm. A basic schematic of transmission line representation is also proposed to evaluate the BPF structure.


Hybrid Electric Vehicles: Architecture and Motor Drives

Electric traction is one of the most promising technologies that can lead to significant improvements in vehicle performance, energy utilization efficiency, and polluting emissions. Among several technologies, hybrid electric vehicle (HEV) traction is the most promising technology that has the advantages of high performance, high fuel efficiency, low emissions, and long operating range. Moreover, the technologies of all the component hardware are technically and markedly available. At present, almost all the major automotive manufacturers are developing hybrid electric vehicles, and some of them have marketed their productions, such as Toyota and Honda. This paper reviews the present technologies of HEVs in the range of drivetrain configuration, electric motor drives, and energy storages


Learning from other subjects helps reducing Brain-Computer Interface calibration time

A major limitation of Brain-Computer Interfaces (BCI) is their long calibration time, as much data from the user must be collected in order to tune the BCI for this target user. In this paper, we propose a new method to reduce this calibration time by using data from other subjects. More precisely, we propose an algorithm to regularize the Common Spatial Patterns (CSP) and Linear Discriminant Analysis (LDA) algorithms based on the data from a subset of automatically selected subjects. An evaluation of our approach showed that our method significantly outperformed the standard BCI design especially when the amount of data from the target user is small. Thus, our approach helps in reducing the amount of data needed to achieve a given performance level.


Location Segmentation, Inference and Prediction for Anticipatory Computing

This paper presents an analysis of continuous cellular tower data representing five months of movement from 215 randomly sampled subjects in a major urban city. We demonstrate the potential of existing community detection methodologies to identify salient locations based on the network generated by tower transitions. The tower groupings from these unsupervised clustering techniques are subsequently validated using data from Bluetooth beacons placed in the homes of the subjects. We then use these inferred locations as states within several dynamic Bayesian networks to predict each subject’s subsequent movements with over 90% accuracy. We also introduce the X-Factor model, a DBN with a latent variable corresponding to abnormal behavior. We conclude with a description of extensions for this model, such as incorporating additional contextual and temporal variables already being logged by the


A Novel Self-Reference Technique for STT-RAM Read and Write Reliability Enhancement

Spin-transfer torque random access memory (STT-RAM) has demonstrated great potential in embedded and stand-alone applications. However, process variations and thermal fluctuations greatly influence the operation reliability of STT-RAM and limit its scalability. In this paper, we propose a new field-assisted access scheme to improve the read/write reliability and performance of STT-RAM. During read operations, an external magnetic field is applied to a magnetic tunneling junction (MTJ) device, generating a resistive sense signal without referring to other devices. Such a self-reference scheme offers a very promising alternative approach to overcome the severe cell-to-cell variations at highly scaled technology node. Furthermore, the external magnetic field can be used to assist the MTJ switching during write operations without introducing extra hardware overhead.


Tendinous insertion of semimembranosus muscle into the lateral meniscus

Forty-two cadaver knees were used for morphologic and MRI observations of the tendinous distal expansions of the semimembranosus m. and the posterior capsular structures of the knee. A tendinous branch of the semimembranosus m. inserting into the posterior horn of the lateral meniscus was found in 43.2% of the knees dissected, besides five already known insertional branches; capsular, direct, anterior and inferior, as well as the oblique popliteal ligament. The tendon had three morphologic types; thin, broad and round. All three types moved the lateral meniscus posteriorly when pulled on. Thus, the semimembranosus m. may also have a protective function for the lateral meniscus as well as the already well established function of protecting the medial meniscus in knee flexion. When a semimembranosus tendon attachment to the posterior horn of the lateral meniscus is present, its normal insertion is difficult to differentiate from a lateral meniscus tear in MRI and this may cause misdiagnosis.


Millisecond Coupling of Local Field Potentials to Synaptic Currents in the Awake Visual Cortex

The cortical local field potential (LFP) is a common measure of population activity, but its relationship to synaptic activity in individual neurons is not fully established. This relationship has been typically studied during anesthesia and is obscured by shared slow fluctuations. Here, we used patch-clamp recordings in visual cortex of anesthetized and awake mice to measure intracellular activity; we then applied a simple method to reveal its coupling to the simultaneously recorded LFP. LFP predicted membrane potential as accurately as synaptic currents, indicating a major role for synaptic currents in the relationship between cortical LFP and intracellular activity. During anesthesia, cortical LFP predicted excitation far better than inhibition; during wakefulness, it predicted them equally well, and visual stimulation further enhanced predictions of inhibition. These findings reveal a central role for synaptic currents, and especially inhibition, in the relationship between the subthreshold activity of individual neurons and the cortical LFP during wakefulness.


The underlying structure of visuospatial working memory in children with mathematical learning disability.

This study examined visual, spatial-sequential, and spatial-simultaneous working memory (WM) performance in children with mathematical learning disability (MLD) and low mathematics achievement (LMA) compared with typically developing (TD) children. Groups were matched on reading decoding performance and verbal intelligence. Besides statistical significance testing, we used bootstrap confidence interval estimation and computed effect sizes. Children were individually tested with six computerized tasks, two for each visuospatial WM subcomponent. We found that both MLD and LMA children had low visuospatial WM function in both spatial-simultaneous and spatial-sequential WM tasks. The WM deficit was most expressed in MLD children and less in LMA children. This suggests that WM scores are distributed along a continuum with TD children achieving top scores and MLD children achieving low scores. The theoretical and practical significance of findings is discussed. Statement of Contribution What is already known on this subject? Working memory plays an important role in mathematical achievement. Children with mathematical learning disability (MLD) usually have low working memory resources. Conflicting results have been reported concerning the role of VSWM in individuals with MLD. What the present study adds? Children with different degree of impairment in math achievement and typically developing children were tested. Visual, spatial-sequential, and spatial-simultaneous working memory tasks were examined. Only spatial-sequential and spatial-simultaneous working memory tasks discriminated the two impairments groups.


The neuroscience of social decision-making.

Given that we live in highly complex social environments, many of our most important decisions are made in the context of social interactions. Simple but sophisticated tasks from a branch of experimental economics known as game theory have been used to study social decision-making in the laboratory setting, and a variety of neuroscience methods have been used to probe the underlying neural systems. This approach is informing our knowledge of the neural mechanisms that support decisions about trust, reciprocity, altruism, fairness, revenge, social punishment, social norm conformity, social learning, and competition. Neural systems involved in reward and reinforcement, pain and punishment, mentalizing, delaying gratification, and emotion regulation are commonly recruited for social decisions. This review also highlights the role of the prefrontal cortex in prudent social decision-making, at least when social environments are relatively stable. In addition, recent progress has been made in understanding the neural bases of individual variation in social decision-making.


Audit Log Management in MongoDB

In the past few years, web-based applications and their data management needs have changed dramatically. Relational databases are often being replaced by other viable alternatives, such as NoSQL databases, for reasons of scalability and heterogeneity. MongoDB, a NoSQL database, is an agile database built for scalability, performance and high availability. It can be deployed in single server environment and also on complex multi-site architectures. MongoDB provides high performance for read and write operations by leveraging in-memory computing. Although researchers have motivated the need for MongoDB, not much appears in the area of log management. Efficient log management techniques are needed for various reasons including security, accountability, and improving the performance of the system. Towards this end, we analyze the different logging methods offered by MongoDB and compare them to the NIST standard. Our analysis indicates that profiling and mongosniff are useful for log management and we present a simple model that combines the two techniques.


Bayesian Information Extraction Network

Dynamic Bayesian networks (DBNs) offer an elegant way to integrate various aspects of language in one model. Many existing algorithms developed for learning and inference in DBNs are applicable to probabilistic language modeling. To demonstrate the potential of DBNs for natural language processing, we employ a DBN in an information extraction task. We show how to assemble wealth of emerging linguistic instruments for shallow parsing, syntactic and semantic tagging, morphological decomposition, named entity recognition etc. in order to incrementally build a robust information extraction system. Our method outperforms previously published results on an established benchmark domain. 1 Information Extraction Information extraction ( IE) is the task of filling in template information from previously unseen text which belongs to a pre-defined domain. The resulting database is suited for formal queries and filtering.IE systems generally work by detecting patterns in the text that help identify significant information. Researchers have shown [Freitag and McCallum, 1999; Ray and Craven, 2001 ] that a probabilistic approach allows the construction of robust and well-performing systems. However, the existing probabilistic systems are generally based on Hidden Markov Models ( HMMs). Due to this relatively impoverished representation, they are unable to take advantage of the wide array of linguistic information used by many non-probabilistic IE systems. In addition, existing HMM -based systems model each target category separately, failing to capture relational information, such as typical target order, or the fact that each element only belongs to a single category. This paper shows how to incorporate a wide array of knowledge into a probabilistic IE system, based on dynamic Bayesian networks ( DBN)—a rich probabilistic representation that generalizes HMMs. Let us illustrateIE by describing seminar announcements which got established as one of the most popular benchmark domains in the field[Califf and Mooney, 1999; Freitag and McCallum, 1999; Soderland, 1999; Roth and Yih, 2001; Ciravegna, 2001 ]. People receive dozens of seminar announcements weekly and need to manually extract information and paste it into personal organizers. The goal of an IE system is to automatically identify target fields such as location and topic of a seminar, date and starting time, ending time and speaker. Announcements come in many formats, but usually follow some pattern. We often find a header with a gist in the form “ PostedBy: john@host.domain; Who: Dr. Steals; When: 1 am; ” and so forth. Also in the body of the message, the speaker usually precedes both location and starting time, which in turn precedes ending time as in: ‘‘Dr. Steals presents in Dean Hall at one am.’’ The task is complicated since some fields may be missing or may contain multiple values. This kind of data falls into the so-called semi-structured text category. Instances obey certain structure and usually contain information for most of the expected fields in some order. There are two other categories: free text and structured text. Instructured text , the positions of the information fields are fixed and values are limited to pre-defined set. Consequently, theIE systems focus on specifying the delimiters and order associated with each field. At the opposite end lies the task of extracting information fromfree textwhich, although unstructured, is assumed to be grammatical. Here IE systems rely more on syntactic, semantic and discourse knowledge in order to assemble relevant information potentially scattered all over a large document. IE algorithms face different challenges depending on the extraction targets and the kind of the text they are embedded in. In some cases, the target is uniquely identifiable (singleslot), while in others, the targets are linked together in multislot association frames. For example, a conference schedule has several slots for related speaker, topic and time of the presentation, while a seminar announcement usually refers to a unique event. Sometimes it is necessary to identify each word in a target slot, while some benefit may be reaped from partial identification of the target, such as labeling the beginning or end of the slot separately. Many applications involve processing of domain-specific jargon like Internetese —a style of writing prevalent in news groups, e-mail messages, bulletin boards and online chat rooms. Such documents do not follow a good grammar, spelling or literary style. Often these are more like a stream-of-consciousness ranting in which asciiart and pseudo-graphic sketches are used and emphasis is provided by all-capitals, or using multiple exclamation signs. As we exemplify below, syntactic analysers easily fail on such Position 1 2 3 4 5 6 7 8 9 10 Tag <speaker > <location> <s-time> Phrase Doctor Steals Presents in Dean Hall at 1 am . Lemma Dr. present in hall at am PoS NNP NNP(VB) VB(NNS) IN NNP NN(NNP) IN CD NN(RB) . Syn.segm. NP NP (VP) VP PP NP NP PP NP NP(VP) Semantic Title LstName LstName Location Time Length 3 6 8 2 4 4 2 1 2 1 Case Upper Upper Upper lower Upper Upper lower lower Table 1: Sample phrase and its representation in multiple feature values for ten tokens.


A Framework for Highly-Available Composed Real-Time Internet Services Bhaskaran Raman Qualifying Examination

Applicationservicesfor theend-userareall importantin today’scommunicationnetworks,andcoulddictatethesuccessor failureof technologyor serviceproviders[39]. It is importantto develop and deploy applicationfunctionality quickly [18]. The ability to compose servicesfrom independentprovidersprovidesa flexible way to quickly build new end-to-endfunctionality. Suchcompositionof services acrossthe network and acrossdifferent serviceproviders becomesespeciallyimportant in the context of growing popularityandheterogeneityin 3G+ accessnetworks and devices[25]. In this work, we provide a framework for constructing such composed services on the Internet. Robustnessandhigh-availability arecrucial for Internet services.While cluster-basedmodelsfor resilienceto failureshave beenbuilt for web-servers[27] aswell asproxy services[11, 4], theseareinadequatein thecontext of composedservices.This is especiallyso whenthe application sessionis long-lived, andfailureshaveto behandledduring asession. In the context of composed services, we address the important and challenging issues of resilience to failures, and adapting to changes in overall performance during longlived sessions. Our framework is basedon a connection-orientedoverlay network of compute-clusterson theInternet.Theoverlay network provides the context for composingservices over the wide-area,and monitoring for livenessand performanceof a session. We have performedinitial analysesof the feasibility of network failure detectionover the wide-areaInternet. And we have a preliminaryevaluation of the overheadassociatedwith suchan overlay network. We presentour plansfor furtherevaluationandrefinement of thearchitecture;andfor examiningissuesrelatedto the creationof theoverlaytopology. 1 Intr oduction Application functionality for the end-useris thedriving force behinddevelopmentof technologyandits adoption. It is importantto be ableto developanddeploy new functionality quickly [18]. Compositionof existing servicesto achieve new functionalityenablessuchquick development throughreuseof alreadydeployed components.Consider for example,thescenarioshown in Figure1. Video-on-demand server Replicated instances Service Provider A


Neural Message Passing for Quantum Chemistry

Supervised learning on molecules has incredible potential to be useful in chemistry, drug discovery, and materials science. Luckily, several promising and closely related neural network models invariant to molecular symmetries have already been described in the literature. These models learn a message passing algorithm and aggregation procedure to compute a function of their entire input graph. At this point, the next step is to find a particularly effective variant of this general approach and apply it to chemical prediction benchmarks until we either solve them or reach the limits of the approach. In this paper, we reformulate existing models into a single common framework we call Message Passing Neural Networks (MPNNs) and explore additional novel variations within this framework. Using MPNNs we demonstrate state of the art results on an important molecular property prediction benchmark; these results are strong enough that we believe future work should focus on datasets with larger molecules or more accurate ground truth labels.


Substrate integrated waveguide (SIW) power amplifier using CBCPW-to-SIW transition for matching network

In this paper, for the first time a novel substrate integrated waveguide (SIW)-based 10W power amplifier (PA), designed with conductor-backed coplanar waveguide (CBCPW)-to-SIW transition matching network (MN), is presented. Transition between CBCPW and SIW is employed for both input and output MN designs, the proposed SIW PA can be easily connected with any microstrip or SIW-based circuits. Asymmetrical and symmetrical types of CBCPW-to-SIW transition MN are proposed. Output SIW-based MN is designed with asymmetrical structure by using one inductive metalized post and input SIW-based MN is designed with symmetrical structure by using two identical inductive metalized posts. One SIW-based 10W PA using GaN HEMT at 3.62 GHz is designed, fabricated, and measured. Measured results show that the maximum power added efficiency (PAE) is 54.24 % with 39.74 dBm output power and the maximum gain is 13.31 dB. At the design frequency of 3.6 GHz, the size of proposed SIW-based PA is comparable with other microstrip-based PAs.


High Performance Single-Chip FPGA Rijndael Algorithm Implementations

This paper describes high performance single-chip FPGA implementations of the new Advanced Encryption Standard (AES) algorithm, Rijndael. The designs are implemented on the Virtex-E FPGA family of devices. FPGAs have proven to be very effective in implementing encryption algorithms. They provide more flexibility than ASIC implementations and produce higher data-rates than equivalent software implementations. A novel, generic, parameterisable Rijndael encryptor core capable of supporting varying key sizes is presented. The 192-bit key and 256-bit key designs run at data rates of 5.8 Gbits/sec and 5.1 Gbits/sec respectively. The 128-bit key encryptor core has a throughput of 7 Gbits/sec which is 3.5 times faster than similar existing hardware designs and 21 times faster than known software implementations, making it the fastest single-chip FPGA Rijndael encryptor core reported to date. A fully pipelined single-chip 128-bit key Rijndael encryptor/decryptor core is also presented. This design runs at a data rate of 3.2 Gbits/sec on a Xilinx Virtex-E XCV3200E-8-CG1156 FPGA device. There are no known singlechip FPGA implementations of an encryptor/decryptor Rijndael design.


AES Proposal : Rijndael


Very Compact FPGA Implementation of the AES Algorithm

In this paper a compact FPGA architecture for the AES algorithm with 128-bit key targeted for low-cost embedded applications is presented. Encryption, decryption and key schedule are all implemented using small resources of only 222 Slices and 3 Block RAMs. This implementation easily fits in a low-cost Xilinx Spartan II XC2S30 FPGA. This implementation can encrypt and decrypt data streams of 150 Mbps, which satisfies the needs of most embedded applications, including wireless communication. Specific features of Spartan II FPGAs enabling compact logic implementation are explored, and a new way of implementing MixColumns and InvMixColumns transformations using shared logic resources is presented.


A compact FPGA implementation of the hash function whirlpool

Recent breakthroughs in cryptanalysis of standard hash functions like SHA-1 and MD5 raise the need for alternatives. A credible alternative to for instance SHA-1 or the SHA-2 family of hash functions is Whirlpool. Whirlpool is a hash function that has been evaluated and approved by NESSIE and is standardized by ISO/IEC. To the best of our knowledge only one FPGA implementation of Whirlpool has been published to date. This implementation is designed for high throughput rates requiring a considerable amount of hardware resources. In this article we present a compact hardware implementation of the hash function Whirlpool. The proposed architecture uses an innovative state representation that makes it possible to reduce the required hardware resources remarkably. The complete implementation requires 1456 CLB-slices and, most notably, no block RAMs.


House Price Prediction : Hedonic Price Model vs . Artificial Neural Network

The objective of this paper is to empirically compare the predictive power of the hedonic model with an artificial neural network model on house price prediction. A sample of 200 houses in Christchurch, New Zealand is randomly selected from the Harcourt website. Factors including house size, house age, house type, number of bedrooms, number of bathrooms, number of garages, amenities around the house and geographical location are considered. Empirical results support the potential of artificial neural network on house price prediction, although previous studies have commented on its black box nature and achieved different conclusions.


Search mode and purchase intention in online shopping behavior

This study focuses on the effect of website visitors' goal-oriented search mode on purchase intention in online environments. In a study of 874 respondents recruited from 13 online shops representing a diversity of product categories and customer segments, the effect of visitors' goal-oriented search mode on purchase intention is found to be moderated by product involvement and product risk. Furthermore, product involvement, product risk, product knowledge and Internet experience are found to have positive effects on the degree of goaloriented search mode of the visitors. Also, product knowledge and Internet experience are reported to have direct positive effects on purchase intention. The results point to the importance of understanding the characteristics of website visitors, and to customize the support and search services offered on the website to the characteristics and preferences of the individual visitor. Customization of this kind may partly be based on immediate visitor history (referrer) and may be used to increase purchase intention, and eventually online sales.


Data-driven Personas: Constructing Archetypal Users with Clickstreams and User Telemetry

User Experience (UX) research teams following a user centered design approach harness personas to better understand a user's workflow by examining that user's behavior, goals, needs, wants, and frustrations. To create target personas these researchers rely on workflow data from surveys, self-reports, interviews, and user observation. However, this data not directly related to user behavior, weakly reflects a user's actual workflow in the product, is costly to collect, is limited to a few hundred responses, and is outdated as soon as a persona's workflows evolve. To address these limitations we present a quantitative bottom-up data-driven approach to create personas. First, we directly incorporate user behavior via clicks gathered automatically from telemetry data related to the actual product use in the field; since the data collection is automatic it is also cost effective. Next, we aggregate 3.5 million clicks from 2400 users into 39,000 clickstreams and then structure them into 10 workflows via hierarchical clustering; we thus base our personas on a large data sample. Finally, we use mixed models, a statistical approach that incorporates these clustered workflows to create five representative personas; updating our mixed model ensures that these personas remain current. We also validated these personas with our product's user behavior experts to ensure that workflows and the persona goals represent actual product use.


Detecting Faulty Nodes with Data Errors for Wireless Sensor Networks

Wireless Sensor Networks (WSN) promise researchers a powerful instrument for observing sizable phenomena with fine granularity over long periods. Since the accuracy of data is important to the whole system's performance, detecting nodes with faulty readings is an essential issue in network management. As a complementary solution to detecting nodes with functional faults, this article, proposes FIND, a novel method to detect nodes with data faults that neither assumes a particular sensing model nor requires costly event injections. After the nodes in a network detect a natural event, FIND ranks the nodes based on their sensing readings as well as their physical distances from the event. FIND works for systems where the measured signal attenuates with distance. A node is considered faulty if there is a significant mismatch between the sensor data rank and the distance rank. Theoretically, we show that average ranking difference is a provable indicator of possible data faults. FIND is extensively evaluated in simulations and two test bed experiments with up to 25 MicaZ nodes. Evaluation shows that FIND has a less than 5% miss detection rate and false alarm rate in most noisy environments.


5.2 Distributed system of digitally controlled microregulators enabling per-core DVFS for the POWER8TM microprocessor

Integrated voltage regulator modules (iVRMs) [1] provide a cost-effective path to realizing per-core dynamic voltage and frequency scaling (DVFS), which can be used to optimize the performance of a power-constrained multi-core processor. This paper presents an iVRM system developed for the POWER8™ microprocessor, which functions as a very fast, accurate low-dropout regulator (LDO), with 90.5% peak power efficiency (only 3.1% worse than an ideal LDO). At low output voltages, efficiency is reduced but still sufficient to realize beneficial energy savings with DVFS. Each iVRM features a bypass mode so that some of the cores can be operated at maximum performance with no regulator loss. With the iVRM area including the input decoupling capacitance (DCAP) (but not the output DCAP inherent to the cores), the iVRMs achieve a power density of 34.5W/mm2, which exceeds that of inductor-based or SC converters by at least 3.4× [2].


New Word Detection in Ancient Chinese Literature

Mining Ancient Chinese corpus is not as convenient as modern Chinese, because there is no complete dictionary of ancient Chinese words which leads to the bad performance of tokenizers. So finding new words in ancient Chinese texts is significant. In this paper, the Apriori algorithm is improved and used to produce candidate character sequences. And a long short-term memory (LSTM) neural network is used to identify the boundaries of the word. Furthermore, we design word confidence feature to measure the confidence score of new words. The experimental results demonstrate that the improved Apriori-like algorithm can greatly improve the recall rate of valid candidate character sequences, and the average accuracy of our method on new word detection raise to 89.7%.


Attacking Deterministic Signature Schemes Using Fault Attacks

Many digital signature schemes rely on random numbers that are unique and non-predictable per signature. Failures of random number generators may have catastrophic effects such as compromising private signature keys. In recent years, many widely-used cryptographic technologies adopted deterministic signature schemes because they are presumed to be safer to implement. In this paper, we analyze the security of deterministic ECDSA and EdDSA signature schemes and show that the elimination of random number generators in these schemes enables new kinds of fault attacks. We formalize these attacks and introduce practical attack scenarios against EdDSA using the Rowhammer fault attack. EdDSA is used in many widely used protocols such as TLS, SSH, and IPSec, and we show that these protocols are not vulnerable to our attack. We formalize the necessary requirements of protocols using these deterministic signature schemes to be vulnerable, and discuss mitigation strategies and their effect on fault attacks against deterministic signature schemes.


Development of a FACS-verified set of basic and self-conscious emotion expressions.

In 2 studies, the authors developed and validated of a new set of standardized emotion expressions, which they referred to as the University of California, Davis, Set of Emotion Expressions (UCDSEE). The precise components of each expression were verified using the Facial Action Coding System (FACS). The UCDSEE is the first FACS-verified set to include the three "self-conscious" emotions known to have recognizable expressions (embarrassment, pride, and shame), as well as the 6 previously established "basic" emotions (anger, disgust, fear, happiness, sadness, and surprise), all posed by the same 4 expressers (African and White males and females). This new set has numerous potential applications in future research on emotion and related topics.


The use of Social Networking Sites (SNSs) by the faculty members of the School of Library & Information Science, PAAET, Kuwait

Purpose – The purpose of this study is to describe the usage of Social Networking Sites (SNSs) by the faculty members of the School of Library and Information Science (SLIS), at the College of Basic Education, the Public Authority for Applied Education and Training (PAAET), Kuwait. Design/methodology/approach – A survey conducted to collect data from 33 faculty members of whom only 21 members were using SNSs, representing 63.6 per cent of the total sample, and 12 members were not using SNSs, representing 36.4 per cent of the total sample. This study revealed that SNSs are used moderately by the faculty members. Findings – This study showed that faculty members who were using SNSs tend to be males, aged between 41 and 50 years, PhD holders, ranked as assistant professors, full-time members, specialized in information technologies with a relatively new experience of teaching ranged from one to five years, and most of the faculty members who were not using SNSs tended to be also males, aged between 41 and 60 years, PhD holders, ranked as lecturers, full-time members specialized in organization of information with a teaching experience ranged from 16 to 20 years. More than half of the faculty members were using SNSs for three years to less than six years, and a large number of them were using SNSs several times a week and were accessing these sites more from their school office, home and school laboratory. There are no any statistical significant differences between the demographic data of participants (gender, age and education level) and either their use or non-use of SNSs. There are no significant differences between the academic rank, teaching status and teaching experience of faculty and their use of SNSs. However, there is a significant relation between the faculty’s area of teaching and their use of SNSs. Faculty members were interested in the use of SNSs. YouTube, Twitter, Facebook and blogs respectively were used mostly by faculty members, but Twitter, Facebook and YouTube were the most famous SNSs they have profiles on. Faculty members have adopted SNSs mainly for the purpose of communicating with others, finding and sharing information with peers and students as well. Tasks on SNSs made by faculty members were mostly to make communication, send/receive messages and find general and specific information. Faculty members’ profiles on SNSs were mostly on Twitter, Facebook, YouTube, blogs, wikis and podcasting respectively. Faculty members confirmed that the use of YouTube, Facebook, blogs, Twitter, wikis and podcasting respectively was at least effective and the use of YouTube, Facebook, Twitter, Blogs and Wikis respectively was at least fairly useful fairly easy to them. Faculty members are in general agreement about the effectiveness of SNSs especially for disseminating and sharing information, communication and informal collaboration. The study showed The researcher would like to thank the Public Authority for Applied Education and Training (PAAET), the state of Kuwait, for supporting this paper (BE-11-10). The current issue and full text archive of this journal is available on Emerald Insight at: www.emeraldinsight.com/0264-0473.htm


The Bluetooth radio system

A few years ago it was recognized that the vision of a truly low-cost, low-power radio-based cable replacement was feasible. Such a ubiquitous link would provide the basis for portable devices to communicate together in an ad hoc fashion by creating personal area networks which have similar advantages to their office environment counterpart, the local area network. Bluetooth/sup TM/ is an effort by a consortium of companies to design a royalty-free technology specification enabling this vision. This article describes the radio system behind the Bluetooth concept. Designing an ad hoc radio system for worldwide usage poses several challenges. The article describes the critical system characteristics and motivates the design choices that have been made.


Patch Autocorrelation Features: a translation and rotation invariant approach for image classification

The autocorrelation is often used in signal processing as a tool for finding repeating patterns in a signal. In image processing, there are various image analysis techniques that use the autocorrelation of an image in a broad range of applications from texture analysis to grain density estimation. This paper provides an extensive review of two recently introduced and related frameworks for image representation based on autocorrelation, namely Patch Autocorrelation Features (PAF) and Translation and Rotation Invariant Patch Autocorrelation Features (TRIPAF). The PAF approach stores a set of features obtained by comparing pairs of patches from an image. More precisely, each feature is the euclidean distance between a particular pair of patches. The proposed approach is successfully evaluated in a series of handwritten digit recognition experiments on the popular MNIST data set. However, the PAF approach has limited applications, because it is not invariant to affine transformations. More recently, the PAF approach was extended to become invariant to image transformations, including (but not limited to) translation and rotation changes. In the TRIPAF framework, several features are extracted from each image patch. Based on these features, a vector of similarity values is computed between each pair of patches. Then, the similarity vectors are clustered together such that the spatial offset between the patches of each pair is roughly the same. Finally, the mean and the standard deviation of each similarity value are computed for each group of similarity vectors. These statistics are concatenated to obtain the TRIPAF feature vector. The TRIPAF vector essentially records information about the repeating patterns within an image at various spatial offsets. After presenting the two approaches, several optical character recognition and texture classification experiments are conducted to evaluate the two approaches. Results are reported on the MNIST (98.93%), the Brodatz (96.51%), and the UIUCTex (98.31%) data sets. Both PAF and TRIPAF are fast to compute and produce compact representations in practice, while reaching accuracy levels similar to other state-of-the-art methods.


Baby Talk : Understanding and Generating Image Descriptions

We posit that visually descriptive language offers computer vision researchers both information about the world, and information about how people describe the world. The potential benefit from this source is made more significant due to the enormous amount of language data easily available today. We present a system to automatically generate natural language descriptions from images that exploits both statistics gleaned from parsing large quantities of text data and recognition algorithms from computer vision. The system is very effective at producing relevant sentences for images. It also generates descriptions that are notably more true to the specific image content than previous work.


Single camera lane detection and tracking

In this paper we present a method to detect and track straight lane boundaries, using a forward-looking single camera mounted on-board. The method proved to be robust, even under varying lighting conditions, and in the presence of heavy shadowing cast by vegetation, vehicles, bridges, etc. Moreover, false positive hardly occur. The lane markings are being continuously detected even when the vehicle is performing maneuvers such as excursion or lane change. The performance is achieved by a novel combination of methods, namely, by first finding vanishing point candidates for the lane boundaries, and later by the careful selection of line segments given a vanishing point, and finally by using a smart heuristics to select the whole set of four lane boundaries. Suppression of false positives is further achieved by tracking the vanishing point, and by constraining the lane width based on recent history.


CLAMS: Bringing Quality to Data Lakes

With the increasing incentive of enterprises to ingest as much data as they can in what is commonly referred to as "data lakes", and with the recent development of multiple technologies to support this "load-first" paradigm, the new environment presents serious data management challenges. Among them, the assessment of data quality and cleaning large volumes of heterogeneous data sources become essential tasks in unveiling the value of big data. The coveted use of unstructured and semi-structured data in large volumes makes current data cleaning tools (primarily designed for relational data) not directly adoptable.
 We present CLAMS, a system to discover and enforce expressive integrity constraints from large amounts of lake data with very limited schema information (e.g., represented as RDF triples). This demonstration shows how CLAMS is able to discover the constraints and the schemas they are defined on simultaneously. CLAMS also introduces a scale-out solution to efficiently detect errors in the raw data. CLAMS interacts with human experts to both validate the discovered constraints and to suggest data repairs.
 CLAMS has been deployed in a real large-scale enterprise data lake and was experimented with a real data set of 1.2 billion triples. It has been able to spot multiple obscure data inconsistencies and errors early in the data processing stack, providing huge value to the enterprise.


Distributed Detection of Sensor Worms Using Sequential Analysis and Remote Software Attestations

Recent work has demonstrated that self-propagating worms are a real threat to sensor networks. Since worms can enable an adversary to quickly compromise an entire sensor network, they must be detected and stopped as quickly as possible. To meet this need, we propose a worm propagation detection scheme for sensor networks. The proposed scheme applies a sequential analysis to detect worm propagation by leveraging the intuition that a worm’s communication pattern is different from benign traffic. In particular, a worm in a sensor network requires a long sequence of packets propagating hop-by-hop to each new infected node in turn. We thus have detectors that observe communication patterns in the network, a worm spreading hop-by-hop will quickly create chains of connections that would not be seen in normal traffic. Once detector nodes identify the worm propagation pattern, they initiate remote software attestations to detect infected nodes. Through analysis and simulation, we demonstrate that the proposed scheme effectively and efficiently detects worm propagation. In particular, it blocks worm propagation while restricting the fraction of infected nodes to at most 13.5% with an overhead of at most 0.63 remote attestations per node per time slot.


“Teach Me–Show Me”—End-User Personalization of a Smart Home and Companion Robot

Care issues and costs associated with an increasing elderly population are becoming a major concern for many countries. The use of assistive robots in “smart-home” environments has been suggested as a possible partial solution to these concerns. A challenge is the personalization of the robot to meet the changing needs of the elderly person over time. One approach is to allow the elderly person, or their carers or relatives, to make the robot learn activities in the smart home and teach it to carry out behaviors in response to these activities. The overriding premise being that such teaching is both intuitive and “nontechnical.” To evaluate these issues, a commercially available autonomous robot has been deployed in a fully sensorized but otherwise ordinary suburban house. We describe the design approach to the teaching, learning, robot, and smart home systems as an integrated unit and present results from an evaluation of the teaching component with 20 participants and a preliminary evaluation of the learning component with three participants in a human-robot interaction experiment. Participants reported findings using a system usability scale and ad-hoc Likert questionnaires. Results indicated that participants thought that this approach to robot personalization was easy to use, useful, and that they would be capable of using it in real-life situations both for themselves and for others.


A 20 Gb/s 0.4 pJ/b Energy-Efficient Transmitter Driver Utilizing Constant- ${\rm G}_{\rm m}$  Bias

This paper describes a transmitter driver based on a CMOS inverter with a resistive feedback. By employing the proposed driver topology, the pre-driver can be greatly simplified, resulting in a remarkable reduction of the overall driver power consumption. It also offers another advantage that the implementation of equalization is straightforward, compared with a conventional voltage-mode driver. Furthermore, the output impedance remains relatively constant while the data is being transmitted, resulting in good signal integrity. For evaluation of the driver performance, a fully functional 20 Gb/s transmitter is implemented, including a PRBS generator, a serializer, and a half-rate clock generator. In order to enhance the overall speed of the digital circuits for 20 Gb/s data transmission, the resistive feedback is applied to the time-critical inverters, which enables shorter rise/fall times. The prototype chip is fabricated in a 65 nm CMOS technology. The implemented driver circuit operates up to the data rate of 20 Gb/s, exhibiting an energy efficiency of 0.4 pJ/b for the output swing of 250 mVpp,diff.


A Technique for Data Deduplication using Q-Gram Concept with Support Vector Machine

Several systems that rely on consistent data to offer high quality services, such as digital libraries and e-commerce brokers, may be affected by the existence of duplicates, quasi-replicas, or near-duplicate entries in their repositories. Because of that, there have been significant investments from private and government organizations in developing methods for removing replicas from its data repositories.In this paper, we have proposed accordingly. In the previous work, duplicate record detection was done using three different similarity measures and neural network. In the previous work, we have generated feature vector based on similarity measures and then, neural network was used to find the duplicate records. In this paper, we have developed Q-gram concept with support vector machine for deduplication process. The similarity function, which we are used Dice coefficient,Damerau–Levenshtein distance,Tversky index for similarity measurement. Finally, support vector machine is used for testing whether data record is duplicate or not. A set of data generated from some similarity measures are used as the input to the proposed system. There are two processes which characterize the proposed deduplication technique, the training phase and the testing phase the experimental results showed that the proposed deduplication technique has higher accuracy than the existing method. The accuracy obtained for the proposed deduplication 88%.


A New Travelling Wave Antenna in Microstrip

The radiation characteristics of the first higher order mode of microstrip lines are investigated. As a result, a simple travelling wave antenna element is scribed, having a larger bandwidth compared with resonator antennas. A method to excite the first higher order mode is shown. A single antenna element is treated theoretically and experimentally, and an array of four antenna elements is demonstrated.


The pathogenesis of Campylobacter jejuni-mediated enteritis.

Campylobacter jejuni, a gram-negative spiral shaped bacterium, is a frequent cause of gastrointestinal food-borne illness in humans throughout the world. Illness with C. jejuni ranges from mild to severe diarrheal disease. This article focuses on Campylobacter virulence determinants and their potential role in the development of C. jejuni-mediated enteritis. A model is presented that diagrams the interactions of C. jejuni with the intestinal epithelium. Additional work to identify and characterize C. jejuni virulence determinants is certain to provide novel insights into the diversity of strategies employed by bacterial pathogens to cause disease.